The real‑time reproduction number, impact of interventions and prediction of the epidemic size of COVID‑19 in the center of Iran

Background: the monitoring of reproduction number over time provides feedback on the effectiveness of interventions and on the need to intensify control efforts. hence, we aimed to compute basic (r_0) and real‑time (r_t) reproduction number and predict the trend and the size of the coronavirus disease 2019 (covid‑19) outbreak in the center of iran. materials and methods: we used the 887 confirmed cases of covid‑19 from february 20, 2020, to april 17, 2020 in the center of iran. we considered three scenarios for serial intervals (sis) with gamma distribution. rt was calculated by the sequential bayesian and time‑dependent methods. based on a branching process using the poisson distributed number of new cases per day, the daily incidence and cumulative incidence for the next 30 days were predicted. the analysis was applied in r packages 3.6.3 and stata 12.0. results: the model shows that the r_t of covid‑19 has been decreasing since the onset of the epidemic. according to three scenarios based on different distributions of sis in the past 58 days from the epidemic, r_t has been 1.03 (0.94, 1.14), 1.05 (0.96, 1.15), and 1.08 (0.98, 1.18) and the cumulative incidence cases will be 360 (180, 603), 388 (238, 573), and 444 (249, 707) for the next 30 days, respectively. conclusion: based on the real‑time data extracted from the center of iran, r_t has been decreasing substantially since the beginning of the epidemic, and it is expected to remain almost constant or continue to decline slightly in the next 30 days, which is consequence of the schools and universities shutting down, reduction of working hours, mass screening, and social distancing.