A Model Predictive Control Approach for Cholera Outbreaks with Mobile Sensing

Abstract

Spatially specific intervention is proven to be more cost-effective than the “one-fit-all” strategy in controlling infectious disease outbreaks. However, it presents decision challenges due to partially observable epidemic state information and imperfectly determined model parameters. With deployment of mobile sensor, additional information such as geo-tagged bacteria concentration can be acquired, which can help to improve the understanding of the epidemic state and model parameters. Thus, a mobile sensor dispatching problem deciding further sensing spots should be studied considering the surveillance capacity. To solve this problem, we develop a metapopulation model based predictive control approach for making intelligent public health intervention decisions and sensor spatial dispatch decisions for cholera outbreak control. This approach incorporates a mobile sensor deployment scheme considering the improvement on unmeasurable parameter estimation and epidemic state prediction. Then it optimizes spatially specific intervention strengths to mitigate the harm of the infected population with progressively gained understanding of the system via mobile sensing.