Agent-Based Modeling of Malaria Transmission

Despite global efforts to eradicate malaria, it remains a major health threat to nearly half of the world's population. Recent statistics show that globally, there are over 200 million malaria cases, and estimated deaths are close to half a million. Africa alone accounts for almost 90% of these cases. Numerous studies have been conducted to understand the transmission dynamics of malaria. Mathematical methods have been widely used to model and understand disease dynamics and outbreak patterns. Although mathematical methods have provided good results for homogeneous populations, they impose significant limitations for investigating certain heterogeneities in a population, such as contact rate, birth rate, death rate, egg deposition rate, maturation rate and length of incubation periods in a mosquito population. This paper proposes an agent-based modeling approach that permits the capture of heterogeneous mixing and agent interactions, thus enabling a better understanding of malaria dynamics and outbreak patterns. Our approach is illustrated in a case study using climate and demographic data of the cities of Tripura, Limpopo, and Benin. Our agent-based simulation was validated against the reported cases of malaria collected in these cities. Furthermore, the efficiency of the proposed model was compared with the mathematical models used as a benchmark. A statistical test confirmed that the agent-based model is robust and has the potential to accurately predict the peak seasons of malaria. These results can help healthcare providers and policymakers have intervention mechanisms in place in advance, which can potentially help reduce the malaria transmission rate.