Impact of Seasonal Changes on the Epidemiology of Lassa Fever in a State in North Central, Nigeria

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Its transmission is closely tied to environmental and seasonal dynamics, especially those influencing rodent populations. Understanding these climatic influences is essential for strengthening disease surveillance and implementing timely interventions. Methods A descriptive cross-sectional study was conducted using retrospective epidemiological and climatic data from 2019 to 2024. Confirmed Lassa fever case records and meteorological variables (temperature, rainfall, and humidity) were analyzed using statistical correlation, regression models, and geospatial mapping. Results A consistent seasonal trend was observed, with the majority of cases occurring during the dry season (November–March), peaking in January 2022 and February 2024. Temperature showed a statistically significant positive correlation with Lassa fever incidence (r = 0.282, p = 0.029). Rainfall and humidity displayed weak or non-significant associations, though brief case surges followed isolated rainfall spikes in dry months. Spatial analysis identified Lokoja, Ibaji, and Dekina LGAs as hotspots, likely due to population density, food storage practices, and improved reporting. The regression model yielded modest explanatory power (R² = 0.089), but thresholds such as temperature > 35°C and humidity between 60–80% emerged as potential early warning indicators. Conclusion Seasonal variation, particularly elevated temperatures during the dry season, plays a significant role in Lassa fever incidence in Kogi State. Integrating climatic, ecological, and epidemiological data into a real-time risk alert system under a One Health framework could enhance preparedness and response in high-risk areas. Lassa fever seasonal variation climate change temperature rainfall humidity early warning system Kogi State Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Lassa fever is a viral hemorrhagic illness that has gained increasing attention due to its recurring outbreaks and high case fatality rates in parts of West Africa, particularly in Nigeria [ 1 ] [ 2 ] noted that the disease is caused by the Lassa virus, a single-stranded RNA virus of the Arenaviridae family, and is primarily transmitted to humans through contact with the urine, feces, saliva, or blood of infected multimammate rats ( Mastomys natalensis ), which serve as the natural reservoir. Human-to-human transmission can also occur in healthcare settings, particularly in the absence of appropriate infection prevention and control measures [ 3 ]. Despite ongoing surveillance and intervention efforts, Nigeria continues to experience annual Lassa fever outbreaks with significant morbidity and mortality, especially in high-risk regions such as Kogi State [ 4 ]. Recent studies have begun to explore the environmental and ecological factors that influence the epidemiology of Lassa fever, revealing a strong connection between disease transmission and seasonal or climatic conditions [ 5 , 6 , 7 ]. Seasonal variation, including shifts in rainfall, humidity, and temperature, directly influences rodent behaviour, reproduction cycles, and human interaction with rodent habitats. These variations may alter the timing and intensity of disease outbreaks [ 8 – 10 ]. With the growing threat of climate change, understanding how changing environmental conditions affect infectious disease dynamics is critical for proactive public health planning and response [ 11 ]. Kogi State, located in the North Central region of Nigeria, serves as an important case study due to its ecological diversity, unique geographic positioning, and recorded annual Lassa fever outbreaks. The state’s climate features distinct wet and dry seasons, with varying rainfall intensity and temperature patterns that could influence rodent population densities and human exposure risks [ 12 – 15 ]. However, empirical evidence linking climatic variability to Lassa fever trends in Kogi State remains sparse. Most existing studies on Lassa fever have focused on virology, clinical manifestations, and national surveillance statistics, with limited emphasis on localized climate-disease relationships [ 16 – 18 ]. The relevance of this study is underscored by the need to bridge the gap between environmental health and infectious disease epidemiology in the context of climate change. Between 2019 and 2024, Nigeria witnessed a range of climatic anomalies including delayed rainfall, fluctuating humidity levels, and increasingly warm dry seasons [ 19 ]. These changes could have significant implications for vector-borne and zoonotic diseases, yet few retrospective analyses have been conducted to explore this correlation, especially at the subnational level [ 19 ]. Given Kogi State’s persistent burden of Lassa fever, analyzing how seasonal changes may have influenced disease patterns during this five-year period is both timely and critical. This study will retrospectively analyze epidemiological data on confirmed Lassa fever cases in Kogi State from 2019 to 2024, alongside meteorological data including monthly rainfall, temperature, and relative humidity. The goal is to identify trends, seasonal peaks, and potential correlations between climatic parameters and disease incidence. The findings are expected to contribute to improved understanding of environmental determinants of Lassa fever outbreaks, support the development of climate-informed surveillance systems, and inform local public health strategies. Ultimately, the study aims to provide actionable insights for health authorities, particularly in tailoring early warning systems, enhancing outbreak preparedness, and guiding targeted interventions in communities most vulnerable to the combined effects of climate change and infectious diseases. It is anticipated that this research will not only address a pressing gap in the literature but also serve as a foundation for future multidisciplinary investigations at the intersection of climate science and public health in Nigeria and beyond. METHODOLOGY Research Design This study adopted a retrospective cross-sectional design, relying entirely on secondary data sources. It involved the analysis of confirmed Lassa fever cases from 2019, 2020, 2022, 2023, and 2024 in Kogi State, extracted from the Surveillance Outbreak Response Management and Analysis System (SORMAS) platform managed by the Nigeria Centre for Disease Control (NCDC). Monthly climatic variables temperature, rainfall, and relative humidity were obtained from the Nigerian Meteorological Agency (NiMET). The goal was to examine the correlation between climate variables and Lassa fever incidence. Study Area The study was conducted in Kogi State, located in North-Central Nigeria (Figure 1). The state lies between latitude 6°30′N and 8°50′N and longitude 5°51′E and 7°55′E. Kogi shares boundaries with Benue State to the east, Nasarawa to the north-east, Kwara to the northwest, Niger to the north, Edo and Ondo to the south, and the Federal Capital Territory (FCT) to the north. The state comprises 21 Local Government Areas (LGAs) with varying ecological zones ranging from guinea savannah to rainforest, a factor contributing to its climate diversity. It experiences a tropical climate characterized by distinct dry (November–March) and wet (April–October) seasons. This ecological diversity, coupled with a dense rural population engaged in agriculture and food storage practices, makes Kogi State a high-risk area for Lassa fever outbreaks (NCDC, 2023). Population of the Study The population for this study comprised all laboratory-confirmed cases of Lassa fever reported in Kogi State from January 2019 to December 2024, as well as monthly meteorological data (temperature, rainfall, humidity) obtained from NiMET for the same period. Data Sources This study made use of secondary data only, drawn from the following sources: Epidemiological Data: Confirmed monthly Lassa fever case data were obtained from the SORMAS database, managed by the NCDC in collaboration with the Kogi State Ministry of Health Disease Surveillance and Notification Office (DSNO). Climatic Data: Monthly averages of temperature (°C), rainfall (mm), and relative humidity (%) were collected from the Nigerian Meteorological Agency (NiMET). Sampling Technique Since the study population consisted of all reported cases within the five-year timeframe, total enumeration sampling was employed. This allowed for the inclusion of all available and complete Lassa fever and climate datasets from 2019 to 2024, excluding 2021, which had incomplete surveillance records on the SORMAS platform. Data Collection Procedure Permission was obtained from the Kogi State Ministry of Health and the NCDC to access the SORMAS data on confirmed Lassa fever cases. Climatic data were retrieved in collaboration with NiMET. Data were cleaned and verified for completeness before analysis. Data variables extracted included: Number of Lassa fever cases per month Local Government Area (LGA) of occurrence Mean monthly temperature, rainfall, and humidity Outbreak timing by season (wet vs. dry) Inclusion and Exclusion Criteria Inclusion Criteria All laboratory-confirmed cases of Lassa fever reported in Kogi State between January 2019 and December 2024, as recorded in the SORMAS database. Monthly meteorological data (temperature, rainfall, and humidity) obtained from the Nigerian Meteorological Agency (NiMET) corresponding to the same time frame. Local Government Areas (LGAs) with at least one confirmed Lassa fever case during the study period. Climate data entries with complete and consistent monthly values for the selected climatic indicators. Exclusion Criteria All suspected or probable Lassa fever cases without laboratory confirmation, as per NCDC surveillance definitions. Data for the year 2021, due to incomplete or missing surveillance records on the SORMAS platform. Meteorological entries with missing or incomplete monthly climatic parameters (temperature, humidity, rainfall). LGAs with incomplete or inconsistent Lassa fever case records during the study period. Method of Data Analysis Data were entered and analyzed using IBM SPSS Version 23. The analysis involved: Descriptive statistics: Frequency tables and graphs were used to describe the distribution of cases by month, year, season, and LGA. Pearson correlation analysis: Used to assess the relationship between individual climate variables and Lassa fever incidence. Multiple linear regression analysis: Used to determine the strength and significance of climatic variables (independent variables) in predicting Lassa fever cases (dependent variable). Geospatial mapping: A QGIS-based thematic map was created to visualize the spatial distribution of Lassa fever cases across LGAs in Kogi State. A significance level of 0.05 was adopted to determine statistical significance. Ethical Considerations This study was conducted using anonymized secondary data and posed no direct risk to any human subject. Ethical approval was obtained from the Kogi State Ministry of Health Ethics Committee, and data use was authorized by the Nigeria Centre for Disease Control (NCDC). All data were handled confidentially and used strictly for academic and public health research purposes. Historically, Kogi State has witnessed recurrent Lassa fever outbreaks, with reported cases from at least 19 out of its 21 Local Government Areas (LGAs). Surveillance records between 2019 and 2024 indicated consistent outbreaks with notable hotspots in LGAs such as Lokoja, Dekina, Okene, and Omala [20]. Its diverse ecology, high rodent populations, and socio-cultural practices such as open grain storage and bush burning contribute to the persistence of Lassa fever in the state [5-7]. Results This study presents and interprets the findings from the analysis of Lassa fever data from 2019 to 2024 alongside monthly climate data: temperature, rainfall, and humidity, from the Nigerian Meteorological Agency (NiMET). Analysis was conducted to address the study objectives and answer the guiding research questions. Data were analyzed using descriptive statistics, correlation coefficients, and multiple regression analysis. Across the five-year study period (2019–2024), Lassa fever incidence in Kogi State demonstrated a pronounced seasonal pattern (Figure 2), with consistent surges during the dry season (November to March) and noticeable declines during the wet season (April to October). As shown in Figure 3, the highest monthly case burdens occurred in January 2022 (14 cases) and February 2024 (13 cases), marking the dry season as the critical transmission window. This trend reflects underlying environmental and behavioural dynamics. The dry season in Kogi is characterized by the Harmattan winds, lower vegetation cover, and scarcity of food and water in the wild, which force rodent vectors such as Mastomys natalensis into closer contact with human dwellings. These conditions significantly increase the risk of exposure to Lassa virus through contaminated surfaces, food items, or aerosolized rodent excreta. Furthermore, the observed seasonal surge aligns with earlier findings from Nasarawa and Benue States, where dry periods consistently correlate with heightened rodent activity and Lassa virus transmission risk. This seasonal pattern also mirrors national Lassa fever surveillance data from the NCDC, which consistently identifies Edo, Ondo, and Ebonyi States; all sharing similar climatic features as dry season hotspots for outbreaks [16-18]. The strong seasonal pattern observed in this study underlines the importance of integrating climate seasonality into Lassa fever surveillance, early warning, and response frameworks, particularly in ecologically vulnerable states like Kogi. Pearson correlation analysis revealed the following: Temperature (r = 0.282, p = 0.029): Statistically significant positive correlation. Rainfall (r = -0.17, p = 0.199): Negative, not statistically significant. Humidity (r = -0.109, p = 0.409): Weak negative correlation, not significant. These findings suggest that temperature is the most influential climatic factor associated with Lassa fever incidence in Kogi State. Specifically, as temperature increases, so does the likelihood of human–rodent interaction and subsequent virus transmission. This supports research by Eze et al. (2023) [14] and Redding et al. (2021) [15], which found strong correlations between ambient temperature rise and Lassa fever outbreaks in endemic Nigerian states. Humidity and rainfall showed weak or negative correlations, implying that high rainfall may disperse rodent habitats and reduce indoor infestation, whereas high humidity might inhibit aerosolized viral transmission especially during the peak rainy season. Based on the regression model and seasonal analysis (Figure 5): Temperature >35°C and Humidity between 60–80% during dry months (Dec–April) are the most critical conditions for increased Lassa fever risk. Rainfall spikes during otherwise dry months (e.g., unexpected rains in February 2022 and 2023) preceded short-lived case surges. This confirms that climatic thresholds, not just seasons, drive outbreaks. This insight mirrors earlier modelling studies conducted in Ebonyi State and the Federal Capital Territory (FCT), Abuja, which also identified climatic triggers as predictive outbreak indicators [16]. The top three LGAs by total confirmed cases are: Lokoja, Ibaji and Dekina These LGAs jointly accounted for over 50% of cases reported between 2019 and 2024. The spatial clustering of cases correlates with areas of high population density and significant agricultural activity, which often involve open grain storage and rodent exposure. Additionally, Lokoja, being the state capital, has a high surveillance and reporting efficiency, likely contributing to higher reported figures (Figure 6). These findings align with studies in Plateau and Benue States, where LGA-level differences in infrastructure, food storage, and housing types contributed significantly to disease distribution [17] Table 1 Model Summary and change statistics for Predictive Modelling and Early Warning Systems for Lassa fever outbreaks Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .299a 0.089 0.04 6.307 0.089 1.8 3 55 0.158 Multiple regression analysis was used to define the nature of correlations between temperature, Multiple regression analysis was used to define the nature of correlations between temperature, humidity, rainfall; and the onset of diseases during Lassa fever outbreaks in Kogi State. The model produced an R square value of 0.089, which means that the total impact of temperature, humidity, and rainfall in the occurrence of Lassa fever can only explain 8.9% of the variance in the occurrence of Lassa fever. Adjusted R Square was even lower, 0.04 (4%); it means that the model has a limited explanatory power, and it might not generalise easily (See Table 1). Table 2: multi-regression analysis of climatic factors related to Lassa fever incidence in Kogi State Model Unstandardized Coefficients Standardized Coefficients T Sig. Correlations Collinearity Statistics B Std. Error Beta Zero-order Partial Part Tolerance VIF (Constant) -9.42 8.436 -1.117 0.269 Rainfall (mm) -0.012 0.01 -0.203 -1.183 0.242 -0.17 -0.158 -0.152 0.564 1.775 Temperature (°C) 0.288 0.168 0.228 1.719 0.091 0.257 0.226 0.221 0.939 1.065 Humidity (%) 0.074 0.09 0.139 0.826 0.412 -0.026 0.111 0.106 0.586 1.706 N Lassa fever cases = 0.288T + 0.074H - 0.012R - 9.42 Multicollinearity was ideal as it was determined using Variance Inflation Factor (VIF); in fact, all predictors VIF values were significantly lower than 5. In particular, the values of VIF of rainfall, temperature, and humidity were 1.775, 1.065, and 1.706 respectively (see Table 2) and this showed that the predictors did not experience strong multicollinearity. On analysis of the Unstandardized coefficients, temperature had the most positive relationship with Lassa fever incidence (B = 0.288) indicating that for any one unit change in atmospheric temperature, there will be 28.8% increase in the incidence of Lassa fever in the State. Humidity came next, exhibiting a modest positive impact (B = 0.074), that is, 7.4% increment in incidence of Lassa fever cases though, rainfall was not strongly related to the incidence of Lassa fever (B = -0.012). This shows an inverse relationship of 1.2 % decrease in the incidence of Lassa fever for every one unit increase in rainfall. This trend changed considering the p-values; no coefficients attained the conventional statistical significance value of 0.05. The borderline p-value which was 0.091 indicated that there is weak association between temperature and Lassa fever incidence. Rainfall and humidity on the other hand were found with p-values at 0.242 and 0.412 respectively meaning that they were not having a significant contribution. Table 3: Climatic threshold associated with lassa fever outbreak in Kogi State Factor Dry Season (High Risk) Wet Season (Moderate Risk) Temperature >35°C (strong correlation) 30–34°C (weak correlation) Rainfall Low/zero or sudden spikes Heavy but inconsistent link Humidity 60–80% (optimal) >85% (possible suppression) Table 3 presents the observed climatic thresholds that correlate with the seasonal distribution of Lassa fever outbreaks across Kogi State. The table revealed a stronger association between Lassa fever incidence and the dry season, typically characterized by temperatures exceeding 35°C, moderate humidity levels (60–80%), and low or abruptly fluctuating rainfall. These conditions likely enhance rodent intrusion into human settlements due to declining food and water sources in the wild, and facilitate viral transmission through aerosolized rodent excreta, particularly during the harmattan period. Conversely, during the wet season, although temperatures remain moderately high (30–34°C) and humidity often exceeds 85%, the association with Lassa fever incidence appears weaker and less consistent. High humidity may suppress airborne transmission by increasing particulate settling, while consistent rainfall supports vegetation growth and rodent dispersion, temporarily reducing rodent-human contact in indoor environments. This threshold table serves as a strategic tool for anticipating high-risk periods and guiding public health interventions. It underscores the importance of aligning surveillance and preventive strategies such as community sensitization, rodent control, and early warning alerts with periods marked by the identified high-risk climatic parameters. Table 4: Early warning Climatic Indicators for Lassa fever in Kogi State Risk Level Climate Conditions Recommended Actions Green (Low) Normal wet-season conditions Routine surveillance, public awareness campaigns. Yellow (Moderate) Dry season onset + rising temperatures Increase rodent control, stockpile ribavirin (treatment). Orange (High) Dry month with >35°C + 60–80% humidity Active case-finding, community alerts, hospital preparedness. Red (Critical) Sudden rainfall spike in dry season Emergency response: quarantine, vector control, resource deployment. The multiple regression model yielded: R² = 0.089, indicating that temperature, rainfall, and humidity explain only 8.9% of Lassa fever variance. Unstandardized coefficients indicate: Temperature (B = 0.288): Most significant influence, albeit with borderline p-value (0.091). Humidity (B = 0.074): Modest effect. Rainfall (B = -0.012): Inverse relationship. None of the coefficients attained statistical significance at p < 0.05. Although this suggests that climate data alone cannot predict outbreaks, its integration with epidemiological surveillance could improve early warning systems. As such, a tiered climate-risk framework was proposed (Table 4), categorizing risk levels based on weather conditions. This aligns with WHO’s recommendation for One Health approaches that combine ecological, climatic, and epidemiological data for outbreak preparedness. In Kogi State, such systems would require: Monthly meteorological data sharing between NiMET and the State Ministry of Health. GIS-based risk mapping for rodent control targeting. Prepositioning of medical supplies (e.g., ribavirin) and community alerts during identified high-risk periods. Discussion of Findings The analysis of Lassa fever incidence in Kogi State from 2019 to 2024 reveals a consistent seasonal pattern, with cases predominantly occurring during the dry season (November to March). Outbreak peaks in January 2022 and February 2024 were followed by declines during the rainy months, reinforcing the ecological link between aridity and transmission. This clustering corresponds to stressors affecting rodent behaviour, including food and water scarcity that compel rodents to enter human dwellings in search of sustenance. The Harmattan season, marked by dry and dusty winds, may further facilitate aerosolized transmission of viral particles via contaminated rodent excreta, a mechanism supported by previous studies [18]. Temperature emerged as the most influential climatic factor, with a statistically significant positive correlation with Lassa fever incidence (r = 0.282, p = 0.029). The implication is that elevated temperatures, particularly above 35°C, promote rodent breeding and activity, thereby amplifying human exposure. This finding corroborates patterns observed in other endemic regions such as Edo, Ondo, and Ebonyi States, and aligns with predictive models highlighting temperature as a key driver of outbreak dynamics [19-20]. By contrast, rainfall and humidity demonstrated weaker associations. High rainfall generally disperses rodent habitats and may temporarily reduce indoor infestations, though isolated rainfall spikes during dry months, as seen in February 2022 and 2023, preceded brief surges in incidence, pointing to complex ecological interactions. Humidity exerted a nuanced influence: values between 60–80% appeared to support transmission, whereas levels above 85% likely suppressed aerosol spread by promoting particulate settling, echoing earlier reports from West Africa (Beermann et al., 2023) [8-10]. Spatial distribution patterns further illuminate the epidemiology of Lassa fever in Kogi State. Lokoja, Ibaji, and Dekina consistently accounted for the highest burdens of cases. The clustering in Lokoja is plausibly linked to its urban density, suboptimal food storage practices, and relatively stronger case reporting, while floodplain ecologies in Ibaji and Idah create favourable conditions for rodent proliferation and human–rodent contact. These findings parallel evidence from other riverine communities, where poor drainage, seasonal flooding, and inadequate housing structures intensify exposure risks[15, 21]. Such hotspots require tailored interventions rather than uniform state-wide approaches. Although the multiple regression model demonstrated modest explanatory power (R² = 0.089), the climatic thresholds derived particularly temperatures exceeding 35°C and humidity ranges of 60–80% offer practical early-warning indicators. While climate alone cannot fully predict outbreaks, its integration with ecological and behavioural surveillance strengthens forecasting capacity. This aligns with earlier work advocating for climate-based predictive frameworks that, when combined with local epidemiological intelligence, can substantially enhance outbreak preparedness [22-25]. Addressing the recurrent outbreaks of Lassa fever in Kogi State therefore requires a comprehensive, forward-looking strategy anchored in a One Health framework. Policy and surveillance systems must incorporate meteorological data, particularly forecasts from the Nigerian Meteorological Agency (NiMet), into routine reporting platforms such as the Disease Surveillance and Notification Office (DSNO). This integration would facilitate anticipatory actions, enabling targeted rodent control, prepositioning of protective equipment, and deployment of sensitization campaigns before peak transmission periods [9, 27-28]. Establishing climate-informed early warning systems in high-risk LGAs would further allow for timely, localized interventions. Public health education and behavioural change remain indispensable components of outbreak prevention. Seasonal rodent control programs between October and March, with a focus on household-level interventions such as rodent-proofing and safe grain storage, are critical. Community sensitization should emphasize early care-seeking and practical prevention measures, particularly in persistent epicenters such as Lokoja, Ibaji, and Idah. Health system capacity also demands strengthening, including the prepositioning of essential supplies—ribavirin, personal protective equipment, and diagnostic kits—prior to the onset of the dry season. Expanding laboratory diagnostic capacity in secondary healthcare centers will shorten delays in confirmation and improve case management [29]. Cross-sectoral collaboration remains central to sustained progress. Real-time data sharing between the Nigeria Centre for Disease Control (NCDC), NiMet, and state health departments will synchronize meteorological intelligence with epidemiological surveillance and response [29-31]. In addition, sustained research is needed to refine predictive modelling by integrating behavioural, ecological, and rodent population dynamics with climatic data. Longitudinal studies and community-based surveillance will provide the depth of evidence necessary to improve forecasting and inform policy. Ultimately, the findings affirm that while climatic factors alone cannot fully account for Lassa fever transmission, they interact with ecological and social determinants in complex but predictable ways. Embedding meteorological insights into routine surveillance, focusing interventions on spatial hotspots, and strengthening institutional collaboration represent actionable pathways to sustainable control of this climate-sensitive disease [35-42]. Limitations This study is not without limitations, which should be considered when interpreting the findings. One notable constraint was the absence of epidemiological data for 2021, which created a gap in the continuity of annual trend analysis. This missing year restricts the ability to fully capture potential fluctuations or transitional dynamics in Lassa fever incidence across the study period. Another limitation lies in the use of averaged monthly climate data. While this approach provides broad insight into seasonal relationships, it may obscure short-term variations in temperature, rainfall, and humidity that are directly relevant to rodent behaviour and viral transmission dynamics. More granular, daily or weekly climatic data could reveal patterns of exposure and risk that remain hidden when only monthly averages are considered. Additionally, although demographic information such as age and sex was available in the dataset, it was not incorporated into the statistical modelling due to the scope of the present study. This exclusion limits understanding of how demographic factors may intersect with climatic and ecological variables to shape disease risk. Future studies that integrate these dimensions could yield a more comprehensive epidemiological picture of Lassa fever in the region. Conclusion This study concludes that Lassa fever transmission in Kogi State exhibits seasonal predictability and weak to moderate associations with climatic factors, particularly temperature and humidity. The highest incidence occurs in the dry season, with Idah, Ibaji, and Lokoja identified as spatial hotspots. However, climate is only one part of the broader risk ecosystem that includes rodent ecology, socio-economic vulnerability, and behavioural patterns. Effective Lassa fever control must therefore adopt a multi-sectoral, seasonally informed, and geographically targeted approach. Declarations Abbreviations Not Applicable Ethics approval and consent to participate This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (2013 version) as adopted by the World Medical Association. Ethical approval for the study was secured from the Health Research Ethics Committee of the Kogi State Ministry of Health (a fully registered committee the under National Health Research Ethics Committee), after a defence of the research proposal during the Committee’s ethical screening interview. Consent for Participation Not Applicable Availability of data and materials The datasets generated and analyzed during the current study are not publicly available due to privacy considerations of the participants but are available from the corresponding author upon reasonable request. Competing Interests The authors hereby declare that there are no competing interests Funding This study did not receive any specific grant from any funding institution. Author’s Contributions ONJ, AIA, GIE, SOA, AOM, OPE, AJE, OA, SB, AU, OJA, KM, AJA, MSO, OST, and AAB conceptualized and designed the study and contributed to drafting and revising the manuscript. ONJ, AIA, GIE, SOA, AOM, OPE, AJE, OA, SB, AU, OJA, KM, AJA, MSO, OST, and AAB contributed to data collection, and manuscript review, All authors participated in study design, and critically reviewed the manuscript for important intellectual content. All authors assisted with the literature review, data visualization, and preparation of initial manuscript drafts, All authors provided methodological expertise, and contributed significantly to manuscript revisions. All authors supported data acquisition and provided feedback on the manuscript drafts. All authors contributed to the manuscript structure, final proofreading, and editing for clarity and coherence; all authors have read and approved the final manuscript. Acknowledgments Not Applicable Code Availability Not Applicable Availability of data and materials The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Consent for publication Not applicable. Competing interests All authors declare that they have no competing interests. Clinical Trials Number Not Applicable References Al-Mustapha AI, Adesiyan IM, Orum TG, Ogundijo OA, Lawal AN, Nzedibe OE, Onyeka LO, Muhammad KU, Odetayo L, Oyewo M, Muhammad SO, Atadiose EO, Adebudo LI, Adetunji DA, Jantiku HJ, Akintule AO, Nwachukwu RC, Abubakar AT. Lassa fever in Nigeria: epidemiology and risk perception. Sci Rep. 2024;14(1). doi: 10.1038/s41598-024-78726-3. Balogun OO, Akande OW, Hamer DH. Lassa Fever: An Evolving Emergency in West Africa. Am J Trop Med Hyg. 2020 Nov 23;104(2):466-73. doi: 10.4269/ajtmh.20-0487. PMID: 33236712; PMCID: PMC7866331. Collins A. Preventing Health Care–Associated Infections. Rockville (MD): Agency for Healthcare Research and Quality (US); 2020. Available from: https://www.ncbi.nlm.nih.gov/books/NBK2683/ Aloke C, Obasi NA, Aja PM, Emelike CU, Egwu CO, Jeje O, Edeogu CO, Onisuru OO, Orji OU, Achilonu I. Combating Lassa Fever in West African Sub-Region: Progress, Challenges, and Future Perspectives. Viruses. 2023;15(1):146. doi: 10.3390/v15010146. Ezenwa-Ahanene A, Musa E, Fagbemi A. Understanding the ecological drivers of Lassa fever in North Central Nigeria. Afr J Infect Dis. 2024;18(1):22-9. Ezenwa-Ahanene A, Okoye C, Okorie P, Olaleye DO, Adewale O. Dry season dynamics and Lassa fever transmission in rural communities of Ebonyi State, Nigeria. Int J Infect Dis. 2024;139:87-94. doi: 10.1016/j.ijid.2024.01.004. Ezenwa-Ahanene A, Salawu AT, Adebowale AS. Descriptive epidemiology of Lassa fever, its trend, seasonality, and mortality predictors in Ebonyi State, South-East, Nigeria, 2018-2022. BMC Public Health. 2024;24(1). doi: 10.1186/s12889-024-20840-y. Beermann S, Abdullahi YM, Salami K, Igbokwe E. Development of climate-based early warning tools for Lassa fever outbreaks in Nigeria: A spatiotemporal pilot model. PLoS Negl Trop Dis. 2023;17(6):e0011134. doi: 10.1371/journal.pntd.0011134. Beermann S, Dobler G, Faber M, Frank C, Habedank B, Hagedorn P, Kampen H, Kuhn C, Nygren T, Schmidt-Chanasit J, Schmolz E, Stark K, Ulrich RG, Weiß S, Wilking H. Impact of climate change on vector- and rodent-borne infectious diseases. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2023;8(Suppl 3):33-61. doi: 10.25646/11401. Beermann S, Schumann B, Lang M, Glahn M. Satellite-based early warning systems for Lassa fever: Integrating rodent data and rainfall metrics in West Africa. PLoS Negl Trop Dis. 2023;17(2):e0011156. doi: 10.1371/journal.pntd.0011156. Shafique M, Khurshid M, Muzammil S, Arshad MI, Malik IR, Rasool MH, Khalid A, Khalid R, Asghar R, Baloch Z, Aslam B. Traversed dynamics of climate change and One Health. Environ Sci Eur. 2024;36(1). doi: 10.1186/s12302-024-00931-8. Cadmus EO, Olayinka A, Bamidele O. Climatic factors and seasonal variation in Lassa fever transmission in Nigeria. Int J Infect Dis. 2023;128:55-64. doi: 10.1016/j.ijid.2023.03.009. Cadmus EO, Olufemi AO, Ezeobi B, Owolabi OJ, Aluko A. Climatic predictors of Lassa fever outbreak patterns in Southern Nigeria. Afr J Infect Dis. 2023;17(1):34-45. doi: 10.21010/ajid.v17i1.5. Cadmus S, Taiwo OJ, Akinseye VO, Cadmus EO, Famokun G, Fagbemi S, Ansumana R, Omoluabi A, Ayinmode AB, Oluwayelu DO, Odemuyiwa SO, Tomori O. Ecological correlates and predictors of Lassa fever incidence in Ondo State, Nigeria 2017–2021: an emerging urban trend. Sci Rep. 2023;13(1). doi: 10.1038/s41598-023-47820-3. Adebayo AO, Oladipo EO, Amusan AA. Seasonal variation in the abundance and distribution of Mastomys natalensis in central Nigeria. J Trop Ecol. 2021;37(2):111-20. doi: 10.1017/S0266467421000030. Redding DW, Gibb R, Dan-Nwafor CC, Ilori EA, Yashe RU, Oladele SH, Amedu MO, Iniobong A, Attfield LA, Donnelly CA, Abubakar I, Jones KE, Ihekweazu C. Geographical drivers and climate-linked dynamics of Lassa fever in Nigeria. Nat Commun. 2021;12(1):5759. doi: 10.1038/s41467-021-25910-y. Redding DW, Moses LM, Cunningham AA, Wood JLN, Jones KE. Environmental-mechanistic modelling of the impact of climate change on the emergence of Lassa fever in West Africa. Nat Commun. 2021;12(1):1-11. doi: 10.1038/s41467-021-21394-2. Redding DW, Tiedt S, Lo I, Jones KE. Predicting the global mammalian viral sharing network using phylogeography. Nat Commun. 2021;12(1):1-12. doi: 10.1038/s41467-021-21034-5. Edokpa DO, Ede PN, Diagi BE, Ajiere SI. Rainfall and Temperature Variations in a Dry Tropical Environment of Nigeria. J Atmos Sci Res. 2023;6(2):50-7. doi: 10.30564/jasr.v6i2.5527. Nigeria Centre for Disease Control (NCDC). Lassa Fever Situation Reports and Case Statistics (2019–2024). Abuja: NCDC; 2024. Available from: https://www.ncdc.gov.ng Adebayo AM, Fowotade A, Adeniji JA, Nwabuisi C. Environmental and demographic factors influencing the incidence of Lassa fever in riverine communities in Nigeria. Pan Afr Med J. 2022;42:110. doi: 10.11604/pamj.2022.42.110.31045. Haque S, Mengersen KM, Barr I, Wang L, Yang W, Vardoulakis S, Bambrick H, Hu W. Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations. Environ Res. 2024;249:118568. doi: 10.1016/j.envres.2024.118568. Borham A, Abdel Motaal K, ElSersawy N, Ahmed YF, Mahmoud S, Musaibah AS, Abdelnaser A. Climate change and zoonotic disease outbreaks: emerging evidence from epidemiology and toxicology. Int J Environ Res Public Health. 2025;22(6):883. doi: 10.3390/ijerph22060883. Mills C, Donnelly CA. Climate-based modelling and forecasting of dengue in three endemic departments of Peru. PLoS Negl Trop Dis. 2024;18(12):e0012596. doi: 10.1371/journal.pntd.0012596. Villanueva-Miranda I, Xiao G, Xie Y. Artificial intelligence in early warning systems for infectious disease surveillance: a systematic review. Front Public Health. 2025 Jun 23;13:1609615. doi: 10.3389/fpubh.2025.1609615. PMID: 40626156; PMCID: PMC12230060. Okpachi CA, Okon UA, Okunromade O, Williams-Enenche L, Ojotule A. Evaluation of Lassa fever surveillance system in Kogi State, north-central Nigeria. J Interv Epidemiol Public Health. 2025 Aug 14;8:Abstract ELIC2025253 (Oral 104). Tambo E, Adetunde OT, Olalubi OA. Re-emerging Lassa fever outbreaks in Nigeria: re-enforcing “One Health” community surveillance and emergency response practice. Infect Dis Poverty. 2018;7:37. doi: 10.1186/s40249-018-0421-8. Eneh SC, Obi CG, Ephraim Ikpongifono U, Dauda Z, Udoewah SA, Anokwuru CC, Onukansi FO, Ikhuoria OV, Ojo TO, Madukaku CU, Orabueze IN, Chizoba AF. The resurgence of Lassa fever in Nigeria: economic impact, challenges, and strategic public health interventions. Front Public Health. 2025 Jul 16;13:1574459. doi: 10.3389/fpubh.2025.1574459. PMID: 40740381; PMCID: PMC12307277. Agbonlahor DE, Akpede GO, Happi CT, Tomori O. 52 years of Lassa fever outbreaks in Nigeria, 1969–2020: an epidemiologic analysis of the temporal and spatial trends. Am J Trop Med Hyg. 2021;105(4):974-85. doi: 10.4269/ajtmh.20-1160. Aiyedun JO, Musa H, Saka MJ. Rodent infestation and risk factors for Lassa fever transmission in Nigeria: a cross-sectional survey. Afr J Infect Dis. 2021;15(3):66-74. doi: 10.21010/ajid.v15i3.9. Akinbobola AO, Ogunlowo OO, Kolawole TO. Climate change and health vulnerability in Nigeria: integrating meteorological data into public health planning. Int J Environ Res Public Health. 2022;19(13):7892. doi: 10.3390/ijerph19137892. Kiryluk HD, Beard CB, Holcomb KM. The use of environmental data in descriptive and predictive models of vector-borne disease in North America. J Med Entomol. 2024 May 13;61(3):595-602. doi: 10.1093/jme/tjae031. PMID: 38431876; PMCID: PMC11078578. Johnston ASA, Boyd RJ, Watson JW, Paul A, Evans LC, Gardner EL, Boult VL. Predicting population responses to environmental change from individual-level mechanisms: towards a standardized mechanistic approach. Proc Biol Sci. 2019 Oct 23;286(1913):20191916. doi: 10.1098/rspb.2019.1916. PMID: 31615360; PMCID: PMC6834044. Anyamba A, Small JL, Britch SC, Tucker CJ, Linthicum KJ, Maloney S. Climate anomalies and the risk of vector-borne diseases in West Africa: emerging patterns and opportunities for predictive modelling. Sci Rep. 2023;13(1):11904. doi: 10.1038/s41598-023-39215-6. Besson ME, Pépin M, Metral PA. Lassa fever: critical review and prospects for control. Trop Med Infect Dis. 2024;9(8):178. doi: 10.3390/tropicalmed9080178. Bonwitt J, Sáez AM, Lamin JM, Ansumana R, Dawson M, Brown H, Sahr F. At home with Mastomys and Rattus: human–rodent interactions and potential for primary transmission of Lassa virus in domestic spaces. PLoS Negl Trop Dis. 2020;14(2):e0008108. doi: 10.1371/journal.pntd.0008108. Buckee CO, Tatem AJ, Metcalf CJE. Seasonal population movements and the surveillance and control of infectious diseases. Trends Parasitol. 2017;33(1):10-20. doi: 10.1016/j.pt.2016.10.006. Carlson CJ, Albery GF, Merow C, Trisos CH, Zipfel CM, Eskew EA, Gibb R. Climate change increases cross-species viral transmission risk. Nature. 2022;607(7919):555-62. doi: 10.1038/s41586-022-04788-w. Collins A. Preventing health care–associated infections. Rockville (MD): Agency for Healthcare Research and Quality (US); 2020. Available from: https://www.ncbi.nlm.nih.gov/books/NBK2683/ Destoumieux-Garzón D, Mavingui P, Boetsch G, Boissier J, Darriet F, Duboz P, Voituron Y. The One Health concept: 10 years old and a long road ahead. Front Vet Sci. 2020;7:14. doi: 10.3389/fvets.2020.00014. Akyala AI, Aremu SO, Jaggu AR, Gyar SS. Emerging trends and associated risk factors influencing mortality and fatality rates of Lassa fever in Nigeria, 2001–2024: a retrospective study. J Interv Epidemiol Public Health. 2025;8(ConfProc5):00216. doi: 10.37432/jieph-confpro5-00216. Akyala AI, Aremu SO, Jaggu AR, Gyar SS. Assessment of cutting-edge machine learning models to significantly enhance predictions of Lassa fever outbreaks using whole genome sequencing. J Interv Epidemiol Public Health. 2025;8(ConfProc5):00263. doi: 10.37432/jieph-confpro5-00263. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Tropical Diseases, Travel Medicine and Vaccines → Version 1 posted Editorial decision: Revision requested 20 Oct, 2025 Reviews received at journal 15 Oct, 2025 Reviews received at journal 12 Oct, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers agreed at journal 23 Sep, 2025 Reviewers invited by journal 22 Sep, 2025 Editor assigned by journal 22 Sep, 2025 Submission checks completed at journal 22 Sep, 2025 First submitted to journal 17 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":246247,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Kogi state\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7638265/v1/38a282fb7d767b3a4d45acc8.png"},{"id":92734833,"identity":"8e52dc8a-6f49-4693-9cfe-5eaf3471b381","added_by":"auto","created_at":"2025-10-03 16:25:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24031,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of Lassa fever cases by season in Kogi State\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7638265/v1/6228a3a48a2cf963f524cec9.png"},{"id":92736009,"identity":"abe9eacd-5696-4a34-b3aa-fa61a77b953b","added_by":"auto","created_at":"2025-10-03 16:33:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33343,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between seasonal variations and incidence of Lassa fever in Kogi State from 2019 to 2024\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7638265/v1/c19510f8efda320dc665fa47.png"},{"id":92736011,"identity":"e78e320a-7948-4ae1-be07-408153867154","added_by":"auto","created_at":"2025-10-03 16:33:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59518,"visible":true,"origin":"","legend":"\u003cp\u003ea,b,c: Showing association of temperature, rainfall and humidity with lassa fever\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7638265/v1/fe208e42dc27fca5f44791ce.png"},{"id":92734837,"identity":"644ab1ab-92ee-4475-992b-65637903f5c6","added_by":"auto","created_at":"2025-10-03 16:25:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":81754,"visible":true,"origin":"","legend":"\u003cp\u003eChart showing climatic conditions associated with increased Lassa Fever Outbreaks in Kogi State\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7638265/v1/12b100e78423aa427ac63ba2.png"},{"id":92734836,"identity":"a533a5cd-cbcb-426b-9077-de225549c654","added_by":"auto","created_at":"2025-10-03 16:25:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":135597,"visible":true,"origin":"","legend":"\u003cp\u003eShowing geographical distribution of Lassa fever across local government areas in the state\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7638265/v1/ec5b874d8c992ee89b1fbb02.png"},{"id":104739536,"identity":"3f5af3c8-eb9a-484c-a968-77d2d81d2d93","added_by":"auto","created_at":"2026-03-16 16:08:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1936287,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7638265/v1/919f93cd-b726-4ba0-839e-b08b39d4fa0c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Seasonal Changes on the Epidemiology of Lassa Fever in a State in North Central, Nigeria","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eLassa fever is a viral hemorrhagic illness that has gained increasing attention due to its recurring outbreaks and high case fatality rates in parts of West Africa, particularly in Nigeria [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] noted that the disease is caused by the Lassa virus, a single-stranded RNA virus of the Arenaviridae family, and is primarily transmitted to humans through contact with the urine, feces, saliva, or blood of infected multimammate rats (\u003cem\u003eMastomys natalensis\u003c/em\u003e), which serve as the natural reservoir. Human-to-human transmission can also occur in healthcare settings, particularly in the absence of appropriate infection prevention and control measures [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite ongoing surveillance and intervention efforts, Nigeria continues to experience annual Lassa fever outbreaks with significant morbidity and mortality, especially in high-risk regions such as Kogi State [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent studies have begun to explore the environmental and ecological factors that influence the epidemiology of Lassa fever, revealing a strong connection between disease transmission and seasonal or climatic conditions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Seasonal variation, including shifts in rainfall, humidity, and temperature, directly influences rodent behaviour, reproduction cycles, and human interaction with rodent habitats. These variations may alter the timing and intensity of disease outbreaks [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. With the growing threat of climate change, understanding how changing environmental conditions affect infectious disease dynamics is critical for proactive public health planning and response [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eKogi State, located in the North Central region of Nigeria, serves as an important case study due to its ecological diversity, unique geographic positioning, and recorded annual Lassa fever outbreaks. The state\u0026rsquo;s climate features distinct wet and dry seasons, with varying rainfall intensity and temperature patterns that could influence rodent population densities and human exposure risks [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, empirical evidence linking climatic variability to Lassa fever trends in Kogi State remains sparse. Most existing studies on Lassa fever have focused on virology, clinical manifestations, and national surveillance statistics, with limited emphasis on localized climate-disease relationships [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe relevance of this study is underscored by the need to bridge the gap between environmental health and infectious disease epidemiology in the context of climate change. Between 2019 and 2024, Nigeria witnessed a range of climatic anomalies including delayed rainfall, fluctuating humidity levels, and increasingly warm dry seasons [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These changes could have significant implications for vector-borne and zoonotic diseases, yet few retrospective analyses have been conducted to explore this correlation, especially at the subnational level [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven Kogi State\u0026rsquo;s persistent burden of Lassa fever, analyzing how seasonal changes may have influenced disease patterns during this five-year period is both timely and critical.\u003c/p\u003e\u003cp\u003eThis study will retrospectively analyze epidemiological data on confirmed Lassa fever cases in Kogi State from 2019 to 2024, alongside meteorological data including monthly rainfall, temperature, and relative humidity. The goal is to identify trends, seasonal peaks, and potential correlations between climatic parameters and disease incidence. The findings are expected to contribute to improved understanding of environmental determinants of Lassa fever outbreaks, support the development of climate-informed surveillance systems, and inform local public health strategies.\u003c/p\u003e\u003cp\u003eUltimately, the study aims to provide actionable insights for health authorities, particularly in tailoring early warning systems, enhancing outbreak preparedness, and guiding targeted interventions in communities most vulnerable to the combined effects of climate change and infectious diseases. It is anticipated that this research will not only address a pressing gap in the literature but also serve as a foundation for future multidisciplinary investigations at the intersection of climate science and public health in Nigeria and beyond.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cp\u003e\u003cstrong\u003eResearch Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adopted a retrospective cross-sectional design, relying entirely on secondary data sources. It involved the analysis of confirmed Lassa fever cases from 2019, 2020, 2022, 2023, and 2024 in Kogi State, extracted from the Surveillance Outbreak Response Management and Analysis System (SORMAS) platform managed by the Nigeria Centre for Disease Control (NCDC). Monthly climatic variables temperature, rainfall, and relative humidity were obtained from the Nigerian Meteorological Agency (NiMET). The goal was to examine the correlation between climate variables and Lassa fever incidence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in Kogi State, located in North-Central Nigeria (Figure 1). The state lies between latitude 6\u0026deg;30\u0026prime;N and 8\u0026deg;50\u0026prime;N and longitude 5\u0026deg;51\u0026prime;E and 7\u0026deg;55\u0026prime;E. Kogi shares boundaries with Benue State to the east, Nasarawa to the north-east, Kwara to the northwest, Niger to the north, Edo and Ondo to the south, and the Federal Capital Territory (FCT) to the north.\u003c/p\u003e\n\u003cp\u003eThe state comprises 21 Local Government Areas (LGAs) with varying ecological zones ranging from guinea savannah to rainforest, a factor contributing to its climate diversity. It experiences a tropical climate characterized by distinct dry (November\u0026ndash;March) and wet (April\u0026ndash;October) seasons. This ecological diversity, coupled with a dense rural population engaged in agriculture and food storage practices, makes Kogi State a high-risk area for Lassa fever outbreaks (NCDC, 2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe population for this study comprised all laboratory-confirmed cases of Lassa fever reported in Kogi State from January 2019 to December 2024, as well as monthly meteorological data (temperature, rainfall, humidity) obtained from NiMET for the same period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study made use of secondary data only, drawn from the following sources:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eEpidemiological Data:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Confirmed monthly Lassa fever case data were obtained from the SORMAS database, managed by the NCDC in collaboration with the Kogi State Ministry of Health Disease Surveillance and Notification Office (DSNO).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eClimatic Data:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Monthly averages of temperature (\u0026deg;C), rainfall (mm), and relative humidity (%) were collected from the Nigerian Meteorological Agency (NiMET).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSampling Technique\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince the study population consisted of all reported cases within the five-year timeframe, total enumeration sampling was employed. This allowed for the inclusion of all available and complete Lassa fever and climate datasets from 2019 to 2024, excluding 2021, which had incomplete surveillance records on the SORMAS platform.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePermission was obtained from the Kogi State Ministry of Health and the NCDC to access the SORMAS data on confirmed Lassa fever cases. Climatic data were retrieved in collaboration with NiMET. Data were cleaned and verified for completeness before analysis.\u003c/p\u003e\n\u003cp\u003eData variables extracted included:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eNumber of Lassa fever cases per month\u003c/li\u003e\n \u003cli\u003eLocal Government Area (LGA) of occurrence\u003c/li\u003e\n \u003cli\u003eMean monthly temperature, rainfall, and humidity\u003c/li\u003e\n \u003cli\u003eOutbreak timing by season (wet vs. dry)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion and Exclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eAll laboratory-confirmed cases of Lassa fever reported in Kogi State between January 2019 and December 2024, as recorded in the SORMAS database.\u003c/li\u003e\n \u003cli\u003eMonthly meteorological data (temperature, rainfall, and humidity) obtained from the Nigerian Meteorological Agency (NiMET) corresponding to the same time frame.\u003c/li\u003e\n \u003cli\u003eLocal Government Areas (LGAs) with at least one confirmed Lassa fever case during the study period.\u003c/li\u003e\n \u003cli\u003eClimate data entries with complete and consistent monthly values for the selected climatic indicators.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eAll suspected or probable Lassa fever cases without laboratory confirmation, as per NCDC surveillance definitions.\u003c/li\u003e\n \u003cli\u003eData for the year 2021, due to incomplete or missing surveillance records on the SORMAS platform.\u003c/li\u003e\n \u003cli\u003eMeteorological entries with missing or incomplete monthly climatic parameters (temperature, humidity, rainfall).\u003c/li\u003e\n \u003cli\u003eLGAs with incomplete or inconsistent Lassa fever case records during the study period.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eMethod of Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were entered and analyzed using IBM SPSS Version 23. The analysis involved:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eDescriptive statistics: Frequency tables and graphs were used to describe the distribution of cases by month, year, season, and LGA.\u003c/li\u003e\n \u003cli\u003ePearson correlation analysis: Used to assess the relationship between individual climate variables and Lassa fever incidence.\u003c/li\u003e\n \u003cli\u003eMultiple linear regression analysis: Used to determine the strength and significance of climatic variables (independent variables) in predicting Lassa fever cases (dependent variable).\u003c/li\u003e\n \u003cli\u003eGeospatial mapping: A QGIS-based thematic map was created to visualize the spatial distribution of Lassa fever cases across LGAs in Kogi State.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA significance level of 0.05 was adopted to determine statistical significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted using anonymized secondary data and posed no direct risk to any human subject. Ethical approval was obtained from the Kogi State Ministry of Health Ethics Committee, and data use was authorized by the Nigeria Centre for Disease Control (NCDC). All data were handled confidentially and used strictly for academic and public health research purposes.\u003c/p\u003e\n\u003cp\u003eHistorically, Kogi State has witnessed recurrent Lassa fever outbreaks, with reported cases from at least 19 out of its 21 Local Government Areas (LGAs). Surveillance records between 2019 and 2024 indicated consistent outbreaks with notable hotspots in LGAs such as Lokoja, Dekina, Okene, and Omala [20]. Its diverse ecology, high rodent populations, and socio-cultural practices such as open grain storage and bush burning contribute to the persistence of Lassa fever in the state [5-7].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis study presents and interprets the findings from the analysis of Lassa fever data from 2019 to 2024 alongside monthly climate data: temperature, rainfall, and humidity, from the Nigerian Meteorological Agency (NiMET). Analysis was conducted to address the study objectives and answer the guiding research questions. Data were analyzed using descriptive statistics, correlation coefficients, and multiple regression analysis. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc188440570\"\u003eAcross the five-year study period (2019\u0026ndash;2024), Lassa fever incidence in Kogi State demonstrated a pronounced seasonal pattern (Figure 2), with consistent surges during the dry season (November to March) and noticeable declines during the wet season (April to October). As shown in Figure 3, the highest monthly case burdens occurred in January 2022 (14 cases) and February 2024 (13 cases), marking the dry season as the critical transmission window.\u003c/p\u003e\n\u003cp\u003eThis trend reflects underlying environmental and behavioural dynamics. The dry season in Kogi is characterized by the Harmattan winds, lower vegetation cover, and scarcity of food and water in the wild, which force rodent vectors such as \u003cem\u003eMastomys natalensis\u003c/em\u003e into closer contact with human dwellings. These conditions significantly increase the risk of exposure to Lassa virus through contaminated surfaces, food items, or aerosolized rodent excreta.\u003c/p\u003e\n\u003cp\u003eFurthermore, the observed seasonal surge aligns with earlier findings from Nasarawa and Benue States, where dry periods consistently correlate with heightened rodent activity and Lassa virus transmission risk. This seasonal pattern also mirrors national Lassa fever surveillance data from the NCDC, which consistently identifies Edo, Ondo, and Ebonyi States; all sharing similar climatic features as dry season hotspots for outbreaks [16-18].\u003c/p\u003e\n\u003cp\u003eThe strong seasonal pattern observed in this study underlines the importance of integrating climate seasonality into Lassa fever surveillance, early warning, and response frameworks, particularly in ecologically vulnerable states like Kogi.\u003c/p\u003e\n\u003cp\u003ePearson correlation analysis revealed the following:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eTemperature (r = 0.282, p = 0.029): Statistically significant positive correlation.\u003c/li\u003e\n \u003cli\u003eRainfall (r = -0.17, p = 0.199): Negative, not statistically significant.\u003c/li\u003e\n \u003cli\u003eHumidity (r = -0.109, p = 0.409): Weak negative correlation, not significant.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese findings suggest that temperature is the most influential climatic factor associated with Lassa fever incidence in Kogi State. Specifically, as temperature increases, so does the likelihood of human\u0026ndash;rodent interaction and subsequent virus transmission.\u003c/p\u003e\n\u003cp\u003eThis supports research by Eze \u003cem\u003eet al.\u003c/em\u003e (2023) [14] and Redding \u003cem\u003eet al.\u003c/em\u003e (2021) [15], which found strong correlations between ambient temperature rise and Lassa fever outbreaks in endemic Nigerian states.\u003c/p\u003e\n\u003cp\u003eHumidity and rainfall showed weak or negative correlations, implying that high rainfall may disperse rodent habitats and reduce indoor infestation, whereas high humidity might inhibit aerosolized viral transmission especially during the peak rainy season.\u003c/p\u003e\n\u003cp\u003eBased on the regression model and seasonal analysis (Figure 5):\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eTemperature \u0026gt;35\u0026deg;C and Humidity between 60\u0026ndash;80% during dry months (Dec\u0026ndash;April) are the most critical conditions for increased Lassa fever risk.\u003c/li\u003e\n \u003cli\u003eRainfall spikes during otherwise dry months (e.g., unexpected rains in February 2022 and 2023) preceded short-lived case surges.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis confirms that climatic thresholds, not just seasons, drive outbreaks. This insight mirrors earlier modelling studies conducted in Ebonyi State and the Federal Capital Territory (FCT), Abuja, which also identified climatic triggers as predictive outbreak indicators [16].\u003c/p\u003e\n\u003cp\u003eThe top three LGAs by total confirmed cases are: Lokoja, Ibaji and Dekina\u003c/p\u003e\n\u003cp\u003eThese LGAs jointly accounted for over 50% of cases reported between 2019 and 2024.\u003c/p\u003e\n\u003cp\u003eThe spatial clustering of cases correlates with areas of high population density and significant agricultural activity, which often involve open grain storage and rodent exposure. Additionally, Lokoja, being the state capital, has a high surveillance and reporting efficiency, likely contributing to higher reported figures (Figure 6).\u003c/p\u003e\n\u003cp\u003eThese findings align with studies in Plateau and Benue States, where LGA-level differences in infrastructure, food storage, and housing types contributed significantly to disease distribution [17]\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eModel Summary and change statistics for Predictive Modelling and Early Warning Systems for Lassa fever outbreaks\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR Square\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted R Square\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Error of the Estimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eChange Statistics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR Square Change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF Change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edf1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edf2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSig. F Change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.299a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eMultiple regression analysis was used to define the nature of correlations between temperature,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultiple regression analysis was used to define the nature of correlations between temperature, humidity, rainfall; and the onset of diseases during Lassa fever outbreaks in Kogi State. The model produced an R square value of 0.089, which means that the total impact of temperature, humidity, and rainfall in the occurrence of Lassa fever can only explain 8.9% of the variance in the occurrence of Lassa fever. Adjusted R Square was even lower, 0.04 (4%); it means that the model has a limited explanatory power, and it might not generalise easily (See Table 1).\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: multi-regression analysis of climatic factors related to Lassa fever incidence in Kogi State\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"690\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnstandardized Coefficients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandardized Coefficients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCollinearity Statistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eZero-order\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003ePartial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003ePart\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eTolerance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-9.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e8.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-1.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eRainfall (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-1.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1.775\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e1.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eHumidity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1.706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eN\u003csub\u003eLassa fever cases\u0026nbsp;\u003c/sub\u003e= 0.288T + 0.074H - 0.012R - 9.42\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMulticollinearity was ideal as it was determined using Variance Inflation Factor (VIF); in fact, all predictors VIF values were significantly lower than 5. In particular, the values of VIF of rainfall, temperature, and humidity were 1.775, 1.065, and 1.706 respectively (see Table 2) and this showed that the predictors did not experience strong multicollinearity.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOn analysis of the Unstandardized coefficients, temperature had the most positive relationship with Lassa fever incidence (B = 0.288) indicating that for any one unit change in atmospheric temperature, there will be 28.8% increase in the incidence of Lassa fever in the State. Humidity\u0026nbsp;\u003c/strong\u003ecame next, exhibiting a modest positive impact (B = 0.074), that is, 7.4% increment in incidence of Lassa fever cases though, rainfall was not strongly related to the incidence of Lassa fever (B = -0.012). This shows an inverse relationship of 1.2 % decrease in the incidence of Lassa fever for every one unit increase in rainfall. This trend changed considering the p-values; no coefficients attained the conventional statistical significance value of 0.05. The borderline p-value which was 0.091 indicated that there is weak association between temperature and Lassa fever incidence. Rainfall and humidity on the other hand were found with p-values at 0.242 and 0.412 respectively meaning that they were not having a significant contribution.\u003c/p\u003e\n\u003cp\u003eTable 3: Climatic threshold associated with lassa fever outbreak in Kogi State\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"587\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 253px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDry Season (High Risk)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWet Season (Moderate Risk)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;35\u0026deg;C (strong correlation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30\u0026ndash;34\u0026deg;C (weak correlation)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow/zero or sudden spikes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHeavy but inconsistent link\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHumidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u0026ndash;80% (optimal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;85% (possible suppression)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 presents the observed climatic thresholds that correlate with the seasonal distribution of Lassa fever outbreaks across Kogi State. The table revealed a stronger association between Lassa fever incidence and the dry season, typically characterized by temperatures exceeding 35\u0026deg;C, moderate humidity levels (60\u0026ndash;80%), and low or abruptly fluctuating rainfall. These conditions likely enhance rodent intrusion into human settlements due to declining food and water sources in the wild, and facilitate viral transmission through aerosolized rodent excreta, particularly during the harmattan period.\u003c/p\u003e\n\u003cp\u003eConversely, during the wet season, although temperatures remain moderately high (30\u0026ndash;34\u0026deg;C) and humidity often exceeds 85%, the association with Lassa fever incidence appears weaker and less consistent. High humidity may suppress airborne transmission by increasing particulate settling, while consistent rainfall supports vegetation growth and rodent dispersion, temporarily reducing rodent-human contact in indoor environments.\u003c/p\u003e\n\u003cp\u003eThis threshold table serves as a strategic tool for anticipating high-risk periods and guiding public health interventions. It underscores the importance of aligning surveillance and preventive strategies such as community sensitization, rodent control, and early warning alerts with periods marked by the identified high-risk climatic parameters.\u003c/p\u003e\n\u003cp\u003eTable 4: Early warning Climatic Indicators for Lassa fever in Kogi State\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClimate Conditions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecommended Actions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eGreen (Low)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eNormal wet-season conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eRoutine surveillance, public awareness campaigns.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eYellow (Moderate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eDry season onset + rising temperatures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eIncrease rodent control, stockpile ribavirin (treatment).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eOrange (High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eDry month with \u0026gt;35\u0026deg;C + 60\u0026ndash;80% humidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eActive case-finding, community alerts, hospital preparedness.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eRed (Critical)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eSudden rainfall spike in dry season\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eEmergency response: quarantine, vector control, resource deployment.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe multiple regression model yielded:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eR\u0026sup2; = 0.089, indicating that temperature, rainfall, and humidity explain only 8.9% of Lassa fever variance.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUnstandardized coefficients indicate:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eTemperature (B = 0.288): Most significant influence, albeit with borderline p-value (0.091).\u003c/li\u003e\n \u003cli\u003eHumidity (B = 0.074): Modest effect.\u003c/li\u003e\n \u003cli\u003eRainfall (B = -0.012): Inverse relationship.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNone of the coefficients attained statistical significance at p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eAlthough this suggests that climate data alone cannot predict outbreaks, its integration with epidemiological surveillance could improve early warning systems. As such, a tiered climate-risk framework was proposed (Table 4), categorizing risk levels based on weather conditions.\u003c/p\u003e\n\u003cp\u003eThis aligns with WHO\u0026rsquo;s recommendation for One Health approaches that combine ecological, climatic, and epidemiological data for outbreak preparedness.\u003c/p\u003e\n\u003cp\u003eIn Kogi State, such systems would require:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eMonthly meteorological data sharing between NiMET and the State Ministry of Health.\u003c/li\u003e\n \u003cli\u003eGIS-based risk mapping for rodent control targeting.\u003c/li\u003e\n \u003cli\u003ePrepositioning of medical supplies (e.g., ribavirin) and community alerts during identified high-risk periods.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Discussion of Findings","content":"\u003cp\u003eThe analysis of Lassa fever incidence in Kogi State from 2019 to 2024 reveals a consistent seasonal pattern, with cases predominantly occurring during the dry season (November to March). Outbreak peaks in January 2022 and February 2024 were followed by declines during the rainy months, reinforcing the ecological link between aridity and transmission. This clustering corresponds to stressors affecting rodent behaviour, including food and water scarcity that compel rodents to enter human dwellings in search of sustenance. The Harmattan season, marked by dry and dusty winds, may further facilitate aerosolized transmission of viral particles via contaminated rodent excreta, a mechanism supported by previous studies [18].\u003c/p\u003e\n\u003cp\u003eTemperature emerged as the most influential climatic factor, with a statistically significant positive correlation with Lassa fever incidence (r = 0.282, p = 0.029). The implication is that elevated temperatures, particularly above 35\u0026deg;C, promote rodent breeding and activity, thereby amplifying human exposure. This finding corroborates patterns observed in other endemic regions such as Edo, Ondo, and Ebonyi States, and aligns with predictive models highlighting temperature as a key driver of outbreak dynamics [19-20]. By contrast, rainfall and humidity demonstrated weaker associations. High rainfall generally disperses rodent habitats and may temporarily reduce indoor infestations, though isolated rainfall spikes during dry months, as seen in February 2022 and 2023, preceded brief surges in incidence, pointing to complex ecological interactions. Humidity exerted a nuanced influence: values between 60\u0026ndash;80% appeared to support transmission, whereas levels above 85% likely suppressed aerosol spread by promoting particulate settling, echoing earlier reports from West Africa (Beermann et al., 2023) [8-10].\u003c/p\u003e\n\u003cp\u003eSpatial distribution patterns further illuminate the epidemiology of Lassa fever in Kogi State. Lokoja, Ibaji, and Dekina consistently accounted for the highest burdens of cases. The clustering in Lokoja is plausibly linked to its urban density, suboptimal food storage practices, and relatively stronger case reporting, while floodplain ecologies in Ibaji and Idah create favourable conditions for rodent proliferation and human\u0026ndash;rodent contact. These findings parallel evidence from other riverine communities, where poor drainage, seasonal flooding, and inadequate housing structures intensify exposure risks[15, 21]. Such hotspots require tailored interventions rather than uniform state-wide approaches.\u003c/p\u003e\n\u003cp\u003eAlthough the multiple regression model demonstrated modest explanatory power (R\u0026sup2; = 0.089), the climatic thresholds derived particularly temperatures exceeding 35\u0026deg;C and humidity ranges of 60\u0026ndash;80% offer practical early-warning indicators. While climate alone cannot fully predict outbreaks, its integration with ecological and behavioural surveillance strengthens forecasting capacity. This aligns with earlier work advocating for climate-based predictive frameworks that, when combined with local epidemiological intelligence, can substantially enhance outbreak preparedness [22-25].\u003c/p\u003e\n\u003cp\u003eAddressing the recurrent outbreaks of Lassa fever in Kogi State therefore requires a comprehensive, forward-looking strategy anchored in a One Health framework. Policy and surveillance systems must incorporate meteorological data, particularly forecasts from the Nigerian Meteorological Agency (NiMet), into routine reporting platforms such as the Disease Surveillance and Notification Office (DSNO). This integration would facilitate anticipatory actions, enabling targeted rodent control, prepositioning of protective equipment, and deployment of sensitization campaigns before peak transmission periods [9, 27-28]. Establishing climate-informed early warning systems in high-risk LGAs would further allow for timely, localized interventions.\u003c/p\u003e\n\u003cp\u003ePublic health education and behavioural change remain indispensable components of outbreak prevention. Seasonal rodent control programs between October and March, with a focus on household-level interventions such as rodent-proofing and safe grain storage, are critical. Community sensitization should emphasize early care-seeking and practical prevention measures, particularly in persistent epicenters such as Lokoja, Ibaji, and Idah. Health system capacity also demands strengthening, including the prepositioning of essential supplies\u0026mdash;ribavirin, personal protective equipment, and diagnostic kits\u0026mdash;prior to the onset of the dry season. Expanding laboratory diagnostic capacity in secondary healthcare centers will shorten delays in confirmation and improve case management [29].\u003c/p\u003e\n\u003cp\u003eCross-sectoral collaboration remains central to sustained progress. Real-time data sharing between the Nigeria Centre for Disease Control (NCDC), NiMet, and state health departments will synchronize meteorological intelligence with epidemiological surveillance and response \u0026nbsp;[29-31]. In addition, sustained research is needed to refine predictive modelling by integrating behavioural, ecological, and rodent population dynamics with climatic data. Longitudinal studies and community-based surveillance will provide the depth of evidence necessary to improve forecasting and inform policy.\u003c/p\u003e\n\u003cp\u003eUltimately, the findings affirm that while climatic factors alone cannot fully account for Lassa fever transmission, they interact with ecological and social determinants in complex but predictable ways. Embedding meteorological insights into routine surveillance, focusing interventions on spatial hotspots, and strengthening institutional collaboration represent actionable pathways to sustainable control of this climate-sensitive disease [35-42].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is not without limitations, which should be considered when interpreting the findings. One notable constraint was the absence of epidemiological data for 2021, which created a gap in the continuity of annual trend analysis. This missing year restricts the ability to fully capture potential fluctuations or transitional dynamics in Lassa fever incidence across the study period.\u003c/p\u003e\n\u003cp\u003eAnother limitation lies in the use of averaged monthly climate data. While this approach provides broad insight into seasonal relationships, it may obscure short-term variations in temperature, rainfall, and humidity that are directly relevant to rodent behaviour and viral transmission dynamics. More granular, daily or weekly climatic data could reveal patterns of exposure and risk that remain hidden when only monthly averages are considered.\u003c/p\u003e\n\u003cp\u003eAdditionally, although demographic information such as age and sex was available in the dataset, it was not incorporated into the statistical modelling due to the scope of the present study. This exclusion limits understanding of how demographic factors may intersect with climatic and ecological variables to shape disease risk. Future studies that integrate these dimensions could yield a more comprehensive epidemiological picture of Lassa fever in the region.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study concludes that Lassa fever transmission in Kogi State exhibits seasonal predictability and weak to moderate associations with climatic factors, particularly temperature and humidity. The highest incidence occurs in the dry season, with Idah, Ibaji, and Lokoja identified as spatial hotspots. However, climate is only one part of the broader risk ecosystem that includes rodent ecology, socio-economic vulnerability, and behavioural patterns. Effective Lassa fever control must therefore adopt a multi-sectoral, seasonally informed, and geographically targeted approach.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (2013 version) as adopted by the World Medical Association. Ethical approval for the study was secured from the Health Research Ethics Committee of the Kogi State Ministry of Health (a fully registered committee the under National Health Research Ethics Committee), after a defence of the research proposal during the Committee\u0026rsquo;s ethical screening interview.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Participation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to privacy considerations of the participants but are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors hereby declare that there are no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any specific grant from any funding institution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eONJ, AIA, GIE, SOA, AOM, OPE, AJE, OA, SB, AU, OJA, KM, AJA, MSO, OST, and AAB conceptualized and designed the study and contributed to drafting and revising the manuscript. ONJ, AIA, GIE, SOA, AOM, OPE, AJE, OA, SB, AU, OJA, KM, AJA, MSO, OST, and AAB contributed to data collection, and manuscript review, All authors participated in study design, and critically reviewed the manuscript for important intellectual content. All authors assisted with the literature review, data visualization, and preparation of initial manuscript drafts, All authors provided methodological expertise, and contributed significantly to manuscript revisions. All authors supported data acquisition and provided feedback on the manuscript drafts. All authors contributed to the manuscript structure, final proofreading, and editing for clarity and coherence; all authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trials Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-Mustapha AI, Adesiyan IM, Orum TG, Ogundijo OA, Lawal AN, Nzedibe OE, Onyeka LO, Muhammad KU, Odetayo L, Oyewo M, Muhammad SO, Atadiose EO, Adebudo LI, Adetunji DA, Jantiku HJ, Akintule AO, Nwachukwu RC, Abubakar AT. Lassa fever in Nigeria: epidemiology and risk perception. Sci Rep. 2024;14(1). doi: 10.1038/s41598-024-78726-3.\u003c/li\u003e\n\u003cli\u003eBalogun OO, Akande OW, Hamer DH. Lassa Fever: An Evolving Emergency in West Africa. Am J Trop Med Hyg. 2020 Nov 23;104(2):466-73. doi: 10.4269/ajtmh.20-0487. PMID: 33236712; PMCID: PMC7866331.\u003c/li\u003e\n\u003cli\u003eCollins A. Preventing Health Care\u0026ndash;Associated Infections. Rockville (MD): Agency for Healthcare Research and Quality (US); 2020. Available from: https://www.ncbi.nlm.nih.gov/books/NBK2683/\u003c/li\u003e\n\u003cli\u003eAloke C, Obasi NA, Aja PM, Emelike CU, Egwu CO, Jeje O, Edeogu CO, Onisuru OO, Orji OU, Achilonu I. Combating Lassa Fever in West African Sub-Region: Progress, Challenges, and Future Perspectives. Viruses. 2023;15(1):146. doi: 10.3390/v15010146.\u003c/li\u003e\n\u003cli\u003eEzenwa-Ahanene A, Musa E, Fagbemi A. Understanding the ecological drivers of Lassa fever in North Central Nigeria. Afr J Infect Dis. 2024;18(1):22-9.\u003c/li\u003e\n\u003cli\u003eEzenwa-Ahanene A, Okoye C, Okorie P, Olaleye DO, Adewale O. Dry season dynamics and Lassa fever transmission in rural communities of Ebonyi State, Nigeria. Int J Infect Dis. 2024;139:87-94. doi: 10.1016/j.ijid.2024.01.004.\u003c/li\u003e\n\u003cli\u003eEzenwa-Ahanene A, Salawu AT, Adebowale AS. Descriptive epidemiology of Lassa fever, its trend, seasonality, and mortality predictors in Ebonyi State, South-East, Nigeria, 2018-2022. BMC Public Health. 2024;24(1). doi: 10.1186/s12889-024-20840-y.\u003c/li\u003e\n\u003cli\u003eBeermann S, Abdullahi YM, Salami K, Igbokwe E. Development of climate-based early warning tools for Lassa fever outbreaks in Nigeria: A spatiotemporal pilot model. PLoS Negl Trop Dis. 2023;17(6):e0011134. doi: 10.1371/journal.pntd.0011134.\u003c/li\u003e\n\u003cli\u003eBeermann S, Dobler G, Faber M, Frank C, Habedank B, Hagedorn P, Kampen H, Kuhn C, Nygren T, Schmidt-Chanasit J, Schmolz E, Stark K, Ulrich RG, Wei\u0026szlig; S, Wilking H. Impact of climate change on vector- and rodent-borne infectious diseases. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2023;8(Suppl 3):33-61. doi: 10.25646/11401.\u003c/li\u003e\n\u003cli\u003eBeermann S, Schumann B, Lang M, Glahn M. Satellite-based early warning systems for Lassa fever: Integrating rodent data and rainfall metrics in West Africa. PLoS Negl Trop Dis. 2023;17(2):e0011156. doi: 10.1371/journal.pntd.0011156.\u003c/li\u003e\n\u003cli\u003eShafique M, Khurshid M, Muzammil S, Arshad MI, Malik IR, Rasool MH, Khalid A, Khalid R, Asghar R, Baloch Z, Aslam B. Traversed dynamics of climate change and One Health. Environ Sci Eur. 2024;36(1). doi: 10.1186/s12302-024-00931-8.\u003c/li\u003e\n\u003cli\u003eCadmus EO, Olayinka A, Bamidele O. Climatic factors and seasonal variation in Lassa fever transmission in Nigeria. Int J Infect Dis. 2023;128:55-64. doi: 10.1016/j.ijid.2023.03.009.\u003c/li\u003e\n\u003cli\u003eCadmus EO, Olufemi AO, Ezeobi B, Owolabi OJ, Aluko A. Climatic predictors of Lassa fever outbreak patterns in Southern Nigeria. Afr J Infect Dis. 2023;17(1):34-45. doi: 10.21010/ajid.v17i1.5.\u003c/li\u003e\n\u003cli\u003eCadmus S, Taiwo OJ, Akinseye VO, Cadmus EO, Famokun G, Fagbemi S, Ansumana R, Omoluabi A, Ayinmode AB, Oluwayelu DO, Odemuyiwa SO, Tomori O. Ecological correlates and predictors of Lassa fever incidence in Ondo State, Nigeria 2017\u0026ndash;2021: an emerging urban trend. Sci Rep. 2023;13(1). doi: 10.1038/s41598-023-47820-3.\u003c/li\u003e\n\u003cli\u003eAdebayo AO, Oladipo EO, Amusan AA. Seasonal variation in the abundance and distribution of Mastomys natalensis in central Nigeria. J Trop Ecol. 2021;37(2):111-20. doi: 10.1017/S0266467421000030.\u003c/li\u003e\n\u003cli\u003eRedding DW, Gibb R, Dan-Nwafor CC, Ilori EA, Yashe RU, Oladele SH, Amedu MO, Iniobong A, Attfield LA, Donnelly CA, Abubakar I, Jones KE, Ihekweazu C. Geographical drivers and climate-linked dynamics of Lassa fever in Nigeria. Nat Commun. 2021;12(1):5759. doi: 10.1038/s41467-021-25910-y.\u003c/li\u003e\n\u003cli\u003eRedding DW, Moses LM, Cunningham AA, Wood JLN, Jones KE. Environmental-mechanistic modelling of the impact of climate change on the emergence of Lassa fever in West Africa. Nat Commun. 2021;12(1):1-11. doi: 10.1038/s41467-021-21394-2.\u003c/li\u003e\n\u003cli\u003eRedding DW, Tiedt S, Lo I, Jones KE. Predicting the global mammalian viral sharing network using phylogeography. Nat Commun. 2021;12(1):1-12. doi: 10.1038/s41467-021-21034-5.\u003c/li\u003e\n\u003cli\u003eEdokpa DO, Ede PN, Diagi BE, Ajiere SI. Rainfall and Temperature Variations in a Dry Tropical Environment of Nigeria. J Atmos Sci Res. 2023;6(2):50-7. doi: 10.30564/jasr.v6i2.5527.\u003c/li\u003e\n\u003cli\u003eNigeria Centre for Disease Control (NCDC). Lassa Fever Situation Reports and Case Statistics (2019\u0026ndash;2024). Abuja: NCDC; 2024. Available from: https://www.ncdc.gov.ng\u003c/li\u003e\n\u003cli\u003eAdebayo AM, Fowotade A, Adeniji JA, Nwabuisi C. Environmental and demographic factors influencing the incidence of Lassa fever in riverine communities in Nigeria. Pan Afr Med J. 2022;42:110. doi: 10.11604/pamj.2022.42.110.31045.\u003c/li\u003e\n\u003cli\u003eHaque S, Mengersen KM, Barr I, Wang L, Yang W, Vardoulakis S, Bambrick H, Hu W. Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations. Environ Res. 2024;249:118568. doi: 10.1016/j.envres.2024.118568.\u003c/li\u003e\n\u003cli\u003eBorham A, Abdel Motaal K, ElSersawy N, Ahmed YF, Mahmoud S, Musaibah AS, Abdelnaser A. Climate change and zoonotic disease outbreaks: emerging evidence from epidemiology and toxicology. Int J Environ Res Public Health. 2025;22(6):883. doi: 10.3390/ijerph22060883.\u003c/li\u003e\n\u003cli\u003eMills C, Donnelly CA. Climate-based modelling and forecasting of dengue in three endemic departments of Peru. PLoS Negl Trop Dis. 2024;18(12):e0012596. doi: 10.1371/journal.pntd.0012596.\u003c/li\u003e\n\u003cli\u003eVillanueva-Miranda I, Xiao G, Xie Y. Artificial intelligence in early warning systems for infectious disease surveillance: a systematic review. Front Public Health. 2025 Jun 23;13:1609615. doi: 10.3389/fpubh.2025.1609615. PMID: 40626156; PMCID: PMC12230060.\u003c/li\u003e\n\u003cli\u003eOkpachi CA, Okon UA, Okunromade O, Williams-Enenche L, Ojotule A. Evaluation of Lassa fever surveillance system in Kogi State, north-central Nigeria. J Interv Epidemiol Public Health. 2025 Aug 14;8:Abstract ELIC2025253 (Oral 104).\u003c/li\u003e\n\u003cli\u003eTambo E, Adetunde OT, Olalubi OA. Re-emerging Lassa fever outbreaks in Nigeria: re-enforcing \u0026ldquo;One Health\u0026rdquo; community surveillance and emergency response practice. Infect Dis Poverty. 2018;7:37. doi: 10.1186/s40249-018-0421-8.\u003c/li\u003e\n\u003cli\u003eEneh SC, Obi CG, Ephraim Ikpongifono U, Dauda Z, Udoewah SA, Anokwuru CC, Onukansi FO, Ikhuoria OV, Ojo TO, Madukaku CU, Orabueze IN, Chizoba AF. The resurgence of Lassa fever in Nigeria: economic impact, challenges, and strategic public health interventions. Front Public Health. 2025 Jul 16;13:1574459. doi: 10.3389/fpubh.2025.1574459. PMID: 40740381; PMCID: PMC12307277.\u003c/li\u003e\n\u003cli\u003eAgbonlahor DE, Akpede GO, Happi CT, Tomori O. 52 years of Lassa fever outbreaks in Nigeria, 1969\u0026ndash;2020: an epidemiologic analysis of the temporal and spatial trends. Am J Trop Med Hyg. 2021;105(4):974-85. doi: 10.4269/ajtmh.20-1160.\u003c/li\u003e\n\u003cli\u003eAiyedun JO, Musa H, Saka MJ. Rodent infestation and risk factors for Lassa fever transmission in Nigeria: a cross-sectional survey. Afr J Infect Dis. 2021;15(3):66-74. doi: 10.21010/ajid.v15i3.9.\u003c/li\u003e\n\u003cli\u003eAkinbobola AO, Ogunlowo OO, Kolawole TO. Climate change and health vulnerability in Nigeria: integrating meteorological data into public health planning. Int J Environ Res Public Health. 2022;19(13):7892. doi: 10.3390/ijerph19137892.\u003c/li\u003e\n\u003cli\u003eKiryluk HD, Beard CB, Holcomb KM. The use of environmental data in descriptive and predictive models of vector-borne disease in North America. J Med Entomol. 2024 May 13;61(3):595-602. doi: 10.1093/jme/tjae031. PMID: 38431876; PMCID: PMC11078578.\u003c/li\u003e\n\u003cli\u003eJohnston ASA, Boyd RJ, Watson JW, Paul A, Evans LC, Gardner EL, Boult VL. Predicting population responses to environmental change from individual-level mechanisms: towards a standardized mechanistic approach. Proc Biol Sci. 2019 Oct 23;286(1913):20191916. doi: 10.1098/rspb.2019.1916. PMID: 31615360; PMCID: PMC6834044.\u003c/li\u003e\n\u003cli\u003eAnyamba A, Small JL, Britch SC, Tucker CJ, Linthicum KJ, Maloney S. Climate anomalies and the risk of vector-borne diseases in West Africa: emerging patterns and opportunities for predictive modelling. Sci Rep. 2023;13(1):11904. doi: 10.1038/s41598-023-39215-6.\u003c/li\u003e\n\u003cli\u003eBesson ME, P\u0026eacute;pin M, Metral PA. Lassa fever: critical review and prospects for control. Trop Med Infect Dis. 2024;9(8):178. doi: 10.3390/tropicalmed9080178.\u003c/li\u003e\n\u003cli\u003eBonwitt J, S\u0026aacute;ez AM, Lamin JM, Ansumana R, Dawson M, Brown H, Sahr F. At home with Mastomys and Rattus: human\u0026ndash;rodent interactions and potential for primary transmission of Lassa virus in domestic spaces. PLoS Negl Trop Dis. 2020;14(2):e0008108. doi: 10.1371/journal.pntd.0008108.\u003c/li\u003e\n\u003cli\u003eBuckee CO, Tatem AJ, Metcalf CJE. Seasonal population movements and the surveillance and control of infectious diseases. Trends Parasitol. 2017;33(1):10-20. doi: 10.1016/j.pt.2016.10.006.\u003c/li\u003e\n\u003cli\u003eCarlson CJ, Albery GF, Merow C, Trisos CH, Zipfel CM, Eskew EA, Gibb R. Climate change increases cross-species viral transmission risk. Nature. 2022;607(7919):555-62. doi: 10.1038/s41586-022-04788-w.\u003c/li\u003e\n\u003cli\u003eCollins A. Preventing health care\u0026ndash;associated infections. Rockville (MD): Agency for Healthcare Research and Quality (US); 2020. Available from: https://www.ncbi.nlm.nih.gov/books/NBK2683/\u003c/li\u003e\n\u003cli\u003eDestoumieux-Garz\u0026oacute;n D, Mavingui P, Boetsch G, Boissier J, Darriet F, Duboz P, Voituron Y. The One Health concept: 10 years old and a long road ahead. Front Vet Sci. 2020;7:14. doi: 10.3389/fvets.2020.00014.\u003c/li\u003e\n\u003cli\u003eAkyala AI, Aremu SO, Jaggu AR, Gyar SS. Emerging trends and associated risk factors influencing mortality and fatality rates of Lassa fever in Nigeria, 2001\u0026ndash;2024: a retrospective study. J Interv Epidemiol Public Health. 2025;8(ConfProc5):00216. doi: 10.37432/jieph-confpro5-00216.\u003c/li\u003e\n\u003cli\u003eAkyala AI, Aremu SO, Jaggu AR, Gyar SS. Assessment of cutting-edge machine learning models to significantly enhance predictions of Lassa fever outbreaks using whole genome sequencing. J Interv Epidemiol Public Health. 2025;8(ConfProc5):00263. doi: 10.37432/jieph-confpro5-00263.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tropical-diseases-travel-medicine-and-vaccines","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tdtm","sideBox":"Learn more about [Tropical Diseases, Travel Medicine and Vaccines](http://tdtmvjournal.biomedcentral.com)","snPcode":"40794","submissionUrl":"https://submission.nature.com/new-submission/40794/3","title":"Tropical Diseases, Travel Medicine and Vaccines","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lassa fever, seasonal variation, climate change, temperature, rainfall, humidity, early warning system, Kogi State","lastPublishedDoi":"10.21203/rs.3.rs-7638265/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7638265/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eLassa fever is a recurrent public health threat in Nigeria, particularly in endemic regions such as Kogi State. Its transmission is closely tied to environmental and seasonal dynamics, especially those influencing rodent populations. Understanding these climatic influences is essential for strengthening disease surveillance and implementing timely interventions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA descriptive cross-sectional study was conducted using retrospective epidemiological and climatic data from 2019 to 2024. Confirmed Lassa fever case records and meteorological variables (temperature, rainfall, and humidity) were analyzed using statistical correlation, regression models, and geospatial mapping.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA consistent seasonal trend was observed, with the majority of cases occurring during the dry season (November\u0026ndash;March), peaking in January 2022 and February 2024. Temperature showed a statistically significant positive correlation with Lassa fever incidence (r\u0026thinsp;=\u0026thinsp;0.282, p\u0026thinsp;=\u0026thinsp;0.029). Rainfall and humidity displayed weak or non-significant associations, though brief case surges followed isolated rainfall spikes in dry months. Spatial analysis identified Lokoja, Ibaji, and Dekina LGAs as hotspots, likely due to population density, food storage practices, and improved reporting. The regression model yielded modest explanatory power (R\u0026sup2; = 0.089), but thresholds such as temperature\u0026thinsp;\u0026gt;\u0026thinsp;35\u0026deg;C and humidity between 60\u0026ndash;80% emerged as potential early warning indicators.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eSeasonal variation, particularly elevated temperatures during the dry season, plays a significant role in Lassa fever incidence in Kogi State. Integrating climatic, ecological, and epidemiological data into a real-time risk alert system under a One Health framework could enhance preparedness and response in high-risk areas.\u003c/p\u003e","manuscriptTitle":"Impact of Seasonal Changes on the Epidemiology of Lassa Fever in a State in North Central, Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 16:25:24","doi":"10.21203/rs.3.rs-7638265/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-20T13:42:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-15T21:48:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-12T23:21:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149660564745659391788289795449092827603","date":"2025-09-24T07:14:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45742788351616361510772383931934124606","date":"2025-09-23T09:51:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-22T06:18:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-22T05:11:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-22T05:10:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Diseases, Travel Medicine and Vaccines","date":"2025-09-17T09:08:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tropical-diseases-travel-medicine-and-vaccines","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tdtm","sideBox":"Learn more about [Tropical Diseases, Travel Medicine and Vaccines](http://tdtmvjournal.biomedcentral.com)","snPcode":"40794","submissionUrl":"https://submission.nature.com/new-submission/40794/3","title":"Tropical Diseases, Travel Medicine and Vaccines","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8ac86615-e648-4ceb-b364-768359462d16","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:04:48+00:00","versionOfRecord":{"articleIdentity":"rs-7638265","link":"https://doi.org/10.1186/s40794-026-00294-3","journal":{"identity":"tropical-diseases-travel-medicine-and-vaccines","isVorOnly":false,"title":"Tropical Diseases, Travel Medicine and Vaccines"},"publishedOn":"2026-03-13 15:58:53","publishedOnDateReadable":"March 13th, 2026"},"versionCreatedAt":"2025-10-03 16:25:24","video":"","vorDoi":"10.1186/s40794-026-00294-3","vorDoiUrl":"https://doi.org/10.1186/s40794-026-00294-3","workflowStages":[]},"version":"v1","identity":"rs-7638265","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7638265","identity":"rs-7638265","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}