Identifying and predicting climate change impact on vector-borne disease using machine learning - Case study of Plasmodium falciparum from Africa

Aug 5, 2026

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Vector-borne diseases threaten public health in climate-vulnerable regions, with malaria—caused by Plasmodium falciparum and transmitted by Anopheles mosquitoes—remaining a leading burden in sub-Saharan Africa. This study uses machine learning to identify and predict how climate change may alter malaria transmission dynamics across Africa. Climate variables including temperature, precipitation, humidity, and vegetation cover are combined with epidemiological records, and models such as Maximum Entropy are trained on historical data to project future scenarios. Results support spatial and temporal risk understanding and help inform targeted interventions and adaptation strategies, while providing a framework for other vector-borne diseases.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages5

Reading Time13 min

AccessFree to download

Introduction

Climate change and vector-borne disease transmission

Malaria and Plasmodium falciparum in Africa

From statistical modeling to machine learning

Identifying and predicting climate change impact on vector-borne disease using machine learning: Case study of Plasmodium falciparum from Africa Priyanka Singh 1,*, Sameer Saran2 1 UPES, School of Computer Science, Dehradun-248007, Uttarakhand, India – [priyanka.iirs@gmail.com](priyanka.iirs@gmail.com) 2 Regional Remote Sensing Center-North, ISRO, Dept. of Space, New Delhi-110049, India – [saran.iirs@gmail.com](saran.iirs@gmail.com) Abstract Vector-borne diseases pose a significant threat to human health, particularly in regions vulnerable to climate change. Among these diseases, malaria, caused by the parasite Plasmodium falciparum and transmitted through the Anopheles mosquito, remains a major global health concern, particularly in sub-Saharan Africa. This study explores the use of machine learning techniques to identify and predict the impact of climate change on the transmission dynamics of P. falciparum malaria in Africa. The research utilizes a combination of climate data, epidemiological records, and machine learning algorithms to analyze historical patterns and project future trends in malaria transmission. Key climate variables such as temperature, precipitation, humidity, and vegetation cover are integrated into predictive models to assess their influence on the abundance and distribution of mosquito vectorsand the parasite's lifecycle. Through the application of machine learning models such as Maximum Entropy, this study aims to uncover complex relationships between climatic factors and malaria transmission dynamics. By training these models on historical data, they can accurately predict future scenarios under various climate change scenarios. The findings of this research will provide valuable insights into the potential impact of climate change on the spatial and temporal distribution of P. falciparum malaria in Africa. Such insights are crucial for designing targeted interventions and adaptation strategies to mitigate the anticipated rise in malaria cases and associated morbidity and mortality in the region. Moreover, the methodology developed in this study can serve asa framework for assessing and addressing the impact of climate change on other vector-borne diseases globally. Keywords: Maximum Entropy, Malaria, Machine Learning, Geospatial, Climate Change, Healthcare 1. Introduction Vector-borne diseases, such as malaria, dengue fever, Zika virus, and Lyme disease, pose significant public health challenges worldwide, particularly in regions where environmental conditions are favorable for the proliferation of disease vectors. Among these diseases, malaria remains a major global health concern, with the majority of cases occurring in sub-Saharan Africa. Climate change is increasingly recognized as a key driver influencing the distribution, abundance, and transmission dynamics of vector-borne diseases. In recent years, there has been growing interest in utilizing machine learning techniques to better understand and predict the impact of climate change on these diseases, particularly focusing on malaria caused by the parasite Plasmodium falciparum. Studies examining the relationship between climate change and vectorborne diseases have a rich history dating back several decades. Early research primarily focused on statistical modeling approaches to assess the association between climatic variablesand disease incidence. These studies laid the groundwork for understanding the complex interactions between climate, vectors, hosts, and pathogens in disease transmission cycles. However, traditional statistical methods often have limitations in capturing nonlinear relationships and complex interactions within large and heterogeneous datasets. Seasonal variations in vector abundance have a significant impact on the seasonal dynamics and geographic distributions of vector borne parasites abundance (Chavasse et al., 1999; Emerson, Bailey, Mahdi, Walraven, & Lindsay, 2000; Mabaso, Craig, Vounatsou, & Sm

FAQs

Why focus on Plasmodium falciparum malaria in Africa?

Malaria remains a major global health concern, with most cases concentrated in sub-Saharan Africa. The document highlights P. falciparum as responsible for the majority of malaria-related deaths worldwide.

What data and climate variables are used for the predictive models?

The study integrates climate data and epidemiological records, using variables such as temperature, precipitation, humidity, and vegetation cover to model their effects on mosquito vectors and the parasite lifecycle.

Which machine learning method is emphasized in the study?

Maximum Entropy is highlighted as a key model for uncovering relationships between climatic factors and malaria transmission dynamics, trained on historical data to forecast future scenarios.

Aug 5, 2026

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