Agent-Based Modelling of Malaria Transmission Dynamics

TL;DR

This paper introduces an agent-based model to simulate malaria transmission in heterogeneous populations, validated with real data, and compares favorably to traditional mathematical models for predicting outbreak peaks.

Contribution

It presents a novel agent-based modeling approach that captures heterogeneity in malaria dynamics, improving prediction accuracy over existing mathematical models.

Findings

Model accurately predicts malaria peak seasons.

Agent-based approach outperforms traditional models.

Model validated with real city data.

Abstract

Recent statistics of malaria shows that over 200 million cases and estimated deaths of nearly half a million occur globally. Africa alone accounts for almost 90% of the cases. Several studies have been conducted to understand the disease transmission dynamics. In particular, mathematical methods have been frequently used to model and understand the disease dynamics and outbreak patterns. Although, mathematical methods have provided good results for homogeneous populations, these methods impose significant limitations for studying malaria dynamics in heterogeneous populations, a result of various factors, e.g. spatial and temporal fluctuations, social networks, human movements pattern etc. This paper proposes an agent-based modelling approach that permits modelling and analysing malaria dynamics for heterogenous populations. Our approach is illustrated using the climate and demographic data for the Tripura, Limpopo and Benin cities. Our agent-based simulation has been validated against the reported cases of malaria collected in the cities mentioned. Furthermore, the efficiency of the proposed model has been compared with the mathematical model used as benchmark. A statistical test confirms the proposed model is robust and has potential for predicting the peak seasons of malaria. This potentially makes our methods a useful tool as an intervention mechanism, which will have impact on hospitals, healthcare providers, health organisations.