Enhancing Surveillance and Decision-Making on Dengue Using Agent-Based Modelling and Simulation

Luba Pascoe, Thomas Clemen, Devotha Nyambo, Karen Bradshaw

Proceedings of the Second IJCAI AI for Good Symposium in Africa hosted by Deep Learning Indaba

Pages 50-60.

https://doi.org/10.24963/ijcai.aai4g.2024/8

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Abstract

Dengue fever has spread globally and outbreaks are experienced. This study analyzed the roles of climatic variables in the spread of dengue fever in a Sub-Saharan Africa region using Agent-Based Modelling (ABM) and Simulation. Dengue ABM was implemented using GAMA 1.9.1 framework and simulation results were analyzed using Python 3.9. Initially, the model was simulated for 300 days with 1200 mosquito agents (approximately 0.2 infected) and 2000 human agents (approximately 0.005 infected). Weather data from the Tanzania Meteorological Agency (2012-2022) for the Dar es Salaam region was used. Simulation results showing the dynamics in mosquito agents’ infection status and human agents’ infection status are presented. The fluctuations in dengue incidences with weather parameters are also presented. Analysis of this information aids in informing policy-makers, health data stakeholders, and epidemiologists, and enhances surveillance and decision-making.

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