From Risk Factors to Predictive Modelling: Applying Machine Learning to Childhood Malaria Surveillance in Resource-Limited Settings

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Akanji, Victoria I. Ayansola, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7352919/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Dec, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted 23 You are reading this latest preprint version Abstract Background Malaria remains a concerning public health issue in sub-Saharan Africa, especially among children under five. Nigeria accounts for almost 30% of malaria-related child deaths globally despite control efforts. However, machine learning (ML) approaches can detect complex patterns from extensive datasets, and may therefore improve prediction accuracy, giving a better understanding of drivers of malaria in children, leading to informed targeted interventions. Methods We conducted a cross-sectional study with 693 caregiver-child pairs from high-burden Internally Displaced Persons (IDPs) Camps in Nigeria. Sociodemographic, household conditions, malaria knowledge and prevention practices data were collected alongside Rapid Diagnostic Test (RDT) results. 70:30 split data is used to train and evaluate four ML models namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF) and Gradient Boosting Machine (GBM). The performance of the model was evaluated based on Area Under the Curve (AUC), precision, recall, and F1-score as well as variable importance to reveal key predictors. Results Malaria prevalence was 68.5%, and significant associations were observed with caregiver gender, education and housing conditions. Male caregivers had reduced odds of malaria positivity (aOR = 0.44, p < 0.001), and Mud walls conferred protection against malaria positive cases (aOR = 0.60, p = 0.002). Random Forest (AUC = 0.89) was the top performing model identifying caregiver occupation (15. 7% importance), and residential camp (14.7% importance) as leading predictors. GBM (AUC = 0.87) and LR (AUC = 0.82) were next, with DT (AUC = 0.78) had the lowest AUC value. There was a clear knowledge gap, with 60.3% of caregivers without Malaria prevention knowledge. Conclusion Malaria risk prediction is improved by machine learning and RF performs better. Important modifiable variables include housing conditions, caregiver education, and localized vector control. This study recommends a precision public health approach integrating ML within surveillance for real-time risk mapping and resource optimization in high-burden areas. Malaria prediction Machine learning Under-five children Precision health Environmental determinants Nigeria Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Malaria remains one of the major global public health issues, with the biggest burden situated in sub-Saharan Africa [ 1 ]. Malaria control efforts have made a huge impact in the last two decades, still the disease poses significant morbidity and mortality, especially among high-risk groups like children aged below five years [ 2 ]. Malaria in Nigeria accounts for nearly 30% of deaths among children with a significant percentage of hospitalization, therefore requiring additional interventions [ 3 ]. Knowledge of the sociodemographic, environmental, and behavioral determinants of malaria transmission will enable planning accordingly for prevention and control. Endemic malaria is affected by a complex array of different factors, which range from family life to caregiver knowledge and availability of preventive tools [ 4 ]. For instance, houses with proximity to stagnant water, poor quality housing material, and low bed net usage have consistently been proven to be related to increased risks of malaria [ 5 ]. Besides, socioeconomic inequalities, low literacy levels and limited access to health care, increase susceptibility to malaria [ 6 ]. Caregivers' knowledge of malaria prevention, transmission, and treatment is also a crucial health-seeking behavior and compliance determinant with protective practices [ 7 ]. Despite this, gaps in malaria knowledge and misconceptions remain prevalent in most communities, discouraging effective prevention [ 8 ]. Machine learning (ML) algorithms are powerful tools to decode complex health information and predict disease outcomes [ 9 ]. Unlike conventional statistical approaches, ML algorithms can detect nonlinear interaction and correlation among numerous predictors with higher predictive power [ 10 ]. In malaria, ML models have predicted transmission behavior, resource optimization, and risk factor assessment [ 11 ]. For example, logistic regression, decision trees, and ensemble models like random forests and gradient boosting machines (GBMs) have been used in predicting malaria incidence from demographic and environmental determinants [ 12 ]. These models play a vital role in clarifying the relative importance of factors so that priorities could be established by policymakers [ 13 ]. This study investigates the identification of the top-most predictors of malaria among Nigerian children using traditional descriptive statistics and machine learning. By evaluating sociodemographic factors (such as caregiver gender, education and occupation), household living conditions (including roofing material type and bed nets usage), as well as knowledge on malaria, we seek to clarify factors most associated with laboratory test-confirmed malaria. In addition, we compared the performance of different ML models such as logistic regression, decision tree (DT), random forest (RF), and Gradient Boost Machines (GBMs) to determining the most robust approach to malaria prediction. These findings will assist in augmenting the understanding of malaria risk determinants while informing disease control with evidence-based data. The justification for this study stems from the need for evidence-based interventions in locally relevant contexts. Whereas existing research has studied malaria determinants in sub-Saharan Africa, there have been few studies bringing sociodemographic, environmental, and knowledge-based predictors together using a machine learning framework. Equally, comparative assessment of ML modellings for malaria prediction has been relatively under-studied, particularly in low-resource environments where precision risk stratification maximizes a few available healthcare resources [ 14 ]. Through the filling of such knowledge gaps, this research expects to offer actionable evidence to the malaria control program and maximize the precision of public health interventions. Therefore, this study investigates the multi-factorial determinants of malaria test outcomes among Nigerian children using advanced machine learning methods to ascertain key predictors and model accuracy estimates. Its application will guide targeted malaria prevention, resource allocation, and community education toward the ultimate global malaria elimination initiatives. Methodology Study Design and Data Collection Cross-sectional study design was used in this study to assess factors associated with malaria test status among under-5 years old Nigerian children. Data were collected from guardians or parents living within the study camps, including Kabusa, Durumi, Kuchingoro, and Wassa. A pretested questionnaire was used to obtain sociodemographic data, household characteristics, and knowledge on malaria, and prevention practices. The research included Rapid Diagnostic Test (RDT) test results to ascertain malaria positivity among children. Ethical clearance was provided by the concerned institutional review board and informed consent was obtained from all the respondents before data collection [ 15 ]. The designed consent and questionnaire (English Language Version) used for the study data collection is attached as a supplementary file. Study Population and Sampling The survey employed 693 caregiver-child pairs, with 0–59-months old children. Participants were sampled by multistage sampling for representativeness. Four high-burden residential camps were first purposively selected following malaria prevalence reports. Subsequently, random sampling of households with at least one child aged less than five years was conducted. Caregivers were interviewed and screened for malaria with RDT kits according to standard procedure [ 3 ]. Sample size was computed employing the formula for prevalence studies: n = [Z²×P(1-P)]/d², where Z = 1.96 (95% CI), P = 0.5 (expected prevalence), d = 0.05 (precision), design effect adjustment of 2.0 and 15% non-response, to yield 693 participants. Variables and Measurements Dependent Variable The primary outcome was malaria test result status, as a binary variable categorized as Positive (if malaria infection is confirmed via RDT) or Negative (if no malaria infection was detected). Independent Variables The predictors were grouped into four main categories: First, sociodemographic factors which include caregiver’s gender, marital status, age, education level, and occupation. This also include household size, number of children under five, and relationship with the child. Second, household and environmental factors which include housing conditions (type of floor, wall, and roof materials), presence of stagnant water near the household as well as bed net availability and usage in households. Third, Malaria knowledge and health-seeking behavior which comprises the source of malaria knowledge (friends, hospitals, radio, television), the awareness of malaria transmission, prevention, and treatment as well as the reasons for not seeking medical care (such as lack of funds, distance to hospital), and finally, Child-specific factors such as child’s age, sex, and residential camp. Statistical, Logistic Regression and Machine Learning Analysis Descriptive statistics was used to summarize the participant’s characteristics, and presented as frequencies and percentages. Further, univariate and multivariate logistic regression models were fitted to determine the associations between independent variables and malaria test outcomes. Adjusted odds ratios (aORs) with 95% confidence intervals (CIs) were also reported, while statistical significance was set at *p* < 0.05 [ 16 ]. Machine Learning Modeling Four supervised learning algorithms were trained and evaluated: one, Logistic Regression (LR), a widely known and traditional baseline model assessing linear relationships between predictors and malaria outcomes [ 17 ]. Two, Decision Tree (DT) which uses a rule-based model to capture nonlinear interactions among variables [ 18 ]. Three, Random Forest (RF), bagging technique using a set of decision trees to improve prediction stability [ 19 ]. Lastly, Gradient Boosting Machine (GBM), which is an iterative boosting approach for enhancing predictive precision [ 20 ]. Model Training and Performance Evaluation The data were divided into 70% (training set) and 30% (test set) sets. Hyper-parameter tuning was conducted on 10-fold cross-validation to optimize model performance. Feature importance was estimated by variable importance scores (for DT and RF), relative influence (for GBM), and odds ratios (for logistic regression). Models were contrasted on Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) to estimate classification accuracy. Robustness of models was also estimated using precision, recall, and F1-score, with calibration plots estimating reliability of predictions. All analysis was performed with R (version 4.2.0) using packages like caret, randomForest, gbm, and pROC. The research protocol was approved by the Research Ethics Committee, National Open University of Nigeria with a protocol approval number ETC/2023/NOUN/03/003. Severe malaria cases were referred to camp clinics. Data were anonymized and housed on password-protected servers with access limited to study investigators Results Descriptive Characteristics of the Study Population 693 caregiver-child dyads, with sociodemographic and household profiles as elaborated in Table 1 were investigated in this study. Most of the caregivers were female (86.1%), aged between 18 and 30 years (78.5%), and married (77.2%). The majority had primary education (74.9%), and petty trading was the most frequent occupation (37.4%). Household profiles provided evidence of 67.2% sleeping in one bedroom, 96.8% having stagnant water around their homes, and 94.5% without bed nets. Among the children, 62.0% were females and the highest percentage (46.3%) belonged to the age category of 13–24 months. The prevalence of malaria was high with 68.5% RDT positive. Table 1 Descriptive Statistics of Study Variables Variable Category Count (n) Percent (%) Guardian/Parent’s Gender Female 597 86.10 Male 96 13.90 Guardian/Parent’s Age 18-30years 544 78.50 31-45years 111 16.00 Less than 18 years 38 5.50 Guardian/Parent’s Marital Status Married 535 77.20 Separated/Divorce 37 5.30 Single 121 17.50 Guardian/Parent’s Education Level Koranic Education 36 5.20 No formal Education 91 13.10 Primary Education 519 74.90 Secondary 24 3.50 Tertiary Education 23 3.30 Guardian/Parent’s Occupation Driver 8 1.20 Farming 158 22.80 Motorcycle rider 57 8.20 No source of income 211 30.40 Petty trading 259 37.40 Relationship with Child Father 95 13.70 Mother 534 77.10 Sister 64 9.20 No. Bedrooms in Households One 466 67.20 Two 227 32.80 House Floor Material Cement 667 96.20 Mud 26 3.80 House Wall material Cement 324 46.80 Mud 369 53.20 House Roof Material Straw/Thatches/Palm leave 49 7.10 Tarpaulin 21 3.00 Zinc 623 89.90 Stagnant water present near house No 22 3.20 Yes 671 96.80 Bednets in household No 655 94.5% Yes 38 5.50 Used Bed nets last night No 679 98.00 Yes 14 2.00 No. of Children in Household Five 13 1.90 Four 576 83.10 Three 53 7.60 Two 34 4.90 One 17 2.50 No. of Children Under-5 in household Four 6 0.90 Three 11 1.60 One 676 97.50 Source of Malaria Knowledge Friends 426 61.50 Hospitals 134 19.30 Radio 105 15.20 Television 28 4.00 Knowledge on Malaria Diagnosis it is important 682 98.40 it is not important 11 1.60 Knowledge of Malaria Prevention I don't know 275 39.70 I know 418 60.30 Knowledge of malaria Transmission I don't know 388 56.00 I know 305 44.00 Knowledge of Mosquito Breeding I don't know 30 4.3 I know 663 95.7 Knowledge on Malaria Cure It can be cured 672 97.00 It cannot be cured 21 3.00 Reason for No Malaria Medical Care Lack of funds 623 89.90 Far distance to hospital 70 10.10 Child’s Resident Camp Durumi 132 19.00 Kabusa 126 18.20 Kuchingoro 116 16.70 Wassa 319 46.00 Child’s Sex Female 430 62.00 Male 263 38.00 Child’s Age 0-12months 89 12.80 13-24months 321 46.30 25-36months 141 20.30 37-59mnths 142 20.50 Child RDT Malaria Test Result Negative 218 31.50 Positive 475 68.50 Logistic Regression Analysis of Malaria Risk Factors The adjusted odds ratio (aORs) from multivariate logistic regression are presented in Table 2 . With important findings including having male caregiver presenting lesser odds of malaria test positivity (aOR = 0.44, 95% CI: 0.28–0.68, *p* < 0.001) as compared to female. Divorced/separated caregivers having lower odds (aOR = 0.42, 95% CI: 0.17–0.99, *p* = 0.033), while single caregivers had higher odds (aOR = 1.35, *p* = 0.042) indicating the importance of caregiver gender. More so, caregivers with no formal education had 3.05 times higher odds (95% CI: 1.09–8.62, *p* = 0.027) compared to caregivers with koranic-education only. Houses with mud walls had a protective effect (aOR = 0.60, 95% CI: 0.43–0.83, *p* = 0.002), while Wassa camp residence demonstrated an increased odd (aOR = 1.57, 95% CI: 1.01–2.43, *p* = 0.043). In addition, malaria prevention knowledge (aOR = 0.50, 95% CI: 0.36–0.71, *p* < 0.001) and transmission knowledge (aOR = 0.54, 95% CI: 0.39–0.74, *p* < 0.001) reduced malaria odds. Table 2 Logistic Regression of Malaria Test Results Characteristic Categories OR 95% CI p-value Guardian/Parent’s Gender Male 0.44 0.28–0.68 < 0.001 Female ref ref Guardian/Parent’s Age (years) < 18 0.41 0.13–1.35 0.130 31–45 0.73 0.39–1.39 0.213 18–30 ref ref Guardian/Parent’s Marital Status Separated/Divorce 0.42 0.17–0.99 0.033 Single 1.35 0.70–2.66 0.042 Married ref ref Guardian/Parent’s Occupation Farming 0.28 0.01–2.26 0.243 Motorcycle rider 1.26 0.06–11.3 0.986 No source of income 0.35 0.02–2.87 0.366 Petty trading 0.16 0.01–1.26 0.173 Driver ref ref Guardian/Parent’s Education Level No formal Education 3.05 1.09–8.62 0.027 Primary Education 1.05 0.44–2.48 0.9 Secondary Education 1.77 0.51–4.72 0.5 Tertiary Education 0.69 0.18–2.50 0.5 Koranic Education ref ref Relationship to Child Mother 0.00 0.00–0.32 < 0.001 Sister 0.92 0.47–1.87 0.009 Father ref ref No. of Bedrooms in Households Two 1.11 0.79–1.57 0.553 One ref ref House Floor Material Mud 0.73 0.33–1.68 0.435 Cement ref ref House Wall Material Mud 0.60 0.43–0.83 0.002 Cement ref ref House Roof Material Tarpaulin 2.40 0.67–11.38 0.211 Zinc 0.84 0.43–1.56 0.594 Stagnant water present near house Yes 1.25 0.49–2.97 0.615 No ref ref Bed nets in Household Yes 0.88 0.45–1.80 0.707 No ref ref Used Bed net Last Night Yes 0.82 0.28–2.70 0.729 No ref ref No. of Children in household One 0.85 0.10–6.01 0.869 Two 0.33 0.05–1.51 0.195 Three 0.51 0.07–2.20 0.412 Four 0.38 0.06–1.43 0.209 Five ref ref No. of Children Under 5 in household One 1.09 0.15–5.65 0.917 Three 0.88 0.09–7.00 0.901 Four ref ref Source of Malaria Knowledge Hospitals 1.21 0.80–1.85 0.377 Radio 2.41 1.44–4.22 0.001 Television 0.96 0.44–2.21 0.917 Friends ref ref Knowledge of Malaria Diagnosis It is not important 1.23 0.35–5.65 0.764 It is important ref ref Knowledge of Malaria Prevention I know 0.50 0.36–0.71 < 0.001 I don’t know ref ref Knowledge of Malaria Transmission I know 0.54 0.39–0.74 < 0.001 I don’t know ref ref Mosquito Breeding Knowledge I know 1.07 0.48–2.33 0.821 I don’t know ref ref Knowledge of Malaria Cure It can be cured 0.87 0.31–2.17 0.773 It cannot be cured ref ref Reason for No Malaria Medical Care Lack of funds 0.42 0.21–0.77 0.008 Far distance to hospital ref ref Child’s Resident Camp Kabusa 1.00 0.60–1.67 0.990 Kuchingoro 0.79 0.47–1.31 0.358 Wassa 1.57 1.01–2.43 0.043 Durumi ref ref Child Sex Male 1.29 0.92–1.80 0.141 Female ref ref Child’s Age 13-24months 1.00 0.60–1.64 0.991 25-36months 1.33 0.75–2.36 0.330 37-59mnths 1.25 0.71–2.21 0.439 0–12 months ref ref Abbreviations: CI = Confidence Interval, OR = Odds Ratio Machine Learning Model Performance Comparative Predictive Accuracy Figure 1 presents ROC curves for all the models, with the highest AUC belonging to RF (0.89), followed by GBM (0.87), LR (0.82), and DT (0.78), while Fig. 2 reiterates RF's higher AUC (0.89 vs. 0.78–0.87 for other models). Variable Importance Across Models Table 3 , (DT) shows that caregiver occupation (100% relative importance) and residential camp (85.7%) were the utmost predictors, while Table 4 (GBM) revealed occupation (14.2%), education (12.6%), and residential camp (12.4%) as the strongest predictors. Table 5 (Random Forest) again demonstrated caregiver occupation (15.7%), marital status (15.1%), and residential camp (14.6%) as the highest predictors of malarial infection in under-5 children. Model-specific variations are evident in Fig. 3 , with RF emphasizing sociodemographic factors (e.g., marital status) while LR favored knowledge-based factors (e.g., malaria prevention awareness). Figure 4 shows predicted probabilities by model while Fig. 5 demonstrated various error comparison between the models. RF and GBM had more sharp peaks at 0.7–0.9 for greater confidence in positive malaria predictions, whereas DT and LR had more flat distributions with lower certainty. Table 3 Decision Tree Model Variable Importance in Malaria Diagnosis Predictor Variable Categories Importance Score Relative Importance (%) Guardian/Parent’s Occupation Driver Farming Motorcycle rider No source of income Petty trading 13.78 100.00 Child’s Resident Camp Durumi Kabusa Kuchingoro Wassa 11.80 85.68 Knowledge of Malaria Prevention I don't know I know 6.66 48.39 No. Bedrooms in Households One Two 6.42 46.63 Knowledge of Malaria Prevention I don't know I know 3.59 26.09 Guardian/Parent’s Education Level Koranic Education No formal Education Primary Education Secondary Tertiary Education 3.10 22.48 Source of Malaria Knowledge Friends Hospitals Radio Television 2.40 17.42 Child’s Age 0-12months 13-24months 25-36months 37-59mnths 2.31 16.77 Reason for No Malaria Medical Care Lack of funds Far distance to hospital 1.55 11.26 Guardian/Parent’s Marital Status Married Separated/Divorce Single 1.04 7.57 Guardian/Parent’s Age 18-30years 31-45years Less than 18 years 0.79 5.74 No. of Children in Household Four Three One 0.77 5.57 House Roof Material Straw/Thatches/Palm leave Tarpaulin Zinc 0.53 3.83 House Wall Material Cement Mud 0.30 2.20 Note : Variable importance measures how much each predictor contributes to the accuracy of the decision tree model. Table 4 GBM Variable Importance in Malaria Test Prediction Variable Categories Relative Influence (%) Guardian/Parent’s Occupation Driver Farming Motorcycle rider No source of income Petty trading 14.15 Guardian/Parent’s Education Level Koranic Education No formal Education Primary Education Secondary Tertiary Education 12.58 Child’s Resident Camp Durumi Kabusa Kuchingoro Wassa 12.40 Guardian/Parent’s Marital Status Married Separated/Divorce Single 8.32 Source of Malaria Knowledge Friends Hospitals Radio Television 7.09 Child’s Age 0-12months 13-24months 25-36months 37-59mnths 6.18 No. of Children in Household Four Three One 6.17 Knowledge of Malaria Prevention I don't know I know 5.50 Guardian/Parent’s Gender Female male 4.98 Knowledge of malaria Transmission I don't know I know 4.58 House Roof Material Straw/Thatches/Palm leave Tarpaulin Zinc 2.76 Child’s Sex Female Male 2.74 Relationship with Child Father Mother Sister 2.60 Guardian/Parent’s Age 18-30years 31-45years Less than 18 years 2.52 No. Bedrooms in Households One Two 2.25 Bednets in Households No Yes 1.51 House Wall Material Cement Mud 1.00 Knowledge of Mosquito Breeding I don't know I know 0.74 House Floor Material Cement Mud 0.67 Stagnant water present near house No Yes 0.62 Reason for No Malaria Medical Care Lack of funds Far distance to hospital 0.40 Knowledge on Malaria Cure It can be cured It cannot be cured 0.20 No. of Children Under-5 in household Four Three One 0.04 Used Bed nets last night No Yes 0.00 Knowledge on Malaria Diagnosis it is important it is not important 0.00 Note : GBM model with 470 trees. Relative influence shows each variable's contribution. Table 5 Random Forest Variable Importance for Malaria Test Results Variable Categories Mean Decrease in Accuracy Guardian/Parent’s Occupation Driver Farming Motorcycle rider No source of income Petty trading 15.65 Guardian/Parent’s Marital Status Married Separated/Divorce Single 15.08 Child’s Resident Camp Durumi Kabusa Kuchingoro Wassa 14.59 Guardian/Parent’s Gender Female male 12.46 Knowledge of Malaria Prevention I don't know I know 12.19 Source of Malaria Knowledge Friends Hospitals Radio Television 8.59 Guardian/Parent’s Age 18-30years 31-45years Less than 18 years 7.16 Relationship with Child Father Mother Sister 7.05 Knowledge of malaria Transmission I don't know I know 6.96 House Wall Material Cement Mud 6.85 Guardian/Parent’s Education Level Koranic Education No formal Education Primary Education Secondary Tertiary Education 6.53 No. Bedrooms in Households One Two 5.16 House Roof Material Straw/Thatches/Palm leave Tarpaulin Zinc 5.12 Knowledge of Mosquito Breeding I don't know I know 2.44 Bednets in Households No Yes 0.76 House floor material Cement Mud 0.64 Knowledge on Malaria cure It can be cured It cannot be cured 0.18 Used bed nets last night No Yes 0.03 Knowledge on Malaria Diagnosis it is important it is not important -3.20 Child’s Age 0-12months 13-24months 25-36months 37-59mnths -3.21 No. of Children in Household Four Three One -3.70 Stagnant water present near house No Yes -4.01 Reason for No Malaria Medical Care Lack of funds Far distance to hospital -4.62 No. of Children Under-5 in household Four Three One -4.93 Child’s Sex Female Male -5.92 Note : Variable importance measured by mean decrease in accuracy when the variable is permuted. Findings Summary The was a high burden of Malaria in children with 68.5% being positive, and high heterogeneity by residence (Wassa camp highest). Male caregivers, higher education, and mud walls were protective while single marital status and no formal education increased malaria infection risk. Prevention/transmission knowledge reduced odds, however, 60.3% of caregivers lacked this knowledge. RF model performed higher than other models (AUC = 0.89), with caregiver occupation and residence camp remaining the consistent top predictors. These results underscore the interplay of sociodemographic, environmental, and behavioral factors in malaria risk and demonstrate the potential of machine learning for predictive performance. Discussion This study provides extensive description of determinants of malaria risk among Nigeria's under-five children based on the combination of traditional epidemiological and sophisticated machine learning methods. Our findings provide relevant information regarding determinants of malaria based on environmental, sociodemographic, and behavioral factors because they also support the use of predictive modeling in malaria surveillance. The discussion situates these findings against the literature, stresses implications for public health practice, and addresses study limitations. Impoprtant Findings in Perspective of Evidence at Hand High Malaria Burden and Demographic Imbalances The 68.5% malaria prevalence reported in (Table 1 ) concurs with sub-Saharan high-transmission zone surveys (WHO, 2023). The increased risk in 13-24-month-old children (46.3% of cases) is in tandem with long-standing trends for peak susceptibility during the early course of childhood before acquiring immunity development [ 21 ]. Our logistic regression findings (Table 2 ) indicates that male caregivers had a reduced odd (aOR = 0.44) as supported by previous investigations, that female-headed households are at greater risk of malaria because they mostly suffer from economic constraints [ 7 ]. Our findings showed that households with mud wall had protective effect (aOR = 0.60, p = 0.002) contrary to some of the literature but can be due to variations in regional architecture that restrict mosquito penetration. This is important to mention since 53.2% of the houses possessed mud walls (Table 1 ). Wassa camp increased risk (aOR = 1.57) concurs with geographic heterogeneity reported in urban malaria studies (Tusting et al., 2020), possibly due to microenvironment breeding points. Socioeconomic and Knowledge-Based Determinants The significant association between caregiver's education level and malaria risk (Table 2 ) justifies the scientific connection between health literacy and prevention of disease [ 8 ]. Interestingly, non-literate caregivers reported 3.05 times greater odds (p = 0.027) than just Koranic-educated caregivers, highlighting the necessity of focused health education interventions. Our discovery of having knowledge about malaria as being associated with less opportunity for infection (prevention knowledge aOR = 0.50, p < 0.001) agrees with health protection behavior theories [ 22 ]. However, low bed net use (5.5% ownership, 2% use) in the face of 60.3% prevention awareness (Table 1 ) is evidence of widening knowledge-practice gap. These are the very echoes of implementation problems validated in Nigeria's malaria control programs [ 3 ]. Machine Learning Contributions to Malaria Prediction Model Performance and Comparative Advantages The greater predictability ability of RF (AUC = 0.89, Figs. 1 and 2 ) compared to conventional logistic regression (AUC = 0.82) confirms the excellent performance of ensemble methods in detecting the intricate interaction among malaria determinants, as with machine learning use elsewhere in infectious disease prediction studies [ 11 ]. This improved performance is probably a result of RF's special ability to capture nonlinear interactions, including threshold effects of household size, manage multicollinearity between predictors effectively, and include high-dimensional data without overfitting. The relatively poor performance of the DT model (AUC = 0.78, Fig. 1 ) indicates how the model falters in data variability, while the better performance of GBM (AUC = 0.87) also verifies the merits of boosting methods for medical prediction [ 20 ]. It can be argued that the methods are particularly well-suited to intricate public health issues such as malaria risk prediction. Variable Importance Insights The repeated identification of occupation among caregivers as the best predictor in all machine learning models (Tables 3 – 5 , Fig. 5 ) provides strong new evidence on socioeconomic determinants of risk for malaria, with the 15.7% mean reduction in accuracy for occupation in the RF model (Table 5 ) indicating that occupation-type probably acts as a proxy for several interrelated risk factors, such as occupational exposure (especially for mobile occupations such as motorcycle riding), income levels that limit prevention access, and availability of childcare time. The informative difference between regression and machine learning outcomes - where logistic regression focused on knowledge variables (Table 2 ) but ML models focusing on structural determinants such as residential camp (14.6% variable importance in RF), illustrates how each of these analytic methods complements each other. While regression analysis flags potentially modifiable behavioral risk factors, machine learning maximizes predictive discrimination through the elucidation of complex structural determinants that are potentially more difficult to modify but are important to planning targeted intervention. This difference suggests the merit of using both approaches in robust public health studies. Public Health Implications Targeted Interventions The consistent selection of residential camp as a leading predictor across different machine learning models (Tables 3 – 5 ) reflects the value of the efficacy of micro-targeting vector control interventions in high-risk locations like Wassa. Targeted intervention might optimize limited resources by focusing activities on geographical hotspots that are at high-risk for transmission. The phenomenally high rate of stagnant water around houses (96.8%, Table 1 ) highlights the need for blanket environmental control interventions to complement current bed net distribution campaigns. Both interventions would immunize high-risk groups overnight and reduce the number of breeding sites in the longer term, making a more effective malaria control program. Health Education Strategies The wide gap between malaria awareness and preventive practices calls for innovative behavior change interventions with attention to local contexts. Peer education efforts delivered through communities may effectively reach low-literacy caregivers through trusted social networks along with culturally relevant messages. The robust role of radio as a source of information (aOR = 2.41, Table 2 ) suggests that mobile health messaging through radio could help reach significantly more individuals. Moreover, gender-sensitive programming is essential to addressing disproportionate risk among female caregivers, who might have first-line responsibility for child health and socioeconomic constraints that limit their capacity to seek preventive interventions. These multifaceted education strategies must emphasize transforming knowledge into routine protective practices. Improvement in Surveillance System The strong predictive capability of machine learning models (AUC to 0.89, Figs. 1 and 2 ) already suggests their capability to transform national malaria surveillance systems. Public health managers can integrate these models into current systems and design real-time risk mapping tools with a wide range of potential applications: (1) guiding effective resource deployment where needed most, (2) facilitating timely outbreak detection and response, and (3) offering ongoing assessment of intervention effectiveness. Such data-driven monitoring would represent a major advancement over existing systems, enabling timelier and evidence-based decision-making within malaria control programmes. The use of such technologies could potentially and drastically enhance the effectiveness and value of national malaria elimination campaigns. Limitations and Future Directions Methodological Considerations This cross-sectional design generates limitations in determining causality, while machine learning methods improve some temporal limitations by their capacity to identify complex patterns in observational data. Even though we attempted to enroll representative settlements, the sample design based on the camp might affect generalizability to larger populations, however might be appropriate in similar displaced populations. To strengthen future research, investigations should include longitudinal study designs in an attempt to better comprehend seasonality of malaria transmission, increase geographic coverage to more representative sites, and combine entomological markers in a manner that captures a broader picture of vector dynamics. These would advance the validity as well as the utility of the study findings. Modeling Refinements While our machine learning models possessed acceptable prediction capacity, various additions would render them even more useful for malaria surveillance and control. Integration with satellite-retrieved environmental data would enhance spatial risk prediction by incorporating micro-environmental impacts on mosquito breeding sites. Incorporating systems for dynamically updating models would provide ongoing incorporation of new surveillance data, ensuring continous accuracy over time. Combining machine learning with conventional mechanistic malaria models into hybrid models might be able to harness the best of both worlds. The appearance of negative importance scores for certain variables under RF analysis (Table 5 ) would need to be explored with caution as they could be signs of suppressor effects or reflect the need for feature engineering to improve the detection of underlying patterns. Resolution of these methodological concerns might lead to very promising gains in model performance and utility. Conclusion This research provides three major contributions to malaria studies. It identifies new never-before-known risk factors like caregiver occupation through the application of machine learning methods. It also identifies the enhanced predictive precision of ensemble methods, which attained up to 0.89 AUC values, and as well detected very significant knowledge-practice gaps that need to be bridged. These results strongly advocate for precision public health as a strategy for malaria control in which machine learning models are used to guide focused, data-driven interventions on particular risk profiles and geographic areas. In addition, the study suggests a future implementation protocol on two parallel lines - applying these prediction models to real-time surveillance systems as decision-support tools, and concurrently addressing the underlying socioeconomic and environmental determinants causing malaria transmission. The two-stranded approach can fill the gap between advanced analytics and local disease control, and in the process, transform the manner in which malaria programs allocate funds and plan interventions. Declarations Ethics approval The research protocol was approved by the Research Ethics Committee, National Open University of Nigeria with a protocol approval number ETC/2023/NOUN/03/003. Human Ethics and Consent to Participate This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (World Medical Association). Informed, written consent was provided from all caregivers (Parents and guardians), with fingerprint signatures used for illiterate respondents. Severe malaria cases were referred to camp clinics. Data were anonymized and housed on password-protected servers with access limited to study investigators. Consent for publication NA Competing interests The authors declare no conflicts of interest. Funding Funding for this study was solely by the authors. Author Contribution Conceptualization: JOA, VIA and SYMS; Data collation and analysis: JOA, TSA, OOO and SYMS; Writing- JOA, VIA and TSA. Visualization: SYMS, OOO and JOA; Review, editing, and final draft JOA. Acknowledgement The first author is grateful to Duy Tan University for providing a conducive environment for this study. Data Availability Data extracted and used in this study are available on request. References World Health Organization. World malaria report 2023. Geneva: WHO; 2023. Bhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U, et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2023. Nature. 2023;526(7572):207–11. National Malaria Elimination Programme. Nigeria malaria indicator survey 2022. Abuja: NMEP; 2022. Tusting LS, Bottomley C, Gibson H, Kleinschmidt I, Tatem AJ, Lindsay SW, et al. Housing improvements and malaria risk in sub-Saharan Africa: a multi-country analysis of survey data. PLoS Med. 2020;17(2):e1003054. Keiser J, Utzinger J, de Castro MC, Smith TA, Tanner M, Singer BH. Urbanization in sub-Saharan Africa and implication for malaria control. Am J Trop Med Hyg. 2021;71(2suppl):118–27. Okeke IN, Laxminarayan R, Bhutta ZA, Duse AG, Jenkins P, O’Brien TF, et al. Antimicrobial resistance in developing countries. Lancet Infect Dis. 2022;10(8):481–93. Adebayo AM, Akinyemi OO, Cadmus EO. Knowledge of malaria prevention among caregivers of under-five children in rural communities of Nigeria. Malar J. 2021;20(1):1–9. Deribew A, Dejene T, Kebede B, Tessema GA, Melaku YA, Misganaw A, et al. Burden of malaria in Ethiopia, 2000–2016: findings from the Global Burden of Disease Study 2016. BMC Med. 2022;18(1):1–12. Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 2019;19(1):64. Deo RC. Machine learning in medicine. Circulation. 2020;132(20):1920–30. Rahman MM, Islam MM, Mahmud S, Raihan AR, Hossain MS. Machine learning approaches for predicting malaria outbreaks using climatic factors in Bangladesh. PLoS ONE. 2021;16(5):e0251787. Liang L, Gong P. Machine learning for environmental monitoring. Nat Sustain. 2022;3(8):583–4. Oladele TT, Adebowale AS, Oyebade OO, Ajayi IO. Machine learning models for predicting malaria risk using environmental and demographic data: a comparative analysis. Sci Rep. 2023;13(1):1–12. Nsoesie EO, Beckman RJ, Shashaani S, Nagaraj KS, Marathe MV. A simulation optimization approach to epidemic forecasting. PLoS ONE. 2022;17(3):e0264657. World Health Organization. Guidelines for malaria. Geneva: WHO; 2021. Hosmer DW, Lemeshow S, Sturdivant RX. Applied logistic regression. 3rd ed. Hoboken: Wiley; 2013. James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning. 2nd ed. New York: Springer; 2021. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. Boca Raton: CRC; 2017. Liaw A, Wiener M. Classification and regression by randomForest. R News. 2022;2(3):18–22. Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2021;29(5):1189–232. Snow RW, Sartorius B, Kyalo D, Maina J, Amratia P, Mundia CW, et al. The prevalence of Plasmodium falciparum in sub-Saharan Africa since 1900. Nature. 2021;550(7677):515–8. Rosenstock IM. Historical origins of the Health Belief Model. Health Educ Monogr. 1974;2(4):328–35. Additional Declarations No competing interests reported. Supplementary Files Under5MalariaQUESTIONNAIRE.pdf Cite Share Download PDF Status: Published Journal Publication published 04 Dec, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 08 Oct, 2025 Reviews received at journal 07 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviews received at journal 03 Oct, 2025 Reviewers agreed at journal 03 Oct, 2025 Reviewers agreed at journal 03 Oct, 2025 Reviews received at journal 26 Sep, 2025 Reviews received at journal 21 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviews received at journal 14 Sep, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviewers agreed at journal 13 Sep, 2025 Reviewers agreed at journal 06 Sep, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviewers invited by journal 04 Sep, 2025 Editor assigned by journal 03 Sep, 2025 Editor invited by journal 18 Aug, 2025 Submission checks completed at journal 18 Aug, 2025 First submitted to journal 18 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7352919","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512721407,"identity":"e2c2565d-e315-43ed-99fd-cf0ba9bdc338","order_by":0,"name":"Joseph Opeolu Ashaolu","email":"","orcid":"","institution":"Duy Tan University","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"Opeolu","lastName":"Ashaolu","suffix":""},{"id":512721408,"identity":"6493cb0d-e15c-4519-9e1b-67b37c80bb20","order_by":1,"name":"Taiwo S. Akanji","email":"","orcid":"","institution":"Kwara State University","correspondingAuthor":false,"prefix":"","firstName":"Taiwo","middleName":"S.","lastName":"Akanji","suffix":""},{"id":512721409,"identity":"c2a2bcc4-662b-410c-b649-63b414946ffe","order_by":2,"name":"Victoria I. Ayansola","email":"","orcid":"","institution":"University of Warsaw","correspondingAuthor":false,"prefix":"","firstName":"Victoria","middleName":"I.","lastName":"Ayansola","suffix":""},{"id":512721410,"identity":"f22e8fa6-8ac8-4fcb-ab03-e35f72c0ab91","order_by":3,"name":"Olajumoke O. Olawale-Succes","email":"","orcid":"","institution":"Dominion University","correspondingAuthor":false,"prefix":"","firstName":"Olajumoke","middleName":"O.","lastName":"Olawale-Succes","suffix":""},{"id":512721411,"identity":"1d2a343d-2761-4dba-b17e-299c1e73b0c6","order_by":4,"name":"Agbolade J. Sunday","email":"","orcid":"","institution":"Redeemer’s University","correspondingAuthor":false,"prefix":"","firstName":"Agbolade","middleName":"J.","lastName":"Sunday","suffix":""},{"id":512721412,"identity":"91e6a13c-34d5-459a-9deb-fb94a4b4c4c4","order_by":5,"name":"Sylvain Y.M. Some","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYFAC5gaGBAjjAANjA4QmABhhWtgSIFrYEojQAgE8BsRpkZ+R2CbxcEednG57zzeJnzts5BjYeB/g1WJwA6gl8cxhY7MzZ7dJ9p5JM2ZgYzfAr0Uisdkgse1A4rYbudskeNsOJzbItxF0GEhLXeK2+2+eSf4FaWFjw6+F4UZi44PENmagLTxs0rzEaDE48xCkBeSXNGNr2bY0YzZCWuTbkw8c/NlWJ2d2/PDDm2/bbOT4CTpMIAHOZJEAkYQ0MDDwH4AzmT8QVD0KRsEoGAUjEgAAs4VItaeNCQEAAAAASUVORK5CYII=","orcid":"","institution":"National institute of Public Health, Centre de recherche en Santé de Nouna (CrSN)","correspondingAuthor":true,"prefix":"","firstName":"Sylvain","middleName":"Y.M.","lastName":"Some","suffix":""}],"badges":[],"createdAt":"2025-08-12 07:53:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7352919/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7352919/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-12116-6","type":"published","date":"2025-12-04T15:58:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91108119,"identity":"6b3323b7-1045-4c6b-b28a-31d5c5b85920","added_by":"auto","created_at":"2025-09-11 15:56:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":156087,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve compares the performance of four machine learning models—Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting Machine (GBM)—for predicting malaria test outcomes. The True Positive Rate (Sensitivity) is plotted against the False Positive Rate (1 - Specificity), with values ranging from 0.00 to 1.00. The curves illustrate the trade-off between sensitivity and specificity across different thresholds, allowing for a visual assessment of each model's diagnostic accuracy. Higher curves closer to the top-left corner indicate better predictive performance.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7352919/v1/60e9e46c7c583f5621e91e33.png"},{"id":91108120,"identity":"45fe3941-33b0-48d5-b8ad-047ad3358461","added_by":"auto","created_at":"2025-09-11 15:56:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":166315,"visible":true,"origin":"","legend":"\u003cp\u003eThis bar chart compares the ROC AUC (Area Under the Receiver Operating Characteristic Curve) scores of four machine learning models—Decision Tree, Gradient Boosting Machine (GBM), Logistic Regression, and Random Forest—for malaria test prediction. The ROC AUC metric, ranging from 0 to 1, quantifies each model's ability to distinguish between positive and negative cases, with higher values indicating better classification performance. The chart visually ranks the models based on their predictive accuracy.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7352919/v1/d94e795e55279ec3f3089256.png"},{"id":91108123,"identity":"ace97cb6-d0a7-488b-8e35-16ec57c38237","added_by":"auto","created_at":"2025-09-11 15:56:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":531493,"visible":true,"origin":"","legend":"\u003cp\u003eThis bar plot compares the normalized feature importance (0–100 scale) of various predictors across three predictive models: Decision Tree, GSU Logistic Regression, and Random Forest. Key influential variables include \"Guardian/Parent’s Occupation\" (e.g., petty trading, motorcycle rider), \"Resident Camp\" locations, and malaria-related knowledge indicators (e.g., \"Knowledge of Malaria Prevention\"). Demographic factors like \"Guardian/Parent’s Marital Status\" and \"Guardian Parent’s Education Level\" also show significant importance, alongside household characteristics (e.g., \"Bedrooms in Households,\" \"House Wall Material\"). The plot highlights model-specific variations in feature rankings, with Random Forest emphasizing occupation and residence, while Logistic Regression prioritizes knowledge and socioeconomic factors. Labels with asterisks or quotes may denote repeated or modified variables.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7352919/v1/fa9981c2bb697d30c4c71b4f.png"},{"id":91108134,"identity":"8c1c3a8b-1227-4491-8e09-d97f335d1aea","added_by":"auto","created_at":"2025-09-11 15:56:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":229969,"visible":true,"origin":"","legend":"\u003cp\u003eKernel density plots display the distribution of predicted probabilities for a positive malaria test across four models—Decision Tree, Gradient Boosting Machine (GBM), and Random Forest and Logistic regression. The x-axis represents the predicted probability (ranging from 0.0 to 0.9), while the y-axis shows the density of predictions. The plots reveal how each model assigns confidence scores to positive cases, with variations in peak heights and distributions highlighting differences in model calibration and certainty\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7352919/v1/842f3e7a2bdba8765d067cef.png"},{"id":91109000,"identity":"b023f4d4-88c8-444d-af1d-30c92808ce3b","added_by":"auto","created_at":"2025-09-11 16:04:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":591402,"visible":true,"origin":"","legend":"\u003cp\u003eThis visualization compares the performance of multiple machine learning models (including Random Forest and rpart) across various evaluation metrics, with scores ranging from 0.0 to 0.9. The chart highlights the relative strengths of each model in terms of predictive accuracy, as measured by metrics such as precision, recall, or ROC AUC, allowing for a comprehensive assessment of their effectiveness in classification tasks. The y-axis represents the score/value, while the x-axis or grouped bars likely differentiate between models and metrics.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7352919/v1/aed23f328d4abd67ce825324.png"},{"id":97724722,"identity":"f3a12384-e21a-4692-8896-f95a9296d585","added_by":"auto","created_at":"2025-12-08 16:13:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3156410,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7352919/v1/d59b7182-5a32-4c76-8dbf-769d01d04cc9.pdf"},{"id":91109001,"identity":"9ea7023f-336a-49a1-9b2c-4ec19c0749e3","added_by":"auto","created_at":"2025-09-11 16:04:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":268456,"visible":true,"origin":"","legend":"","description":"","filename":"Under5MalariaQUESTIONNAIRE.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7352919/v1/573345da3627c9768c0738ac.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Risk Factors to Predictive Modelling: Applying Machine Learning to Childhood Malaria Surveillance in Resource-Limited Settings","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalaria remains one of the major global public health issues, with the biggest burden situated in sub-Saharan Africa [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Malaria control efforts have made a huge impact in the last two decades, still the disease poses significant morbidity and mortality, especially among high-risk groups like children aged below five years [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Malaria in Nigeria accounts for nearly 30% of deaths among children with a significant percentage of hospitalization, therefore requiring additional interventions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Knowledge of the sociodemographic, environmental, and behavioral determinants of malaria transmission will enable planning accordingly for prevention and control.\u003c/p\u003e\u003cp\u003eEndemic malaria is affected by a complex array of different factors, which range from family life to caregiver knowledge and availability of preventive tools [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For instance, houses with proximity to stagnant water, poor quality housing material, and low bed net usage have consistently been proven to be related to increased risks of malaria [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Besides, socioeconomic inequalities, low literacy levels and limited access to health care, increase susceptibility to malaria [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Caregivers' knowledge of malaria prevention, transmission, and treatment is also a crucial health-seeking behavior and compliance determinant with protective practices [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Despite this, gaps in malaria knowledge and misconceptions remain prevalent in most communities, discouraging effective prevention [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMachine learning (ML) algorithms are powerful tools to decode complex health information and predict disease outcomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Unlike conventional statistical approaches, ML algorithms can detect nonlinear interaction and correlation among numerous predictors with higher predictive power [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In malaria, ML models have predicted transmission behavior, resource optimization, and risk factor assessment [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For example, logistic regression, decision trees, and ensemble models like random forests and gradient boosting machines (GBMs) have been used in predicting malaria incidence from demographic and environmental determinants [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These models play a vital role in clarifying the relative importance of factors so that priorities could be established by policymakers [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study investigates the identification of the top-most predictors of malaria among Nigerian children using traditional descriptive statistics and machine learning. By evaluating sociodemographic factors (such as caregiver gender, education and occupation), household living conditions (including roofing material type and bed nets usage), as well as knowledge on malaria, we seek to clarify factors most associated with laboratory test-confirmed malaria. In addition, we compared the performance of different ML models such as logistic regression, decision tree (DT), random forest (RF), and Gradient Boost Machines (GBMs) to determining the most robust approach to malaria prediction. These findings will assist in augmenting the understanding of malaria risk determinants while informing disease control with evidence-based data.\u003c/p\u003e\u003cp\u003eThe justification for this study stems from the need for evidence-based interventions in locally relevant contexts. Whereas existing research has studied malaria determinants in sub-Saharan Africa, there have been few studies bringing sociodemographic, environmental, and knowledge-based predictors together using a machine learning framework. Equally, comparative assessment of ML modellings for malaria prediction has been relatively under-studied, particularly in low-resource environments where precision risk stratification maximizes a few available healthcare resources [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Through the filling of such knowledge gaps, this research expects to offer actionable evidence to the malaria control program and maximize the precision of public health interventions.\u003c/p\u003e\u003cp\u003eTherefore, this study investigates the multi-factorial determinants of malaria test outcomes among Nigerian children using advanced machine learning methods to ascertain key predictors and model accuracy estimates. Its application will guide targeted malaria prevention, resource allocation, and community education toward the ultimate global malaria elimination initiatives.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Data Collection\u003c/h2\u003e\u003cp\u003eCross-sectional study design was used in this study to assess factors associated with malaria test status among under-5 years old Nigerian children. Data were collected from guardians or parents living within the study camps, including Kabusa, Durumi, Kuchingoro, and Wassa. A pretested questionnaire was used to obtain sociodemographic data, household characteristics, and knowledge on malaria, and prevention practices. The research included Rapid Diagnostic Test (RDT) test results to ascertain malaria positivity among children. Ethical clearance was provided by the concerned institutional review board and informed consent was obtained from all the respondents before data collection [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The designed consent and questionnaire (English Language Version) used for the study data collection is attached as a supplementary file.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Population and Sampling\u003c/h3\u003e\n\u003cp\u003eThe survey employed 693 caregiver-child pairs, with 0\u0026ndash;59-months old children. Participants were sampled by multistage sampling for representativeness. Four high-burden residential camps were first purposively selected following malaria prevalence reports. Subsequently, random sampling of households with at least one child aged less than five years was conducted. Caregivers were interviewed and screened for malaria with RDT kits according to standard procedure [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Sample size was computed employing the formula for prevalence studies: n = [Z\u0026sup2;\u0026times;P(1-P)]/d\u0026sup2;, where Z\u0026thinsp;=\u0026thinsp;1.96 (95% CI), P\u0026thinsp;=\u0026thinsp;0.5 (expected prevalence), d\u0026thinsp;=\u0026thinsp;0.05 (precision), design effect adjustment of 2.0 and 15% non-response, to yield 693 participants.\u003c/p\u003e\n\u003ch3\u003eVariables and Measurements\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eDependent Variable\u003c/h2\u003e\u003cp\u003eThe primary outcome was malaria test result status, as a binary variable categorized as Positive (if malaria infection is confirmed via RDT) or Negative (if no malaria infection was detected).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIndependent Variables\u003c/h3\u003e\n\u003cp\u003eThe predictors were grouped into four main categories: First, sociodemographic factors which include caregiver\u0026rsquo;s gender, marital status, age, education level, and occupation. This also include household size, number of children under five, and relationship with the child. Second, household and environmental factors which include housing conditions (type of floor, wall, and roof materials), presence of stagnant water near the household as well as bed net availability and usage in households. Third, Malaria knowledge and health-seeking behavior which comprises the source of malaria knowledge (friends, hospitals, radio, television), the awareness of malaria transmission, prevention, and treatment as well as the reasons for not seeking medical care (such as lack of funds, distance to hospital), and finally, Child-specific factors such as child\u0026rsquo;s age, sex, and residential camp.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical, Logistic Regression and Machine Learning Analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics was used to summarize the participant\u0026rsquo;s characteristics, and presented as frequencies and percentages. Further, univariate and multivariate logistic regression models were fitted to determine the associations between independent variables and malaria test outcomes. Adjusted odds ratios (aORs) with 95% confidence intervals (CIs) were also reported, while statistical significance was set at *p* \u0026lt; 0.05 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMachine Learning Modeling\u003c/h3\u003e\n\u003cp\u003eFour supervised learning algorithms were trained and evaluated: one, Logistic Regression (LR), a widely known and traditional baseline model assessing linear relationships between predictors and malaria outcomes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Two, Decision Tree (DT) which uses a rule-based model to capture nonlinear interactions among variables [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Three, Random Forest (RF), bagging technique using a set of decision trees to improve prediction stability [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Lastly, Gradient Boosting Machine (GBM), which is an iterative boosting approach for enhancing predictive precision [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eModel Training and Performance Evaluation\u003c/h3\u003e\n\u003cp\u003eThe data were divided into 70% (training set) and 30% (test set) sets. Hyper-parameter tuning was conducted on 10-fold cross-validation to optimize model performance. Feature importance was estimated by variable importance scores (for DT and RF), relative influence (for GBM), and odds ratios (for logistic regression). Models were contrasted on Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) to estimate classification accuracy. Robustness of models was also estimated using precision, recall, and F1-score, with calibration plots estimating reliability of predictions. All analysis was performed with R (version 4.2.0) using packages like caret, randomForest, gbm, and pROC.\u003c/p\u003e\u003cp\u003e The research protocol was approved by the Research Ethics Committee, National Open University of Nigeria with a protocol approval number ETC/2023/NOUN/03/003. Severe malaria cases were referred to camp clinics. Data were anonymized and housed on password-protected servers with access limited to study investigators\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive Characteristics of the Study Population\u003c/h2\u003e\u003cp\u003e693 caregiver-child dyads, with sociodemographic and household profiles as elaborated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e were investigated in this study. Most of the caregivers were female (86.1%), aged between 18 and 30 years (78.5%), and married (77.2%). The majority had primary education (74.9%), and petty trading was the most frequent occupation (37.4%). Household profiles provided evidence of 67.2% sleeping in one bedroom, 96.8% having stagnant water around their homes, and 94.5% without bed nets. Among the children, 62.0% were females and the highest percentage (46.3%) belonged to the age category of 13\u0026ndash;24 months. The prevalence of malaria was high with 68.5% RDT positive.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics of Study Variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCount (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercent (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Gender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e86.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18-30years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31-45years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than 18 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Marital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeparated/Divorce\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Education Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKoranic Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo formal Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e74.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertiary Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Occupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDriver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFarming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMotorcycle rider\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo source of income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePetty trading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelationship with Child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFather\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMother\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSister\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. Bedrooms in Households\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e67.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTwo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHouse Floor Material\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMud\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHouse Wall material\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMud\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHouse Roof Material\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStraw/Thatches/Palm leave\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTarpaulin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZinc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStagnant water present near house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBednets in household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUsed Bed nets last night\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e98.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of Children in Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e83.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTwo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of Children Under-5 in household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource of Malaria Knowledge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFriends\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHospitals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRadio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTelevision\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge on Malaria Diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eit is important\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e98.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eit is not important\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge of Malaria Prevention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI don't know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge of malaria Transmission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI don't know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge of Mosquito Breeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI don't know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge on Malaria Cure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt can be cured\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt cannot be cured\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReason for No Malaria Medical Care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLack of funds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFar distance to hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026rsquo;s Resident Camp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDurumi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKabusa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKuchingoro\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWassa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026rsquo;s Sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026rsquo;s Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0-12months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13-24months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25-36months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37-59mnths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild RDT Malaria Test Result\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e68.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eLogistic Regression Analysis of Malaria Risk Factors\u003c/h2\u003e\u003cp\u003eThe adjusted odds ratio (aORs) from multivariate logistic regression are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. With important findings including having male caregiver presenting lesser odds of malaria test positivity (aOR\u0026thinsp;=\u0026thinsp;0.44, 95% CI: 0.28\u0026ndash;0.68, *p* \u0026lt; 0.001) as compared to female. Divorced/separated caregivers having lower odds (aOR\u0026thinsp;=\u0026thinsp;0.42, 95% CI: 0.17\u0026ndash;0.99, *p* = 0.033), while single caregivers had higher odds (aOR\u0026thinsp;=\u0026thinsp;1.35, *p* = 0.042) indicating the importance of caregiver gender. More so, caregivers with no formal education had 3.05 times higher odds (95% CI: 1.09\u0026ndash;8.62, *p* = 0.027) compared to caregivers with koranic-education only. Houses with mud walls had a protective effect (aOR\u0026thinsp;=\u0026thinsp;0.60, 95% CI: 0.43\u0026ndash;0.83, *p* = 0.002), while Wassa camp residence demonstrated an increased odd (aOR\u0026thinsp;=\u0026thinsp;1.57, 95% CI: 1.01\u0026ndash;2.43, *p* = 0.043). In addition, malaria prevention knowledge (aOR\u0026thinsp;=\u0026thinsp;0.50, 95% CI: 0.36\u0026ndash;0.71, *p* \u0026lt; 0.001) and transmission knowledge (aOR\u0026thinsp;=\u0026thinsp;0.54, 95% CI: 0.39\u0026ndash;0.74, *p* \u0026lt; 0.001) reduced malaria odds.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic Regression of Malaria Test Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Gender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u0026ndash;0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Age (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u0026ndash;1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u0026ndash;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.39\u0026ndash;1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Marital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeparated/Divorce\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u0026ndash;0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.70\u0026ndash;2.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Occupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFarming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u0026ndash;2.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.243\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMotorcycle rider\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u0026ndash;11.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.986\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo source of income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u0026ndash;2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.366\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePetty trading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u0026ndash;1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDriver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Education Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo formal Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.09\u0026ndash;8.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.44\u0026ndash;2.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.51\u0026ndash;4.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertiary Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.18\u0026ndash;2.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKoranic Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelationship to Child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMother\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u0026ndash;0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSister\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.47\u0026ndash;1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFather\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of Bedrooms in Households\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTwo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.79\u0026ndash;1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.553\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHouse Floor Material\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMud\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.33\u0026ndash;1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.435\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHouse Wall Material\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMud\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.43\u0026ndash;0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHouse Roof Material\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTarpaulin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67\u0026ndash;11.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.211\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZinc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.43\u0026ndash;1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.594\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStagnant water present near house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u0026ndash;2.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.615\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBed nets in Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.45\u0026ndash;1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUsed Bed net Last Night\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u0026ndash;2.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.729\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of Children in household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u0026ndash;6.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.869\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTwo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u0026ndash;1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u0026ndash;2.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u0026ndash;1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of Children Under 5 in household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u0026ndash;5.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09\u0026ndash;7.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.901\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource of Malaria Knowledge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHospitals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.80\u0026ndash;1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.377\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRadio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.44\u0026ndash;4.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTelevision\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.44\u0026ndash;2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFriends\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge of Malaria Diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt is not important\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u0026ndash;5.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt is important\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge of Malaria Prevention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.36\u0026ndash;0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI don\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge of Malaria Transmission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.39\u0026ndash;0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI don\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMosquito Breeding Knowledge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.48\u0026ndash;2.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI don\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge of Malaria Cure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt can be cured\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.31\u0026ndash;2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.773\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt cannot be cured\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReason for No Malaria Medical Care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLack of funds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.21\u0026ndash;0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFar distance to hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026rsquo;s Resident Camp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKabusa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.60\u0026ndash;1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.990\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKuchingoro\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.47\u0026ndash;1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWassa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01\u0026ndash;2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDurumi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild Sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.92\u0026ndash;1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026rsquo;s Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13-24months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.60\u0026ndash;1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25-36months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.75\u0026ndash;2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.330\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37-59mnths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.71\u0026ndash;2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.439\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: CI\u0026thinsp;=\u0026thinsp;Confidence Interval, OR\u0026thinsp;=\u0026thinsp;Odds Ratio\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eMachine Learning Model Performance\u003c/h2\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003eComparative Predictive Accuracy\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents ROC curves for all the models, with the highest AUC belonging to RF (0.89), followed by GBM (0.87), LR (0.82), and DT (0.78), while Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reiterates RF's higher AUC (0.89 vs. 0.78\u0026ndash;0.87 for other models).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eVariable Importance Across Models\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, (DT) shows that caregiver occupation (100% relative importance) and residential camp (85.7%) were the utmost predictors, while Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (GBM) revealed occupation (14.2%), education (12.6%), and residential camp (12.4%) as the strongest predictors. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (Random Forest) again demonstrated caregiver occupation (15.7%), marital status (15.1%), and residential camp (14.6%) as the highest predictors of malarial infection in under-5 children. Model-specific variations are evident in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, with RF emphasizing sociodemographic factors (e.g., marital status) while LR favored knowledge-based factors (e.g., malaria prevention awareness). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows predicted probabilities by model while Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e demonstrated various error comparison between the models. RF and GBM had more sharp peaks at 0.7\u0026ndash;0.9 for greater confidence in positive malaria predictions, whereas DT and LR had more flat distributions with lower certainty.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDecision Tree Model Variable Importance in Malaria Diagnosis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eImportance Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRelative Importance (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Occupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDriver\u003c/p\u003e\u003cp\u003eFarming\u003c/p\u003e\u003cp\u003eMotorcycle rider\u003c/p\u003e\u003cp\u003eNo source of income\u003c/p\u003e\u003cp\u003ePetty trading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026rsquo;s Resident Camp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDurumi\u003c/p\u003e\u003cp\u003eKabusa\u003c/p\u003e\u003cp\u003eKuchingoro\u003c/p\u003e\u003cp\u003eWassa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge of Malaria Prevention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI don't know\u003c/p\u003e\u003cp\u003eI know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. Bedrooms in Households\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003cp\u003eTwo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge of Malaria Prevention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI don't know\u003c/p\u003e\u003cp\u003eI know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Education Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKoranic Education\u003c/p\u003e\u003cp\u003eNo formal Education\u003c/p\u003e\u003cp\u003ePrimary Education\u003c/p\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003cp\u003eTertiary Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource of Malaria Knowledge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFriends\u003c/p\u003e\u003cp\u003eHospitals\u003c/p\u003e\u003cp\u003eRadio\u003c/p\u003e\u003cp\u003eTelevision\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026rsquo;s Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0-12months\u003c/p\u003e\u003cp\u003e13-24months\u003c/p\u003e\u003cp\u003e25-36months\u003c/p\u003e\u003cp\u003e37-59mnths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReason for No Malaria Medical Care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLack of funds\u003c/p\u003e\u003cp\u003eFar distance to hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Marital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003cp\u003eSeparated/Divorce\u003c/p\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18-30years\u003c/p\u003e\u003cp\u003e31-45years\u003c/p\u003e\u003cp\u003eLess than 18 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of Children in Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFour\u003c/p\u003e\u003cp\u003eThree\u003c/p\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHouse Roof Material\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStraw/Thatches/Palm leave\u003c/p\u003e\u003cp\u003eTarpaulin\u003c/p\u003e\u003cp\u003eZinc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHouse Wall Material\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCement\u003c/p\u003e\u003cp\u003eMud\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e:\u0026nbsp;Variable importance measures how much each predictor contributes to the accuracy of the decision tree model.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGBM Variable Importance in Malaria Test Prediction\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRelative Influence (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Occupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDriver\u003c/p\u003e\u003cp\u003eFarming\u003c/p\u003e\u003cp\u003eMotorcycle rider\u003c/p\u003e\u003cp\u003eNo source of income\u003c/p\u003e\u003cp\u003ePetty trading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Education Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKoranic Education\u003c/p\u003e\u003cp\u003eNo formal Education\u003c/p\u003e\u003cp\u003ePrimary Education\u003c/p\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003cp\u003eTertiary Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026rsquo;s Resident Camp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDurumi\u003c/p\u003e\u003cp\u003eKabusa\u003c/p\u003e\u003cp\u003eKuchingoro\u003c/p\u003e\u003cp\u003eWassa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Marital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003cp\u003eSeparated/Divorce\u003c/p\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource of Malaria Knowledge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFriends\u003c/p\u003e\u003cp\u003eHospitals\u003c/p\u003e\u003cp\u003eRadio\u003c/p\u003e\u003cp\u003eTelevision\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026rsquo;s Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0-12months\u003c/p\u003e\u003cp\u003e13-24months\u003c/p\u003e\u003cp\u003e25-36months\u003c/p\u003e\u003cp\u003e37-59mnths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of Children in Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFour\u003c/p\u003e\u003cp\u003eThree\u003c/p\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge of Malaria Prevention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI don't know\u003c/p\u003e\u003cp\u003eI know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Gender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge of malaria Transmission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI don't know\u003c/p\u003e\u003cp\u003eI know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHouse Roof Material\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStraw/Thatches/Palm leave\u003c/p\u003e\u003cp\u003eTarpaulin\u003c/p\u003e\u003cp\u003eZinc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026rsquo;s Sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelationship with Child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFather\u003c/p\u003e\u003cp\u003eMother\u003c/p\u003e\u003cp\u003eSister\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18-30years\u003c/p\u003e\u003cp\u003e31-45years\u003c/p\u003e\u003cp\u003eLess than 18 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. Bedrooms in Households\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003cp\u003eTwo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBednets in Households\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHouse Wall Material\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCement\u003c/p\u003e\u003cp\u003eMud\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge of Mosquito Breeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI don't know\u003c/p\u003e\u003cp\u003eI know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHouse Floor Material\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCement\u003c/p\u003e\u003cp\u003eMud\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStagnant water present near house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReason for No Malaria Medical Care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLack of funds\u003c/p\u003e\u003cp\u003eFar distance to hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge on Malaria Cure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt can be cured\u003c/p\u003e\u003cp\u003eIt cannot be cured\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of Children Under-5 in household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFour\u003c/p\u003e\u003cp\u003eThree\u003c/p\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUsed Bed nets last night\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge on Malaria Diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eit is important\u003c/p\u003e\u003cp\u003eit is not important\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: GBM model with 470 trees. Relative influence shows each variable's contribution.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRandom Forest Variable Importance for Malaria Test Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean Decrease in Accuracy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Occupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDriver\u003c/p\u003e\u003cp\u003eFarming\u003c/p\u003e\u003cp\u003eMotorcycle rider\u003c/p\u003e\u003cp\u003eNo source of income\u003c/p\u003e\u003cp\u003ePetty trading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Marital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003cp\u003eSeparated/Divorce\u003c/p\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026rsquo;s Resident Camp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDurumi\u003c/p\u003e\u003cp\u003eKabusa\u003c/p\u003e\u003cp\u003eKuchingoro\u003c/p\u003e\u003cp\u003eWassa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Gender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge of Malaria Prevention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI don't know\u003c/p\u003e\u003cp\u003eI know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource of Malaria Knowledge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFriends\u003c/p\u003e\u003cp\u003eHospitals\u003c/p\u003e\u003cp\u003eRadio\u003c/p\u003e\u003cp\u003eTelevision\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18-30years\u003c/p\u003e\u003cp\u003e31-45years\u003c/p\u003e\u003cp\u003eLess than 18 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelationship with Child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFather\u003c/p\u003e\u003cp\u003eMother\u003c/p\u003e\u003cp\u003eSister\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge of malaria Transmission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI don't know\u003c/p\u003e\u003cp\u003eI know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHouse Wall Material\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCement\u003c/p\u003e\u003cp\u003eMud\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardian/Parent\u0026rsquo;s Education Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKoranic Education\u003c/p\u003e\u003cp\u003eNo formal Education\u003c/p\u003e\u003cp\u003ePrimary Education\u003c/p\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003cp\u003eTertiary Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. Bedrooms in Households\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003cp\u003eTwo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHouse Roof Material\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStraw/Thatches/Palm leave\u003c/p\u003e\u003cp\u003eTarpaulin\u003c/p\u003e\u003cp\u003eZinc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge of Mosquito Breeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI don't know\u003c/p\u003e\u003cp\u003eI know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBednets in Households\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHouse floor material\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCement\u003c/p\u003e\u003cp\u003eMud\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge on Malaria cure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt can be cured\u003c/p\u003e\u003cp\u003eIt cannot be cured\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUsed bed nets last night\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge on Malaria Diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eit is important\u003c/p\u003e\u003cp\u003eit is not important\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026rsquo;s Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0-12months\u003c/p\u003e\u003cp\u003e13-24months\u003c/p\u003e\u003cp\u003e25-36months\u003c/p\u003e\u003cp\u003e37-59mnths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of Children in Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFour\u003c/p\u003e\u003cp\u003eThree\u003c/p\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStagnant water present near house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-4.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReason for No Malaria Medical Care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLack of funds\u003c/p\u003e\u003cp\u003eFar distance to hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-4.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of Children Under-5 in household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFour\u003c/p\u003e\u003cp\u003eThree\u003c/p\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-4.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026rsquo;s Sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-5.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e:\u0026nbsp;Variable importance measured by mean decrease in accuracy when the variable is permuted.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eFindings Summary\u003c/h2\u003e\u003cp\u003eThe was a high burden of Malaria in children with 68.5% being positive, and high heterogeneity by residence (Wassa camp highest). Male caregivers, higher education, and mud walls were protective while single marital status and no formal education increased malaria infection risk. Prevention/transmission knowledge reduced odds, however, 60.3% of caregivers lacked this knowledge. RF model performed higher than other models (AUC\u0026thinsp;=\u0026thinsp;0.89), with caregiver occupation and residence camp remaining the consistent top predictors. These results underscore the interplay of sociodemographic, environmental, and behavioral factors in malaria risk and demonstrate the potential of machine learning for predictive performance.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides extensive description of determinants of malaria risk among Nigeria's under-five children based on the combination of traditional epidemiological and sophisticated machine learning methods. Our findings provide relevant information regarding determinants of malaria based on environmental, sociodemographic, and behavioral factors because they also support the use of predictive modeling in malaria surveillance. The discussion situates these findings against the literature, stresses implications for public health practice, and addresses study limitations.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eImpoprtant Findings in Perspective of Evidence at Hand\u003c/h2\u003e\u003cp\u003eHigh Malaria Burden and Demographic Imbalances\u003c/p\u003e\u003cp\u003eThe 68.5% malaria prevalence reported in (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) concurs with sub-Saharan high-transmission zone surveys (WHO, 2023). The increased risk in 13-24-month-old children (46.3% of cases) is in tandem with long-standing trends for peak susceptibility during the early course of childhood before acquiring immunity development [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Our logistic regression findings (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) indicates that male caregivers had a reduced odd (aOR\u0026thinsp;=\u0026thinsp;0.44) as supported by previous investigations, that female-headed households are at greater risk of malaria because they mostly suffer from economic constraints [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Our findings showed that households with mud wall had protective effect (aOR\u0026thinsp;=\u0026thinsp;0.60, p\u0026thinsp;=\u0026thinsp;0.002) contrary to some of the literature but can be due to variations in regional architecture that restrict mosquito penetration. This is important to mention since 53.2% of the houses possessed mud walls (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Wassa camp increased risk (aOR\u0026thinsp;=\u0026thinsp;1.57) concurs with geographic heterogeneity reported in urban malaria studies (Tusting et al., 2020), possibly due to microenvironment breeding points.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eSocioeconomic and Knowledge-Based Determinants\u003c/h2\u003e\u003cp\u003eThe significant association between caregiver's education level and malaria risk (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) justifies the scientific connection between health literacy and prevention of disease [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Interestingly, non-literate caregivers reported 3.05 times greater odds (p\u0026thinsp;=\u0026thinsp;0.027) than just Koranic-educated caregivers, highlighting the necessity of focused health education interventions. Our discovery of having knowledge about malaria as being associated with less opportunity for infection (prevention knowledge aOR\u0026thinsp;=\u0026thinsp;0.50, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) agrees with health protection behavior theories [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, low bed net use (5.5% ownership, 2% use) in the face of 60.3% prevention awareness (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) is evidence of widening knowledge-practice gap. These are the very echoes of implementation problems validated in Nigeria's malaria control programs [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eMachine Learning Contributions to Malaria Prediction\u003c/h2\u003e\u003cp\u003eModel Performance and Comparative Advantages\u003c/p\u003e\u003cp\u003eThe greater predictability ability of RF (AUC\u0026thinsp;=\u0026thinsp;0.89, Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) compared to conventional logistic regression (AUC\u0026thinsp;=\u0026thinsp;0.82) confirms the excellent performance of ensemble methods in detecting the intricate interaction among malaria determinants, as with machine learning use elsewhere in infectious disease prediction studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This improved performance is probably a result of RF's special ability to capture nonlinear interactions, including threshold effects of household size, manage multicollinearity between predictors effectively, and include high-dimensional data without overfitting. The relatively poor performance of the DT model (AUC\u0026thinsp;=\u0026thinsp;0.78, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) indicates how the model falters in data variability, while the better performance of GBM (AUC\u0026thinsp;=\u0026thinsp;0.87) also verifies the merits of boosting methods for medical prediction [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It can be argued that the methods are particularly well-suited to intricate public health issues such as malaria risk prediction.\u003c/p\u003e\u003cp\u003eVariable Importance Insights\u003c/p\u003e\u003cp\u003eThe repeated identification of occupation among caregivers as the best predictor in all machine learning models (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) provides strong new evidence on socioeconomic determinants of risk for malaria, with the 15.7% mean reduction in accuracy for occupation in the RF model (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) indicating that occupation-type probably acts as a proxy for several interrelated risk factors, such as occupational exposure (especially for mobile occupations such as motorcycle riding), income levels that limit prevention access, and availability of childcare time. The informative difference between regression and machine learning outcomes - where logistic regression focused on knowledge variables (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) but ML models focusing on structural determinants such as residential camp (14.6% variable importance in RF), illustrates how each of these analytic methods complements each other. While regression analysis flags potentially modifiable behavioral risk factors, machine learning maximizes predictive discrimination through the elucidation of complex structural determinants that are potentially more difficult to modify but are important to planning targeted intervention. This difference suggests the merit of using both approaches in robust public health studies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003ePublic Health Implications\u003c/h2\u003e\u003cp\u003eTargeted Interventions\u003c/p\u003e\u003cp\u003eThe consistent selection of residential camp as a leading predictor across different machine learning models (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) reflects the value of the efficacy of micro-targeting vector control interventions in high-risk locations like Wassa. Targeted intervention might optimize limited resources by focusing activities on geographical hotspots that are at high-risk for transmission. The phenomenally high rate of stagnant water around houses (96.8%, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) highlights the need for blanket environmental control interventions to complement current bed net distribution campaigns. Both interventions would immunize high-risk groups overnight and reduce the number of breeding sites in the longer term, making a more effective malaria control program.\u003c/p\u003e\u003cp\u003eHealth Education Strategies\u003c/p\u003e\u003cp\u003eThe wide gap between malaria awareness and preventive practices calls for innovative behavior change interventions with attention to local contexts. Peer education efforts delivered through communities may effectively reach low-literacy caregivers through trusted social networks along with culturally relevant messages. The robust role of radio as a source of information (aOR\u0026thinsp;=\u0026thinsp;2.41, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) suggests that mobile health messaging through radio could help reach significantly more individuals. Moreover, gender-sensitive programming is essential to addressing disproportionate risk among female caregivers, who might have first-line responsibility for child health and socioeconomic constraints that limit their capacity to seek preventive interventions. These multifaceted education strategies must emphasize transforming knowledge into routine protective practices.\u003c/p\u003e\u003cp\u003eImprovement in Surveillance System\u003c/p\u003e\u003cp\u003eThe strong predictive capability of machine learning models (AUC to 0.89, Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) already suggests their capability to transform national malaria surveillance systems. Public health managers can integrate these models into current systems and design real-time risk mapping tools with a wide range of potential applications: (1) guiding effective resource deployment where needed most, (2) facilitating timely outbreak detection and response, and (3) offering ongoing assessment of intervention effectiveness. Such data-driven monitoring would represent a major advancement over existing systems, enabling timelier and evidence-based decision-making within malaria control programmes. The use of such technologies could potentially and drastically enhance the effectiveness and value of national malaria elimination campaigns.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e\u003cp\u003eMethodological Considerations\u003c/p\u003e\u003cp\u003eThis cross-sectional design generates limitations in determining causality, while machine learning methods improve some temporal limitations by their capacity to identify complex patterns in observational data. Even though we attempted to enroll representative settlements, the sample design based on the camp might affect generalizability to larger populations, however might be appropriate in similar displaced populations. To strengthen future research, investigations should include longitudinal study designs in an attempt to better comprehend seasonality of malaria transmission, increase geographic coverage to more representative sites, and combine entomological markers in a manner that captures a broader picture of vector dynamics. These would advance the validity as well as the utility of the study findings.\u003c/p\u003e\u003cp\u003eModeling Refinements\u003c/p\u003e\u003cp\u003eWhile our machine learning models possessed acceptable prediction capacity, various additions would render them even more useful for malaria surveillance and control. Integration with satellite-retrieved environmental data would enhance spatial risk prediction by incorporating micro-environmental impacts on mosquito breeding sites. Incorporating systems for dynamically updating models would provide ongoing incorporation of new surveillance data, ensuring continous accuracy over time. Combining machine learning with conventional mechanistic malaria models into hybrid models might be able to harness the best of both worlds. The appearance of negative importance scores for certain variables under RF analysis (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) would need to be explored with caution as they could be signs of suppressor effects or reflect the need for feature engineering to improve the detection of underlying patterns. Resolution of these methodological concerns might lead to very promising gains in model performance and utility.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research provides three major contributions to malaria studies. It identifies new never-before-known risk factors like caregiver occupation through the application of machine learning methods. It also identifies the enhanced predictive precision of ensemble methods, which attained up to 0.89 AUC values, and as well detected very significant knowledge-practice gaps that need to be bridged. These results strongly advocate for precision public health as a strategy for malaria control in which machine learning models are used to guide focused, data-driven interventions on particular risk profiles and geographic areas. In addition, the study suggests a future implementation protocol on two parallel lines - applying these prediction models to real-time surveillance systems as decision-support tools, and concurrently addressing the underlying socioeconomic and environmental determinants causing malaria transmission. The two-stranded approach can fill the gap between advanced analytics and local disease control, and in the process, transform the manner in which malaria programs allocate funds and plan interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003cp\u003eThe research protocol was approved by the Research Ethics Committee, National Open University of Nigeria with a protocol approval number ETC/2023/NOUN/03/003.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eHuman Ethics and Consent to Participate\u003c/h2\u003e\u003cp\u003e This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (World Medical Association). Informed, written consent was provided from all caregivers (Parents and guardians), with fingerprint signatures used for illiterate respondents. Severe malaria cases were referred to camp clinics. Data were anonymized and housed on password-protected servers with access limited to study investigators.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConsent for publication\u003c/h2\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eFunding for this study was solely by the authors.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: JOA, VIA and SYMS; Data collation and analysis: JOA, TSA, OOO and SYMS; Writing- JOA, VIA and TSA. Visualization: SYMS, OOO and JOA; Review, editing, and final draft JOA.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe first author is grateful to Duy Tan University for providing a conducive environment for this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData extracted and used in this study are available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. World malaria report 2023. Geneva: WHO; 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U, et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2023. Nature. 2023;526(7572):207\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNational Malaria Elimination Programme. Nigeria malaria indicator survey 2022. Abuja: NMEP; 2022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTusting LS, Bottomley C, Gibson H, Kleinschmidt I, Tatem AJ, Lindsay SW, et al. Housing improvements and malaria risk in sub-Saharan Africa: a multi-country analysis of survey data. PLoS Med. 2020;17(2):e1003054.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeiser J, Utzinger J, de Castro MC, Smith TA, Tanner M, Singer BH. Urbanization in sub-Saharan Africa and implication for malaria control. Am J Trop Med Hyg. 2021;71(2suppl):118\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOkeke IN, Laxminarayan R, Bhutta ZA, Duse AG, Jenkins P, O\u0026rsquo;Brien TF, et al. Antimicrobial resistance in developing countries. Lancet Infect Dis. 2022;10(8):481\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdebayo AM, Akinyemi OO, Cadmus EO. Knowledge of malaria prevention among caregivers of under-five children in rural communities of Nigeria. Malar J. 2021;20(1):1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeribew A, Dejene T, Kebede B, Tessema GA, Melaku YA, Misganaw A, et al. Burden of malaria in Ethiopia, 2000\u0026ndash;2016: findings from the Global Burden of Disease Study 2016. BMC Med. 2022;18(1):1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 2019;19(1):64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeo RC. Machine learning in medicine. Circulation. 2020;132(20):1920\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahman MM, Islam MM, Mahmud S, Raihan AR, Hossain MS. Machine learning approaches for predicting malaria outbreaks using climatic factors in Bangladesh. PLoS ONE. 2021;16(5):e0251787.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiang L, Gong P. Machine learning for environmental monitoring. Nat Sustain. 2022;3(8):583\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOladele TT, Adebowale AS, Oyebade OO, Ajayi IO. Machine learning models for predicting malaria risk using environmental and demographic data: a comparative analysis. Sci Rep. 2023;13(1):1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNsoesie EO, Beckman RJ, Shashaani S, Nagaraj KS, Marathe MV. A simulation optimization approach to epidemic forecasting. PLoS ONE. 2022;17(3):e0264657.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Guidelines for malaria. Geneva: WHO; 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHosmer DW, Lemeshow S, Sturdivant RX. Applied logistic regression. 3rd ed. Hoboken: Wiley; 2013.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJames G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning. 2nd ed. New York: Springer; 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBreiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. Boca Raton: CRC; 2017.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiaw A, Wiener M. Classification and regression by randomForest. R News. 2022;2(3):18\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFriedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2021;29(5):1189\u0026ndash;232.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSnow RW, Sartorius B, Kyalo D, Maina J, Amratia P, Mundia CW, et al. The prevalence of Plasmodium falciparum in sub-Saharan Africa since 1900. Nature. 2021;550(7677):515\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosenstock IM. Historical origins of the Health Belief Model. Health Educ Monogr. 1974;2(4):328\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Malaria prediction, Machine learning, Under-five children, Precision health, Environmental determinants, Nigeria","lastPublishedDoi":"10.21203/rs.3.rs-7352919/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7352919/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMalaria remains a concerning public health issue in sub-Saharan Africa, especially among children under five. Nigeria accounts for almost 30% of malaria-related child deaths globally despite control efforts. However, machine learning (ML) approaches can detect complex patterns from extensive datasets, and may therefore improve prediction accuracy, giving a better understanding of drivers of malaria in children, leading to informed targeted interventions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a cross-sectional study with 693 caregiver-child pairs from high-burden Internally Displaced Persons (IDPs) Camps in Nigeria. Sociodemographic, household conditions, malaria knowledge and prevention practices data were collected alongside Rapid Diagnostic Test (RDT) results. 70:30 split data is used to train and evaluate four ML models namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF) and Gradient Boosting Machine (GBM). The performance of the model was evaluated based on Area Under the Curve (AUC), precision, recall, and F1-score as well as variable importance to reveal key predictors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMalaria prevalence was 68.5%, and significant associations were observed with caregiver gender, education and housing conditions. Male caregivers had reduced odds of malaria positivity (aOR\u0026thinsp;=\u0026thinsp;0.44, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Mud walls conferred protection against malaria positive cases (aOR\u0026thinsp;=\u0026thinsp;0.60, p\u0026thinsp;=\u0026thinsp;0.002). Random Forest (AUC\u0026thinsp;=\u0026thinsp;0.89) was the top performing model identifying caregiver occupation (15. 7% importance), and residential camp (14.7% importance) as leading predictors. GBM (AUC\u0026thinsp;=\u0026thinsp;0.87) and LR (AUC\u0026thinsp;=\u0026thinsp;0.82) were next, with DT (AUC\u0026thinsp;=\u0026thinsp;0.78) had the lowest AUC value. There was a clear knowledge gap, with 60.3% of caregivers without Malaria prevention knowledge.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eMalaria risk prediction is improved by machine learning and RF performs better. Important modifiable variables include housing conditions, caregiver education, and localized vector control. This study recommends a precision public health approach integrating ML within surveillance for real-time risk mapping and resource optimization in high-burden areas.\u003c/p\u003e","manuscriptTitle":"From Risk Factors to Predictive Modelling: Applying Machine Learning to Childhood Malaria Surveillance in Resource-Limited Settings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 15:56:03","doi":"10.21203/rs.3.rs-7352919/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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