Outbreak Volatility and High Lethality: A Comparative Burden Analysis of Cholera and Lassa Fever in Nigeria (2019–2024) with Policy, Preparedness and Resource Implications

Outbreak Volatility and High Lethality: A Comparative Burden Analysis of Cholera and Lassa Fever in Nigeria (2019–2024) with Policy, Preparedness and Resource Implications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Outbreak Volatility and High Lethality: A Comparative Burden Analysis of Cholera and Lassa Fever in Nigeria (2019–2024) with Policy, Preparedness and Resource Implications Joseph Gbenga Solomom, Adamu Ishaku Akyala, Joseph M. Ibbih, Jide Idris This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8463403/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background Despite the fact that Lassa fever and cholera are two of Nigeria's most deadly epidemic-prone diseases, few studies have examined their combined burden to aid in effective preparedness planning. This research study provides a comparative examination of epidemiological trends from 2019 to 2024, with implications for health-care financing and resource allocation. Methods A retrospective analysis of surveillance data (NCDC, WHO) was conducted. Descriptive statistics, Bayesian Structural Time Series, seasonal decomposition, and change-point detection identified temporal and spatial trends. Odds ratios (ORs) assessed demographic risks, while proportional state contributions quantified geographic concentration of burden. Results Between 2019 and 2024, there were 204,329 cholera and 3,654 Lassa fever cases reported. Cholera disproportionately affected children under the age of 15 (OR = 1.71), but Lassa fever was more common in adults aged 25 to 44. Males were more affected by Lassa fever, and females by cholera. Cholera outbreaks were very variable (111,062 cases in 2021; +15,325% from 2020), demanding significant surge-response costs, whereas Lassa fever caused smaller but persistent seasonal outbreaks with a consistently high CFR of 22.1%, indicating the severe resource load of case management. Cholera was predominant in northern states (Bauchi, Borno, and Kano), whereas Lassa fever was concentrated in Ondo and Edo (66.3% of cases). Conclusion Conclusion: Nigeria is dealing with two epidemics: cholera, a climate-driven illness that spawns outbreaks, and Lassa fever, a deadly rodent-borne chronic disease. These trends impose considerable and continuous financial demands on the healthcare sector. Regionally diverse preparation, including WASH infrastructure in the north and ecological/diagnostic investments in the south, paves the way for more cost-effective epidemic management. Predictive modelling, early warning systems, and targeted funding are important to lowering the health and economic consequences of these epidemics. Cholera Lassa Fever Nigeria Temporal Trends Disease Burden Geographical Distribution Case Fatality Ratio. Public Health Preparedness Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Epidemic-prone diseases continue to pose significant threats to health security in sub-Saharan Africa, with Nigeria shouldering a disproportionate burden. Cholera and Lassa fever, in particular, have killed a large number of people, slowed economic growth, and upended health-care systems. ¹⁻³ Vibrio cholerae is the cause of cholera, an acute diarrhoeal illness spread through contaminated food and water and closely related with poor water, sanitation, and hygiene (WASH) conditions. According to WHO estimates, the annual global burden is disproportionately high in Sub-Saharan Africa, with 1.3 to 4.0 million cases and 21,000 to 143,000 deaths. ⁴ ⁵ Flooding, population dislocation, climate change, and poor WASH infrastructure exacerbate rapid outbreaks, high infection rates, and large fatality rates from untreated cholera.⁶⁻⁸ According to recent data, Nigeria experienced repeated and significant cholera outbreaks from 2019 to 2024, with notable geographical variation and irregular surges in cases and fatalities.Lassa fever, a deadly viral ailment, primarily affects West Africans, particularly Nigerians. It is caused by the Lassa virus, which is often transmitted by contact with a multimammate rat, a common type of rodent. People can become infected by eating food contaminated by the urine or droppings of these rats, and, in some cases, the disease can spread from person to person, especially in healthcare settings.¹¹ ¹² Over the years, Nigeria has seen many outbreaks of Lassa fever, particularly during the dry season, with an increase in reported cases and clusters of infections in hospitals, putting healthcare workers at risk.¹³⁻¹⁵ Recent reports show that Lassa fever cases have been rising and spreading to more areas in Nigeria between 2017 and 2024.¹⁶ ¹⁷ Over the past ten years, Nigeria has made great strides in disease response and surveillance. To help health practitioners report instances more efficiently across many regions, a new digital platform called SORMAS was introduced in 2020. Additionally, the COVID-19 pandemic contributed to the development of testing abilities, which are essential for identifying cases of illnesses like cholera and Lassa fever.¹⁸-¹⁹ However, it is impossible to present a whole picture of the disease's impact due to the wide variations in diagnostic service availability and quality across the nation. ²⁰ A major contributing factor to the spread of cholera in Nigeria is the absence of sanitary facilities and clean water. Cholera outbreaks are common because of differences in access to basic water, sanitation, and hygiene services, according to studies. In a similar vein, rodent populations, living conditions, and environmental factors all contribute to the persistence of Lassa fever. This emphasises the value of comprehensive systems that address farming operations, housing, rodent control, and food storage. ⁵ ¹² ¹⁶ The majority of research has focused on one disease at a time and for a short period of time, despite improvements in illness tracking and a growing awareness of several diseases. In order to better understand how cholera and Lassa fever are changing, how modern diagnostics are assisting, and how factors like sanitation affect their spread, few studies have looked at long-term trends in these diseases.In order to: (1) differentiate between actual changes in disease transmission and changes brought about by improved reporting; (2) pinpoint areas in need of focused health interventions, like immunisation campaigns and sanitation projects; and (3) guide the prudent use of scarce public health resources, it is imperative to analyse these trends over a number of years.⁷ ¹⁰ ¹³ ¹⁷ In order to provide valuable information for improved readiness, targeted response, and policy initiatives, this project aims to assess the trends and effects of cholera and Lassa fever in Nigeria between 2019 and 2024. 2. Methods 2.1 Study Design This study used a retrospective epidemiological methodology, with secondary surveillance and demographic datasets collected over a six-year period (2019–2024). This methodology enabled a thorough examination of cholera epidemiology in Nigeria, employing temporal, geographical, and population-based analysis to reveal patterns and trends in disease incidence and distribution. 2.2 Study Area Nigeria, Africa's most populous country, is divided into 36 states and the Federal Capital Territory (FCT), each with around 774 Local Government Areas (LGAs). The national population, which is expected to approach 220 million by 2024, is characterised by great ethnic diversity and ecological heterogeneity. Climatic zones span from arid Sahelian savannah in the north to humid rainforest environments in the south.These ecological gradients are crucial because they influence disease transmission dynamics, flooding susceptibility, and the persistence of waterborne diseases like cholera. 2.3 Data Sources From 2019 to 2024, epidemiological information was extracted from the Nigeria Centre for Disease Control's (NCDC) weekly and annual surveillance reports. In addition, updates were integrated from the World Health Organization's Disease Outbreak News (DONs). Population estimates utilized data from the National Bureau of Statistics (NBS) and the United Nations World Population Prospects (WPP). The Nigerian Meteorological Agency (NiMet) provided data on rainfall patterns, flooding events, and climatic variability to help examine the environmental elements that influence health outcomes. 2.4 Indicators Incidence rates per 100,000 people, annual case numbers, deaths attributable to Lassa fever and cholera, case fatality ratios (CFR), and other metrics were used to evaluate the epidemiological impact of these diseases. To further understand temporal patterns, epidemic peaks were studied on a monthly and annual basis. Recurring tendencies were identified by looking at multi-year cycles. Statistically significant changes in epidemic patterns were found during the study period when differences in transmission intensity across time were evaluated using joinpoint regression. The magnitude, severity, and progression of Lassa fever and cholera in Nigeria were fully depicted by these measurements when combined. 2.5 Data Analysis The yearly and cumulative morbidity and death metrics, stratified by age, gender, state, and year, were determined using descriptive epidemiological methods. Joinpoint regression was used for trend analysis, allowing statistically significant changes in incidence to be identified over time. Moving averages were used to smooth out short-term fluctuations and make long-term patterns more understandable. Seasonal influences on transmission dynamics and repeating epidemic cycles were detected using seasonal trend decomposition (STL) approaches. The outputs included temporal trend lines, geographic heatmaps, and detailed epidemic curves to demonstrate case spatial and temporal grouping. IBM SPSS Statistics was utilised for data analysis, while QGIS was employed for spatial mapping. Microsoft Excel was utilised for data administration and presentation. 2.6 Ethical Considerations This study utilized only publicly available secondary surveillance datasets and population statistics. Individual-level identifiers were not employed, and all analyses were conducted on aggregated data provided by national and international health agencies. As a result, ethical clearance was not necessary; however, data handling was conducted in accordance with confidentiality principles and the responsible use of public health information. 3. Results 3.1 Demographic Analysis of Cholera and Lassa Fever (2019–2024) Between 2019 and 2024, there were 204,329 reported cholera cases and 3,654 cases of Lassa fever. The mean age of cholera cases was 20.8 years, while Lassa fever cases averaged 32.6 years. Cholera disproportionately affected children, especially those under 5 years (20.8%) and ages 5–14 (23.7%). In contrast, Lassa fever cases were primarily among adults aged 25–34 (23.6%) and ≥ 45 years (23.2%), with children under 15 contributing less than 16%. In terms of gender, cholera showed a female predominance (52.3% of cases), whereas Lassa fever had a slight male predominance (54.0%). Significant differences were found in age (χ² = 1,609.89, p < 0.0001) and gender distribution (χ² = 1.76, p = 1.76 × 10⁻¹⁴) between the two diseases. Goodness-of-fit tests revealed significant variations within each disease: cholera's age (χ² = 17,327.51, p < 0.0001) and gender (χ² = 442.53, p < 0.0001) distributions were uneven, while Lassa fever also demonstrated significant disparities in age (χ² = 619.41, p < 0.0001) and gender (χ² = 24.34, p = 8.06 × 10⁻⁷). These findings illustrate that cholera primarily impacts younger populations and females, whereas Lassa fever predominantly affects adults, particularly males. This underscores key differences in transmission dynamics and health-seeking behaviors (Table 1 ). Table 1 Demography information associated with disease (cumulative value-2019-2024) Cholera (204,329 cases) Lassa Fever (3654 cases) Cholera vs Lassa fever disease compared N(%) OR P-value N(%) OR P-value P-value Age (ref = 25–34) < 4 42,512 (20.8) 1.45 0.0001 182 (5.0) 0.17 0.0001 < 0.0001 5–14 48,473(23.7) 1.71 369 (10.1) 0.36 15–24 37,839 (18.5) 1.25 686 (18.8) 0.75 25–34 31,434 (15.4) 1 864 (23.6) 1 35–44 21,506 (10.5) 0.65 705 (19.3) 0.77 45+ 22,565 (11.0) 0.68 848 (23.2) 0.98 Gender (ref = Male) Female 106,919(52.3) 1.2 0.0001 1675 (45.8) 0.72 0.0001 < 0.0001 Male 97,410 (47.7) 1 1973 (54.0) 1 *Mean Age for Cholera and Lassa fever is 20.8 and 32.6 respectively 3.2 Heatmaps (2019–2024) The Lassa fever heatmap (2019–2024) shows that the majority of cases affect adults, with peaks in 2020 and 2022, particularly in the 25–34 and 45 + age groups. Lassa fever is primarily an adult sickness, with extremely few cases involving children under the age of 15. The decrease in cases in 2023 and 2024 could be attributed to improved intervention approaches. Cholera, on the other hand, mostly affects children under the age of 15, notably those under the age of four and those aged five to fourteen, with peak incidence expected in 2021. Despite a drop in frequency following this peak, cholera showed signs of resurgence in 2024. In general, cholera is a major risk to children connected with WASH vulnerabilities, whereas Lassa fever mostly affects adults (Fig. 1 ). 3.2 National Burden Between 2019 and 2024, Nigeria reported 204,329 cholera cases and 6,838 fatalities, with a case fatality ratio of 3.3%.However, Lassa fever had a far higher CFR of 22.1% at the time, with 3,654 cases and 809 deaths.Cholera peaked in 2021 with 111,062 cases (50.8 per 100,000 people). Significant increases were observed in 2024 (18,782 cases) and 2019 (45,986 cases).Lassa fever incidence was low, ranging from 0.01 to 0.56 cases per 100,000, and seldom exceeded 1,200 cases per year, despite the high CFR of 18–22%. Lassa fever had a steady death rate even in years with low prevalence, but cholera deaths increased during outbreaks (mostly in 2019 and 2021). Thus, in 2020, there were 977 cases of cholera and 53 deaths (CFR of 5.4%), but 1,189 cases of Lassa fever and 244 deaths (CFR of 20.5%).This underlines cholera as a disease that erupts regularly, while Lassa fever is a less serious but more devastating endemic risk (Table 2 and Fig. 2 ). Table 2 Epidemiological burden: Annual case counts and death Cholera Lassa fever Year Incidence count (cases) Cholera incidence rate (per 100,000) Mortality count* CFR (%) Incidence count (cases) Lassa incidence rate (per 100,000) Mortality count CFR (%) Population** 2019 45986 21.95 1803 3.92 843 0.4 174 20.64 209,485,641 2020 977 0.46 53 5.4 1189 0.56 244 20.52 213,996,181 2021 111062 50.82 3604 3.3 511 0.23 102 19.96 218,529,286 2022 23839 10.68 592 2.5 1028 0.46 189 18.39 223,150,896 2023 3683 1.62 128 3.5 13 0.01 - 227,882,945 2024 18782 8.07 600 3.2 70 0.03 - 232,679,478 Overall 204,329 6780 3.3 3,654 809 22.1 Case fatality ratio -CFR (cumulative cases & deaths) *NSPACC 2024 & NCDC 2019 − 204 **World Bank, (2025).Population,tool-Nigeria. https://data.worldbank.org/indicator/SP.POP.TOTL?end=2024&locations=NG&start=1960 -Case fatality ratio (CFR). -Incidence rate per 100,000 population. 3.3 Epidemic Curves of Confirmed Cholera and Lassa Fever Cases In Nigeria, the epidemic curves for Lassa fever and cholera show unique tendencies. While Lassa fever is endemic and has a steady but declining annual case count, cholera outbreaks show significant interannual variability, with spectacular surges during epidemic years. Figure 3 illustrates Lassa fever cases from 2019 to 2024, with recurring peaks over the dry season (December-April), highlighting the disease's zoonotic transmission characteristics. Unlike Lassa fever, which is a recurring endemic threat, Fig. 3 illustrates cholera cases with irregular peaks associated with the rainy season and flooding, illustrating the disease's climate-sensitivity. Major Outbreak Year and Change Point Detection In 2021, there was the largest cholera outbreak, with 3,604 deaths (CFR 3.2%) and 111,062 probable cases.By 2022, there were 23,763 cases and 592 deaths (CFR 2.5%), and by the end of 2023, there were 3,683 suspected cases. However, by Epidemiological Week 43 in 2024, there had been a comeback, with 18,782 suspected cases and 600 deaths.Lassa fever and cholera had distinct outbreak patterns. With a weekly case count of 3,672.4, cholera endured numerous epidemic waves.Notably, Nigeria recorded its most severe cholera outbreak in week 33 of 2021, with a peak of 9,382 cases. Lassa fever reached a peak of 472 cases in week 7 of 2020, with a lower threshold of 236.1 per week. Cholera's volatility is demonstrated by change-point analysis, which reveals a large 15,325% increase in 2021 followed by sharp decline. Lassa fever declined by 97.9% in 2023, despite a comeback in 2024 (+ 574.5%), which could indicate underreporting or reduced transmission. These findings underscore the explosive, climate-driven cholera epidemics and the cyclical recurrence of Lassa fever, emphasising the importance of effective surveillance and response strategies (Fig. 3 ). 3.4. Regional Distribution of Cholera and Lassa Fever Different regional patterns influenced by socioeconomic and environmental factors can be seen in the geographic spread of Lassa fever and cholera. Cholera is most common in Northern Nigeria, especially in the North-East and North-West regions. States like Kano, Borno, Yobe, and Bauchi are particularly affected because of flooding, conflict, and a lack of proper water and sanitation infrastructure.Due to the Mastomys natalensis rodent reservoir, Lassa fever is primarily found in the South-West, South-South, and South-East zones; Edo, Ondo, and Ebonyi are important hotspots (Figs. 4 and 5) .Environmental factors like agricultural practices and human-rodent interactions are the root cause of the endemicity. Due to limited access to healthcare, case fatality ratios (CFRs) differ by region, with the highest mortality rates occurring in impoverished states.WASH improvements in cholera-prone northern regions and community rodent control and health education in Lassa fever-endemic southern regions are examples of targeted public health initiatives that are necessary to address these enduring patterns. 3.5 Distribution of Cholera and Lassa Fever Across States The spread of Lassa fever and cholera in Nigerian states fluctuated dramatically between 2019 and 2024 due to ecological and regional factors. The northern states had the most cases of cholera (19,546), followed by Borno (16,742), Kano (13,550), and Jigawa (13,296) (Fig. 6 ). Other devastated states, including as Zamfara, Katsina, Sokoto, and Yobe, are dealing with flooding, a lack of safe water, and conflict-related displacement. In addition, there were sporadic occurrences in Bayelsa, Cross River, and Ogun, which typically peak during the rainy season, which occurs around week 28 of the year (Fig. 8). On the other hand, Lassa fever infections were predominantly concentrated in southern states, particularly Ondo (1,238 cases) and Edo (1,183), which together represent more than half of the national total (Fig. 7 ). Bauchi, Ebonyi, and Taraba were also hotspots for rodent populations. Peaks were visible throughout the dry season, especially between weeks 3 and 6. Lassa fever instances were reported in numerous other states, but each had less than 100 cases. Lassa fever was documented in all six geopolitical zones, but the South-West, South-South, and South-East were the severely afflicted (Figs. 8 and 9). Overall, cholera is more common in the north, whereas Lassa fever is mostly endemic in the south, indicating a regional divide in Nigeria's transmittable disease dynamics. 3.6. Lassa Fever and Cholera Cases based on states (2019–2024) According to the data, cholera and Lassa fever have distinct regional clusters. The majority of cholera cases occur in the country's north, with the greatest rates in Bauchi (9.6%), Borno (8.2%), and Kano (6.6%). More than 42% of cholera cases in the country occur in six northern states, which are linked to sanitary issues, flooding, insecurity, and limited access to water.Lassa fever, on the other hand, is most common in the southern states, particularly Ondo and Edo, where it accounts for 66.3% of all cases. Cases reported in Bauchi and Ebonyi show the presence of the Mastomys natalensis rodent. Lassa fever, which is more specific than cholera, is said to occur only infrequently in several places (Table 3 ). Table 3 States with the highest Cholera and Lassa Fever Cases (2019–2024) Cholera Cases Lassa Fever Cases State Cases % of National Burden Zone State Cases % of National Burden Zone Bauchi 19,546 9.60% North-East Ondo 1,238 33.90% South-West Borno 16,742 8.20% North-East Edo 1,183 32.40% South-South Kano 13,550 6.60% North-West Bauchi 291 8.00% North-East Jigawa 13,296 6.50% North-West Ebonyi 205 5.60% South-East Zamfara 12,079 5.90% North-West Taraba 159 4.40% North-East Katsina 10,851 5.30% North-West Kogi 99 2.70% North-Central Sokoto 8,511 4.20% North-West Plateau 89 2.40% North-Central Yobe 6,491 3.20% North-East Benue 64 1.80% North-Central Kebbi 5,956 2.90% North-West Enugu 41 1.10% South-East Niger 3,027 1.50% North-Central Nasarawa 35 1.00% North-Central Adamawa 996 0.50% North-East Gombe 33 0.90% North-East Bayelsa 983 0.50% South-South Delta 27 0.70% South-South Cross River 760 0.40% South-South Oyo 25 0.70% South-West Ogun 295 0.10% South-West Kaduna 22 0.60% North-West Ebonyi 283 0.10% South-East Rivers 13 0.40% South-South 3.7. Comparative Seasonal Dynamics of Cholera and Lassa Fever The long-term trajectory of disease incidence is shown by the trend component.Although their courses diverged, cholera and Lassa fever both steadily increased year after year during the research period.Between 2018 and 2021, cholera rates spiked during major outbreaks, then declined in 2022, while there were still occasional seasonal spikes.Conversely, Lassa fever exhibited erratic growth, reaching its highest points in 2020 and 2023 as the endemic region grew and diagnostic capabilities advanced.The seasonal component displays anticipated patterns.Because of watery transmission, cholera outbreaks increased throughout the rainy season (July to September).On the other hand, Lassa fever increased between December and March, which also happened to be the time when human-rodent contact increased.Lassa fever stayed constant, with the exception of a significant decline in 2023 (Fig. 10 ).These results highlight the importance of disease-specific outbreak prediction models and methods, as well as the significance of long-term surveillance and seasonal forecast for epidemic preparedness. During the dry season, when rodent activity increases human exposure to Mastomys natalensis, Fig. 11 shows monthly case trends from Bayesian Structural Time Series Models, showing persistent peaks from January to March.Although it was not statistically significant, a negative association (r = -0.7846, p = 0.0646) was discovered.The seasonality of cholera is more variable, peaking between June and October after periods of heavy rainfall and flooding.In areas with insufficient WASH systems, case fatality rates are higher throughout the major epidemic years of 2019, 2022, and 2023 (Table 4 ).The lack of a significant association (r=-0.0540,p = 0.9313) indicates that environmental shocks have a bigger impact on the incidence of cholera than steady trends.In conclusion, cholera is brought on by rainfall and poor sanitation, whereas Lassa fever peaks predictably during dry months.The timing of preparations should be exact, with an emphasis on rodent control, early Lassa fever diagnosis, and enhanced WASH activities before the rainy season for cholera. Table 4 Table for collated data from WASHNORM (2021) — Access to basic water supply services by state Rank State Basic water access (%) — 2021 (WASHNORM) 1 Lagos 96 2 Ogun 94 3 Anambra 92 4 Jigawa 89 5 Osun 83 6 Ekiti 81 7 Imo 81 8 Oyo 79 9 Delta 78 10 Adamawa 77 11 Rivers 76 12 Edo 75 13 Kwara 74 14 Yobe 72 15 FCT 70 16 Borno 68 17 Enugu 68 18 Bauchi 67 19 Gombe 65 20 Kogi 62 3.8 Preparedness issues Monitoring, laboratory capability, and WASH infrastructure gaps make it difficult to plan for cholera and Lassa fever outbreaks in Nigeria.Even while SORMAS implementation and the Integrated Disease Surveillance and Response (IDSR) platform have progressed, reporting remains unequal, particularly in rural and conflict-affected areas (Fig. 12 ).Cholera preparedness is complicated by a lack of access to clean water and sanitation, particularly in the Northeast and Northwest.Unequal resource distribution leads to higher mortality rates and case management delays.Preventive measures, such as oral cholera immunisation, are routinely overlooked. Lassa fever is an issue caused by a lack of community education and poor rodent control. Despite improved testing capabilities, early detection is impeded by sample transport delays and PPE shortages. Preparedness is hampered by bigger issues such as limited resources and inadequate intersectoral collaboration. A sustainable plan requires investments in strong health-care institutions and effective community communication. 4. Discussion This analysis reveals that between 2019 and 2024, cholera and Lassa fever in Nigeria exhibit distinct demographic, temporal, and geographic tendencies.There were noticeable demographic differences: children under the age of 15 were disproportionately affected by cholera (nearly 45% of cases), and those aged < 4 years (OR = 1.45) and 5–14 years (OR = 1.71) had higher risks.Adults aged 25 to 44 accounted for the majority of the Lassa fever burden, whereas younger children were at much lower risk.Males were more likely to develop Lassa fever, whereas females were more likely to contract cholera.These distinctions highlight various modes of transmission: Lassa fever is impacted by ecological and occupational dangers, whereas cholera is associated with environmental WASH exposures.The findings are similar with previous NCDC monitoring reports 21 – 22 , which show that Lassa fever is more prevalent in the south while cholera is more common in the north. The greater likelihood in this study’s analysis is in line with Gidado et al. 23 report that over 60% of cholera cases occurred in youngsters under the age of fifteen.Lassa fever disproportionately affected adults in Ondo and Edo, according to Olayinka et al. 24 This is in line with our conclusion that these two states accounted for 66.3% of all cases nationwide. Whereas Lassa fever had smaller but recurrent seasonal peaks with consistently high fatality (CFR 22.1%), cholera showed tremendous epidemiological instability, peaking at 111,062 cases in 2021 and surpassing weekly outbreak criteria eleven times. Change-point detection shows that the pandemic of cholera increased quickly (+ 15,325% between 2020 and 2021), but then sharply decreased, whereas Bayesian decomposition validates the expected dry-season peak of Lassa fever. All of these results point to the cholera's climate-sensitive, epidemic-prone profile, while Lassa fever's profile is endemic but deadly. The combination of change-point detection and proportionate state-level burden analysis in this research study adds new dimensions to these patterns, which are consistent with prior surveillance findings. 21 25 While Lassa fever remained a serious issue, with Ondo and Edo accounting for 66.3% of cases, Bauchi, Borno, and Kano contributed significantly to the country's cholera burden.This fine-grained attribution helps to justify spatially particular treatments. While Ghana and Sierra Leone indicate identical seasonal patterns throughout West Africa, 29 30 Nigeria's double load is unique: the country remains the hub of Lassa fever, and the 2021 cholera outbreak was one of the worst in the world in the previous ten years. This underscores Nigeria's critical role in regional health security and preparation initiatives. The findings are significant from both an economic and epidemiological perspective. Cholera's explosive epidemics result in high emergency expenses for IV fluids, oral rehydration treatments, swift deployments, and reactive immunisation attempts. On the other hand, despite being less common, Lassa fever incurs large costs per patient due to lengthy hospital admissions, ribavirin therapy, infection control procedures, and lost production from premature mortality. One-size-fits-all readiness is useless, as evidenced by these various cost profiles. Regionally diverse solutions provide the most cost-effective route : Cholera (North-East/North-West): Recurring emergency response costs can be decreased by investments in WASH infrastructure, continuous oral cholera vaccine use in hotspots, and flood preparedness. Lassa fever (South-West/South-South): Increasing ecological surveillance, rodent control, and diagnostic capacity can lower treatment expenses and mortality, improving economic efficiency. Nigeria will continue to face cyclical epidemics in the absence of such expenditures, which would put a burden on the healthcare system, drain household budgets, and take limited funds away from long-term development. The Special Contributions of the Study This study builds on earlier studies in four ways: State-level proportional load attribution enables focused responses; Change-point detection was used to evaluate the volatility of the cholera outbreak; The anticipated diseases calendars for both Lassa virus and bacteria (Vibrio cholerae) are validated by Bayesian seasonal decomposition; and one of Nigeria's most comprehensive dual-disease analytical frameworks is produced by combining epidemiological and economic perspectives. 5. Conclusion This study compares cholera and Lassa fever in Nigeria (2019–2024) in detail.While cholera, an outbreak that is climate-sensitive in the north, predominantly kills children, Lassa fever, an endemic southern zoonosis, has a high adult death rate.While temporal investigations emphasised the rainy-season surges of cholera and the dry-season peaks of Lassa fever, change-point detection showed the explosive volatility of cholera in contrast to the chronic recurrence of Lassa fever.Cholera economically necessitates costly emergency procedures, whereas Lassa fever causes large output losses and high per-case administration expenses.Region-specific strategies, such as northern WASH and flood control and southern ecological surveillance and diagnostic expansion, are employed for the best mitigation. Improved early-warning systems, predictive modelling, and focused funding are essential for preventing cyclical epidemics and preserving national health security in Nigeria's Integrated Disease Surveillance and Response framework. Declarations Author Contribution Author ContributionsJoseph S. G. conceived the study topic, conducted the data analysis, and led the interpretation of findings. He drafted the manuscript, including the results, discussion, and conclusion sections. Adamu A. led the study design, guided data collection, and critically reviewed the manuscript for methodological and scientific rigor. Joseph I.M. provided technical guidance on the framing and refinement of the manuscript title and reviewed the manuscript for intellectual content. Jide I. coordinated access to national surveillance data from the Nigeria Centre for Disease Control and Prevention (NCDC), ensured data accuracy and validation, and reviewed the manuscript. All authors read and approved the final version of the manuscript. Data Availability Yes. This study used existing secondary data derived from routinely collected national surveillance reports of the Nigeria Centre for Disease Control and Prevention. No primary data were collected. References Belleek BK et al. Cholera in Africa: a climate change crisis. Pan Afr Med J. 2025. Nigeria Centre for Disease Control. Cholera Situation Reports. Abuja: NCDC; 2019–24. Akingbola A et al. Cholera outbreak in Nigeria: history and socioeconomic drivers. BMC Public Health. 2025. World Health Organization. Cholera fact sheet. Geneva: WHO; 2024. UNICEF, National Bureau of Statistics. WASHNORM Report 2021: National Outcome Routine Mapping. Abuja: UNICEF/NBS; 2021. Eneh S et al. Cholera outbreak trends in Nigeria: policy implications. Front Public Health. 2024. Reuters. Nigeria reports 359 cholera deaths in first nine months of year. 2024 Oct. Maffioli EM et al. 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Diagnostic capacity expansion in Nigeria during COVID-19. Implementation report; 2021–2. Springer. Assessment of preparedness of Nigeria’s diagnostic and laboratory network. Springer; 2025. Nigeria Centre for Disease Control (NCDC). Annual Cholera Situation Reports 2019–2024. Abuja: NCDC; 2024. Nigeria Centre for Disease Control (NCDC). Lassa Fever Epidemiological Reports 2019–2024. Abuja: NCDC; 2024. Gidado S, Awosanya E, Haladu S, et al. Cholera outbreak in a naïve rural community in Northern Nigeria, 2010: the importance of hand washing with soap, potable water, and sanitation. Pan Afr Med J. 2018;30:5–13. Olayinka AT, Oyemakinde A, Balogun MS, et al. Lassa fever epidemiology in Nigeria, 2012–2019: analysis of national surveillance data. Int J Infect Dis. 2020;95:218–25. World Health Organization (WHO). Cholera and Lassa fever: regional epidemiological updates 2019–2023. Geneva: WHO; 2023. Fichet-Calvet E, Rogers DJ. Risk maps of Lassa fever in West Africa. PLoS Negl Trop Dis. 2009;3(3):e388. Adewuyi P, Akinyemi O, Umar A, et al. Epidemiological profile of cholera in Nigeria: trends and lessons for control. BMC Public Health. 2022;22:456. Musa EO, Woyessa AB, Adepoju KA, et al. Case fatality rates of Lassa fever in Nigeria: a systematic review. Trop Med Int Health. 2019;24(9):1044–53. Ilunga BK, Nkrumah KN, Sesay J, et al. Seasonal drivers of cholera outbreaks in Sierra Leone, 2012–2018. BMC Infect Dis. 2020;20:765. Amoako SY, Adomako M, Nartey T, et al. Epidemiological patterns of Lassa fever and cholera in Ghana: implications for integrated surveillance. Pan Afr Med J. 2021;38:114. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviews received at journal 31 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers invited by journal 21 Jan, 2026 Editor invited by journal 31 Dec, 2025 Editor assigned by journal 29 Dec, 2025 Submission checks completed at journal 29 Dec, 2025 First submitted to journal 27 Dec, 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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2","display":"","copyAsset":false,"role":"figure","size":188276,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncidence rate and CFR of Lassa fever and Cholera\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8463403/v1/4303965e0167fd0b7ca1ac59.png"},{"id":101019460,"identity":"97f65511-47e9-4991-a564-2f504bc8d280","added_by":"auto","created_at":"2026-01-24 00:38:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":248039,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEpidemic Curve (weekly cases)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8463403/v1/4a02245ac74cb51aa27a4376.png"},{"id":101204427,"identity":"57ce8397-b1e3-4942-8518-7d42070c56e3","added_by":"auto","created_at":"2026-01-27 09:43:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":72141,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional Distribution of Cholera Cases in Nigeria, 2019–2024\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8463403/v1/8d38f3303a3154ebf2229224.png"},{"id":101204426,"identity":"566ada06-6668-4e11-b8a3-c0483e16e62d","added_by":"auto","created_at":"2026-01-27 09:43:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":78800,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional Distribution of Lassa Fever Cases in Nigeria, 2019–2024\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8463403/v1/8e9a8c4d6144b160241dc5d9.png"},{"id":101204361,"identity":"c338dd48-1a42-4bbb-bc64-ff9eb956af3a","added_by":"auto","created_at":"2026-01-27 09:42:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":172555,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStates with the Highest Cholera Cases in Nigeria, 2019–2024\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8463403/v1/8b724dfc4bfc799dc9a4c52b.png"},{"id":101019469,"identity":"a88cae0b-4793-45c2-8f48-5c0c86da6b78","added_by":"auto","created_at":"2026-01-24 00:38:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":333919,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStates with the Highest Lassa Fever Cases in Nigeria, 2019–2024\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8463403/v1/4146bd2d028af40da55bdd26.png"},{"id":101019492,"identity":"3653e50e-3823-44d4-b645-2d93a14ded86","added_by":"auto","created_at":"2026-01-24 00:38:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":125870,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeographic Distribution of Confirmed Cholera Cases, Nigeria, 2019–2024 (Cumulative)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003eChoropleth map of cumulative cholera burden. Transmission was concentrated in the North-East and North-West, with scattered outbreaks in the South-South and South-West.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8463403/v1/32ec6a7eede6c7ffff22e7eb.png"},{"id":101019463,"identity":"c8899494-b2ac-4a04-bb21-21add0f01ecf","added_by":"auto","created_at":"2026-01-24 00:38:49","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":124874,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeographic Distribution of Confirmed Lassa Fever Cases, Nigeria, 2019–2024 (Cumulative)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003eChoropleth map of cumulative Lassa fever burden. Distribution was concentrated in the South-West, South-South, and South-East zones, consistent with known endemic foci.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8463403/v1/5ddd563cff732812ed4fde4e.png"},{"id":101204508,"identity":"029228ca-a56a-40b5-b301-15bd6648ce9f","added_by":"auto","created_at":"2026-01-27 09:43:24","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":377651,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Decomposed Trend and Seasonal Component for Cholera and Lassa Fever in Nigeria\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8463403/v1/72f97b0a1e121b391a76a5e5.png"},{"id":101019479,"identity":"24ef3c68-a1c7-4d80-8049-cb04d1c75c1e","added_by":"auto","created_at":"2026-01-24 00:38:50","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":113322,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSeasonal Pattern on average monthly cases\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-8463403/v1/b7c995aa09fe8862b4a4d789.png"},{"id":101019487,"identity":"d6a0b1ae-8d88-4046-af3b-9235a97b8fd1","added_by":"auto","created_at":"2026-01-24 00:38:50","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":161725,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCFR trends (2019–2024) for Cholera and Lassa fever, with key surveillance/diagnostic milestones\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-8463403/v1/b17868f4b9f18d60ad4d56dd.png"},{"id":103056265,"identity":"825aaf8b-bed7-40df-82d0-3522ac8e4770","added_by":"auto","created_at":"2026-02-20 09:00:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3372667,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8463403/v1/8c9075d1-14d8-4269-bc1a-55d4aa9e57e3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Outbreak Volatility and High Lethality: A Comparative Burden Analysis of Cholera and Lassa Fever in Nigeria (2019–2024) with Policy, Preparedness and Resource Implications","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEpidemic-prone diseases continue to pose significant threats to health security in sub-Saharan Africa, with Nigeria shouldering a disproportionate burden. Cholera and Lassa fever, in particular, have killed a large number of people, slowed economic growth, and upended health-care systems. \u0026sup1;⁻\u0026sup3; Vibrio cholerae is the cause of cholera, an acute diarrhoeal illness spread through contaminated food and water and closely related with poor water, sanitation, and hygiene (WASH) conditions. According to WHO estimates, the annual global burden is disproportionately high in Sub-Saharan Africa, with 1.3 to 4.0\u0026nbsp;million cases and 21,000 to 143,000 deaths. ⁴ ⁵ Flooding, population dislocation, climate change, and poor WASH infrastructure exacerbate rapid outbreaks, high infection rates, and large fatality rates from untreated cholera.⁶⁻⁸\u003c/p\u003e \u003cp\u003eAccording to recent data, Nigeria experienced repeated and significant cholera outbreaks from 2019 to 2024, with notable geographical variation and irregular surges in cases and fatalities.Lassa fever, a deadly viral ailment, primarily affects West Africans, particularly Nigerians. It is caused by the Lassa virus, which is often transmitted by contact with a multimammate rat, a common type of rodent. People can become infected by eating food contaminated by the urine or droppings of these rats, and, in some cases, the disease can spread from person to person, especially in healthcare settings.\u0026sup1;\u0026sup1; \u0026sup1;\u0026sup2; Over the years, Nigeria has seen many outbreaks of Lassa fever, particularly during the dry season, with an increase in reported cases and clusters of infections in hospitals, putting healthcare workers at risk.\u0026sup1;\u0026sup3;⁻\u0026sup1;⁵ Recent reports show that Lassa fever cases have been rising and spreading to more areas in Nigeria between 2017 and 2024.\u0026sup1;⁶ \u0026sup1;⁷ Over the past ten years, Nigeria has made great strides in disease response and surveillance. To help health practitioners report instances more efficiently across many regions, a new digital platform called SORMAS was introduced in 2020. Additionally, the COVID-19 pandemic contributed to the development of testing abilities, which are essential for identifying cases of illnesses like cholera and Lassa fever.\u0026sup1;⁸-\u0026sup1;⁹ However, it is impossible to present a whole picture of the disease's impact due to the wide variations in diagnostic service availability and quality across the nation. \u0026sup2;⁰\u003c/p\u003e \u003cp\u003eA major contributing factor to the spread of cholera in Nigeria is the absence of sanitary facilities and clean water. Cholera outbreaks are common because of differences in access to basic water, sanitation, and hygiene services, according to studies. In a similar vein, rodent populations, living conditions, and environmental factors all contribute to the persistence of Lassa fever. This emphasises the value of comprehensive systems that address farming operations, housing, rodent control, and food storage. ⁵ \u0026sup1;\u0026sup2; \u0026sup1;⁶ The majority of research has focused on one disease at a time and for a short period of time, despite improvements in illness tracking and a growing awareness of several diseases. In order to better understand how cholera and Lassa fever are changing, how modern diagnostics are assisting, and how factors like sanitation affect their spread, few studies have looked at long-term trends in these diseases.In order to: (1) differentiate between actual changes in disease transmission and changes brought about by improved reporting; (2) pinpoint areas in need of focused health interventions, like immunisation campaigns and sanitation projects; and (3) guide the prudent use of scarce public health resources, it is imperative to analyse these trends over a number of years.⁷ \u0026sup1;⁰ \u0026sup1;\u0026sup3; \u0026sup1;⁷ In order to provide valuable information for improved readiness, targeted response, and policy initiatives, this project aims to assess the trends and effects of cholera and Lassa fever in Nigeria between 2019 and 2024.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eThis study used a retrospective epidemiological methodology, with secondary surveillance and demographic datasets collected over a six-year period (2019\u0026ndash;2024). This methodology enabled a thorough examination of cholera epidemiology in Nigeria, employing temporal, geographical, and population-based analysis to reveal patterns and trends in disease incidence and distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study Area\u003c/h2\u003e \u003cp\u003eNigeria, Africa's most populous country, is divided into 36 states and the Federal Capital Territory (FCT), each with around 774 Local Government Areas (LGAs). The national population, which is expected to approach 220\u0026nbsp;million by 2024, is characterised by great ethnic diversity and ecological heterogeneity. Climatic zones span from arid Sahelian savannah in the north to humid rainforest environments in the south.These ecological gradients are crucial because they influence disease transmission dynamics, flooding susceptibility, and the persistence of waterborne diseases like cholera.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Sources\u003c/h2\u003e \u003cp\u003eFrom 2019 to 2024, epidemiological information was extracted from the Nigeria Centre for Disease Control's (NCDC) weekly and annual surveillance reports. In addition, updates were integrated from the World Health Organization's Disease Outbreak News (DONs). Population estimates utilized data from the National Bureau of Statistics (NBS) and the United Nations World Population Prospects (WPP). The Nigerian Meteorological Agency (NiMet) provided data on rainfall patterns, flooding events, and climatic variability to help examine the environmental elements that influence health outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Indicators\u003c/h2\u003e \u003cp\u003eIncidence rates per 100,000 people, annual case numbers, deaths attributable to Lassa fever and cholera, case fatality ratios (CFR), and other metrics were used to evaluate the epidemiological impact of these diseases. To further understand temporal patterns, epidemic peaks were studied on a monthly and annual basis. Recurring tendencies were identified by looking at multi-year cycles. Statistically significant changes in epidemic patterns were found during the study period when differences in transmission intensity across time were evaluated using joinpoint regression. The magnitude, severity, and progression of Lassa fever and cholera in Nigeria were fully depicted by these measurements when combined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data Analysis\u003c/h2\u003e \u003cp\u003eThe yearly and cumulative morbidity and death metrics, stratified by age, gender, state, and year, were determined using descriptive epidemiological methods. Joinpoint regression was used for trend analysis, allowing statistically significant changes in incidence to be identified over time. Moving averages were used to smooth out short-term fluctuations and make long-term patterns more understandable. Seasonal influences on transmission dynamics and repeating epidemic cycles were detected using seasonal trend decomposition (STL) approaches. The outputs included temporal trend lines, geographic heatmaps, and detailed epidemic curves to demonstrate case spatial and temporal grouping. IBM SPSS Statistics was utilised for data analysis, while QGIS was employed for spatial mapping. Microsoft Excel was utilised for data administration and presentation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Ethical Considerations\u003c/h2\u003e \u003cp\u003eThis study utilized only publicly available secondary surveillance datasets and population statistics. Individual-level identifiers were not employed, and all analyses were conducted on aggregated data provided by national and international health agencies. As a result, ethical clearance was not necessary; however, data handling was conducted in accordance with confidentiality principles and the responsible use of public health information.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographic Analysis of Cholera and Lassa Fever (2019\u0026ndash;2024)\u003c/h2\u003e \u003cp\u003eBetween 2019 and 2024, there were 204,329 reported cholera cases and 3,654 cases of Lassa fever. The mean age of cholera cases was 20.8 years, while Lassa fever cases averaged 32.6 years. Cholera disproportionately affected children, especially those under 5 years (20.8%) and ages 5\u0026ndash;14 (23.7%). In contrast, Lassa fever cases were primarily among adults aged 25\u0026ndash;34 (23.6%) and \u0026ge;\u0026thinsp;45 years (23.2%), with children under 15 contributing less than 16%. In terms of gender, cholera showed a female predominance (52.3% of cases), whereas Lassa fever had a slight male predominance (54.0%). Significant differences were found in age (χ\u0026sup2; = 1,609.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and gender distribution (χ\u0026sup2; = 1.76, p\u0026thinsp;=\u0026thinsp;1.76 \u0026times; 10⁻\u0026sup1;⁴) between the two diseases.\u003c/p\u003e \u003cp\u003eGoodness-of-fit tests revealed significant variations within each disease: cholera's age (χ\u0026sup2; = 17,327.51, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and gender (χ\u0026sup2; = 442.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) distributions were uneven, while Lassa fever also demonstrated significant disparities in age (χ\u0026sup2; = 619.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and gender (χ\u0026sup2; = 24.34, p\u0026thinsp;=\u0026thinsp;8.06 \u0026times; 10⁻⁷). These findings illustrate that cholera primarily impacts younger populations and females, whereas Lassa fever predominantly affects adults, particularly males. This underscores key differences in transmission dynamics and health-seeking behaviors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemography information associated with disease (cumulative value-2019-2024)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eCholera\u003c/p\u003e \u003cp\u003e(204,329 cases)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eLassa Fever\u003c/p\u003e \u003cp\u003e(3654 cases)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCholera vs Lassa fever disease compared\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(ref\u0026thinsp;=\u0026thinsp;25\u0026ndash;34)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42,512 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e182 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48,473(23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e369 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37,839 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e686 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31,434 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e864 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,506 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e705 (19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22,565 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e848 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(ref\u0026thinsp;=\u0026thinsp;Male)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106,919(52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1675 (45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97,410 (47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1973 (54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003e*Mean Age for Cholera and Lassa fever is 20.8 and 32.6 respectively\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Heatmaps (2019\u0026ndash;2024)\u003c/h2\u003e \u003cp\u003eThe Lassa fever heatmap (2019\u0026ndash;2024) shows that the majority of cases affect adults, with peaks in 2020 and 2022, particularly in the 25\u0026ndash;34 and 45\u0026thinsp;+\u0026thinsp;age groups. Lassa fever is primarily an adult sickness, with extremely few cases involving children under the age of 15. The decrease in cases in 2023 and 2024 could be attributed to improved intervention approaches. Cholera, on the other hand, mostly affects children under the age of 15, notably those under the age of four and those aged five to fourteen, with peak incidence expected in 2021. Despite a drop in frequency following this peak, cholera showed signs of resurgence in 2024. In general, cholera is a major risk to children connected with WASH vulnerabilities, whereas Lassa fever mostly affects adults (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 National Burden\u003c/h2\u003e \u003cp\u003eBetween 2019 and 2024, Nigeria reported 204,329 cholera cases and 6,838 fatalities, with a case fatality ratio of 3.3%.However, Lassa fever had a far higher CFR of 22.1% at the time, with 3,654 cases and 809 deaths.Cholera peaked in 2021 with 111,062 cases (50.8 per 100,000 people). Significant increases were observed in 2024 (18,782 cases) and 2019 (45,986 cases).Lassa fever incidence was low, ranging from 0.01 to 0.56 cases per 100,000, and seldom exceeded 1,200 cases per year, despite the high CFR of 18\u0026ndash;22%. Lassa fever had a steady death rate even in years with low prevalence, but cholera deaths increased during outbreaks (mostly in 2019 and 2021). Thus, in 2020, there were 977 cases of cholera and 53 deaths (CFR of 5.4%), but 1,189 cases of Lassa fever and 244 deaths (CFR of 20.5%).This underlines cholera as a disease that erupts regularly, while Lassa fever is a less serious but more devastating endemic risk (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEpidemiological burden: Annual case counts and death\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCholera\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003eLassa fever\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncidence count\u003c/p\u003e \u003cp\u003e(cases)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCholera incidence rate (per 100,000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMortality count*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCFR (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncidence count\u003c/p\u003e \u003cp\u003e(cases)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLassa incidence rate (per 100,000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMortality count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCFR (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePopulation**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e209,485,641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e213,996,181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e19.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e218,529,286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e223,150,896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e227,882,945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e232,679,478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204,329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3,654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCase fatality ratio -CFR (cumulative cases \u0026amp; deaths)\u003c/p\u003e \u003cp\u003e*NSPACC 2024 \u0026amp; NCDC 2019\u0026thinsp;\u0026minus;\u0026thinsp;204\u003c/p\u003e \u003cp\u003e**World Bank, (2025).Population,tool-Nigeria.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.worldbank.org/indicator/SP.POP.TOTL?end=2024\u0026amp;locations=NG\u0026amp;start=1960\u003c/span\u003e\u003cspan address=\"https://data.worldbank.org/indicator/SP.POP.TOTL?end=2024\u0026amp;locations=NG\u0026amp;start=1960\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e-Case fatality ratio (CFR).\u003c/p\u003e \u003cp\u003e-Incidence rate per 100,000 population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Epidemic Curves of Confirmed Cholera and Lassa Fever Cases\u003c/h2\u003e \u003cp\u003eIn Nigeria, the epidemic curves for Lassa fever and cholera show unique tendencies. While Lassa fever is endemic and has a steady but declining annual case count, cholera outbreaks show significant interannual variability, with spectacular surges during epidemic years. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates Lassa fever cases from 2019 to 2024, with recurring peaks over the dry season (December-April), highlighting the disease's zoonotic transmission characteristics. Unlike Lassa fever, which is a recurring endemic threat, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates cholera cases with irregular peaks associated with the rainy season and flooding, illustrating the disease's climate-sensitivity.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMajor Outbreak Year and Change Point Detection\u003c/strong\u003e \u003cp\u003eIn 2021, there was the largest cholera outbreak, with 3,604 deaths (CFR 3.2%) and 111,062 probable cases.By 2022, there were 23,763 cases and 592 deaths (CFR 2.5%), and by the end of 2023, there were 3,683 suspected cases. However, by Epidemiological Week 43 in 2024, there had been a comeback, with 18,782 suspected cases and 600 deaths.Lassa fever and cholera had distinct outbreak patterns. With a weekly case count of 3,672.4, cholera endured numerous epidemic waves.Notably, Nigeria recorded its most severe cholera outbreak in week 33 of 2021, with a peak of 9,382 cases. Lassa fever reached a peak of 472 cases in week 7 of 2020, with a lower threshold of 236.1 per week. Cholera's volatility is demonstrated by change-point analysis, which reveals a large 15,325% increase in 2021 followed by sharp decline. Lassa fever declined by 97.9% in 2023, despite a comeback in 2024 (+\u0026thinsp;574.5%), which could indicate underreporting or reduced transmission. These findings underscore the explosive, climate-driven cholera epidemics and the cyclical recurrence of Lassa fever, emphasising the importance of effective surveillance and response strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Regional Distribution of Cholera and Lassa Fever\u003c/h2\u003e \u003cp\u003eDifferent regional patterns influenced by socioeconomic and environmental factors can be seen in the geographic spread of Lassa fever and cholera. Cholera is most common in Northern Nigeria, especially in the North-East and North-West regions. States like Kano, Borno, Yobe, and Bauchi are particularly affected because of flooding, conflict, and a lack of proper water and sanitation infrastructure.Due to the Mastomys natalensis rodent reservoir, Lassa fever is primarily found in the South-West, South-South, and South-East zones; Edo, Ondo, and Ebonyi are important hotspots (Figs.\u0026nbsp;4 and 5) .Environmental factors like agricultural practices and human-rodent interactions are the root cause of the endemicity. Due to limited access to healthcare, case fatality ratios (CFRs) differ by region, with the highest mortality rates occurring in impoverished states.WASH improvements in cholera-prone northern regions and community rodent control and health education in Lassa fever-endemic southern regions are examples of targeted public health initiatives that are necessary to address these enduring patterns.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Distribution of Cholera and Lassa Fever Across States\u003c/h2\u003e \u003cp\u003eThe spread of Lassa fever and cholera in Nigerian states fluctuated dramatically between 2019 and 2024 due to ecological and regional factors. The northern states had the most cases of cholera (19,546), followed by Borno (16,742), Kano (13,550), and Jigawa (13,296) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Other devastated states, including as Zamfara, Katsina, Sokoto, and Yobe, are dealing with flooding, a lack of safe water, and conflict-related displacement. In addition, there were sporadic occurrences in Bayelsa, Cross River, and Ogun, which typically peak during the rainy season, which occurs around week 28 of the year (Fig.\u0026nbsp;8).\u003c/p\u003e \u003cp\u003eOn the other hand, Lassa fever infections were predominantly concentrated in southern states, particularly Ondo (1,238 cases) and Edo (1,183), which together represent more than half of the national total (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Bauchi, Ebonyi, and Taraba were also hotspots for rodent populations. Peaks were visible throughout the dry season, especially between weeks 3 and 6. Lassa fever instances were reported in numerous other states, but each had less than 100 cases. Lassa fever was documented in all six geopolitical zones, but the South-West, South-South, and South-East were the severely afflicted (Figs.\u0026nbsp;8 and 9). Overall, cholera is more common in the north, whereas Lassa fever is mostly endemic in the south, indicating a regional divide in Nigeria's transmittable disease dynamics.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Lassa Fever and Cholera Cases based on states (2019\u0026ndash;2024)\u003c/h2\u003e \u003cp\u003eAccording to the data, cholera and Lassa fever have distinct regional clusters. The majority of cholera cases occur in the country's north, with the greatest rates in Bauchi (9.6%), Borno (8.2%), and Kano (6.6%). More than 42% of cholera cases in the country occur in six northern states, which are linked to sanitary issues, flooding, insecurity, and limited access to water.Lassa fever, on the other hand, is most common in the southern states, particularly Ondo and Edo, where it accounts for 66.3% of all cases. Cases reported in Bauchi and Ebonyi show the presence of the Mastomys natalensis rodent. Lassa fever, which is more specific than cholera, is said to occur only infrequently in several places (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStates with the highest Cholera and Lassa Fever Cases (2019\u0026ndash;2024)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eCholera Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eLassa Fever Cases\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eState\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% of National Burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eState\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e% of National Burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZone\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBauchi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19,546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e9.60%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOndo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e33.90%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSouth-West\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16,742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEdo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSouth-South\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKano\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13,550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth-West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBauchi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNorth-East\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJigawa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13,296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth-West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEbonyi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSouth-East\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZamfara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12,079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth-West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTaraba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNorth-East\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKatsina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth-West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKogi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNorth-Central\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSokoto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth-West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNorth-Central\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBenue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNorth-Central\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKebbi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth-West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnugu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSouth-East\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth-Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNasarawa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNorth-Central\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdamawa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGombe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNorth-East\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBayelsa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSouth-South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDelta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSouth-South\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCross River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSouth-South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOyo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSouth-West\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOgun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSouth-West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKaduna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNorth-West\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEbonyi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSouth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSouth-South\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Comparative Seasonal Dynamics of Cholera and Lassa Fever\u003c/h2\u003e \u003cp\u003eThe long-term trajectory of disease incidence is shown by the trend component.Although their courses diverged, cholera and Lassa fever both steadily increased year after year during the research period.Between 2018 and 2021, cholera rates spiked during major outbreaks, then declined in 2022, while there were still occasional seasonal spikes.Conversely, Lassa fever exhibited erratic growth, reaching its highest points in 2020 and 2023 as the endemic region grew and diagnostic capabilities advanced.The seasonal component displays anticipated patterns.Because of watery transmission, cholera outbreaks increased throughout the rainy season (July to September).On the other hand, Lassa fever increased between December and March, which also happened to be the time when human-rodent contact increased.Lassa fever stayed constant, with the exception of a significant decline in 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e10\u003c/span\u003e).These results highlight the importance of disease-specific outbreak prediction models and methods, as well as the significance of long-term surveillance and seasonal forecast for epidemic preparedness.\u003c/p\u003e \u003cp\u003eDuring the dry season, when rodent activity increases human exposure to Mastomys natalensis, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows monthly case trends from Bayesian Structural Time Series Models, showing persistent peaks from January to March.Although it was not statistically significant, a negative association (r = -0.7846, p\u0026thinsp;=\u0026thinsp;0.0646) was discovered.The seasonality of cholera is more variable, peaking between June and October after periods of heavy rainfall and flooding.In areas with insufficient WASH systems, case fatality rates are higher throughout the major epidemic years of 2019, 2022, and 2023 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).The lack of a significant association (r=-0.0540,p\u0026thinsp;=\u0026thinsp;0.9313) indicates that environmental shocks have a bigger impact on the incidence of cholera than steady trends.In conclusion, cholera is brought on by rainfall and poor sanitation, whereas Lassa fever peaks predictably during dry months.The timing of preparations should be exact, with an emphasis on rodent control, early Lassa fever diagnosis, and enhanced WASH activities before the rainy season for cholera.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTable for collated data from WASHNORM (2021) \u0026mdash; Access to basic water supply services by state\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eState\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBasic water access (%) \u0026mdash; 2021 (WASHNORM)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLagos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOgun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnambra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJigawa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOsun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEkiti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOyo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdamawa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e 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align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBorno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnugu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBauchi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGombe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKogi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Preparedness issues\u003c/h2\u003e \u003cp\u003eMonitoring, laboratory capability, and WASH infrastructure gaps make it difficult to plan for cholera and Lassa fever outbreaks in Nigeria.Even while SORMAS implementation and the Integrated Disease Surveillance and Response (IDSR) platform have progressed, reporting remains unequal, particularly in rural and conflict-affected areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e12\u003c/span\u003e).Cholera preparedness is complicated by a lack of access to clean water and sanitation, particularly in the Northeast and Northwest.Unequal resource distribution leads to higher mortality rates and case management delays.Preventive measures, such as oral cholera immunisation, are routinely overlooked. Lassa fever is an issue caused by a lack of community education and poor rodent control. Despite improved testing capabilities, early detection is impeded by sample transport delays and PPE shortages. Preparedness is hampered by bigger issues such as limited resources and inadequate intersectoral collaboration. A sustainable plan requires investments in strong health-care institutions and effective community communication.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis analysis reveals that between 2019 and 2024, cholera and Lassa fever in Nigeria exhibit distinct demographic, temporal, and geographic tendencies.There were noticeable demographic differences: children under the age of 15 were disproportionately affected by cholera (nearly 45% of cases), and those aged\u0026thinsp;\u0026lt;\u0026thinsp;4 years (OR\u0026thinsp;=\u0026thinsp;1.45) and 5\u0026ndash;14 years (OR\u0026thinsp;=\u0026thinsp;1.71) had higher risks.Adults aged 25 to 44 accounted for the majority of the Lassa fever burden, whereas younger children were at much lower risk.Males were more likely to develop Lassa fever, whereas females were more likely to contract cholera.These distinctions highlight various modes of transmission: Lassa fever is impacted by ecological and occupational dangers, whereas cholera is associated with environmental WASH exposures.The findings are similar with previous NCDC monitoring reports\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, which show that Lassa fever is more prevalent in the south while cholera is more common in the north. The greater likelihood in this study\u0026rsquo;s analysis is in line with Gidado et al.\u003csup\u003e23\u003c/sup\u003e report that over 60% of cholera cases occurred in youngsters under the age of fifteen.Lassa fever disproportionately affected adults in Ondo and Edo, according to Olayinka et al.\u003csup\u003e24\u003c/sup\u003e This is in line with our conclusion that these two states accounted for 66.3% of all cases nationwide. Whereas Lassa fever had smaller but recurrent seasonal peaks with consistently high fatality (CFR 22.1%), cholera showed tremendous epidemiological instability, peaking at 111,062 cases in 2021 and surpassing weekly outbreak criteria eleven times. Change-point detection shows that the pandemic of cholera increased quickly (+\u0026thinsp;15,325% between 2020 and 2021), but then sharply decreased, whereas Bayesian decomposition validates the expected dry-season peak of Lassa fever. All of these results point to the cholera's climate-sensitive, epidemic-prone profile, while Lassa fever's profile is endemic but deadly.\u003c/p\u003e \u003cp\u003eThe combination of change-point detection and proportionate state-level burden analysis in this research study adds new dimensions to these patterns, which are consistent with prior surveillance findings.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e While Lassa fever remained a serious issue, with Ondo and Edo accounting for 66.3% of cases, Bauchi, Borno, and Kano contributed significantly to the country's cholera burden.This fine-grained attribution helps to justify spatially particular treatments. While Ghana and Sierra Leone indicate identical seasonal patterns throughout West Africa,\u003csup\u003e29 30\u003c/sup\u003e Nigeria's double load is unique: the country remains the hub of Lassa fever, and the 2021 cholera outbreak was one of the worst in the world in the previous ten years. This underscores Nigeria's critical role in regional health security and preparation initiatives.\u003c/p\u003e \u003cp\u003eThe findings are significant from both an economic and epidemiological perspective. Cholera's explosive epidemics result in high emergency expenses for IV fluids, oral rehydration treatments, swift deployments, and reactive immunisation attempts. On the other hand, despite being less common, Lassa fever incurs large costs per patient due to lengthy hospital admissions, ribavirin therapy, infection control procedures, and lost production from premature mortality. One-size-fits-all readiness is useless, as evidenced by these various cost profiles.\u003c/p\u003e \u003cp\u003e \u003cem\u003eRegionally diverse solutions provide the most cost-effective route\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eCholera (North-East/North-West): Recurring emergency response costs can be decreased by investments in WASH infrastructure, continuous oral cholera vaccine use in hotspots, and flood preparedness.\u003c/p\u003e \u003cp\u003eLassa fever (South-West/South-South): Increasing ecological surveillance, rodent control, and diagnostic capacity can lower treatment expenses and mortality, improving economic efficiency. Nigeria will continue to face cyclical epidemics in the absence of such expenditures, which would put a burden on the healthcare system, drain household budgets, and take limited funds away from long-term development.\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe Special Contributions of the Study\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThis study builds on earlier studies in four ways: State-level proportional load attribution enables focused responses; Change-point detection was used to evaluate the volatility of the cholera outbreak; The anticipated diseases calendars for both Lassa virus and bacteria (Vibrio cholerae) are validated by Bayesian seasonal decomposition; and one of Nigeria's most comprehensive dual-disease analytical frameworks is produced by combining epidemiological and economic perspectives.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study compares cholera and Lassa fever in Nigeria (2019\u0026ndash;2024) in detail.While cholera, an outbreak that is climate-sensitive in the north, predominantly kills children, Lassa fever, an endemic southern zoonosis, has a high adult death rate.While temporal investigations emphasised the rainy-season surges of cholera and the dry-season peaks of Lassa fever, change-point detection showed the explosive volatility of cholera in contrast to the chronic recurrence of Lassa fever.Cholera economically necessitates costly emergency procedures, whereas Lassa fever causes large output losses and high per-case administration expenses.Region-specific strategies, such as northern WASH and flood control and southern ecological surveillance and diagnostic expansion, are employed for the best mitigation. Improved early-warning systems, predictive modelling, and focused funding are essential for preventing cyclical epidemics and preserving national health security in Nigeria's Integrated Disease Surveillance and Response framework.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor ContributionsJoseph S. G. conceived the study topic, conducted the data analysis, and led the interpretation of findings. He drafted the manuscript, including the results, discussion, and conclusion sections. Adamu A. led the study design, guided data collection, and critically reviewed the manuscript for methodological and scientific rigor. Joseph I.M. provided technical guidance on the framing and refinement of the manuscript title and reviewed the manuscript for intellectual content. Jide I. coordinated access to national surveillance data from the Nigeria Centre for Disease Control and Prevention (NCDC), ensured data accuracy and validation, and reviewed the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eYes. This study used existing secondary data derived from routinely collected national surveillance reports of the Nigeria Centre for Disease Control and Prevention. No primary data were collected.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBelleek BK et al. Cholera in Africa: a climate change crisis. Pan Afr Med J. 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNigeria Centre for Disease Control. Cholera Situation Reports. Abuja: NCDC; 2019\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkingbola A et al. Cholera outbreak in Nigeria: history and socioeconomic drivers. BMC Public Health. 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Cholera fact sheet. Geneva: WHO; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNICEF, National Bureau of Statistics. WASHNORM Report 2021: National Outcome Routine Mapping. Abuja: UNICEF/NBS; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEneh S et al. Cholera outbreak trends in Nigeria: policy implications. Front Public Health. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReuters. Nigeria reports 359 cholera deaths in first nine months of year. 2024 Oct.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaffioli EM et al. Allocation of oral cholera vaccines in Africa. Vaccine. 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGavi, the Vaccine Alliance. Oral cholera vaccine support. Geneva: Gavi; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Mustapha AI et al. Lassa fever in Nigeria: epidemiology and risk perception. Sci Rep. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Lassa fever fact sheet. Geneva: WHO; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArruda L et al. One Health approaches to Lassa fever. One Health. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsogun D et al. Review of the epidemiology of Lassa fever in Nigeria. Infect Dis Poverty. 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlayemi A et al. Arenavirus diversity and phylogeography of Mastomys in West Africa. PLoS Pathog. 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Centers for Disease Control and Prevention. About Lassa fever. Atlanta: CDC; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEzenwa-Ahanene A et al. Descriptive epidemiology of Lassa fever\u0026mdash;Ebonyi state. BMC Public Health. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe Guardian. Nigeria to host Lassa fever treatment trials; Ondo, Edo, Bauchi as key sites. 2024 May.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eExemplars in Global Health. SORMAS in Nigeria: digital surveillance scale-up. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. NCDC. Diagnostic capacity expansion in Nigeria during COVID-19. Implementation report; 2021\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpringer. Assessment of preparedness of Nigeria\u0026rsquo;s diagnostic and laboratory network. Springer; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNigeria Centre for Disease Control (NCDC). Annual Cholera Situation Reports 2019\u0026ndash;2024. Abuja: NCDC; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNigeria Centre for Disease Control (NCDC). Lassa Fever Epidemiological Reports 2019\u0026ndash;2024. Abuja: NCDC; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGidado S, Awosanya E, Haladu S, et al. Cholera outbreak in a na\u0026iuml;ve rural community in Northern Nigeria, 2010: the importance of hand washing with soap, potable water, and sanitation. Pan Afr Med J. 2018;30:5\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlayinka AT, Oyemakinde A, Balogun MS, et al. Lassa fever epidemiology in Nigeria, 2012\u0026ndash;2019: analysis of national surveillance data. Int J Infect Dis. 2020;95:218\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (WHO). Cholera and Lassa fever: regional epidemiological updates 2019\u0026ndash;2023. Geneva: WHO; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFichet-Calvet E, Rogers DJ. Risk maps of Lassa fever in West Africa. PLoS Negl Trop Dis. 2009;3(3):e388.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdewuyi P, Akinyemi O, Umar A, et al. Epidemiological profile of cholera in Nigeria: trends and lessons for control. BMC Public Health. 2022;22:456.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMusa EO, Woyessa AB, Adepoju KA, et al. Case fatality rates of Lassa fever in Nigeria: a systematic review. Trop Med Int Health. 2019;24(9):1044\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIlunga BK, Nkrumah KN, Sesay J, et al. Seasonal drivers of cholera outbreaks in Sierra Leone, 2012\u0026ndash;2018. BMC Infect Dis. 2020;20:765.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmoako SY, Adomako M, Nartey T, et al. Epidemiological patterns of Lassa fever and cholera in Ghana: implications for integrated surveillance. Pan Afr Med J. 2021;38:114.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cholera, Lassa Fever, Nigeria, Temporal Trends, Disease Burden, Geographical Distribution, Case Fatality Ratio. Public Health Preparedness","lastPublishedDoi":"10.21203/rs.3.rs-8463403/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8463403/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDespite the fact that Lassa fever and cholera are two of Nigeria's most deadly epidemic-prone diseases, few studies have examined their combined burden to aid in effective preparedness planning. This research study provides a comparative examination of epidemiological trends from 2019 to 2024, with implications for health-care financing and resource allocation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective analysis of surveillance data (NCDC, WHO) was conducted. Descriptive statistics, Bayesian Structural Time Series, seasonal decomposition, and change-point detection identified temporal and spatial trends. Odds ratios (ORs) assessed demographic risks, while proportional state contributions quantified geographic concentration of burden.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBetween 2019 and 2024, there were 204,329 cholera and 3,654 Lassa fever cases reported. Cholera disproportionately affected children under the age of 15 (OR\u0026thinsp;=\u0026thinsp;1.71), but Lassa fever was more common in adults aged 25 to 44. Males were more affected by Lassa fever, and females by cholera. Cholera outbreaks were very variable (111,062 cases in 2021; +15,325% from 2020), demanding significant surge-response costs, whereas Lassa fever caused smaller but persistent seasonal outbreaks with a consistently high CFR of 22.1%, indicating the severe resource load of case management. Cholera was predominant in northern states (Bauchi, Borno, and Kano), whereas Lassa fever was concentrated in Ondo and Edo (66.3% of cases).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eConclusion: Nigeria is dealing with two epidemics: cholera, a climate-driven illness that spawns outbreaks, and Lassa fever, a deadly rodent-borne chronic disease. These trends impose considerable and continuous financial demands on the healthcare sector. Regionally diverse preparation, including WASH infrastructure in the north and ecological/diagnostic investments in the south, paves the way for more cost-effective epidemic management. Predictive modelling, early warning systems, and targeted funding are important to lowering the health and economic consequences of these epidemics.\u003c/p\u003e","manuscriptTitle":"Outbreak Volatility and High Lethality: A Comparative Burden Analysis of Cholera and Lassa Fever in Nigeria (2019–2024) with Policy, Preparedness and Resource Implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-24 00:38:44","doi":"10.21203/rs.3.rs-8463403/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-18T12:44:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-08T20:27:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20471164002477431838153883159927505556","date":"2026-02-03T02:45:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40450282415154177990153576135508826352","date":"2026-02-02T13:57:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-31T17:25:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315489027618070417584550180778933424909","date":"2026-01-29T19:00:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40009910366672089008295808779995857037","date":"2026-01-28T21:13:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270733365405888599106615019985512789047","date":"2026-01-27T02:06:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T17:48:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-31T10:14:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-30T04:53:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-30T04:53:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-12-28T00:06:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4cbd7632-0cd8-4742-8a17-4509a732666b","owner":[],"postedDate":"January 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-24T00:38:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-24 00:38:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8463403","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8463403","identity":"rs-8463403","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}