Background: Estimation of the Basic Reproduction Number of an infectious disease is an important issue for controlling the infection. Here, we aimed to estimate the Basic Reproduction Number (0) of COVID-19 in Iran. Methods: To estimate 0 in Iran and Tehran, the capital, we used 3 different methods: exponential growth rate, maximum likelihood, and Bayesian time-dependent. Daily Number of confirmed cases and serial intervals with a mean of 4. 27 days and a standard deviation of 3. 44 days with gamma distribution were used. Sensitivity analysis was performed to show the importance of generation time in estimating 0. Results: The epidemic was in its exponential growth 11 days after the beginning of the epidemic (Feb 19, 2020) with doubling time of 1. 74 (CI: 1. 58-1. 93) days in Iran and 1. 83 (CI: 1. 39-2. 71) in Tehran. Nationwide, the value of 0 from February 19 to 29 using exponential growth method, maximum likelihood, and Bayesian time-dependent methods was 4. 70 (95% CI: 4. 23-5. 23), 3. 90 (95% CI: 3. 47-4. 36), and 3. 23 (95% CI: 2. 94-3. 51), respectively. In addition, in Tehran, 0 was 5. 14 (95% CI: 4. 15-6. 37), 4. 20 (95% CI: 3. 385. 14), and 3. 94 (95% CI: 3. 45-4. 40) for exponential growth, maximum likelihood, and Bayesian time-dependent methods, respectively. Bayesian time dependent methods usually provide less biased estimates. The results of sensitivity analyses demonstrated that changes in the mean generation time affect estimates of 0. Conclusion: The estimate of 0 for the COVID-19 ranged from 3. 94 to 5. 14 in Tehran and from 3. 23 to 4. 70 in nationwide using different methods, which were significantly larger than 1, indicating the potential of COVID-19 to cause an outbreak.

Background: Determining the epidemic threshold parameter helps health providers calculate the coverage while guiding them in planning the process of vaccination strategy. Since the trend and mechanism of influenza is very similar in different countries, we planned a study with the mentioned goal by using data of US from 2017 to 2018. Study design: A secondary study. Methods: R0 and corresponding vaccination coverage are estimated using the national and state-level data of the US from the 40th in 2017 to the 5th week in 2018. Four methods maximum likelihood (ML), exponential growth (EG), time-dependent Reproduction Numbers (TD), and sequential Bayesian (SB) are used to calculate minimum vaccination coverage. The gamma distribution is considered as the distribution and the generation of time. Results: The peak of epidemy in most states has occurred in the 15th week after the beginning of the epidemics. The generation time obey the Gamma distribution with mean and standard deviation of 3. 6 and 1. 6, respectively, was utilized for the generation time. The R0 (vaccination coverage) equaled 1. 94 (48. 4%), 1. 80 (44. 4%), 3. 06 (67. 3%), and 2. 11 (52. 6%) for EG, ML, SB, and TD methods at the national level, respectively. Conclusion: The R0 estimations were in the range of 1. 8-3. 06, indicating that an epidemic has occurred in the US (R0>1). Thus, it is required to vaccinate at least 44. 4% to 67. 3% to prevent the next epidemics of influenza. The findings of this study assist futures studies to apply disease control by vaccination strategies in order to prevent a national disaster.