Should We Mitigate or Suppress the Next Pandemic? Time-Horizons and Costs Shape Optimal Social Distancing Strategies

Abstract

The COVID-19 pandemic has called for swift action from local governments, which have instated Nonpharmaceutical Interventions (NPIs) to curb the spread of SARS-Cov-2. The quick and decisive decision to save lives through blunt instruments has raised questions about the conditions under which decision-makers should employ mitigation or suppression strategies to tackle the COVID-19 pandemic. More broadly, there are still debates over which set of strategies should be adopted to control different pandemics, and the lessons learned for SARS-Cov-2 may not apply to a new pathogen. While curbing SARS-Cov-2 required blunt instruments, it is unclear whether a less-transmissible and less-deadly emerging pathogen would justify the same response. This paper illuminates this question using a parsimonious transmission model by formulating the social distancing lives vs. livelihoods dilemma as a boundary value problem. In this setup, society balances the costs and benefits of social distancing contingent on the costs of reducing transmission relative to the burden imposed by the disease. To the best of our knowledge, our approach is distinct in the sense that strategies emerge from the problem structure rather than being imposed a priori. We find that the relative time-horizon of the pandemic (i.e., the time it takes to develop effective vaccines and treatments) and the relative cost of social distancing influence the choice of the optimal policy. Unsurprisingly, we find that the appropriate policy response depends on these two factors. We discuss the conditions under which each policy archetype (suppression vs. mitigation) appears to be the most appropriate.

Publication
Pre-print (medRxiv)
Pedro Nascimento de Lima
Pedro Nascimento de Lima

I’m a Ph.D. Candidate in Policy Analysis working at the intersection of Simulation Modeling (ABM, Systems Dynamics, Microsim), Policy Analysis and Decision Making Under Deep Uncertainty.

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