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Disease-Dependent Interaction Policies to Support Health and Economic Outcomes During the COVID-19 Epidemic
18 Pages Posted: 13 Oct 2020More...
Background: Lockdowns and stay-at-home orders have partially mitigated the spread of Covid-19. However, en masse mitigation — applying to all individuals irrespective of disease status — has come with substantial socioeconomic costs. In this paper we demonstrate how individualized policies based on disease status can reduce transmission risk while minimizing impacts on economic outcomes.
Methods: We introduce an optimal control approach that identifies personalized interaction rates according to an individual’s test status. However, optimal control policies can be fragile given mis-specification of parameters or mis-estimation of the current disease state. Hence, we design feedback control policies informed by optimal control solutions to modulate interaction rates of susceptible and recovered individuals based on estimates of the epidemic state.
Findings: We identify personalized interaction rates based such that recovered individuals elevate their interactions and susceptible individuals remain at home before returning to pre-lockdown levels. Critically, the timing of return-to-work policies depends strongly on isolation efficiency of infectious individuals. As we show, feedback control policies can yield mitigation policies with similar population-wide infection rates to total shutdown but with significantly lower economic costs and with greater robustness to uncertainty compared to optimal control policies. The switching policy enables susceptible individuals to return to work when recovered levels are sufficiently higher than circulating incidence.
Interpretation: Our analysis shows that test-driven improvements in isolation efficiency of infectious individuals can inform disease-dependent interaction policies that mitigate transmission while enhancing the return of individuals to pre-pandemic economic activity.
Funding: This work was supported by grants from the Army Research Office (W911NF1910384), National Institutes of Health (1R01AI46592-01), and the National Science Foundation (1806606 and 2032082).
Declaration of Interests: The authors declare no competing interests.
Keywords: Infectious Disease Modeling, Personalized Intervention Strategy, Control Theory and Optimization
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