COVID-19 infection control parameters in Iran: an epidemiological modeling :

Abstract

Abstract Background: We aimed to evaluate Iran's current COVID-19 infection, emphasizing the number of infection detection and the disease's reproductive number in its high peak in November and after the lockdown in December. Materials and Methods: Using the Johns Hopkins Bloomberg School of Public Health Contact Tracing Evaluation and Strategic Support Application, we used the average weekly new cases and average case mortality in November and December 2020. The average case isolation and identification time (25%, Four days) and the average case contact within the household and community were entered into the application. We examined Two modeling systems with 50% and 70% case isolation for the November period as alternative scenarios for the current infection control rate. Results: Our modeling showed only 11% and 30% of the infections were detected in November and December. The disease's reproductive number is similar to the natural reproductive number of the disease (2-3) in November. The two models used to increase the rate of case isolation to 50% and 70% did not significantly change the reproductive number. Conclusion: The priority in Iran for COVID-19 infection control should be a dramatic increase in the number of testing to achieve the correct number of case detection and fulfill the contact tracing criteria to reduce the disease spread.