Epidemiological Modelling of Malaria, Tuberculosis and Cholera from 2010 to 2023 in Nigeria
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.