COVID-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications

Rowland, Michael A.; Swannack, Todd M.; Mayo, Michael L.; Parno, Matthew; Farthing, Matthew; Dettwiller, Ian; George, Glover; England, William; Reif, Molly; Cegan, Jeffrey; Trump, Benjamin; Linkov, Igor; Lafferty, Brandon; Bridges, Todd

The SARS-CoV-2 virus is responsible for the novel coronavirus disease 2019 (COVID-19), which has spread to populations throughout the continental United States. Most state and local governments have adopted some level of “social distancing” policy, but infections have continued to spread despite these efforts. Absent a vaccine, authorities have few other tools by which to mitigate further spread of the virus. This begs the question of how effective social policy really is at reducing new infections that, left alone, could potentially overwhelm the existing hospitalization capacity of many states. We developed a mathematical model that captures correlations between some state-level “social distancing” policies and infection kinetics for all U.S. states, and use it to illustrate the link between social policy decisions, disease dynamics, and an effective reproduction number that changes over time, for case studies of Massachusetts, New Jersey, and Washington states. In general, our findings indicate that the potential for second waves of infection, which result after reopening states without an increase to immunity, can be mitigated by a return of social distancing policies as soon as possible after the waves are detected.