IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0242128.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242128
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0242128&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0242128?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Facundo Piguillem & Liyan Shi, 2022. "Optimal Covid-19 Quarantine and Testing Policies," The Economic Journal, Royal Economic Society, vol. 132(647), pages 2534-2562.
    3. David Howarth, 2023. "English tort law and the pandemic: the dog that has not barked," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 48(3), pages 577-607, July.
    4. 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.
    5. Yubin Lee & Byung-Woo Kim & Shin-Woo Kim & Hyunjin Son & Boyoung Park & Heeyoung Lee & Myoungsoon You & Moran Ki, 2021. "Precautionary Behavior Practices and Psychological Characteristics of COVID-19 Patients and Quarantined Persons," IJERPH, MDPI, vol. 18(11), pages 1-15, June.
    6. 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.
    7. 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.
    8. Yun Jo & Andy Hong & Hyungun Sung, 2021. "Density or Connectivity: What Are the Main Causes of the Spatial Proliferation of COVID-19 in Korea?," IJERPH, MDPI, vol. 18(10), pages 1-16, May.
    9. Bibha Dhungel & Md. Shafiur Rahman & Md. Mahfuzur Rahman & Aliza K. C. Bhandari & Phuong Mai Le & Nushrat Alam Biva & Stuart Gilmour, 2022. "Reliability of Early Estimates of the Basic Reproduction Number of COVID-19: A Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 19(18), pages 1-14, September.
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    2. Sara K Al-Harbi & Salma M Al-Tuwairqi, 2022. "Modeling the effect of lockdown and social distancing on the spread of COVID-19 in Saudi Arabia," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-40, April.
    3. Hwang, Eunju, 2022. "Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    4. Fu, Xinjie & Wang, JinRong, 2024. "Dynamic behaviors and non-instantaneous impulsive vaccination of an SAIQR model on complex networks," Applied Mathematics and Computation, Elsevier, vol. 465(C).
    5. Mayer Alvo & Jingrui Mu, 2023. "COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    6. Sharma, Natasha & Verma, Atul Kumar & Gupta, Arvind Kumar, 2021. "Spatial network based model forecasting transmission and control of COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    7. Asamoah, Joshua Kiddy K. & Owusu, Mark A. & Jin, Zhen & Oduro, F. T. & Abidemi, Afeez & Gyasi, Esther Opoku, 2020. "Global stability and cost-effectiveness analysis of COVID-19 considering the impact of the environment: using data from Ghana," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    8. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "Dynamic tracking with model-based forecasting for the spread of the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    9. Torsten Thalheim & Tyll Krüger & Jörg Galle, 2022. "Indirect Virus Transmission via Fomites Can Counteract Lock-Down Effectiveness," IJERPH, MDPI, vol. 19(21), pages 1-14, October.
    10. Nur Hannani Bi Rahman & Shazmin Shareena A. Azis & Ibrahim Sipan, 2021. "COVID-19: Standard Operating Procedure Improvement For Green Office Building Using Indoor Environmental Quality," LARES lares-2021-4dqg, Latin American Real Estate Society (LARES).
    11. Yang, Chao & Wan, Zhiyang & Yuan, Quan & Zhou, Yang & Sun, Maopeng, 2023. "Travel before, during and after the COVID-19 pandemic: Exploring factors in essential travel using empirical data," Journal of Transport Geography, Elsevier, vol. 110(C).
    12. Li, Tingting & Guo, Youming, 2022. "Optimal control and cost-effectiveness analysis of a new COVID-19 model for Omicron strain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    13. Han, Lili & Song, Sha & Pan, Qiuhui & He, Mingfeng, 2023. "The impact of multiple population-wide testing and social distancing on the transmission of an infectious disease," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    14. Li, Tingting & Guo, Youming, 2022. "Modeling and optimal control of mutated COVID-19 (Delta strain) with imperfect vaccination," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    15. Altun, Ishak & Sahin, Hakan & Aslantas, Mustafa, 2021. "A new approach to fractals via best proximity point," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    16. Samad Noeiaghdam & Sanda Micula & Juan J. Nieto, 2021. "A Novel Technique to Control the Accuracy of a Nonlinear Fractional Order Model of COVID-19: Application of the CESTAC Method and the CADNA Library," Mathematics, MDPI, vol. 9(12), pages 1-26, June.
    17. Rabih Ghostine & Mohamad Gharamti & Sally Hassrouny & Ibrahim Hoteit, 2021. "Mathematical Modeling of Immune Responses against SARS-CoV-2 Using an Ensemble Kalman Filter," Mathematics, MDPI, vol. 9(19), pages 1-13, September.
    18. Ahumada, M. & Ledesma-Araujo, A. & Gordillo, L. & Marín, J.F., 2023. "Mutation and SARS-CoV-2 strain competition under vaccination in a modified SIR model," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    19. Bandekar, Shraddha Ramdas & Ghosh, Mini, 2022. "A co-infection model on TB - COVID-19 with optimal control and sensitivity analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 1-31.
    20. Usama H. Issa & Ashraf Balabel & Mohammed Abdelhakeem & Medhat M. A. Osman, 2021. "Developing a Risk Model for Assessment and Control of the Spread of COVID-19," Risks, MDPI, vol. 9(2), pages 1-15, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0242128. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.