YSPH Biostatistics Seminar: "Personalized Decision-Making for Infectious Disease Control: Causal Inference and Complex Dependence"

NOTE: BIS 526 students are required to attend in person. Others are invited to attend in person, but may also attend via Zoom.

Speaker- Ivana Malenica

Title- Personalized Decision-Making for Infectious Disease Control: Causal Inference and Complex Dependence

Abstract

Effective management of emerging and existing epidemics requires strategic decisions on where, when, and to whom interventions should be applied. However, personalized decision-making in infectious disease applications introduces new and unique statistical challenges. For instance, the individuals at risk of infection are unknown, the true outcome of interest (positive infection status) is often a latent variable, and the presence of complex dependence reduces data to a single observation. In this work, we investigate an adaptive sequential design under latent outcome structures and unspecified dependence through space and time. The statistical problem is addressed within a nonparametric model that respects the unknown dependence structure. I will begin by formalizing a treatment allocation strategy that utilizes up-to-date data to inform who is at risk of infection in real-time. The optimal allocation strategy, or optimal policy, maximizes the mean latent outcome under a resource constraint. The proposed estimator learns the optimal policy over time and exploits the double-robust structure of the efficient influence function of the target parameters of interest. In the second part of the talk, I will present the study of data-adaptive inference on the mean under the optimal policy, where the target parameter adapts over time in response to the observed data (state of the epidemic). The superior performance of the proposed framework is demonstrated through an agent-based simulation of a residential university environment during the COVID-19 pandemic.