Abstract
New epidemics of infectious diseases can emerge any time, as illustrated by the emergence of chikungunya virus (CHIKV) and Zika virus (ZIKV) in Latin America. During new epidemics, public health officials face difficult decisions regarding spatial targeting of interventions to optimally allocate limited resources. We used a large-scale, data-driven, agent-based simulation model (ABM) to explore CHIKV mitigation strategies, including strategies based on previous DENV outbreaks. Our model represents CHIKV transmission in a realistic population of Colombia with 45 million individuals in 10.6 million households, schools, and workplaces. Our model uses high-resolution probability maps for the occurrence of the Ae. aegypti mosquito vector to estimate mosquito density in Colombia. We found that vector control in all 521 municipalities with mosquito populations led to 402,940 fewer clinical cases of CHIKV compared to a baseline scenario without intervention. We also explored using data about previous dengue virus (DENV) epidemics to inform CHIKV mitigation strategies. Compared to the baseline scenario, 314,437 fewer cases occurred when we simulated vector control only in 301 municipalities that had previously reported DENV, illustrating the value of available data from previous outbreaks. When varying the implementation parameters for vector control, we found that faster implementation and scale-up of vector control led to the greatest proportionate reduction in cases. Using available data for epidemic simulations can strengthen decision making against new epidemic threats.
Highlights
Infectious disease epidemics remain an important global health problem
We found that census and climate data, in conjunction with information about previous dengue virus (DENV) outbreaks, can be useful in an agent-based simulation model (ABM) framework for exploring chikungunya virus (CHIKV) mitigation strategies
We used a data-driven, agent-based model of CHIKV transmission in Colombia that approximated the 2014– 2016 epidemic to explore mitigation strategies that may help prepare for future outbreaks
Summary
Infectious disease epidemics remain an important global health problem. New epidemic diseases appear constantly and existing diseases continue to spread into new areas. Health systems are often overwhelmed by newly emerging epidemics and health officials have to select a package of mitigation strategies, often under significant time pressure, with incomplete data, and with insufficient resources. Not all emerging pathogens are entirely novel and, in some cases, experience has already been gained with a similar pathogen in the past. Information from such previous experiences, when quantified and readily available, could help to improve resiliency against future, unknown threats. We used a large-scale, data-driven, agent-based simulation model (ABM) to explore CHIKV mitigation strategies, including strategies based on previous DENV outbreaks. We found that census and climate data, in conjunction with information about previous DENV outbreaks, can be useful in an ABM framework for exploring CHIKV mitigation strategies
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