ABSTRACT
The sudden emergence of epidemics, such as COVID-19, entails economic and social challenges requiring immediate attention from policy makers. An essential building block in implementing mitigation policies (e.g., lockdowns, testing, and vaccination) is the identification of potential hotspots, defined as locations that contribute significantly to the spatial diffusion of infections. During the initial stages of an epidemic, information related to the pathways of spatial diffusion of infection is not fully observable, making the detection of hotspots difficult. This work proposes a data-driven framework to identify hotspots using advanced analytical methodologies, specifically, a combination of interpretable long short-term memory (LSTM) model, multi-task learning, and transfer learning. Our methodology considers mobility within- and across-locations, which is the primary driving factor for the diffusion of infection over a network of connected locations. Additionally, to augment the signals of infection diffusion and the emergence of hotspots, we use transfer learning from past influenza transmission data, which follow a similar transmission mechanism as COVID-19. To illustrate the practical importance of our framework in deciding on lockdown policies, we compare the hotspots-based policy with a pure infection load-based policy and the state-wide lockdown policy used in practice. We show that the hotspots-based lockdown policy can achieve up to 21% improvement in reducing new infections as compared to an infection-based lockdown policy. In addition, we illustrate that locking down only top few hotspot counties can achieve almost similar performance as a state-wide lockdown policy used in practice. Finally, we demonstrate that the inclusion of transfer learning improves hotspot prediction accuracy by 53.4%. We also compare our model performance with the commonly used compartmental epidemiological model and demonstrate the superior prediction performance. Our paper addresses a practical problem with hotspot identification framework, which policy makers can use to improve mitigation decisions related to the control of epidemics.