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This project forked from akshayjin/chikungunya_simulation

Mosquito-borne diseases, such as chikungunya, dengue and malaria are reemerging and expanding to new and formerly unaffected places leading to a need for models which can track their evolution. This evolution is driven by the interactions between hosts and vectors and thus, is heavily dependent on factors like population distribution, human mobility and weather conditions. Capturing these interactions is very complex because of their chaotic nature. We propose a generalized ABM which captures these interactions at a micro-scale by explicitly modeling each human and mosquito to predict the complex trajectory of the infection. The model has been integrated with GIS, census and climate data to effectively model the host and agent behavior and as a proof of concept, is also trained and validated using 2013-14 Caribbean Chikungunya epidemic data. Two popular infection intervention strategies: LSM (Larval Source Management) and ITN + IRS (Insecticide-Treated Nets + Indoor Residual Spraying), were also implemented and analyzed in terms of cost-effectiveness and mosquito control

License: GNU General Public License v3.0