2026· Journal of Applied Sciences and Environmental Management· Vol 30, pp. 1995-1998· 0 citations
TL;DR
This study investigated the temporal trends of malaria, tuberculosis, and cholera in Nigeria from 2010 to 2023 using epidemiological and statistical modelling approaches and provides evidence-based recommendations for targeted disease control strategies in Nigeria.
Abstract
Infectious diseases such as malaria, tuberculosis, and cholera remain major public health concerns in Nigeria. This study investigated the temporal trends of malaria, tuberculosis, and cholera in Nigeria from 2010 to 2023 using epidemiological and statistical modelling approaches. Secondary data were obtained from national and international health databases alongside environmental variables including rainfall and temperature. Descriptive statistics, correlation analysis, SEIR modelling, logistic growth modelling, and ARIMA forecasting techniques were applied using SPSS and Python software. The results reveal significant correlations between disease prevalence and environmental factors, emphasizing the role of climate variables in disease dynamics. The SEIR model accurately simulated malaria transmission patterns, highlighting critical intervention points. Logistic growth modelling identified the impact of healthcare interventions on tuberculosis prevalence, while ARIMA forecasts provided actionable insights for cholera outbreak preparedness. Graphical visualizations, including time-series trends, correlation heatmaps, and model-based projections, underscore the value of these methods in understanding and mitigating infectious diseases. This study not only advances the application of statistical and mathematical models in public health but also provides evidence-based recommendations for targeted disease control strategies in Nigeria. The findings demonstrate the potential of integrating statistical and mathematical approaches in public health decision-making, paving the way for improved disease surveillance and management in resource-constrained settings Environmental variables, particularly rainfall and temperature, showed notable relationships with disease transmission patterns. The study highlights the importance of integrating statistical and mathematical models into disease surveillance and public health decision-making in Nigeria.
The analysis revealed a consistent clear bimodal (two-peak) pattern each year in malaria incidence per province with notable provincial differences in burden and timing, and the need for geographically and seasonally tailored malaria interventions.
S. V. Alohoutade, R. Hounsell, Codjo Dandonougbo et al.· PLOS Global Public Health· 0 citations
Dengue fever has emerged as a major public health concern in Pakistan, particularly in Balochistan, where climatic variability and limited surveillance infrastructure have contributed to increasing disease burden. This study aimed to investigate the relationship between climatic factors and dengue transmission across multiple districts of Balochistan using a retrospective district-level epidemiological approach. Dengue surveillance data were collected from Public Health Laboratories, District Health Offices (DHOs), dengue surveillance units, and People’s Primary Healthcare Initiative (PPHI) centers, while climatic variables including temperature, rainfall, and humidity were obtained from national and global meteorological sources. The integrated datasets were analyzed using RStudio to assess seasonal trends, spatial distribution, and climate–dengue associations through descriptive statistics, correlation analysis, and negative binomial regression modeling. A total of 26,482 confirmed dengue cases were identified from 125,780 diagnostic tests conducted during 2024. The highest disease burden was observed in Turbat, Panjgur, Jhal Magsi, and Khuzdar districts. Dengue incidence showed a strong seasonal pattern, with cases increasing sharply during the summer and post-monsoon months and peaking in September. Regression analysis demonstrated that increasing temperature was significantly associated with higher dengue incidence, while rainfall exhibited delayed effects through enhanced vector breeding conditions. The combined influence of high temperature and high rainfall produced the greatest transmission intensity. The findings indicate that dengue transmission in Balochistan is highly climate-sensitive and increasingly shifting toward an endemic transmission pattern. Strengthening climate-informed surveillance systems, improving early warning mechanisms, enhancing vector control strategies, and promoting community awareness are essential for reducing the growing dengue burden in the region.
Iqra batool, Abdul Rehman, Ali Nawaz et al.· International Journal of Agr...· 0 citations
Introduction: The occurrence of infectious diseases can be strongly driven by social determinants of health, such as barriers to accessing medical services, socioeconomic vulnerability, and inadequate housing. Objective: To describe the epidemiology of malaria, tuberculosis, viral hepatitis, and AIDS among the population of Amazonas, with an emphasis on Black and Indigenous populations. Methodology: This is a retrospective descriptive study using a quantitative approach to public data to perform descriptive statistical and temporal trend analyses. Only data from the epidemiological bulletin published on the website of the Amazonas Health Surveillance Foundation (FVS-RCP) covering the years 2021 to 2025 were included. Results: During the period, malaria recorded the highest number of cases (300,755), with the majority (60%) occurring in males. Of this total, 136,313 cases (45.3%) were among Indigenous people and 5,461 (1.8%) among the Black population. The remaining cases occurred among White, *Pardo* (mixed-race), and Asian individuals (141,774; 47.1%). Regarding tuberculosis, there were 19,779 cases; of these, 934 (4.7%) were Indigenous and 626 (3.2%) were Black, with 65% being male. Among White, *Pardo*, and Asian individuals, there were 1,560 (7.9%) cases. As for viral hepatitis, there was a total of 3,024 cases; of these, 153 (3.1%) occurred among Indigenous people and 142 (4.7%) among Black people. Among White, *Pardo*, and Asian individuals, there were 3,024 (9.7%) cases. Regarding AIDS cases, there were 3,720 recorded instances during the period; of these, 119 (3.2%) involved Black individuals and only 45 (1.2%) involved Indigenous people, while the remainder occurred among individuals of mixed-race (*pardos*), White, and Asian backgrounds (164 = 4.4%). Conclusion: Curbing the spread of infectious diseases among minority groups—such as Indigenous communities and traditional populations—requires coordinated actions involving territorial protection, specialized primary care, and respect for interculturality.
Arimatéia Portela De Azevedo, Hercules Moraes de Mattos, Denilson Moraes Vieira et al.· Revista de Estudos Interdisc...· 0 citations
Background and aims Malaria remains a public health concern in Bangladesh, despite a notable decline in reported cases since 2012 and a brief resurgence in 2014. Aligned with the United Nations Sustainable Development Goal (SDG) 3.3, which targets the elimination of malaria by 2030, Bangladesh has implemented multiple control and prevention strategies. This study assesses the significance of the decline in malaria cases and applies Bayesian hierarchical models to capture spatial dynamics of malaria-related vulnerability, map district-level risk, and inform targeted interventions. Methods A nationwide spatial analysis was conducted using district-level malaria data from the Bangladesh Disaster-related Statistics (BDRS) 2021, which report cumulative counts of “population suffering from malaria due to disaster” for 2015–2020. These data were used as a proxy indicator of relative malaria vulnerability. National malaria time-series data were obtained from Bangladesh’s National Strategic Plan for Malaria Elimination (2021–2025) published by the Asia Pacific Malaria Elimination Network (APMEN), together with malaria surveillance summaries from the World Malaria Reports (2024,2025) published by the World Health Organization (WHO). District-level rainfall data were obtained from the Bangladesh Water Development Board. Bayesian hierarchical disease mapping models were used to assess spatial dependence and district-level malaria risk. Spatial visualization and autocorrelation analyses (Global Moran’s I, Geary’s C, and Local Moran’s I) were performed using R (version 4.4.0), and Bayesian model estimation was carried out using WinBUGS via Markov Chain Monte Carlo methods. Results The analysis showed evidence of a trend in decreasing malaria cases by a value of −0.691 in the Mann-Kendall trend test. However, spatial analysis revealed significant clustering and geographic heterogeneity. Persistent high-risk clusters were identified in the southeastern hilly regions. Additionally the presence of excess zeros in the data justified the use of zero-inflated models. Conclusion The findings show significant national decline and offer valuable insights into the geographical variability in malaria-related vulnerability in Bangladesh. Rather than being direct indicators of malaria transmission, the results should be understood as representing relative risk patterns based on data related to disasters. Under current data limitations, these results provide evidence to boost spatially informed malaria control methods and assist geographically focused interventions.
Ummehani Mimi, M. Karim, Sultana Begum et al.· PLoS ONE· 0 citations
Malaria elimination is shaped by complex interactions among climatic, environmental, socioeconomic, demographic, health-system, and intervention-related factors. However most studies examine only subsets of these drivers, limiting understanding of their combined influence on epidemiological risks. In this study, we integrated 25 years of data from 44 African countries on malaria burden and control, climate, environmental and land-use conditions, socioeconomic and demographic characteristics, and health-system capacity within a unified geospatial, explainable machine-learning, and forecasting framework to characterize spatiotemporal patterns of malaria, quantify the relative contributions of key determinants, and generate 10-year Africa-wide and country-specific forecasts of malaria incidence and mortality rates per 1,000 people at risk. We identified and mapped malaria incidence and mortality hotspots using the Getis-Ord Gi* statistic. Our analyses showed that both incidence and mortality burden remained highly heterogeneous across Africa, with persistent hotspots concentrated in the West and Central Africa. The explainable machine-learning model, that achieved high predictive performance (i.e., XGBoost for incidence, holdout R$^2$ = 0.92; Random Forest for mortality, holdout R$^2$ = 0.91), identified lower availability of hospital beds (per 1,000 people), higher mortality rate attributed to unsafe WASH (per 100,000), and lower percentage (%) of people using handwashing facilities as the top three most influential determinants of higher risk of infection across Africa whereas higher mortality rate attributed to unsafe WASH (per 100,000), lower % of people using at least basic sanitation services, and access to electricity (%) were associated with worse mortality outcomes. Forecasting models also demonstrated strong predictive accuracy (Naive persistence and Elastic Net, holdout R$^2$ = 0.98 for incidence and 0.97 for mortality). Assuming current intervention and structural conditions persist, Africa-wide malaria incidence was projected to remain broadly stable, with a modest upward trend by 2035, whereas mortality was projected to decline initially and subsequently remain relatively unchanged. However, substantial country-level heterogeneity showed both emerging transmission hotspots and persistently high-burden countries. this suggests there is a need for sustained control and accelerated elimination efforts. Overall, this study demonstrates that integrating geospatial analysis, explainable machine learning, and forecasting provides a robust framework for understanding malaria dynamics, identifying the key determinants of burden, anticipating future trends, and supporting geographically targeted malaria control across Africa. Beyond malaria, our analyses can be applied as a generalizable approach for infectious disease surveillance, early-warning systems, hotspot detection, resource prioritization, and precision public health using large-scale longitudinal health data.
Cholera remains a critical public health challenge in Dhaka, Bangladesh, influenced by complex environmental and demographic drivers. This study analyzed around 20 years of 2% surveillance data (2004–2024) from icddr,b (n = 37,361) to investigate long-term trends and climatic associations using distributed lag non-linear models (DLNMs). Climate data (temperature, rainfall, humidity) were obtained from NASA’s Earth Data archives. Findings reveal a significant demographic shift, with adults (>18 years) accounting for 65% of the 6,141 confirmed cholera cases. Seasonality was pronounced; 48% of cases occurred during the pre-monsoon period. While incidence peaked between 2004 and 2008, a recent resurgence was noted from 2019 to 2024. Environmental analysis showed a positive association between temperature and cholera risk, with relative risk (RR) rising from 0.75 at 20°C to 1.10 at 40°C. Precipitation emerged as a major driver, with risk peaking at 150 mm (RR = 1.5), high humidity (90%) also correlated with increased risk (RR = 1.10). The high burden among adults necessitates age-inclusive interventions, including targeted vaccination and WASH measures. Integrating climate data into early warning systems and expanding surveillance to informal settlements are vital for future outbreak prediction and control.
M. Amin, Md. Asif Ahsan, Z. Khan et al.· PLoS Neglected Tropical Dise...· 0 citations