Development and Deployment of a Machine Learning–Based Predictive Model for COVID- 19 Infection Using Patient Demographic and Symptom Data in Nigeria

Development and Deployment of a Machine Learning–Based Predictive Model for COVID- 19 Infection Using Patient Demographic and Symptom Data in Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Deployment of a Machine Learning–Based Predictive Model for COVID- 19 Infection Using Patient Demographic and Symptom Data in Nigeria Olanrewaju Eniade, Ezekiel Ukwenga, Uchenna Akuka, Opeyemi Adeniyi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8435681/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Timely identification of COVID-19 cases is critical for clinical management and public health control, particularly in resource-limited settings. While RT-PCR testing remains the gold standard, limited accessibility during peak transmission highlights the role of predictive tools. Objective This study aimed to develop and deploy a machine learning–based predictive model for COVID-19 infection using demographic and symptom data from patients in Nigeria. Methods Patient records were preprocessed, including cleaning, encoding of categorical variables, and feature selection. Logistic regression, random forest, and gradient boosting models were compared using ten-fold cross-validation. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, precision, and F1-score. The best-performing model was deployed as a web-based decision-support tool via R Shiny. Results A total of 43,442 patient records were included, with 3712 (8.5%) confirmed positive cases. COVID-19 positivity was significantly associated with male sex, older age, and symptoms such as cough, fever, and dyspnea (all p < .05). Logistic regression achieved an AUROC of 0.93 , sensitivity of 0.91 , specificity of 0.76, and F1-score of 0.95. The model demonstrated strong recall but a slightly low specificity. Conclusion We developed and deployed a lightweight, interpretable predictive model for COVID-19, available as a Shiny application ( http://bit.ly/41LxW9p ). This tool may be externally validated and subsequently proposed to support rapid triage and early decision-making in resource-constrained settings. Predictive model Machine-Learning COVID-19 Logistic Regression Figures Figure 1 Introduction The global situation of COVID-19 in 2025 demonstrates why timely detection remains an important aspect of public health strategy, despite the shift of the pandemic into an endemic phase (Filip et al., 2022 ). The World Health Organization ( 2024 ) reports that there have been more than 778 million confirmed cases and more than 7.1 million deaths globally as of November 2025, with ongoing surges due to the emergence of new strains of the virus, such as Omicron. Although vaccination campaigns have markedly reduced severe morbidity and mortality, preventing an estimated 20 million deaths in the first year of global roll-out (Ghafari et al., 2024 ), the virus persists in generating immune-evasive subvariants that present renewed risks to vulnerable populations. Innovations in diagnostics, from at-home tests to wastewater surveillance, have improved monitoring, yet gaps persist in real-time data sharing and global equity (Overview of Testing for SARS-CoV-2 (COVID-19), 2021). The emergence of variants under monitoring, such as those flagged by the European Centre for Disease Prevention and Control (ECDC) in October 2025, reminds us that the virus's evolutionary potential demands vigilant detection to inform booster campaigns and policy adjustments (European Centre for Disease Prevention and Control, 2025 ). The continuing economic effects, with global debt levels climbing by 30% since 2019 because of the costs of responding to the epidemic, show how undetected spread can make recovery take longer (World Bank, 2022 ). There is also the social effect as well, since rising rates of anxiety and despair have been linked to isolation measures, and educational disruptions are affecting millions of youths (Sakaretsanou et al., 2025 ). As surveillance transitions from the national level in certain countries, private and community testing becomes essential(“Chapter 53. Public Health Surveillance: A Tool for Targeting and Monitoring Interventions,” 2006). Ultimately, early detection improves societal resilience by safeguarding healthcare capacity. It is still important to combine timely diagnostic strategies with vaccination and public health education to keep COVID-19 under control and make the world better prepared for future pandemics (Sakaretsanou et al., 2025 ). To reach the health-related Sustainable Development Goals, it is important to make sure that everyone has equal access to COVID-19 diagnostics (Sampedro, 2021 ). In Nigeria, diagnostic constraints for COVID-19 persist, with RT-PCR as the primary confirmation method facing significant barriers in accessibility and efficiency. With a population exceeding 200 million, Nigeria's healthcare system is strained by geographic disparities, where most molecular laboratories are concentrated in urban centres like Lagos and Abuja, leaving rural areas underserved (Dan-Nwafor et al., 2020 ). During peak periods, reagent shortages and staffing limitations further exacerbate delays, as seen in earlier waves where testing capacity lagged demand (Al-Mustapha et al., 2021 ). Cost is another hurdle; private testing can exceed ₦50,000 (approximately $ 30 USD), unaffordable for over 40% of Nigerians living below the poverty line, leading to under-testing and likely underestimation of cases (Afolalu et al., 2021 ; World Bank, 2019 ). Official cumulative figures stand at around 266,675 cases and 3,155 deaths as of late 2025, but experts suggest these represent only a fraction of the true burden due to low testing rates (Nigeria Centre for Disease Control and Prevention, 2025; Trading Economics, 2025 ). Stigma and false information tend to discourage people from getting tested. Many avoid facilities because they are worried about quarantine's economic impact on their source of livelihood, and social media rumours about vaccines, likewise, discourage uptake (Obi-Ani et al., 2020 ). The Covid-19 pandemic disrupted routine services, with isolation centres overwhelmed and maternal-child health programmes interrupted, compounding diagnostic delays (Afolalu et al., 2021 ). Geographic factors amplify issues: in northern states like Borno, conflict and insecurity hinder lab access, while southern flooding disrupts logistics (Ohia et al., 2020 ). Mobile testing units and community outreach have helped, but coverage is still uneven, and testing per capita remains lower than world averages (Al-Mustapha et al., 2021 ). The Nigeria Centre for Disease Control (NCDC) has scaled up surveillance, but resource limitations persist, with only a few states maintaining active testing (Nigeria Centre for Disease Control and Prevention, 2025). Additionally, economic pressures from the pandemic, which include job losses in the informal sector, have reduced healthcare seeking, and it further puts a strain on diagnostic systems (World Bank, 2019 ). In 2025, with Covid-19 variants reaching Nigeria, these issues could lead to localised outbreaks if not addressed (Nigeria Centre for Disease Control and Prevention, 2025). To sum it up, Nigeria's diagnostic landscape shows how structural barriers perpetuate under-detection. Consequently, it necessitates innovative approaches to enhance timeliness and equity in response (Dan-Nwafor et al., 2020 ). Machine learning (ML) provides a compelling rationale for advancing healthcare, particularly in predictive diagnostics, by uncovering patterns in complex data that traditional methods might miss. Machine learning (ML) approaches have been increasingly applied to infectious disease prediction and decision support because of their capacity to handle high-dimensional clinical data (Lalmuanawma et al., 2020 ). ML algorithms, such as random forests and neural networks, excel at processing high-dimensional datasets, identifying non-linear relationships, and improving predictions through iterative learning (Rahman et al., 2024 ). In the management of infectious disease, ML has transformed outbreak forecasting, as exemplified in models that predicted COVID-19 trajectories more accurately than classical epidemiology (Eweoya et al., 2023 ). For instance, ML integrates diverse inputs like symptoms, demographics, and vital signs to stratify risk, enabling personalised care and resource optimisation (Ajuwon et al., 2023 ). Its ability to handle noisy or incomplete data is particularly valuable in LMICs, where records are often inconsistent (Al-Mustapha et al., 2021 ). The rationale emanates from ML's scalability: once trained, models deploy cost-effectively on mobile devices, which improves access in remote areas (Busari & Samson, 2022 ). In healthcare, ML’s symptom-based models support early detection, achieving over 80% accuracy for COVID-19 positivity (Zoabi et al., 2021 ). Beyond diagnostics, ML aids in drug discovery, patient monitoring, and policy simulation. It has seen applications in Africa's disease surveillance, enhancing response to Ebola and malaria (Chimbunde et al., 2023 ). The rationale includes substantial cost savings: predictive models can reduce unnecessary tests by 20–30%, which aids in freeing budgets in strained systems (Ghafari et al., 2024 ). In oncology, ML detects cancers earlier from imaging with 95% accuracy; in cardiology, it predicts heart attacks from ECG data (Ghafari et al., 2024 ). For pandemics, ML simulates scenarios, informing lockdown policies and vaccine distribution. In LMICs, ML bridges gaps, e.g., mobile apps for tuberculosis diagnosis using chest X-rays (Chimbunde et al., 2023 ). Future directions involve integration with the Internet of Things (IoT) for real-time monitoring and blockchain for secure data sharing. In mental health, ML analyses social media for depression signals; in genomics, it identifies disease mutations (Ajuwon et al., 2023 ). ML's adaptability to evolving threats like COVID variants ensures sustained relevance in 2025 and beyond. In precision medicine, ML tailors treatments based on genetic profiles, improving efficacy (Rahman et al., 2024 ). For resource allocation, it predicts demand for supplies, as seen in COVID logistics (Eweoya et al., 2023 ). Despite these advancements, gaps persist in ML applications tailored to African and Nigerian contexts, where models often focus on forecasting case trajectories or mortality risks rather than frontline triage (Alie et al., 2024 ; Chimbunde et al., 2023 ). Across Africa, ML has been applied to predict mortality in Ethiopia or forecast in South Africa, but adoption in Nigeria lags due to data scarcity and lack of localisation (Alie et al., 2024 ; Chimbunde et al., 2023 ; Yan, Zhang, Goncalves, Xiao, Wang, Guo, Sun, Tang, Jin, et al., 2020; Yan, Zhang, Goncalves, Xiao, Wang, Guo, Sun, Tang, Jing, et al., 2020). Gaps include insufficient external validation on diverse Nigerian populations, where urban bias in datasets overlooks rural dynamics (Ajuwon et al., 2023 ). Moreover, few models incorporate socio-cultural factors like stigma, which affect reporting (Obi-Ani et al., 2020 ). Specific gaps in Africa include limited open datasets for training when most models rely on global data that may not capture local variants or comorbidities (Chimbunde et al., 2023 ). Also, there are ethical gaps, like algorithmic bias from underrepresented ethnic groups (Ajuwon et al., 2023 ). Additionally, a language barrier exists due to the storage of some data in local dialects, which complicates feature extraction (Ajuwon et al., 2023 ). Comparative gaps also exist since many African countries lack standardised digital records (Chimbunde et al., 2023 ). Bridging these gaps requires collaborative efforts with NCDC for data access and validation (Dan-Nwafor et al., 2020 ). These are methodological, ethical, and operational considerations that give the groundwork towards a contextually adequate machine-learning solution. Developing on this framework, this study attempts to apply these principles to practice by creating and implementing a predictive model of COVID-19 infection using patient demographic and symptom data in Nigeria, which is based on machine-learning. This will serve as a complementary triage aid to prioritise testing and isolation. This approach is justified by Nigeria’s pandemic experience, with approximately 267,000 confirmed cases and 3,155 deaths by November 2025: these figures are widely regarded as underestimates due to low testing rates, stigma, and access barriers (Afolalu et al., 2021 ; Trading Economics, 2025 ). By leveraging local data and focusing on primary care deployment, this model offers a practical pathway to reduce delays and improve equity in resource-limited settings (Eweoya et al., 2023 ). Justification includes addressing diagnostic gaps in rural areas, where RT-PCR delays can exceed a week, which will allow the model to flag high-risk cases for immediate action (Dan-Nwafor et al., 2020 ). Demographic inputs like age and gender, combined with symptoms such as fever and cough, provide accessible predictors, with studies showing high accuracy in similar settings (Olowe et al., 2023; Zoabi et al., 2021 ). Deploying such models via web-based tools like Shiny applications further enhances accessibility for frontline clinicians and public health practitioners. The model's deployment via a web application aligns with Nigeria's growing digital health landscape, enhancing triage efficiency (Al-Mustapha et al., 2021 ). Methods Study Design and Data Source We conducted a modelling using patient records obtained from the Nigerian Centre for Disease Control (NCDC). The dataset comprised a total of 43,442 patients with ages ranging from 5 to 121 years, who were tested for COVID-19. Variables included in the study were. Demographic information (age, sex) and self-reported symptoms, alongside laboratory-confirmed COVID-19 test results. Data preprocessing Data was cleaned and standardised using the janitor and stringr packages in R. The outcome variable was binary: POSITIVE or NEGATIVE . Predictors included sex, age, and 15 commonly reported COVID-19 symptoms, which were recoded into binary variables (Yes = 1, No = 0). Records with missing or indeterminate outcomes were excluded. Model Development Three supervised ML algorithms were tested: Logistic regression (generalised linear model), Random forest (ensemble decision trees), and Extreme Gradient Boosting (XGBoost). Data were split into training and testing sets (stratified by outcome). Ten-fold cross-validation was used for model tuning and evaluation. Predictors were normalised, and zero-variance variables were removed. Evaluation Metrics Performance was assessed using AUROC, accuracy, sensitivity, specificity, precision, recall, and F1-score. All analysis was done using the R programming language. Deployment The best-performing model was deployed as a user-friendly Shiny application ( http://bit.ly/41LxW9p ). The app allows users to enter patient characteristics and symptoms and outputs the predicted probability of COVID-19 positivity. Results Demographic and Clinical Factors Associated With COVID-19 Disease The results in Table 1 present the Demographic and Clinical Factors Associated With COVID-19 Disease. A total of 43442 patient records were analysed, with a mean age of 36.9 ± 8.21SD, and 3712 (8.5%) tested positive. The mean age of patients with COVID-19–positive cases was 32.9 ± 13.60 years, compared with 40.2 ± 16.9 among negatives (p < .001). Males (9.2%) were more frequently positive compared to females (7.2%) p < .001). The prevalence of symptoms differed significantly between groups. For instance, more of those who tested positive for COVID-19 had cough (28.0%) compared to those who did not (8.2%), P < 0.001. Patients who tested positive presented more with diarrhoea (18.7% vs. 8.5%), difficulty breathing or dyspnea (26.6% vs. 8.5%), and fever (22.9% vs. 8.3%) compared with those who tested negative. Fatigue or general weakness was also more frequent among positives (12.7% vs. 8.5%). Headache was slightly less common among positives (6.0%) compared to negatives (8.6%). By contrast, joint pain or arthritis was markedly more frequent among positives (31.1% vs. 8.5%). Throat-related symptoms were also notable: thoracic sore throat was present in 36.0% of positives compared to 8.3% of negatives, while sore throat or pharyngitis was reported in 36.0% of positives versus 8.3% of negatives. Other systemic symptoms were more common among positives, including muscle pain (20.6% vs. 8.5%), nausea (19.4% vs. 8.5%), runny nose (25.8% vs. 8.3%), and vomiting (18.7% vs. 8.5%). Acute respiratory distress syndrome and cough with sputum showed lower prevalence overall, with small differences between groups: 7.4% of positives had acute respiratory distress compared with 8.5% of negatives, and cough with sputum was observed in 7.7% of positives versus 8.5% of negatives. Table 1 Demographic and Clinical Factors Associated With COVID-19 Disease Variable Negative (n = 39,730) Positive (n = 3,712) p value Sex < .001 Female 14,247 (92.8) 1,106 (7.2) Male 25,483 (90.7) 2,606 (9.2) Age, mean (SD) 32.92 (13.60) 40.24 (16.91) < .001 Cough < .001 No 39,177 (91.8) 3,497 (8.2) Yes 553 (72.0) 215 (28.0) Diarrhea < .001 No 39,582 (91.5) 3,678 (8.5) Yes 148 (81.3) 34 (18.7) Difficulty breathing/dyspnea < .001 No 39,581 (91.5) 3,658 (8.5) Yes 149 (73.4) 54 (26.6) Fever < .001 No 39,085 (91.7) 3,520 (8.3) Yes 645 (77.1) 192 (22.9) Fatigue or general weakness No Yes 39319 (91.5) 411(87.3) 3652(8.5) 60(12.7) < 0.001 Headache < 0.001 No 38855(91.4) 3656(8.6) Yes 875(94.0) 56(6.0) Joint pain or arthritis < 0.001 No 39699(91 . 5) 3698(8 . 5) Yes 31(68 . 9) 14(31.1) Thorax sore throat No 39467 (91 . 7) 3564(8.3) Yes 263(64 . 0) 148(36.0) Muscle pain, n (%) < .001 No 39,653 (91.5) 3,692 (8.5) Yes 77 (79.4) 20 (20.6) Nausea, n (%) < .001 No 39,555 (91.5) 3,670 (8.5) Yes 175 (80.6) 42 (19.4) Runny nose < .001 No 39,327 (91.7) 3,572 (8.3) Yes 403 (74.2) 140 (25.8) Sore throat or pharyngitis < .001 No 39,467 (91.7) 3,564 (8.3) Yes 263 (64.0) 148 (36.0) Vomiting < .001 No 39,621 (91.5) 3,687 (8.5) Yes 109 (81.3) 25 (18.7) Acute respiratory distress syndrome < .001 No 39,680 (91.5) 3,708 (8.5) Yes 50 (92.6) 4 (7.4) Cough with sputum < .001 No 39,694 (91.5) 3,709 (8.5) Yes 36 (92.3) 3 (7.7) Predictive Performance of Models for COVID-19 Classification The performance metrics, presented in Table 2 , varied across the three supervised machine learning algorithms. The most balanced performance was demonstrated by logistic regression, which was characterized by high accuracy (0.91), sensitivity (0.998), specificity (0.71), precision (0.913), recall (0.998) and F1 score (0.953). Furthermore, its overall level of discrimination was high with an AUC of 0.854. XGBoost followed with a high accuracy (0.911) and specificity (0.71), but very low sensitivity (0.029). Also, it had a low Precision (0.444), Recall (0.029), and F1 score (0.055). Random Forest followed closely behind with a high accuracy of 0.912, high specificity (0.997) and AUC score (0.700). However, it had low sensitivity (0.024), precision (0.46), and very low recall (0.046). These findings revealed that logistic regression outperformed other models in the accuracy of finding true positives and having a more favourable sensitivity-specificity ratio. Table 2 Performance metrics of the supervised ML algorithms Model Accuracy Sensitivity Specificity Precision Recall F1 ROC AUC GLM 0.911 0.998 0.71 0.913 0.998 0.953 0.854 (0.830–0.878) Random Forest 0.912 0.024 0.997 0.46 0.024 0.046 0.700 (0.682–0.718) XGBoost 0.911 0.029 0.996 0.444 0.029 0.055 0.689 (0.671–0.707) Variable importance Variable importance results (Fig. 1 ) indicated that age was the most important predictor in the model, which contributed more than any other feature. Symptoms such as cough and runny nose were also proven to be high contributors. Other symptoms such as fever, sore throat or pharyngitis, soreness in the thorax or the chest, headache, and difficulty breathing contributed moderately to the prediction. Lower-ranked variables, which included fatigue or general weakness, vomiting, nausea, diarrhoea, muscle pain, cough with sputum, joint pain or arthritis and acute respiratory distress syndrome, added comparatively less to the model. In general, the findings indicate that demographic variables, especially age, and the main respiratory symptoms were the most significant contributors to the predictive ability of the model, with those symptoms that are more severe/less commonly reported being insignificant. Deployment The final logistic regression model was integrated into a Shiny web application ( http://bit.ly/41LxW9p ). Users can enter demographic and symptom data to obtain a real-time probability of COVID-19 positivity. Discussion (dup: abstract ?) Discussion This study examined the demographic and clinical disparities between COVID-19-positive and negative cases in Nigeria and tested the suitability of machine learning models in predicting the occurrence of COVID-19 based on routinely collected symptoms and demographic data. Generally, the results reveal that there are clear epidemiological differences between the infected and non-infected individuals. It also indicates the clinical importance of an accurate and affordable predictive model that can be implemented in a real-life environment. The members of this cohort with COVID-19 were much younger compared to those who tested negative. This is contrary to studies conducted in hospital settings where emphasis has been given to older age as an indicator of severe disease and not infection (Gallo Marin et al., 2021; Zhou et al., 2020 ). In the Nigerian context, this might be indicative of the younger populations having greater uptake of the tests, or differences in healthcare-seeking behaviour among economically active individuals compared to the older population. The positivity rate in males was far higher than that of females, which agrees with previous findings that indicate the sex differences in exposure patterns, biological vulnerability, and health-seeking behaviour in the epidemiology of COVID-19 (Gallo Marin et al., 2021). There were noticeable variations in symptomatic patterns among COVID-19-infected and uninfected persons. Central systemic and respiratory manifestations such as cough, fever, dyspnea, runny nose, sore throat, as well as thoracic discomfort were much more common in positives, which supports their diagnostic importance in screening (Gallo Marin et al., 2021). Gastrointestinal symptoms like diarrhoea, nausea, and vomiting were also notably more prevalent in positives. This finding supports mounting evidence on COVID-19 presentations with extrapulmonary manifestations (Zhang et al., 2021). Muscle pain and fatigue were also more common, as they can be the systemic inflammatory reaction to acute viral infection (Zhang et al., 2021). Surprisingly, there were unusual patterns of some symptoms that were traditionally attributed to viral illness. Headaches were vastly less frequent in people with COVID-19, as well as acute respiratory distress syndrome, and cough with sputum in positives were equally less frequent among the positive cases. The results were probably caused by the dominance of the mild to moderate cases present in the community compared to more severe respiratory complications that are more prevalent among hospitalised patients. The significantly greater prevalence of joint pains or arthritis and throat-related symptoms in positives implied that musculoskeletal and upper respiratory tract reactions may be one of the most evident manifestations in this group and cannot be neglected when screening (Zarpoosh & Amirian, 2023). In the variable importance analysis, age showed the strongest predictive power, and it is thus of prime importance in categorising the status of infection among this population. Although older age is usually highlighted in hospital-based research as a disease predictor, its quality in the model indicates that age is also a discriminatory factor for the risk of infection in the community and surveillance. Perhaps this is due to age-related variations in exposure patterns, social life, and immune responses. The common respiratory symptoms like cough and runny nose, with sex and fever, were also high determinants in the categorisation of infection status. Fever, sore throat or pharyngitis, thoracic sore throat, headache, and difficulty breathing had low importance. This suggests that although they are related to infection, they do not have incremental and dominant discriminatory power when combined with age and core respiratory symptoms. It is worth noting that some of the symptoms that had been statistically more prevalent among positives, including fatigue, nausea, vomiting, diarrhoea, and muscle pain, were given relatively lower importance by the model. This highlights the difference between the prevalence of symptoms and predictive contribution. Furthermore, the symptoms are widespread across a wide variety of infectious and non-infectious diseases and thus have low specificity for COVID-19 on their own. Similarly, the most severe disease-related symptoms, such as acute respiratory distress syndrome and sputum-containing cough (Gallo Marin et al., 2021), were the lowest-ranking symptoms, probably because of their low incidence amongst this non-hospitalised population, and their low utility in identifying cases early. Interestingly, though the proportion of positivity of joint pain or arthritis was high in the cohort’s distribution, it had very little contribution to prediction. This is probably due to its low prevalence in the data. Also, this suggested that the contribution of features with extreme proportions but very low occurrence to overall model performance was minimal. Findings from the variable importance suggested that predictors that were both highly prevalent and differentially distributed between infected and uninfected persons were the most useful in classification. This study developed three machine–learning–based predictive models (Logistic regression, Random Forest, and Extreme Gradient Boosting) for COVID-19 infection in Nigeria and deployed the best-performing model. The models were trained using easily obtainable demographic and symptom data. Of the three models, Logistic regression demonstrated the clinically most useful balance of sensitivity and specificity with high discrimination and significantly better performance in detecting true positives than the ensemble models. Despite its simplicity, logistic regression achieved strong discriminatory performance, aligning with previous research showing that non-complex models are often preferable in clinical decision-making (Rudin, 2019 ). Furthermore, the model’s high sensitivity suggests potential utility for rapid triage and early isolation, particularly where confirmatory testing is limited. However, the modest specificity highlights the need for confirmatory laboratory testing before clinical decisions. By deploying the model as an R Shiny application, the feasibility for real-time use in clinical and public health settings is demonstrated. Similar digital tools have been shown to improve early screening, optimise resource allocation, and support epidemic response (Ahamad et al., 2020 ; Lalmuanawma et al., 2020 ). Strengths and Limitations Integrating the final model into an R Shiny web application demonstrates the feasibility of delivering real-time, point-of-care risk estimates with simple inputs. This aligns with novel digital tools that aid early screening of the virus. In addition, it can support resource allocation during another outbreak of the disease. A major strength of this study is the translation of a data-driven model into an accessible platform that can be assessed in clinical and public health systems. Nonetheless, there are some limitations to this study. There is a reliance on self-reported symptoms, potential selection and reporting bias, which relates to the individual getting tested and a lack of external validation beyond the original population the model was trained on. Future research should focus on validating and recalibrating the model across diverse Nigerian settings. This includes utilizing additional predictors like comorbidities, vaccination status, and other laboratory performance. Conclusion We developed a machine learning-based predictive model for COVID-19 infection in Nigeria using demographic and symptom data. The developed model was deployed into a web-based application for the prediction of COVID-19 infection. The logistic regression model achieved high sensitivity and was integrated into an accessible R Shiny application ( http://bit.ly/41LxW9p ). This tool may be externally validated and subsequently proposed to support rapid triage and early decision-making in resource-constrained settings. Abbreviations AI Artificial Intelligence AUC Area Under the Curve AUROC Area Under the Receiver Operating Characteristic Curve COVID 19 –Coronavirus Disease 2019 ECG Electrocardiogram ECDC European Centre for Disease Prevention and Control F1 score –Harmonic mean of precision and recall GLM Generalized Linear Model IoT Internet of Things LMICs Low–and Middle–Income Countries ML Machine Learning NCDC Nigeria Centre for Disease Control (and Prevention) PCR Polymerase Chain Reaction RT PCR –Reverse Transcription Polymerase Chain Reaction ROC Receiver Operating Characteristic SD Standard Deviation USD United States Dollar WHO World Health Organisation XGBoost Extreme Gradient Boosting Declarations Ethics approval and Consent to participate : The data used in this study were obtained from the Nigeria Centre for Disease Control (NCDC). The study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval for this study was obtained from the Adeleke University Ethical Review Committee (approval no: AUERC/FBMS/IND/13), Nigeria. Formal authorization to use the data for this study was granted by the NCDC. No identifying information was collected, which assured the privacy and confidentiality of study participants. The requirement for informed consent specifically to this study was waived by the Ethical Review Board due to the use of secondary, de-identified data. This study did not pose any form of risk to the health of the participants and their environment. All data provided were fully de-identified prior to analysis. The dataset was stored on password-protected computers accessible only to the study investigators. Consent for publication : All authors gave consent for this publication Availability of data and materials : The data sets used for this study are not publicly available (We do not have the right to redistribute the data). However, a request to reuse the data can be sent to NCDC. Competing interests : There are no competing interests. Funding : There was no funding for this research Authors' contributions. Olanrewaju Eniade conceived the study, developed the analysis plan, led the data analysis and model development, and drafted the manuscript. Ezekiel Ukwenga contributed to the introduction, data analysis, and results. Uchenna Akuka contributed to the methods and discussion. Opeyemi Adeniyi and Elonna Obak contributed to reviewing and editing the introduction and discussion. Adeagbo Omolola and Olaitan Peter B contributed to reviewing and editing the introduction. Olowe Rita and Opakunle Tolulope contributed to reviewing and editing the discussion. Olugbenga Adekunle Olowe is the senior author and provided overall supervision, technical review, and critical intellectual input. All authors reviewed and approved the final manuscript. Olanrewaju Eniade is the guarantor of the study. Acknowledgements: The authors acknowledge the Nigeria Centre for Disease Control, the Ministry of Health, and the University of Ibadan for providing access to the data, and Mr Oluwatobi Ojo for his valuable technical feedback. References Afolalu OO, Atekoja OE, Oyewumi ZO, Adeyeye SO, Jolayemi KI, Akingbade O. (2021). Perceived impact of coronavirus pandemic on uptake of healthcare services in south west nigeria. Pan African Medical Journal , 40 . https://doi.org/10.11604/pamj.2021.40.26.28279 Ahamad MM, Aktar S, Rashed-Al-Mahfuz M, Uddin S, Liò P, Xu H, Summers MA, Quinn JMW, Moni MA. (2020). A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients. Expert Systems with Applications , 160 . https://doi.org/10.1016/j.eswa.2020.113661 Ajuwon BI, Awotundun ON, Richardson A, Roper K, Sheel M, Rahman N, Salako A, Lidbury BA. 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Public Health Surveillance: A Tool for Targeting and Monitoring Interventions. (2006). In Disease Control Priorities in Developing Countries (2nd Edition) . https://doi.org/10.1596/978-0-8213-6179-5/chpt-53 Chimbunde E, Sigwadhi LN, Tamuzi JL, Okango EL, Daramola O, Ngah VD, Nyasulu PS. (2023). Machine learning algorithms for predicting determinants of COVID-19 mortality in South Africa. Frontiers in Artificial Intelligence , 6 . https://doi.org/10.3389/frai.2023.1171256 Dan-Nwafor C, Ochu CL, Elimian K, Oladejo J, Ilori E, Umeokonkwo C, Steinhardt L, Igumbor E, Wagai J, Okwor T, Aderinola O, Mba N, Hassan A, Dalhat M, Jinadu K, Badaru S, Arinze C, Jafiya A, Disu Y, Ihekweazu C. Nigeria’s public health response to the COVID-19 pandemic: January to May 2020. J Global Health. 2020;10(2). https://doi.org/10.7189/JOGH.10.020399 . European Centre for Disease Prevention and Control. SARS-CoV-2 variants of concern as of 28 November 2025. European Centre for Disease Prevention and Control; 2025. Eweoya IO, Odetunmibi OA, Odun-Ayo IA, Agbele KK, Adedotun AF, Akingbade TJ. Machine Learning Approach for the Prediction of COVID-19 Spread in Nigeria Using SIR Model. Int J Sustainable Dev Plann. 2023;18(12). https://doi.org/10.18280/ijsdp.181210 . Filip R, Gheorghita Puscaselu R, Anchidin-Norocel L, Dimian M, Savage WK. (2022). Global Challenges to Public Health Care Systems during the COVID-19 Pandemic: A Review of Pandemic Measures and Problems. In Journal of Personalized Medicine (Vol. 12, Issue 8). https://doi.org/10.3390/jpm12081295 Ghafari M, Hall M, Golubchik T, Ayoubkhani D, House T, MacIntyre-Cockett G, Fryer HR, Thomson L, Nurtay A, Kemp SA, Ferretti L, Buck D, Green A, Trebes A, Piazza P, Lonie LJ, Studley R, Rourke E, Smith DL, Lythgoe K. Prevalence of persistent SARS-CoV-2 in a large community surveillance study. Nature. 2024;626(8001). https://doi.org/10.1038/s41586-024-07029-4 . Lalmuanawma S, Hussain J, Chhakchhuak L. (2020). Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. In Chaos, Solitons and Fractals (Vol. 139). https://doi.org/10.1016/j.chaos.2020.110059 Nigeria Centre for Disease Control and Prevention. (2025, September 19). NCDC Public Health Advisory following the Suspected Viral Haemorrhagic-Fever Events in Abuja: What Nigerians Should Know . Nigeria Centre for Disease Control and Prevention. Obi-Ani NA, Anikwenze C, Isiani MC. Social media and the Covid-19 pandemic: Observations from Nigeria. Cogent Arts Humanit. 2020;7(1). https://doi.org/10.1080/23311983.2020.1799483 . Ohia C, Bakarey AS, Ahmad T. COVID-19 and Nigeria: putting the realities in context. Int J Infect Dis. 2020;95. https://doi.org/10.1016/j.ijid.2020.04.062 . Overview of Testing for SARS-CoV-2 (COVID-19), Cdc.Gov. (2021). Rahman A, Debnath T, Kundu D, Khan MSI, Aishi AA, Sazzad S, Sayduzzaman M, Band SS. (2024). Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities. In AIMS Public Health (Vol. 11, Issue 1). https://doi.org/10.3934/publichealth.2024004 Rudin C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. In Nature Machine Intelligence (Vol. 1, Issue 5). https://doi.org/10.1038/s42256-019-0048-x Sakaretsanou AK, Bakola M, Chatzeli T, Charalambous G, Jelastopulu E. (2025). Mental Health Impacts of the COVID-19 Pandemic on College Students: A Literature Review with Emphasis on Vulnerable and Minority Populations. In Healthcare (Switzerland) (Vol. 13, Issue 13). https://doi.org/10.3390/healthcare13131572 Sampedro R. The Sustainable Development Goals (SDG). Carreteras. 2021;4(232). https://doi.org/10.1201/9781003080220-8 . Trading Economics. Nigeria Coronavirus COVID-19 Cases. Trading Economics; 2025. World Bank. (2019). Poverty headcount ratio at national poverty lines (% of population) Thailand. In The World ank . World Bank. (2022). Chapter 1. The economic impacts of the COVID-19 crisis. World Development Report 2022 . World Health Organization. COVID-19 epidemiological update – 15 July 2024. July 2024. World Health Organization; 2024. Yan L, Zhang H-T, Xiao YY, Wang M, Sun C, Liang J, Li S, Zhang M, Guo Y, Xiao YY, Tang X, Cao H, Tan X, Huang N, Jiao B, Luo A, Cao Z, Xu H, Yuan Y. (2020). Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan. MedRxiv . Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L, Wei Y, Li H, Wu X, Xu J, Tu S, Zhang Y, Chen H, Cao B. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229). https://doi.org/10.1016/S0140-6736(20)30566-3 . Zoabi Y, Deri-Rozov S, Shomron N. Machine learning-based prediction of COVID-19 diagnosis based on symptoms. Npj Digit Med. 2021;4(1). https://doi.org/10.1038/s41746-020-00372-6 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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10:25:11","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122576,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8435681/v1/bfb5eb5806bc59616975dd2d.html"},{"id":101075151,"identity":"7f781947-ec47-4c53-94d2-f9285961a99b","added_by":"auto","created_at":"2026-01-25 10:25:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210990,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRanking of Predictor Importance for Symptom and Demographic Features\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8435681/v1/d7088f6c51bf0e16d9e265b3.png"},{"id":101280924,"identity":"291140df-8fc3-4add-90db-31d159a24d52","added_by":"auto","created_at":"2026-01-28 05:10:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1265773,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8435681/v1/cfe4b384-0db9-4b4a-99d0-d9acb32dac2b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Deployment of a Machine Learning–Based Predictive Model for COVID- 19 Infection Using Patient Demographic and Symptom Data in Nigeria","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global situation of COVID-19 in 2025 demonstrates why timely detection remains an important aspect of public health strategy, despite the shift of the pandemic into an endemic phase (Filip et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The World Health Organization (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reports that there have been more than 778\u0026nbsp;million confirmed cases and more than 7.1\u0026nbsp;million deaths globally as of November 2025, with ongoing surges due to the emergence of new strains of the virus, such as Omicron. Although vaccination campaigns have markedly reduced severe morbidity and mortality, preventing an estimated 20\u0026nbsp;million deaths in the first year of global roll-out (Ghafari et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the virus persists in generating immune-evasive subvariants that present renewed risks to vulnerable populations.\u003c/p\u003e \u003cp\u003eInnovations in diagnostics, from at-home tests to wastewater surveillance, have improved monitoring, yet gaps persist in real-time data sharing and global equity (Overview of Testing for SARS-CoV-2 (COVID-19), 2021). The emergence of variants under monitoring, such as those flagged by the European Centre for Disease Prevention and Control (ECDC) in October 2025, reminds us that the virus's evolutionary potential demands vigilant detection to inform booster campaigns and policy adjustments (European Centre for Disease Prevention and Control, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The continuing economic effects, with global debt levels climbing by 30% since 2019 because of the costs of responding to the epidemic, show how undetected spread can make recovery take longer (World Bank, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There is also the social effect as well, since rising rates of anxiety and despair have been linked to isolation measures, and educational disruptions are affecting millions of youths (Sakaretsanou et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As surveillance transitions from the national level in certain countries, private and community testing becomes essential(\u0026ldquo;Chapter 53. Public Health Surveillance: A Tool for Targeting and Monitoring Interventions,\u0026rdquo; 2006). Ultimately, early detection improves societal resilience by safeguarding healthcare capacity. It is still important to combine timely diagnostic strategies with vaccination and public health education to keep COVID-19 under control and make the world better prepared for future pandemics (Sakaretsanou et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). To reach the health-related Sustainable Development Goals, it is important to make sure that everyone has equal access to COVID-19 diagnostics (Sampedro, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Nigeria, diagnostic constraints for COVID-19 persist, with RT-PCR as the primary confirmation method facing significant barriers in accessibility and efficiency. With a population exceeding 200\u0026nbsp;million, Nigeria's healthcare system is strained by geographic disparities, where most molecular laboratories are concentrated in urban centres like Lagos and Abuja, leaving rural areas underserved (Dan-Nwafor et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). During peak periods, reagent shortages and staffing limitations further exacerbate delays, as seen in earlier waves where testing capacity lagged demand (Al-Mustapha et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Cost is another hurdle; private testing can exceed ₦50,000 (approximately \u003cspan\u003e$\u003c/span\u003e30 USD), unaffordable for over 40% of Nigerians living below the poverty line, leading to under-testing and likely underestimation of cases (Afolalu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; World Bank, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Official cumulative figures stand at around 266,675 cases and 3,155 deaths as of late 2025, but experts suggest these represent only a fraction of the true burden due to low testing rates (Nigeria Centre for Disease Control and Prevention, 2025; Trading Economics, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Stigma and false information tend to discourage people from getting tested. Many avoid facilities because they are worried about quarantine's economic impact on their source of livelihood, and social media rumours about vaccines, likewise, discourage uptake (Obi-Ani et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Covid-19 pandemic disrupted routine services, with isolation centres overwhelmed and maternal-child health programmes interrupted, compounding diagnostic delays (Afolalu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Geographic factors amplify issues: in northern states like Borno, conflict and insecurity hinder lab access, while southern flooding disrupts logistics (Ohia et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Mobile testing units and community outreach have helped, but coverage is still uneven, and testing per capita remains lower than world averages (Al-Mustapha et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Nigeria Centre for Disease Control (NCDC) has scaled up surveillance, but resource limitations persist, with only a few states maintaining active testing (Nigeria Centre for Disease Control and Prevention, 2025). Additionally, economic pressures from the pandemic, which include job losses in the informal sector, have reduced healthcare seeking, and it further puts a strain on diagnostic systems (World Bank, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In 2025, with Covid-19 variants reaching Nigeria, these issues could lead to localised outbreaks if not addressed (Nigeria Centre for Disease Control and Prevention, 2025). To sum it up, Nigeria's diagnostic landscape shows how structural barriers perpetuate under-detection. Consequently, it necessitates innovative approaches to enhance timeliness and equity in response (Dan-Nwafor et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Machine learning (ML) provides a compelling rationale for advancing healthcare, particularly in predictive diagnostics, by uncovering patterns in complex data that traditional methods might miss. Machine learning (ML) approaches have been increasingly applied to infectious disease prediction and decision support because of their capacity to handle high-dimensional clinical data (Lalmuanawma et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). ML algorithms, such as random forests and neural networks, excel at processing high-dimensional datasets, identifying non-linear relationships, and improving predictions through iterative learning (Rahman et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the management of infectious disease, ML has transformed outbreak forecasting, as exemplified in models that predicted COVID-19 trajectories more accurately than classical epidemiology (Eweoya et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For instance, ML integrates diverse inputs like symptoms, demographics, and vital signs to stratify risk, enabling personalised care and resource optimisation (Ajuwon et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Its ability to handle noisy or incomplete data is particularly valuable in LMICs, where records are often inconsistent (Al-Mustapha et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The rationale emanates from ML's scalability: once trained, models deploy cost-effectively on mobile devices, which improves access in remote areas (Busari \u0026amp; Samson, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In healthcare, ML\u0026rsquo;s symptom-based models support early detection, achieving over 80% accuracy for COVID-19 positivity (Zoabi et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Beyond diagnostics, ML aids in drug discovery, patient monitoring, and policy simulation. It has seen applications in Africa's disease surveillance, enhancing response to Ebola and malaria (Chimbunde et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The rationale includes substantial cost savings: predictive models can reduce unnecessary tests by 20\u0026ndash;30%, which aids in freeing budgets in strained systems (Ghafari et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In oncology, ML detects cancers earlier from imaging with 95% accuracy; in cardiology, it predicts heart attacks from ECG data (Ghafari et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For pandemics, ML simulates scenarios, informing lockdown policies and vaccine distribution. In LMICs, ML bridges gaps, e.g., mobile apps for tuberculosis diagnosis using chest X-rays (Chimbunde et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Future directions involve integration with the Internet of Things (IoT) for real-time monitoring and blockchain for secure data sharing. In mental health, ML analyses social media for depression signals; in genomics, it identifies disease mutations (Ajuwon et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). ML's adaptability to evolving threats like COVID variants ensures sustained relevance in 2025 and beyond. In precision medicine, ML tailors treatments based on genetic profiles, improving efficacy (Rahman et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For resource allocation, it predicts demand for supplies, as seen in COVID logistics (Eweoya et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these advancements, gaps persist in ML applications tailored to African and Nigerian contexts, where models often focus on forecasting case trajectories or mortality risks rather than frontline triage (Alie et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chimbunde et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Across Africa, ML has been applied to predict mortality in Ethiopia or forecast in South Africa, but adoption in Nigeria lags due to data scarcity and lack of localisation (Alie et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chimbunde et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yan, Zhang, Goncalves, Xiao, Wang, Guo, Sun, Tang, Jin, et al., 2020; Yan, Zhang, Goncalves, Xiao, Wang, Guo, Sun, Tang, Jing, et al., 2020). Gaps include insufficient external validation on diverse Nigerian populations, where urban bias in datasets overlooks rural dynamics (Ajuwon et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, few models incorporate socio-cultural factors like stigma, which affect reporting (Obi-Ani et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Specific gaps in Africa include limited open datasets for training when most models rely on global data that may not capture local variants or comorbidities (Chimbunde et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Also, there are ethical gaps, like algorithmic bias from underrepresented ethnic groups (Ajuwon et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, a language barrier exists due to the storage of some data in local dialects, which complicates feature extraction (Ajuwon et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Comparative gaps also exist since many African countries lack standardised digital records (Chimbunde et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Bridging these gaps requires collaborative efforts with NCDC for data access and validation (Dan-Nwafor et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese are methodological, ethical, and operational considerations that give the groundwork towards a contextually adequate machine-learning solution. Developing on this framework, this study attempts to apply these principles to practice by creating and implementing a predictive model of COVID-19 infection using patient demographic and symptom data in Nigeria, which is based on machine-learning. This will serve as a complementary triage aid to prioritise testing and isolation. This approach is justified by Nigeria\u0026rsquo;s pandemic experience, with approximately 267,000 confirmed cases and 3,155 deaths by November 2025: these figures are widely regarded as underestimates due to low testing rates, stigma, and access barriers (Afolalu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Trading Economics, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). By leveraging local data and focusing on primary care deployment, this model offers a practical pathway to reduce delays and improve equity in resource-limited settings (Eweoya et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Justification includes addressing diagnostic gaps in rural areas, where RT-PCR delays can exceed a week, which will allow the model to flag high-risk cases for immediate action (Dan-Nwafor et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Demographic inputs like age and gender, combined with symptoms such as fever and cough, provide accessible predictors, with studies showing high accuracy in similar settings (Olowe et al., 2023; Zoabi et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Deploying such models via web-based tools like Shiny applications further enhances accessibility for frontline clinicians and public health practitioners. The model's deployment via a web application aligns with Nigeria's growing digital health landscape, enhancing triage efficiency (Al-Mustapha et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Data Source\u003c/h2\u003e \u003cp\u003eWe conducted a modelling using patient records obtained from the Nigerian Centre for Disease Control (NCDC). The dataset comprised a total of \u003cem\u003e43,442\u003c/em\u003e patients with ages ranging from 5 to 121 years, who were tested for COVID-19. Variables included in the study were. Demographic information (age, sex) and self-reported symptoms, alongside laboratory-confirmed COVID-19 test results.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData preprocessing\u003c/h3\u003e\n\u003cp\u003eData was cleaned and standardised using the janitor and stringr packages in R. The outcome variable was binary: \u003cem\u003ePOSITIVE\u003c/em\u003e or \u003cem\u003eNEGATIVE\u003c/em\u003e. Predictors included sex, age, and 15 commonly reported COVID-19 symptoms, which were recoded into binary variables (Yes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0). Records with missing or indeterminate outcomes were excluded.\u003c/p\u003e\n\u003ch3\u003eModel Development\u003c/h3\u003e\n\u003cp\u003eThree supervised ML algorithms were tested:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eLogistic regression (generalised linear model),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRandom forest (ensemble decision trees), and\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExtreme Gradient Boosting (XGBoost).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eData were split into training and testing sets (stratified by outcome). Ten-fold cross-validation was used for model tuning and evaluation. Predictors were normalised, and zero-variance variables were removed.\u003c/p\u003e\n\u003ch3\u003eEvaluation Metrics\u003c/h3\u003e\n\u003cp\u003ePerformance was assessed using AUROC, accuracy, sensitivity, specificity, precision, recall, and F1-score. All analysis was done using the R programming language.\u003c/p\u003e\n\u003ch3\u003eDeployment\u003c/h3\u003e\n\u003cp\u003eThe best-performing model was deployed as a user-friendly Shiny application (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bit.ly/41LxW9p\u003c/span\u003e\u003cspan address=\"http://bit.ly/41LxW9p\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The app allows users to enter patient characteristics and symptoms and outputs the predicted probability of COVID-19 positivity.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and Clinical Factors Associated With COVID-19 Disease\u003c/h2\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e present the Demographic and Clinical Factors Associated With COVID-19 Disease. A total of \u003cem\u003e43442\u003c/em\u003e patient records were analysed, with a mean age of 36.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.21SD, and \u003cem\u003e3712\u003c/em\u003e (8.5%) tested positive. The mean age of patients with COVID-19\u0026ndash;positive cases was \u003cem\u003e32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.60\u003c/em\u003e years, compared with \u003cem\u003e40.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9\u003c/em\u003e among negatives (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Males (9.2%) were more frequently positive compared to females (7.2%) p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The prevalence of symptoms differed significantly between groups. For instance, more of those who tested positive for COVID-19 had cough (28.0%) compared to those who did not (8.2%), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Patients who tested positive presented more with diarrhoea (18.7% vs. 8.5%), difficulty breathing or dyspnea (26.6% vs. 8.5%), and fever (22.9% vs. 8.3%) compared with those who tested negative. Fatigue or general weakness was also more frequent among positives (12.7% vs. 8.5%). Headache was slightly less common among positives (6.0%) compared to negatives (8.6%). By contrast, joint pain or arthritis was markedly more frequent among positives (31.1% vs. 8.5%). Throat-related symptoms were also notable: thoracic sore throat was present in 36.0% of positives compared to 8.3% of negatives, while sore throat or pharyngitis was reported in 36.0% of positives versus 8.3% of negatives. Other systemic symptoms were more common among positives, including muscle pain (20.6% vs. 8.5%), nausea (19.4% vs. 8.5%), runny nose (25.8% vs. 8.3%), and vomiting (18.7% vs. 8.5%).\u003c/p\u003e \u003cp\u003eAcute respiratory distress syndrome and cough with sputum showed lower prevalence overall, with small differences between groups: 7.4% of positives had acute respiratory distress compared with 8.5% of negatives, and cough with sputum was observed in 7.7% of positives versus 8.5% of negatives.\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\u003e\u003cb\u003eDemographic and Clinical Factors Associated With COVID-19 Disease\u003c/b\u003e\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=\"char\" char=\".\" 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=\"left\" 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\u003eNegative (n\u0026thinsp;=\u0026thinsp;39,730)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive (n\u0026thinsp;=\u0026thinsp;3,712)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14,247 (92.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,106 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25,483 (90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,606 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.92 (13.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.24 (16.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCough\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,177 (91.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,497 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e553 (72.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e215 (28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiarrhea\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,582 (91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,678 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e148 (81.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDifficulty breathing/dyspnea\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,581 (91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,658 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e149 (73.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFever\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,085 (91.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,520 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e645 (77.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e192 (22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFatigue or general weakness\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39319 (91.5)\u003c/p\u003e \u003cp\u003e411(87.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3652(8.5)\u003c/p\u003e \u003cp\u003e60(12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeadache\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38855(91.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3656(8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e875(94.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56(6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJoint pain or arthritis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan fontcategory=\"NonProportional\" class=\"\" name=\"Emphasis\"\u003e39699(91 . 5)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan fontcategory=\"NonProportional\" class=\"\" name=\"Emphasis\"\u003e3698(8 . 5)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan fontcategory=\"NonProportional\" class=\"\" name=\"Emphasis\"\u003e31(68 . 9)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14(31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThorax sore throat\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan fontcategory=\"NonProportional\" class=\"\" name=\"Emphasis\"\u003e39467 (91 . 7)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3564(8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan fontcategory=\"NonProportional\" class=\"\" name=\"Emphasis\"\u003e263(64 . 0)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148(36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMuscle pain, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,653 (91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,692 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77 (79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNausea, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,555 (91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,670 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175 (80.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRunny nose\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,327 (91.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,572 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e403 (74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSore throat or pharyngitis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,467 (91.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,564 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e263 (64.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148 (36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVomiting\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,621 (91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,687 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109 (81.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAcute respiratory distress syndrome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,680 (91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,708 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50 (92.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCough with sputum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,694 (91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,709 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36 (92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePredictive Performance of Models for COVID-19 Classification\u003c/h3\u003e\n\u003cp\u003eThe performance metrics, presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, varied across the three supervised machine learning algorithms. The most balanced performance was demonstrated by logistic regression, which was characterized by high accuracy (0.91), sensitivity (0.998), specificity (0.71), precision (0.913), recall (0.998) and F1 score (0.953). Furthermore, its overall level of discrimination was high with an AUC of 0.854. XGBoost followed with a high accuracy (0.911) and specificity (0.71), but very low sensitivity (0.029). Also, it had a low Precision (0.444), Recall (0.029), and F1 score (0.055). Random Forest followed closely behind with a high accuracy of 0.912, high specificity (0.997) and AUC score (0.700). However, it had low sensitivity (0.024), precision (0.46), and very low recall (0.046). These findings revealed that logistic regression outperformed other models in the accuracy of finding true positives and having a more favourable sensitivity-specificity ratio.\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\u003ePerformance metrics of the supervised ML algorithms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrecision\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRecall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eROC AUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.854 (0.830\u0026ndash;0.878)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.700 (0.682\u0026ndash;0.718)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXGBoost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.689 (0.671\u0026ndash;0.707)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eVariable importance\u003c/h2\u003e \u003cp\u003eVariable importance results (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) indicated that age was the most important predictor in the model, which contributed more than any other feature. Symptoms such as cough and runny nose were also proven to be high contributors. Other symptoms such as fever, sore throat or pharyngitis, soreness in the thorax or the chest, headache, and difficulty breathing contributed moderately to the prediction. Lower-ranked variables, which included fatigue or general weakness, vomiting, nausea, diarrhoea, muscle pain, cough with sputum, joint pain or arthritis and acute respiratory distress syndrome, added comparatively less to the model. In general, the findings indicate that demographic variables, especially age, and the main respiratory symptoms were the most significant contributors to the predictive ability of the model, with those symptoms that are more severe/less commonly reported being insignificant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDeployment\u003c/h2\u003e \u003cp\u003eThe final logistic regression model was integrated into a Shiny web application (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bit.ly/41LxW9p\u003c/span\u003e\u003cspan address=\"http://bit.ly/41LxW9p\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Users can enter demographic and symptom data to obtain a real-time probability of COVID-19 positivity.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDiscussion (dup: abstract ?)\u003c/h3\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the demographic and clinical disparities between COVID-19-positive and negative cases in Nigeria and tested the suitability of machine learning models in predicting the occurrence of COVID-19 based on routinely collected symptoms and demographic data. Generally, the results reveal that there are clear epidemiological differences between the infected and non-infected individuals. It also indicates the clinical importance of an accurate and affordable predictive model that can be implemented in a real-life environment.\u003c/p\u003e \u003cp\u003eThe members of this cohort with COVID-19 were much younger compared to those who tested negative. This is contrary to studies conducted in hospital settings where emphasis has been given to older age as an indicator of severe disease and not infection (Gallo Marin et al., 2021; Zhou et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the Nigerian context, this might be indicative of the younger populations having greater uptake of the tests, or differences in healthcare-seeking behaviour among economically active individuals compared to the older population. The positivity rate in males was far higher than that of females, which agrees with previous findings that indicate the sex differences in exposure patterns, biological vulnerability, and health-seeking behaviour in the epidemiology of COVID-19 (Gallo Marin et al., 2021).\u003c/p\u003e \u003cp\u003eThere were noticeable variations in symptomatic patterns among COVID-19-infected and uninfected persons. Central systemic and respiratory manifestations such as cough, fever, dyspnea, runny nose, sore throat, as well as thoracic discomfort were much more common in positives, which supports their diagnostic importance in screening (Gallo Marin et al., 2021). Gastrointestinal symptoms like diarrhoea, nausea, and vomiting were also notably more prevalent in positives. This finding supports mounting evidence on COVID-19 presentations with extrapulmonary manifestations (Zhang et al., 2021). Muscle pain and fatigue were also more common, as they can be the systemic inflammatory reaction to acute viral infection (Zhang et al., 2021).\u003c/p\u003e \u003cp\u003eSurprisingly, there were unusual patterns of some symptoms that were traditionally attributed to viral illness. Headaches were vastly less frequent in people with COVID-19, as well as acute respiratory distress syndrome, and cough with sputum in positives were equally less frequent among the positive cases. The results were probably caused by the dominance of the mild to moderate cases present in the community compared to more severe respiratory complications that are more prevalent among hospitalised patients. The significantly greater prevalence of joint pains or arthritis and throat-related symptoms in positives implied that musculoskeletal and upper respiratory tract reactions may be one of the most evident manifestations in this group and cannot be neglected when screening (Zarpoosh \u0026amp; Amirian, 2023).\u003c/p\u003e \u003cp\u003eIn the variable importance analysis, age showed the strongest predictive power, and it is thus of prime importance in categorising the status of infection among this population. Although older age is usually highlighted in hospital-based research as a disease predictor, its quality in the model indicates that age is also a discriminatory factor for the risk of infection in the community and surveillance. Perhaps this is due to age-related variations in exposure patterns, social life, and immune responses. The common respiratory symptoms like cough and runny nose, with sex and fever, were also high determinants in the categorisation of infection status. Fever, sore throat or pharyngitis, thoracic sore throat, headache, and difficulty breathing had low importance. This suggests that although they are related to infection, they do not have incremental and dominant discriminatory power when combined with age and core respiratory symptoms. It is worth noting that some of the symptoms that had been statistically more prevalent among positives, including fatigue, nausea, vomiting, diarrhoea, and muscle pain, were given relatively lower importance by the model. This highlights the difference between the prevalence of symptoms and predictive contribution.\u003c/p\u003e \u003cp\u003eFurthermore, the symptoms are widespread across a wide variety of infectious and non-infectious diseases and thus have low specificity for COVID-19 on their own. Similarly, the most severe disease-related symptoms, such as acute respiratory distress syndrome and sputum-containing cough (Gallo Marin et al., 2021), were the lowest-ranking symptoms, probably because of their low incidence amongst this non-hospitalised population, and their low utility in identifying cases early. Interestingly, though the proportion of positivity of joint pain or arthritis was high in the cohort\u0026rsquo;s distribution, it had very little contribution to prediction. This is probably due to its low prevalence in the data. Also, this suggested that the contribution of features with extreme proportions but very low occurrence to overall model performance was minimal. Findings from the variable importance suggested that predictors that were both highly prevalent and differentially distributed between infected and uninfected persons were the most useful in classification.\u003c/p\u003e \u003cp\u003eThis study developed three machine\u0026ndash;learning\u0026ndash;based predictive models (Logistic regression, Random Forest, and Extreme Gradient Boosting) for COVID-19 infection in Nigeria and deployed the best-performing model. The models were trained using easily obtainable demographic and symptom data. Of the three models, Logistic regression demonstrated the clinically most useful balance of sensitivity and specificity with high discrimination and significantly better performance in detecting true positives than the ensemble models. Despite its simplicity, logistic regression achieved strong discriminatory performance, aligning with previous research showing that non-complex models are often preferable in clinical decision-making (Rudin, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, the model\u0026rsquo;s high sensitivity suggests potential utility for rapid triage and early isolation, particularly where confirmatory testing is limited. However, the modest specificity highlights the need for confirmatory laboratory testing before clinical decisions.\u003c/p\u003e \u003cp\u003eBy deploying the model as an R Shiny application, the feasibility for real-time use in clinical and public health settings is demonstrated. Similar digital tools have been shown to improve early screening, optimise resource allocation, and support epidemic response (Ahamad et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lalmuanawma et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eIntegrating the final model into an R Shiny web application demonstrates the feasibility of delivering real-time, point-of-care risk estimates with simple inputs. This aligns with novel digital tools that aid early screening of the virus. In addition, it can support resource allocation during another outbreak of the disease. A major strength of this study is the translation of a data-driven model into an accessible platform that can be assessed in clinical and public health systems.\u003c/p\u003e \u003cp\u003eNonetheless, there are some limitations to this study. There is a reliance on self-reported symptoms, potential selection and reporting bias, which relates to the individual getting tested and a lack of external validation beyond the original population the model was trained on. Future research should focus on validating and recalibrating the model across diverse Nigerian settings. This includes utilizing additional predictors like comorbidities, vaccination status, and other laboratory performance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe developed a machine learning-based predictive model for COVID-19 infection in Nigeria using demographic and symptom data. The developed model was deployed into a web-based application for the prediction of COVID-19 infection. The logistic regression model achieved high sensitivity and was integrated into an accessible R Shiny application (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bit.ly/41LxW9p\u003c/span\u003e\u003cspan address=\"http://bit.ly/41LxW9p\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e This tool may be externally validated and subsequently proposed to support rapid triage and early decision-making in resource-constrained settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArtificial Intelligence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAUC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea Under the Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAUROC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea Under the Receiver Operating Characteristic Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCOVID\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003e19\u003c/b\u003e\u0026ndash;Coronavirus Disease 2019\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eECG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectrocardiogram\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eECDC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEuropean Centre for Disease Prevention and Control\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eF1\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003escore\u003c/b\u003e\u0026ndash;Harmonic mean of precision and recall\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGLM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralized Linear Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIoT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternet of Things\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLMICs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow\u0026ndash;and Middle\u0026ndash;Income Countries\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eML\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMachine Learning\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNCDC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNigeria Centre for Disease Control (and Prevention)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePCR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase Chain Reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003ePCR\u003c/b\u003e\u0026ndash;Reverse Transcription Polymerase Chain Reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUSD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited States Dollar\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWHO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organisation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eXGBoost\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtreme Gradient Boosting\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and Consent to participate\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The data used in this study were obtained from the Nigeria Centre for Disease Control (NCDC). The study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval for this study was obtained from the Adeleke University Ethical Review Committee (approval no: AUERC/FBMS/IND/13), Nigeria. Formal authorization to use the data for this study was granted by the NCDC. No identifying information was collected, which assured the privacy and confidentiality of study participants. The requirement for informed consent specifically to this study was waived by the Ethical Review Board due to the use of secondary, de-identified data. This study did not pose any form of risk to the health of the participants and their environment. \u0026nbsp;All data provided were fully de-identified prior to analysis. The dataset was stored on password-protected computers accessible only to the study investigators.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e All authors gave consent for this publication\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The data sets used for this study are not publicly available (We do not have the right to redistribute the data). However, a request to reuse the data can be sent to NCDC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e There are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e There was no funding for this research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOlanrewaju Eniade conceived the study, developed the analysis plan, led the data analysis and model development, and drafted the manuscript. Ezekiel Ukwenga contributed to the introduction, data analysis, and results. Uchenna Akuka contributed to the methods and discussion. Opeyemi Adeniyi and Elonna Obak contributed to reviewing and editing the introduction and discussion. Adeagbo Omolola and Olaitan Peter B contributed to reviewing and editing the introduction. Olowe Rita and Opakunle Tolulope contributed to reviewing and editing the discussion. Olugbenga Adekunle Olowe is the senior author and provided overall supervision, technical review, and critical intellectual input. All authors reviewed and approved the final manuscript. Olanrewaju Eniade is the guarantor of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e The authors acknowledge the Nigeria Centre for Disease Control, the Ministry of Health, and the University of Ibadan for providing access to the data, and Mr Oluwatobi Ojo for his valuable technical feedback.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAfolalu OO, Atekoja OE, Oyewumi ZO, Adeyeye SO, Jolayemi KI, Akingbade O. (2021). 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Npj Digit Med. 2021;4(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41746-020-00372-6\u003c/span\u003e\u003cspan address=\"10.1038/s41746-020-00372-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Predictive model, Machine-Learning, COVID-19, Logistic Regression","lastPublishedDoi":"10.21203/rs.3.rs-8435681/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8435681/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTimely identification of COVID-19 cases is critical for clinical management and public health control, particularly in resource-limited settings. While RT-PCR testing remains the gold standard, limited accessibility during peak transmission highlights the role of predictive tools.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to develop and deploy a machine learning\u0026ndash;based predictive model for COVID-19 infection using demographic and symptom data from patients in Nigeria.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePatient records were preprocessed, including cleaning, encoding of categorical variables, and feature selection. Logistic regression, random forest, and gradient boosting models were compared using ten-fold cross-validation. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, precision, and F1-score. The best-performing model was deployed as a web-based decision-support tool via R Shiny.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of \u003cem\u003e43,442\u003c/em\u003e patient records were included, with \u003cem\u003e3712 (8.5%)\u003c/em\u003e confirmed positive cases. COVID-19 positivity was significantly associated with male sex, older age, and symptoms such as cough, fever, and dyspnea (all p\u0026thinsp;\u0026lt;\u0026thinsp;.05). Logistic regression achieved an AUROC of \u003cem\u003e0.93\u003c/em\u003e, sensitivity of \u003cem\u003e0.91\u003c/em\u003e, specificity of 0.76, and F1-score of 0.95. The model demonstrated strong recall but a slightly low specificity.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe developed and deployed a lightweight, interpretable predictive model for COVID-19, available as a Shiny application (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bit.ly/41LxW9p\u003c/span\u003e\u003cspan address=\"http://bit.ly/41LxW9p\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This tool may be externally validated and subsequently proposed to support rapid triage and early decision-making in resource-constrained settings.\u003c/p\u003e","manuscriptTitle":"Development and Deployment of a Machine Learning–Based Predictive Model for COVID- 19 Infection Using Patient Demographic and Symptom Data in Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-25 10:23:30","doi":"10.21203/rs.3.rs-8435681/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f833149-a378-47ff-a0e9-d37311b719c7","owner":[],"postedDate":"January 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-28T05:09:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-25 10:23:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8435681","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8435681","identity":"rs-8435681","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}