Wednesday, September 2, 2020

Won the Challenge on Mobility Intervention for Epidemics

The challenge was designed for participants to compete each others with their own mobility intervention strategies to minimize scores defined by the organizers. Four mobility interventions are allowed as follows: 
  • To confine individuals in their neighborhood.
  • To quarantine individuals in their home.
  • To isolate individuals from others.
  • To hospitalize infected individuals.

It is important to understand that each intervention has different cost and efficiency. The score is the weighted sum of two exponential functions consisting of two dimensions: the number of infections and the number of interventions. It can be seen as an optimization problem with two opposite objectives. If the number of infections increases, the score increase exponentially. Also, if we intervene more and more, the score increases exponentially as well. That is, it is required to find a balanced strategy. The following video is my presentation introducing our solution at the workshop.

In the challenge, our solution is second ranked among all participants compliant with the challenge documents. The first two teams used the depreciated API that provides presymptomatic information and does not require contact tracing.

Due to complexity of social phenomena, it is a big challenge to predict the curves of epidemics that spread via social contacts and to control such epidemics. Misguided policies to mitigate epidemics may result in catastrophic consequences such as financial crisis, massive unemployment, and the surge of the number of critically ill patients exceeding the capacity of hospitals. In particular, under/overestimation of efficacy of interventions can mislead policymakers about perception of evolving situations. To avoid such pitfalls, we propose Expert-in-the-Loop (EITL) prescriptive analytics using mobility intervention for epidemics. Rather than employing a purely data-driven approach, the key advantage of our approach is to leverage experts' best knowledge in estimating disease spreading and the efficacy of interventions which allows us to efficiently narrow down factors and the scope of combinatorial possible worlds. We introduce our experience to develop Expert-in-the-Loop simulations during the Challenge on Mobility Intervention for Epidemics. We demonstrate that misconceptions about the causality can be corrected in the iterations of consulting with experts, developing simulations, and experimentation.

J.-S. Kim, H. Jin, and A. Z├╝fle, “Expert-in-the-Loop Prescriptive Analytics using Mobility Intervention for Epidemics,” 1st ACM SIGKDD International Workshop on Prescriptive Analytics for the Physical World (PAPW 2020), August 2020