Comparison of a Multiplex Real-Time PCR technique with Oxford Nanopore Technologies Next-Generation-Sequencing for identification of SARS-CoV-2 variants of concern (preprint)

Background; The rapid emergence of SARS-CoV-2 variants and their potential to endangering the global health, has increased the demand for a fast-tracking method in comparison to the Next-Generation-Sequencing (NGS) as a gold standard assay, particularly in developing countries. This study was designed to evaluate the performance of a commercial multiplex Real-Time PCR technique (GA SARS-CoV-2 OneStep RT-PCR Kit) for identification of SARS-CoV-2 variants of concern compared to Oxford Nanopore Next-Generation-Sequencing assay. Materials and Methods; A total of 238 SARS-CoV-2 positive respiratory samples from different waves of COVID-19 in Iran were randomly included in this study. To determine the SARS-CoV-2 VOC, the samples were analyzed via the commercial triple target assay, GA SARS-CoV-2 OneStep RT-PCR Kit, and Next-Generation-Sequencing (NGS) as well. Results; The results revealed good concordance between GA SARS-CoV-2 OneStep RT-PCR Kit and NGS for identification of SARS-CoV-2 VOCs. GA SARS-CoV-2 OneStep RT-PCR Kit identified Wuhan, Alpha and Delta variants with 100% relative sensitivity and specificity. Regarding Omicron subvariants of BA.1, BA.2 and BA.4/5 the relative sensitivity of 100%, 100% and 81.5% and the relative specificity of 95.3%, 93.5%, and 100% were observed. Conclusion; Overall, GA SARS-CoV-2 OneStep RT-PCR Kit can be used as a rapid and cost-effective alternative to NGS for identification of SARS-CoV-2 VOCs.

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

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.

Estimating the number of COVID-19-related infections, deaths and hospitalizations in Iran under different physical distancing and isolation scenarios: A compartmental mathematical modeling (preprint)

Background: Iran is one of the countries that has been overwhelmed with COVID-19. We aimed to estimate the total number of COVID-19 related infections, deaths, and hospitalizations in Iran under different physical distancing and isolation scenarios. Methods: We developed a Susceptible-Exposed-Infected-Removed (SEIR) model, parameterized to the COVID-19 pandemic in Iran. We used the model to quantify the magnitude of the outbreak in Iran and assess the effectiveness of isolation and physical distancing under five different scenarios (A: 0% isolation, through E: 40% isolation of all infected cases). We used Monte-Carlo simulation to calculate the 95% uncertainty intervals (UI). Findings: Under scenario A, we estimated 5,196,000 (UI 1,753,000 - 10,220,000) infections to happen till mid-June with 966,000 (UI 467,800 - 1,702,000) hospitalizations and 111,000 (UI 53,400 - 200,000) deaths. Successful implantation of scenario E would reduce the number of infections by 90% (i.e. 550,000) and change the epidemic peak from 66,000 on June 9th to 9,400 on March 1st. Scenario E also reduces the hospitalizations by 92% (i.e. 74,500), and deaths by 93% (i.e. 7,800). Interpretation: With no approved vaccination or therapy, we found physical distancing and isolation that includes public awareness and case-finding/isolation of 40% of infected people can reduce the burden of COVID-19 in Iran by 90% by mid-June.