Accounting for Underreporting in Mathematical Modeling of Transmission and Control of COVID-19 in Iran

• Main Authors: Meead Saberi, Homayoun Hamedmoghadam, Kaveh Madani, Helen M. Dolk, Andrei S. Morgan, Joan K. Morris, Kaveh Khoshnood, Babak Khoshnood
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-07-01
• Series: Frontiers in Physics
• Subjects: COVID-19; Iran; mathematical modeling; underreporting; effective reproduction number
• Online Access: https://www.frontiersin.org/article/10.3389/fphy.2020.00289/full

Iran has been the country most affected by the outbreak of SARS-CoV-2 in the Middle East. With a relatively high case fatality ratio and limited testing capacity, the number of confirmed cases reported is suspected to suffer from significant underreporting. Therefore, understanding the transmission dynamics of COVID-19 and assessing the effectiveness of the interventions that have taken place in Iran while accounting for the uncertain level of underreporting is of critical importance. In this paper, we developed a compartmental transmission model to estimate the time-dependent effective reproduction number since the beginning of the outbreak in Iran. We associate the variations in the effective reproduction number with a timeline of interventions and national events. The estimation method accounts for the underreporting due to low case ascertainment. Our estimates of the effective reproduction number ranged from 0.66 to 1.73 between February and April 2020, with a median of 1.16. We estimate a reduction in the effective reproduction number during this period, from 1.73 (95% CI 1.60–1.87) on 1 March 2020 to 0.69 (95% CI 0.68–0.70) on 15 April 2020, due to various non-pharmaceutical interventions. The series of non-pharmaceutical interventions and the public compliance that took place in Iran are found to be effective in slowing down the speed of the spread of COVID-19. However, we argue that if the impact of underreporting is overlooked, the estimated transmission and control dynamics could mislead public health decisions, policy makers, and the general public.