Abstract
We combine COVID-19 case data with mobility data to estimate a modified susceptible-infected-recovered (SIR) model in the United States. In contrast to a standard SIR model, we find that the incidence of COVID-19 spread is concave in the number of contagious individuals, as would be expected if people have inter-related social networks. This concave shape has a significant impact on forecasted COVID-19 cases. In particular, our model forecasts that the number of COVID cases would only have an exponential growth for a brief period at the beginning of the contagion event or right after a reopening, but would quickly settle into a prolonged period of time with stable, slightly declining levels of disease spread. This pattern is consistent with observed levels of COVID-19 cases in the US, but inconsistent with standard SIR modeling. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19.
Highlights
We combine COVID-19 case data with mobility data to estimate a modified susceptible-infectedrecovered (SIR) model in the United States
Some of the susceptible individuals get infected in each period, where the rate of infection is a function of the number of infectious individuals as well as other factors that shift the rate of transmission
While it may seem that having more stages in the model would make the SEIR model superior to the SIR model, it has been shown that the standard SIR model does a better job at predicting the spread of COVID-19, based on data from Wuhan, China[13]
Summary
We combine COVID-19 case data with mobility data to estimate a modified susceptible-infectedrecovered (SIR) model in the United States. In contrast to a standard SIR model, we find that the incidence of COVID-19 spread is concave in the number of infectious individuals, as would be expected if people have inter-related social networks. This concave shape has a significant impact on forecasted COVID-19 cases. We find that COVID-19 spreads less than proportionately with the number of infectious individuals, a distinct difference from the assumption of standard models We demonstrate that this pattern could be explained by the interconnectedness of people’s social networks. We observe that social distancing greatly reduces the spread of COVID-19
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