This paper is a product of a working group of the Genomics and Population Health Action Collaborative, an ad hoc activity of the Roundtable on Genomics and Precision Health of the National Academies of Sciences, Engineering, and Medicine. The paper should not be viewed as a consensus statement of the Action Collaborative or Roundtable. The paper and the conclusions it draws are the individual opinions of the listed authors.

The control of the initial outbreak and spread of SARS-CoV-2/COVID-19 by the implementation of unprecedented population-wide non-pharmaceutical mitigation measures has led to remarkable success in dampening the pandemic globally. With many countries easing or beginning to lift these measures to restart activities presently, concern is growing regarding the impacts that such reopening of societies could have on the subsequent transmission of the virus. While mathematical models of COVID-19 transmission have played important roles in evaluating the general population-level impacts of these measures for curbing virus transmission, a key need is for models that are able to effectively capture the effects of the spatial and social heterogeneities that drive the epidemic dynamics observed at the local community level. Iterative near-term forecasting that uses new incoming epidemiological and social behavioural data to sequentially update locally-applicable transmission models can overcome this gap, potentially leading to better predictions and intervention actions. Here, we present the development of one such data-driven iterative modelling tool based on publically-available data and an extended SEIR model for forecasting SARS-CoV-2 at the county level in the United States, and demonstrate, using data from the state of Florida, how this tool can be used to explore the outcomes of the social measures proposed for containing the course of the pandemic as a result of easing the initially imposed lockdown in the state. We provide comprehensive results showing the use of the locally identified models for accessing the impacts and societal tradeoffs of using specific strategies involving movement restriction, social distancing and mass testing, and conclude that while it is absolutely vital to continue with these measures over the near-term and likely to the end of March 2021 in all counties for containing the ongoing pandemic before less socially-disruptive vaccination strategies come into play, it could be possible to lift the more disruptive movement restriction/social distancing measures by end of December 2020 if these are accompanied by widespread testing and contact tracing. Our findings further show that such intensified social interventions could potentially also bring about the control of the epidemic in low and some medium incidence counties first, supporting the development and deployment of a geographically-phased approach to reopening the economy of Florida. We have made our data-driven forecasting system publicly available for policymakers and health officials to use in their own locales, with the hope that a more efficient coordinated strategy for controlling SARS-CoV-2 state-wide, based on effective control of viral transmission at the county level, can be developed and successfully implemented.

The control of the initial outbreak and spread of SARS-CoV-2/COVID-19 via the application of population-wide non-pharmaceutical mitigation measures have led to remarkable successes in dampening the pandemic globally. However, with countries beginning to ease or lift these measures fully to restart activities, concern is growing regarding the impacts that such reopening of societies could have on the subsequent transmission of the virus. While mathematical models of COVID-19 transmission have played important roles in evaluating the impacts of these measures for curbing virus transmission, a key need is for models that are able to effectively capture the effects of the spatial and social heterogeneities that drive the epidemic dynamics observed at the local community level. Iterative forecasting that uses new incoming epidemiological and social behavioral data to sequentially update locally-applicable transmission models can overcome this gap, potentially resulting in better predictions and policy actions. Here, we present the development of one such data-driven iterative modelling tool based on publicly available data and an extended SEIR model for forecasting SARS-CoV-2 at the county level in the United States. Using data from the state of Florida, we demonstrate the utility of such a system for exploring the outcomes of the social measures proposed by policy makers for containing the course of the pandemic. We provide comprehensive results showing how the locally identified models could be employed for accessing the impacts and societal tradeoffs of using specific social protective strategies. We conclude that it could have been possible to lift the more disruptive social interventions related to movement restriction/social distancing measures earlier if these were accompanied by widespread testing and contact tracing. These intensified social interventions could have potentially also brought about the control of the epidemic in low- and some medium-incidence county settings first, supporting the development and deployment of a geographically-phased approach to reopening the economy of Florida. We have made our data-driven forecasting system publicly available for policymakers and health officials to use in their own locales, so that a more efficient coordinated strategy for controlling SARS-CoV-2 region-wide can be developed and successfully implemented.

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