Agent-Based Modelling of Malaria Transmission Dynamics
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ID: 283604
2020
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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.
| Reference Key |
konur2020agentbased
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|---|---|
| Authors | Babagana Modu; Nereida Polovina; Savas Konur |
| Journal | arXiv |
| Year | 2020 |
| DOI |
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