Towards a quantitative model of epidemics during conflicts
- Subject Areas
- Computational Biology, Adaptive and Self-Organizing Systems, Agents and Multi-Agent Systems, Data Science, Scientific Computing and Simulation
- complex systems, epidemics, conflicts, human violence, non-linear models, public policy, humanitarian missions, peacekeeping missions, public health, empathetic artificial intelligence
- © 2019 Banerjee
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. Towards a quantitative model of epidemics during conflicts. PeerJ Preprints 7:e27651v11 https://doi.org/10.7287/peerj.preprints.27651v11
Epidemics may both contribute to and arise as a result of conflict. The effects of conflict on infectious diseases are complex and there have been confounding observations of both increase and decrease in disease outbreaks during and after conflicts. However there is no unified mathematical model that explains all these counter-intuitive observations. There is an urgent need for a quantitative framework for modelling conflicts and epidemics. We introduce a set of mathematical models to understand the role of conflicts in epidemics. Our mathematical framework has the potential to explain the counterintuitive observations and the complex role of human conflicts in epidemics. Our work suggests that aid and peacekeeping organizations should take an integrated approach that combines public health measures, socio-economic development, and peacekeeping in the conflict zone. Our approach exemplifies the role of non-linear thinking in complex systems like human societies. We view our work as a step towards a quantitative model of disease spread in conflicts.
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Code for simulations
Code for simulations