Supporting COVID-19 policy response with large-scale mobility-based modeling

Abstract

Social distancing measures, such as restricting occupancy at venues, have been a primary intervention for controlling the spread of COVID-19. However, these mobility restrictions place a significant economic burden on individuals and businesses. To balance these competing demands, policymakers need analytical tools to assess the costs and benefits of different mobility reduction measures. In this paper, we present our work motivated by our interactions with the Virginia Department of Health on a decision-support tool that utilizes large-scale data and epidemiological modeling to quantify the impact of changes in mobility on infection rates. Our model captures the spread of COVID-19 by using a fine-grained, dynamic mobility network that encodes the hourly movements of people from neighborhoods to individual places, with over 3 billion hourly edges. By perturbing the mobility network, we can simulate a wide variety of reopening plans and forecast their impact in terms of new infections and the loss in visits per sector. To deploy this model in practice, we built a robust computational infrastructure to support running millions of model realizations, and we worked with policymakers to develop an intuitive dashboard interface that communicates our model's predictions for thousands of potential policies, tailored to their jurisdiction. The resulting decision-support environment provides policymakers with much-needed analytical machinery to assess the tradeoffs between future infections and mobility restrictions.

Accurately Estimating Total COVID-19 Infections using Information Theory

“…As future work, it may be useful to adapt MdlInfer to give a measure of the quality of the base epidemiological model. We also note that MdlInfer is built on ODE-based epidemiological models; other kinds of epidemic models, e.g., agent-based models [25,52,34,55,23,46], are more suitable in some settings. It would be interesting to extend MdlInfer to incorporate such models.…”

AI for development: implications for theory and practice

“…AI has made significant progress with respect to the first two steps, but not much with the third, especially in the development field. There are signs, however, that the pandemic has served as an accelerator, allowing AI scholars to develop easy-to-use interfaces to support computational infectious diseases epidemiology and near real-time response at the level of policy making (Chang et al, 2021). The performance value of the AI system is therefore defined by its ability to collect relevant information effectively, process it insightfully, and to feed it back into decisionmaking.…”

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios

“…The next advancement relates to the thorny problem faced by every modeling and simulation effort for complex systems: how can we effectively explore the vast parameter space of what-if analyses in which a large number of possibilities on the nearterm time horizon are explored quickly as small, incremental variations of scenarios over the current, large state of the complex system [6]. The scenarios to be explored become numerous due to the multitude of factors at play, which include locationspecific effects, behavioral effects, intervention measures, and so on [2,9,28]. On the one hand, massive simulations of microscopic models cannot typically be run in large numbers of scenarios.…”