Evaluating the Effect of Macro-Level Health Policies on Novel Coronavirus (COVID-19) Epidemic Control in Iran

Abstract

This study aimed to evaluate the effect of macro-level health policies on COVID-19 outbreak control in Iran. This was a descriptive-analytical study of the applied time series performed on April 19, 2020. The effect of four macro-health interventions, including reducing overcrowding, social distancing, limitation of high-risk economic activities, and active case detection, was examined. The Vector autoregression (VAR) was used to investigate the effect of the interventions. The augmented Dickey-Fuller test (ADF) was used to ensure the time stability of the time series and the existence of a unit-root. To analyzing data and estimation VAR models, STATA software was used. P of less than 0.1 was considered significant. The increase in the number of cases with two days’ lag had a positive and significant effect on increasing the number of new cases of the COVID-19 (C=0.176, P=0.097). Adopting an overcrowding reduction policy with both 2-day lags (c=0.095, P=0.066) and 4-day lags (c=0.314, P=0.000) had a negative and significant effect on increasing the number of new cases of the COVID-19. Our study showed that overcrowding reduction and new COVID-19 case detection could play an effective role in controlling the epidemic of COVID-19 in Iran. It seems that the best advice is to stay home and use strategies to identify more patients.