Current social distancing measures to impede COVID-19 (such as shelter-in-place) are economically unsustainable in the long term. Models are needed to understand the implications of possible relaxation options for these measures.We report such models, together with corresponding parameter estimation techniques and prediction outcomes, borrowing insights from another domain; namely, information cascades. Our models assume that the containment of the virus into isolated locales is no longer possible. Instead, we explore options that reduce the rate of spread. We predict COVID-19 contagion trends in different geographic regions under a "what if " scenario to understand the effects of potential policy decisions on regional trends. Our model allows experimentation with other policies that, we hope, can contribute to socially viable outcomes both in terms of health system capacity and economic impact . We apply our model to over 30 highly impacted states in the US and publish the results at URL
Recent re-opening policies in the US, following a period of social distancing measures , introduced a significant increase in daily COVID-19 infections, calling for a roll-back or substantial revisiting of these policies in many states. The situation is suggestive of difficulties modeling the impact of partial distancing/re-opening policies on future epidemic spread for purposes of choosing safe alternatives. More specifically, one needs to understand the impact of manipulating the availability of social interaction venues (e.g., schools, workplaces, and retail establishments) on virus spread. We introduce a model, inspired by social networks research, that answers the above question. Our model compartmentalizes interaction venues into categories we call mixing domains, enabling one to predict COVID-19 contagion trends in different geographic regions under different what if assumptions on partial re-opening of individual domains . We apply our model to several highly impacted states showing (i) how accurately it predicts the extent of current resurgence (from available policy descriptions), and (ii) what alternatives might be more effective at mitigating the second wave. We further compare policies that rely on partial venue closure to policies that espouse wide-spread periodic testing instead (i.e., in lieu of social distancing). Our models predict that the benefits of (mandatory) testing out-shadow the benefits of partial venue closure, suggesting that perhaps more efforts should be directed to such a mitigation strategy.
R
Current
Recent re-opening policies in the US, following a period of
R
to impede
, introduced a significant increase in daily
R
(such as shelter-in-place) are economically unsustainable in the long term. Models are needed
infections, calling for a roll-back or substantial revisiting of these policies in many states. The situation is suggestive of difficulties modeling the impact of partial distancing/re-opening policies on future epidemic spread for purposes of choosing safe alternatives. More specifically, one needs
R
implications of possible relaxation options for these measures.We report such models, together with corresponding parameter estimation techniques and prediction outcomes, borrowing insights from another domain; namely, information cascades. Our models assume that the containment of the virus into isolated locales is no longer possible. Instead, we explore options that reduce the rate of spread. We
impact of manipulating the availability of social interaction venues (e.g., schools, workplaces, and retail establishments) on virus spread. We introduce a model, inspired by social networks research, that answers the above question. Our model compartmentalizes interaction venues into categories we call mixing domains, enabling one to
R
a "what if " scenario to understand the effects of potential policy decisions on regional trends. Our model allows experimentation with other policies that, we hope, can contribute to socially viable outcomes both in terms of health system capacity and economic impact
different what if assumptions on partial re-opening of individual domains
R
over 30
several
R
in the US and publish the results at URL
showing (i) how accurately it predicts the extent of current resurgence (from available policy descriptions), and (ii) what alternatives might be more effective at mitigating the second wave. We further compare policies that rely on partial venue closure to policies that espouse wide-spread periodic testing instead (i.e., in lieu of social distancing). Our models predict that the benefits of (mandatory) testing out-shadow the benefits of partial venue closure, suggesting that perhaps more efforts should be directed to such a mitigation strategy.