Lessons for preparedness and reasons for concern from the early COVID-19 epidemic in Iran

Abstract

Introduction Many countries with an early outbreak of SARS-CoV-2 struggled to gauge the size and start date of the epidemic mainly due to limited testing capacities and a large proportion of undetected asymptomatic and mild infections. Iran was among the first countries with a major outbreak outside China. Methods We constructed a globally representative sample of 802 genomes, including 46 samples from patients inside or with a travel history to Iran. We then performed a phylogenetic analysis to identify clades related to samples from Iran and estimated the start of the epidemic and early doubling times in cases. We leveraged air travel data from 36 exported cases of COVID-19 to estimate the point-prevalence and the basic reproductive number across the country. We also analysed the province-level all-cause mortality data during winter and spring 2020 to estimate under-reporting of COVID-19-related deaths. Finally, we use this information in an SEIR model to reconstruct the early outbreak dynamics and assess the effectiveness of intervention measures in Iran. Results By identifying the most basal clade that contained genomes from Iran, our phylogenetic analysis showed that the age of the root is placed on 2019-12-21 (95 % HPD: 2019-09-07 – 2020-02-14). This date coincides with our estimated epidemic start date on 2019-12-25 (95 %CI: 2019-12-11 – 2020-02-24) based air travel data from exported cases with an early doubling time of 4.0 (95 %CI: 1.4–6.7) days in cases. Our analysis of all-cause mortality showed 21.9 (95 % CI: 16.7–27.2) thousand excess deaths by the end of summer. Our model forecasted the second epidemic peak and suggested that by 2020-08-31 a total of 15.0 (95 %CI: 4.9–25.0) million individuals recovered from the disease across the country. Conclusion These findings have profound implications for assessing the stage of the epidemic in Iran despite significant levels of under-reporting. Moreover, the results shed light on the dynamics of SARS-CoV-2 transmissions in Iran and central Asia. They also suggest that in the absence of border screening, there is a high risk of introduction from travellers from areas with active outbreaks. Finally, they show both that well-informed epidemic models are able to forecast episodes of resurgence following a relaxation of interventions, and that NPIs are key to controlling ongoing epidemics.

A framework for reconstructing SARS-CoV-2 transmission dynamics using excess mortality data

“…1b ). This is in agreement with previous phylogenetic and epidemiological studies suggesting the epidemic started in late December 2019 to early January 2020 14 , 23 . Furthermore, during the first two waves of the Iranian epidemic, the excess mortality was roughly 2.5 times higher than reported COVID-19 fatalities.…”

“…Iran was among the first countries outside mainland China to report a large outbreak of SARS-CoV-2 in February and March 2020 and also acted as a major source for its spread in several countries in the Middle East and elsewhere 11 , 14 – 18 . It was also one of the first countries to experience a second wave of infection after the relaxation of non-pharmaceutical interventions (NPIs) in July 2020 14 , 19 . By the end of year 2021, the country experienced three additional waves, two of which were driven by the Alpha and Delta variants of SARS-CoV-2, in May and September 2021.…”

Investigating the evolutionary origins of the first three SARS-CoV-2 variants of concern

“…We approximate the between-host evolution of SARS-CoV-2 as a haploid population of size N ( t ) which is equal to the number of daily infectious individuals with SARS-CoV-2 worldwide. Since the number of confirmed cases is often a significant underestimation of the true number of infections (e.g., see: [44–45]), we use the number of daily confirmed deaths [46] to back-calculate the number of infectious individuals, N ( t ), from the global median infection fatality rate (IFR) of COVID-19 [47]. We note this approach is still subject to several potential sources of bias including variation in IFR over time (e.g.…”

International Travel-Related Control Measures to contain The Covid-19 Pandemic: An update to a Cochrane Rapid Review

“…Reasons for exclusion Arino 2021 (24) The study does not provide a specific effect measure for the air travel-related control measure Bays 2021 (26) The study does not measure the specified interventions Chang 2020 (27) The study does not assess the impact of an air travel-related control measure Chen 2020 (28) The study does not measure the specified interventions Chen 2021 (29) The study does not provide a specific effect measure for an air travel-related control measure Cheng 2021 (30) The study does not assess the impact of an air travel-related control measure Chevaliera 2021 (31) The study does not provide a specific effect measure for the air travel-related control measure Chinyoka 2021 (32) The study does not measure the specified interventions Colavita 2021 (33) The study does not assess the impact of an air travel-related control measure Cowling 2020 (34) The study does not provide a specific effect measure for the air travel-related control measure Cruz-Pacheco 2020 (35) The study does not provide a specific effect measure for an air travel-related control measure Ghafari 2021 (36) The study does not measure the specified outcomes Goel 2021 (37) The study does not measure the specified outcomes Grout 2021 (38) The study does not include air travellers Haddaway 2021…”