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Reproductive number of coronavirus: A systematic review and meta-analysis based on global level evidence

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  • Md Arif Billah
  • Md Mamun Miah
  • Md Nuruzzaman Khan

Abstract

Background: The coronavirus (SARS-COV-2) is now a global concern because of its higher transmission capacity and associated adverse consequences including death. The reproductive number of coronavirus provides an estimate of the possible extent of the transmission. This study aims to provide a summary reproductive number of coronavirus based on available global level evidence. Methods: A total of three databases were searched on September 15, 2020: PubMed, Web of Science, and Science Direct. The searches were conducted using a pre-specified search strategy to record studies reported the reproductive number of coronavirus from its inception in December 2019. It includes keywords of coronavirus and its reproductive number, which were combined using the Boolean operators (AND, OR). Based on the included studies, we estimated a summary reproductive number by using the meta-analysis. We used narrative synthesis to explain the results of the studies where the reproductive number was reported, however, were not possible to include in the meta-analysis because of the lack of data (mostly due to confidence interval was not reported). Results: Total of 42 studies included in this review whereas 29 of them were included in the meta-analysis. The estimated summary reproductive number was 2.87 (95% CI, 2.39–3.44). We found evidence of very high heterogeneity (99.5%) of the reproductive number reported in the included studies. Our sub-group analysis was found the significant variations of reproductive number across the country for which it was estimated, method and model that were used to estimate the reproductive number, number of case that was considered to estimate the reproductive number, and the type of reproductive number that was estimated. The highest reproductive number was reported for the Diamond Princess Cruise Ship in Japan (14.8). In the country-level, the higher reproductive number was reported for France (R, 6.32, 95% CI, 5.72–6.99) following Germany (R, 6.07, 95% CI, 5.51–6.69) and Spain (R, 3.56, 95% CI, 1.62–7.82). The higher reproductive number was reported if it was estimated by using the Markov Chain Monte Carlo method (MCMC) method and the Epidemic curve model. We also reported significant heterogeneity of the type of reproductive number- a high-value reported if it was the time-dependent reproductive number. Conclusion: The estimated summary reproductive number indicates an exponential increase of coronavirus infection in the coming days. Comprehensive policies and programs are important to reduce new infections as well as the associated adverse consequences including death.

Suggested Citation

  • Md Arif Billah & Md Mamun Miah & Md Nuruzzaman Khan, 2020. "Reproductive number of coronavirus: A systematic review and meta-analysis based on global level evidence," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-17, November.
    • Handle: RePEc:plo:pone00:0242128
    DOI: 10.1371/journal.pone.0242128
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    1. Aldila, Dipo & Khoshnaw, Sarbaz H.A. & Safitri, Egi & Anwar, Yusril Rais & Bakry, Aanisah R.Q. & Samiadji, Brenda M. & Anugerah, Demas A. & GH, M. Farhan Alfarizi & Ayulani, Indri D. & Salim, Sheryl N, 2020. "A mathematical study on the spread of COVID-19 considering social distancing and rapid assessment: The case of Jakarta, Indonesia," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Bagal, Dilip Kumar & Rath, Arati & Barua, Abhishek & Patnaik, Dulu, 2020. "Estimating the parameters of susceptible-infected-recovered model of COVID-19 cases in India during lockdown periods," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Ullah, Saif & Khan, Muhammad Altaf, 2020. "Modeling the impact of non-pharmaceutical interventions on the dynamics of novel coronavirus with optimal control analysis with a case study," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    4. Sarkar, Kankan & Khajanchi, Subhas & Nieto, Juan J., 2020. "Modeling and forecasting the COVID-19 pandemic in India," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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    3. Atsushi Miyawaki & Yusuke Tsugawa, 2022. "Health and Public Health Implications of COVID‐19 in Asian Countries," Asian Economic Policy Review, Japan Center for Economic Research, vol. 17(1), pages 18-36, January.
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    5. Facundo Piguillem & Liyan Shi, 2022. "Optimal Covid-19 Quarantine and Testing Policies," The Economic Journal, Royal Economic Society, vol. 132(647), pages 2534-2562.
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    7. Kirsten Vanderplanken & Stephan Van den Broucke & Isabelle Aujoulat & Joris Adriaan Frank van Loenhout, 2021. "The Relation between Perceived and Actual Understanding and Adherence: Results from a National Survey on COVID-19 Measures in Belgium," IJERPH, MDPI, vol. 18(19), pages 1-10, September.
    8. Isabella Locatelli & Bastien Trächsel & Valentin Rousson, 2021. "Estimating the basic reproduction number for COVID-19 in Western Europe," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-9, March.
    9. Yana Roshchina & Sergey Roshchin & Ksenia Rozhkova, 2021. "Determinants Of Covid-19 Vaccine Hesitancy And Resistance In Russia," HSE Working papers WP BRP 99/SOC/2021, National Research University Higher School of Economics.
    10. Bjarke Frost Nielsen & Kim Sneppen & Lone Simonsen & Joachim Mathiesen, 2021. "Differences in social activity increase efficiency of contact tracing," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(10), pages 1-11, October.
    11. P. Battiston & M. Menegatti, 2022. "Interaction in Prevention: A General Theory and an Application to COVID-19 Pandemic," Economics Department Working Papers 2022-EP02, Department of Economics, Parma University (Italy).

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