evaluating the effect of macro-level health policies on novel coronavirus (covid-19) epidemic control in iran

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 auto-regression (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.