Time Series Modeling of Epidemics: Leading Indicators, Control Groups and Policy Assessment
This article shows how new time series models can used to track the progress of an epidemic, forecast key variables and evaluate the effects of policies. The univariate framework of Harvey and Kattuman (2020) is extended to model the relationship between two or more series, and the role of common trends is discussed. Data on daily deaths from Covid-19 in Italy and the UK provides an example of leading indicators when there is balanced growth. When growth is not balanced, the model can be extended by including a nonstationary component in the leading series. The viability of this model is investigated by examining the relationship between new cases and deaths in the Florida second wave of summer 2020. The balanced growth framework is then used as the basis for policy evaluation by showing how some variables can serve as control groups for a target variable. This approach is used to investigate the consequences of Sweden’s soft lockdown coronavirus policy.