Impact of Non-pharmaceutical Interventions on the Control of COVID-۱۹ in Iran: A Mathematical Modeling Study

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نوع سند: مقاله ژورنالی
زبان: English
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JR_HPM-11-8_023

تاریخ نمایه سازی: 17 مرداد 1403

Abstract:

Background  During the first months of the coronavirus disease ۲۰۱۹ (COVID-۱۹) pandemic, Iran reported high numbers of infections and deaths. In the following months, the burden of this infection decreased significantly, possibly due to the impact of a package of interventions. We modeled the dynamics of COVID-۱۹ infection in Iran to quantify the impacts of these interventions. Methods  We used a modified susceptible–exposed–infected–recovered (SEIR) model to model the COVID-۱۹ epidemic in Iran, from January ۲۱, ۲۰۲۰ to September ۲۱, ۲۰۲۰. We estimated the ۹۵% uncertainty intervals (UIs) using Markov chain Monte Carlo simulation. Under different scenarios, we assessed the effectiveness of non-pharmaceutical interventions (NPIs) including physical distancing measures and self-isolation. We also estimated the time-varying reproduction number (Rt), using our mathematical model and epidemiologic data. Results  If no NPIs were applied, there could have been a cumulative number of ۵۱ ۸۰۰ ۰۰۰ (۹۵% UI: ۱ ۹۱۰ ۰۰۰–۷۷ ۶۰۰ ۰۰۰) COVID-۱۹ infections and ۲۶۶ ۰۰۰ (۹۵% UI: ۱۱۹ ۰۰۰–۴۷۶ ۰۰۰) deaths by September ۲۱, ۲۰۲۰. If physical distancing interventions, such as school/border closures and self-isolation interventions had been introduced a week earlier than they were actually launched, ۳۰.۸% and ۳۵.۲% reduction in the number of deaths and infections respectively could have been achieved by September ۲۱, ۲۰۲۰. The observed daily number of deaths showed that the Rt was one or more than one almost every day during the analysis period. Conclusion  Our models suggest that the NPIs implemented in Iran between January ۲۱, ۲۰۲۰ and September ۲۱, ۲۰۲۰ had significant effects on the spread of the COVID-۱۹ epidemic. Our study also showed that the timely implementation of NPIs showed a profound effect on further reductions in the numbers of infections and deaths. This highlights the importance of forecasting and early detection of future waves of infection and of the need for effective preparedness and response capabilities.

Authors

Mehran Nakhaeizadeh

Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

Sana Eybpoosh

Department of Epidemiology and Biostatistics, Research Centre for Emerging and Reemerging Infectious Diseases, Pasteur Institute of Iran, Tehran, Iran

Yunes Jahani

Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

Milad Ahmadi Gohari

Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

Ali Akbar Haghdoost

Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

Lisa White

Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK

Hamid Sharifi

HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran