Volume 26, Number 8—August 2020
Severe Acute Respiratory Syndrome Coronavirus 2 Transmission Potential, Iran, 2020
To determine the transmission potential of severe acute respiratory syndrome coronavirus 2 in Iran in 2020, we estimated the reproduction number as 4.4 (95% CI 3.9–4.9) by using a generalized growth model and 3.5 (95% CI 1.3–8.1) by using epidemic doubling time. The reproduction number decreased to 1.55 after social distancing interventions were implemented.
Since early 2020, Iran has been experiencing a devastating epidemic of coronavirus disease (COVID-19) (1). To determine the transmission potential of severe acute respiratory syndrome coronavirus 2 and thereby guide outbreak response efforts, we calculated basic reproduction numbers (R0). During the early transmission phase, R0 quantifies the average number of secondary cases generated by a primary case in a completely susceptible population, absent interventions or behavioral changes. R0>1 indicates the possibility of sustained transmission; R0<1 implies that transmission chains cannot sustain epidemic growth. As the epidemic continues, the effective reproduction number (Re) offers a time-dependent record of the average number of secondary cases per case as the number of susceptible persons becomes depleted and control interventions take effect. We used 2 methods to quantify the reproduction number by using the curve of reported COVID-19 cases in Iran and its 5 regions (Appendix Table 1). The Georgia Southern University Institutional Review Board made a non–human subjects determination for this project (H20364), under the G8 exemption category.
For method 1, we used a generalized growth model (2) with the growth rate and its scaling factor to characterize the daily reported incidence. Next, we simulated the calibrated generalized growth model by using a discretized probability distribution of the serial interval and assuming a Poisson error structure (Appendix).
We based method 2 on calculation of the epidemic’s doubling times, which correspond to the times when the cumulative incidence doubles and are estimated by using the curve of cumulative daily reported cases. To quantify parameter uncertainty, we used parametric bootstrapping with a Poisson error structure around the number of new reported cases to derive 95% CIs (3–5). Assuming exponential growth, the epidemic growth rate is equal to ln(2)/doubling time. Assuming that the preinfectious and infectious periods follow an exponential distribution, R0 ≈ (1 + growth rate × serial interval) (Appendix) (6).
For both methods, the serial interval was assumed to follow a gamma distribution; mean (± SD) = 4.41 (± 3.17) days (7; C. You et al., unpub. data, https://www.medrxiv.org/content/10.1101/2020.02.08.20021253v2). We used MATLAB version R2019b (https://www.mathworks.com) and R version 3.6.2 (https://www.r-project.org) for data analyses and creating figures. We determined a priori that α = 0.05.
Using Wikipedia as a starting point, we double-checked the daily reported new cases during February 19–March 19, 2020 (the day before the Iranian New Year) against official Iran press releases and other credible news sources and corrected the data according to official data (Appendix Tables 2, 3, Figure 1). Incident cases for the 5 regions were missing for 2 days (March 2–3), which we excluded from our analysis. Because the reported national number of new cases did not match the sum of new cases reported in Iran’s 5 regions on March 5, we treated each time series as independent and used the data as reported. Using method 1, we estimated R0 data for February 19–March 1, 2020. Using method 2, we estimated R0 from the early transmission phase (February 19–March 1, 2020) and Re based on the growth rate estimated during March 6–19, 2020, when the epidemic slowed, probably reflecting the effect of social distancing.
Using method 1, we estimated an R0 of 4.4 (95% CI 3.9–4.9) for COVID-19 in Iran. We estimated a growth rate of 0.65 (95% CI 0.56–0.75) and a scaling parameter of 0.96 (95% CI 0.93–1.00) (Appendix Table 4). The scaling parameter indicated near-exponential epidemic growth (Figure). Using method 2, we found that during February 19–March 1, the cumulative incidence of confirmed cases in Iran had doubled 8 times. The estimated epidemic doubling time was 1.20 (95% CI 1.05–1.45) days, and the corresponding R0 estimate was 3.50 (95% CI 1.28–8.14). During March 6–19, the cumulative incidence of confirmed cases doubled 1 time; doubling time was 5.46 (95% CI 5.29–5.65) days. The corresponding Re estimate was 1.55 (95% CI 1.06–2.57) (Appendix Table 5, Figures 7, 8). Our results are robust and consistent with Iran’s COVID-19 R0 estimates of 4.7 (A. Ahmadi et al., unpub. data, https://www.medrxiv.org/content/10.1101/2020.03.17.20037671v3) and 4.86 (E. Sahafizadeh, unpub. data, https://www.medrxiv.org/content/10.1101/2020.03.20.20038422v2) but higher than the R0 of 2.72 estimated by N. Ghaffarzadegan and H. Rahmandad (unpub. data, https://www.medrxiv.org/content/10.1101/2020.03.22.20040956v1).
Our study has limitations. Our analysis is based on the number of daily reported cases, whereas it would be ideal to analyze case counts by dates of symptoms onset, which were not available. Case counts could be underreported because of underdiagnosis, given subclinical or asymptomatic cases or limited testing capacity to test persons with mild illness. The rapid increase in case counts might represent a belated realization of epidemic severity and rapid catching up and testing many persons with suspected cases. If the reporting ratio remains constant over the study period, and given the near-exponential growth of the epidemic’s trajectory, our estimates would remain reliable. Although data are not stratified according to imported and local cases, we assumed that persons were infected locally because transmission has probably been ongoing in Iran for some time (8).
Although the COVID-19 epidemic in Iran has slowed substantially, the situation remains dire. Tighter social distancing interventions are needed to bring this epidemic under control.
Ms. Muniz-Rodriguez is a doctoral student in epidemiology and Dr. Fung is an associate professor of epidemiology at Jiann-Ping Hsu College of Public Health, Georgia Southern University. Their research interests include infectious disease epidemiology, digital health, and disaster emergency responses.
G.C. received support from NSF grant 1414374 as part of the joint NSF-NIH-USDA Ecology and Evolution of Infectious Diseases program. I.C.-H.F. received salary support from the Centers for Disease Control and Prevention (CDC) (19IPA1908208). This article is not part of I.C.-H.F’s CDC-sponsored projects.
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Original Publication Date: April 22, 2020
1These first authors contributed equally to this article.
Table of Contents – Volume 26, Number 8—August 2020
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Please use the form below to submit correspondence to the authors or contact them at the following address:
Isaac Chun-Hai Fung, Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, PO Box 7989, Statesboro, GA 30460-7989 USA; ; or Gerardo Chowell, Department of Population Health Sciences, School of Public Health, Georgia State University, Suite 662, Office 640B, Atlanta, GA 30303, USA