- An essential building block in implementing mitigation policies (e.g., lockdown, testing, and vaccination) is the prediction of potential hotspots, defined as the 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. Currently, policymakers rely primarily on the observed new infections for identifying hotspots and deciding on mitigation actions, which are reactive rather than proactive. This work proposes a new data-driven framework to identify hotspots through advanced analytical methodologies, e.g., a combination of the Long Short-Term Memory (LSTM) model, multi-task learning, and transfer learning. Our methodology considers mobility within and across locations, the primary driving factor for the diffusion of infection over a network of connected locations.
- Shuai Hao, Yuqian Xu, Ujjal Kumar Mukherjee, Sridhar Seshadri, Sebastian Souyris, Anton Ivanov, Eren Ahsen, Padmavati Sridhar
- Many educational institutions have partially or fully closed all operations to cope with the challenges of the ongoing COVID-19 pandemic. In this paper, we explore strategies that such institutions can adopt to conduct safe reopening and resume operations during the pandemic. The research is motivated by the University of Illinois at Urbana-Champaign’s (UIUC’s) SHIELD program, (https://www.uillinois.edu/shield), which is a set of policies and strategies, including rapid saliva-based COVID-19 screening, for ensuring safety of students, faculty and staff to conduct in-person operations, at least partially. Specifically, we study how rapid bulk testing, contact tracing and preventative measures such as mask wearing, sanitization, and enforcement of social distancing can allow institutions to manage the epidemic spread.
- Ujjal Kumar Mukherjee, Subhonmesh Bose, Anton Ivanov, Sebastian Souyris, Sridhar Seshadri, Padmavati Sridhar, Ron Watkins, & Yuqian Xu.
- We present a compartmental epidemic model that accounts for the impact of lock-down and different kinds of testing, motivated by the nature of the ongoing COVID-19 outbreak. We consider the testing of symptomatic, contact traced, and randomly chosen asymptomatic populations. Using the model, we first characterize static mobility levels and testing requirements that can dampen the spread asymptotically. We then characterize a threshold-type optimal lock-down policy that minimizes the social impact of an epidemic, modeled via a sum of infection and lockdown costs. Our results are contextualized with realistic parameter values for COVID-19.
- Subhonmesh Bose, Sebastian Souyris, Ujjal Kumar Mukherjee, Anton Ivanov, Sridhar Seshadri, Albert C. England III, Yuqian Xu