Estimation of the serial interval and basic reproduction number of COVID‐19 in Qom, Iran, and three other countries: A data‐driven analysis in the early phase of the outbreak

Aghaali, Mohammad; Kolifarhood, Goodarz; Nikbakht, Roshan; Saadati, Hossein Mozafar; Nazari, Seyed Saeed Hashemi

doi:10.1111/tbed.13656

Cited by 43 publications

(34 citation statements)

References 35 publications

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“…This is consistent with recent studies conducted in Wuhan, China, as well as in the UK, that respectively estimated an 85% and 74% reduction in the average number of daily contacts under physical distancing interventions [15,16]. This is also in line with the results of Khosravi et al and Aghaali et al, who reported a gradual decrease in Rc over time in Shohroud and Qom (central Iran) [12,17]. The gradual decrease in Rc observed in our study is promising and further highlights the possible effectiveness of NPIs implemented in Iran.…”

Section: Discussionsupporting

confidence: 93%

Impact of non-pharmaceutical interventions on the control of COVID-19 in Iran: A mathematical modeling study

Nakhaeizadeh

Eybpoosh

Jahani

et al. 2020

Preprint

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Background During the first months of the COVID-19 pandemic, Iran reported high numbers of infections and deaths in the Middle East region. In the following months, the burden of this infection decreased significantly, possibly due to the impact of a package of interventions. We modeled the dynamics of COVID-19 infection in Iran to quantify the impacts of these interventions. Methods We used a modified susceptible–exposed–infected–recovered (SEIR) model to model the COVID-19 epidemic in Iran, from 21 January to 21 September 2020, using Markov chain Monte Carlo simulation to calculate 95% uncertainty intervals (UI). We used the model to assess the effectiveness of physical distancing measures and self-isolation under different scenarios. We also estimated the control reproductive number (Rc), using our mathematical model and epidemiologic data. Results If no non-pharmaceutical interventions (NPIs) were applied, there could have been a cumulative number of 51,800,000 (95% UI: 19,100,000–77,600,000) COVID-19 infections and 266,000 (95% UI: 119,000–476,000) deaths by September 21 2020. If physical distancing interventions, such as school/border closures and self-isolation interventions, had been introduced a week earlier than they were actually launched, a 30% reduction in the number of infections and deaths could have been achieved by September 21 2020. The observed daily number of deaths showed that the Rc was one or more than one almost every day during the analysis period. Conclusions Our models suggest that the NPIs implemented in Iran between 21 January and 21 September 2020 had significant effects on the spread of the COVID-19 epidemic. Therefore, we recommend that these interventions are considered when designing future control programs, while simultaneously considering innovative approaches that can minimize harmful economic impacts on the community and the state. Our study also showed that the timely implementation of NPIs showed a profound effect on further reductions in the numbers of infections and deaths. This highlights the importance of forecasting and early detection of future waves of infection and of the need for effective preparedness and response capabilities.

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Section: Discussionsupporting

confidence: 93%

Impact of non-pharmaceutical interventions on the control of COVID-19 in Iran: A mathematical modeling study

Nakhaeizadeh

Eybpoosh

Jahani

et al. 2020

Preprint

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“…The authors make the point that estimations based on household clusters may be 20% shorter than the true value of the serial interval. ► A number of authors, including Mettler et al, 40 Kwok et al 15 and Aghaali et al 20 highlighted the possibility of right truncated selection bias, that is, the possibility of infector-infectee pairs with longer serial intervals being under-represented in the sample due to short investigation period. Measures were taken in a number of studies to minimise this possibility.…”

Section: Methods Used For Estimating the Serial Interval And The Genementioning

confidence: 99%

Rapid review of available evidence on the serial interval and generation time of COVID-19

et al. 2020

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The serial interval is the time between symptom onsets in an infector–infectee pair. The generation time, also known as the generation interval, is the time between infection events in an infector–infectee pair. The serial interval and the generation time are key parameters for assessing the dynamics of a disease. A number of scientific papers reported information pertaining to the serial interval and/or generation time for COVID-19. Objective Conduct a review of available evidence to advise on appropriate parameter values for serial interval and generation time in national COVID-19 transmission models for Ireland and on methodological issues relating to those parameters. Methods We conducted a rapid review of the literature covering the period 1 January 2020 and 21 August 2020, following predefined eligibility criteria. Forty scientific papers met our inclusion criteria and were included in the review. Results The mean of the serial interval ranged from 3.03 to 7.6 days, based on 38 estimates, and the median from 1.0 to 6.0 days (based on 15 estimates). Only three estimates were provided for the mean of the generation time. These ranged from 3.95 to 5.20 days. One estimate of 5.0 days was provided for the median of the generation time. Discussion Estimates of the serial interval and the generation time are very dependent on the specific factors that apply at the time that the data are collected, including the level of social contact. Consequently, the estimates may not be entirely relevant to other environments. Therefore, local estimates should be obtained as soon as possible. Careful consideration should be given to the methodology that is used. Real-time estimations of the serial interval/generation time, allowing for variations over time, may provide more accurate estimates of reproduction numbers than using conventionally fixed serial interval/generation time distributions.

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“…District level R 0 estimates are more likely to show pronounced fluctuations than state or national estimates, as the latter are aggregated across a wide range of epidemiological settings. Because district-level R 0 estimates were not available from India, we compared our findings with those from the cities of Qom and Shahroud in Iran [ 29 , 30 ]. Similar to these cities, Jodhpur showed a trend of high values of R 0 in the first 14-30 days, with a subsequent decrease toward 1 [ 29 , 30 ].…”

Section: Discussionmentioning

confidence: 99%

“…Because district-level R 0 estimates were not available from India, we compared our findings with those from the cities of Qom and Shahroud in Iran [ 29 , 30 ]. Similar to these cities, Jodhpur showed a trend of high values of R 0 in the first 14-30 days, with a subsequent decrease toward 1 [ 29 , 30 ]. The initially high R 0 values can be attributed to the suddenness of the outbreak if the surveillance system is robust.…”

Section: Discussionmentioning

confidence: 99%

Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study

Saurabh¹,

Verma²,

Gautam³

et al. 2020

JMIR Public Health Surveill

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Background On March 9, 2020, the first COVID-19 case was reported in Jodhpur, Rajasthan, in the northwestern part of India. Understanding the epidemiology of COVID-19 at a local level is becoming increasingly important to guide measures to control the pandemic. Objective The aim of this study was to estimate the serial interval and basic reproduction number (R0) to understand the transmission dynamics of the COVID-19 outbreak at a district level. We used standard mathematical modeling approaches to assess the utility of these factors in determining the effectiveness of COVID-19 responses and projecting the size of the epidemic. Methods Contact tracing of individuals infected with SARS-CoV-2 was performed to obtain the serial intervals. The median and 95th percentile values of the SARS-CoV-2 serial interval were obtained from the best fits with the weibull, log-normal, log-logistic, gamma, and generalized gamma distributions. Aggregate and instantaneous R0 values were derived with different methods using the EarlyR and EpiEstim packages in R software. Results The median and 95th percentile values of the serial interval were 5.23 days (95% CI 4.72-5.79) and 13.20 days (95% CI 10.90-18.18), respectively. R0 during the first 30 days of the outbreak was 1.62 (95% CI 1.07-2.17), which subsequently decreased to 1.15 (95% CI 1.09-1.21). The peak instantaneous R0 values obtained using a Poisson process developed by Jombert et al were 6.53 (95% CI 2.12-13.38) and 3.43 (95% CI 1.71-5.74) for sliding time windows of 7 and 14 days, respectively. The peak R0 values obtained using the method by Wallinga and Teunis were 2.96 (95% CI 2.52-3.36) and 2.92 (95% CI 2.65-3.22) for sliding time windows of 7 and 14 days, respectively. R0 values of 1.21 (95% CI 1.09-1.34) and 1.12 (95% CI 1.03-1.21) for the 7- and 14-day sliding time windows, respectively, were obtained on July 6, 2020, using method by Jombert et al. Using the method by Wallinga and Teunis, values of 0.32 (95% CI 0.27-0.36) and 0.61 (95% CI 0.58-0.63) were obtained for the 7- and 14-day sliding time windows, respectively. The projection of cases over the next month was 2131 (95% CI 1799-2462). Reductions of transmission by 25% and 50% corresponding to reasonable and aggressive control measures could lead to 58.7% and 84.0% reductions in epidemic size, respectively. Conclusions The projected transmission reductions indicate that strengthening control measures could lead to proportionate reductions of the size of the COVID-19 epidemic. Time-dependent instantaneous R0 estimation based on the process by Jombart et al was found to be better suited for guiding COVID-19 response at the district level than overall R0 or instantaneous R0 estimation by the Wallinga and Teunis method. A data-driven approach at the local level is proposed to be useful in guiding public health strategy and surge capacity planning.

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Estimation of the serial interval and basic reproduction number of COVID‐19 in Qom, Iran, and three other countries: A data‐driven analysis in the early phase of the outbreak

Cited by 43 publications

References 35 publications

Impact of non-pharmaceutical interventions on the control of COVID-19 in Iran: A mathematical modeling study

Impact of non-pharmaceutical interventions on the control of COVID-19 in Iran: A mathematical modeling study

Rapid review of available evidence on the serial interval and generation time of COVID-19

Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study

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