Abstract
Novel coronavirus (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an epidemic declared by the World Health Organization (WHO). Till now in June 13, 2020, the total COVID-19 cases in different countries around the world are 77,56,905 with 4,28,576 deaths and 3,974,422 recovered. The virus has taken spread in India as well, whereas of June 13, 2020, 3,09,603 cases are confirmed with 8,890 deaths and 1,54,330 recovery. It this situation, it is vital to know the potential danger posed by the pandemic and the epidemic trajectory. In this paper, the basic reproduction number (R0) of COVID-19 from the early epidemic data in India is estimated. The course of the pandemic in India as well as the worst affected seven states in India, namely Maharashtra, Tamil Nadu, Delhi, Gujarat, Uttar Pradesh, Rajasthan and West Bengal is also analyzed. The early outbreak data from the Ministry of Health and Family Welfare (MoHFW), Government of India, are collected for the analysis. The two R packages ‘R0’ and ‘earlyR’ to estimate the basic reproduction number are used. An attempt is also made to forecast near-future incidence cases based on statistical methods. The results show that R0 varies from 1.53 to 3.25 accounting to different methodologies and serial intervals adopted, whereas WHO estimations are from 2 to 2.5. Due to effect of lockdown, the time-dependent reproduction number has reduced to near about 1.22. It is predicted that by July 15, cumulative number of COVID-19 cases may reach around 1.2 million if the current effective reproduction number remains same over the next one month. Finally, it can be concluded that in the coming months, the novel coronavirus will pose a severe challenge to the Indian healthcare system. Thus, it is necessary to predict how the virus may spread so that the healthcare system may be prepared in advance. The time-dependent reproduction number shows the positive effect of lockdown, as this number has gone down.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, Q. et al.: Early transmission dynamics in Wuhan, China, of novel coronavirus‐infected pneumonia. N. Engl. J. Med., (2020)
WHO: Pneumonia of unknown cause—China (https://www.who.int/csr/don/05‐January‐ 2020‐pneumonia‐of‐unkown‐cause‐china/en/; accessed January 30, 2020), (2020)
Chan, J.F.et al.: A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person‐to‐person transmission: a study of a family cluster. Lancet, (2020)
Website:www.Worldometers/ info/coronavirus
Data from Ministry of Health and Family Welfare (MoHFW) website(www.mohfw.gov.in) the Government of India and Indian COVID 19 tracker (www. covid19india.org)
Ma, Y., Zhao, Y., Liu, J., He, X., Wang, B., Fu, S., Yan, J., Niu, J., Luo, B.: Effects of temperature variation and humidity on the mortality of covid-19 in Wuhan. medRxiv, 2020
Araujo, M.B., Naimi, B.: Spread of SARS-CoV-2 Coronavirus likely to be constrained by climate. medRxiv, 2020
Salman, S., Salem, M.L.: The mystery behind childhood sparing by COVID-19. Int. J. Cancer Biomed. Res. 10 (3 Apr 2020)
Luo, W., Majumder, M., Liu, D., Poirier, C., Mandl, K., Lipsitch, M, Santillana, M.: The role of absolute humidity on transmission rates of the covid-19 outbreak. (2020)
Nsoesie, E.O., Brownstein, J.S., Ramakrishnan, N., Marathe, M.V.: A systematicreview of studies on forecasting the dynamics of influenza outbreaks. Influenza. Other. Respir. Viruses. 8(3), 309–316 (2014)
Chretien, J.-P., Riley, S., George, D.B.: Mathematical modeling of the West Africa Ebola epidemic. eLife. 4, e09186 (2015)
WHO Ebola Response Team: Ebola virus disease in West Africa—the first 9 months of the epidemic and forward projections. N. Engl. J. Med. 371(16), 1481–1495 (2014)
WHO Ebola Response Team: West African Ebola epidemic after one year-slowing but not yet under control. N. Engl. J. Med. 372(6), 584–587 (2015)
WHO Ebola Response Team: Ebola virus disease among children in West Africa. New Engl. J. Med. 372(13), 1274–1277 (2015)
Goldstein, E., Cobey, S., Takahashi, S., Miller, J.C., Lipsitch, M.: Predicting the epidemic sizes of influenza A/H1N1, A/H3N2, and B: a statistical method. PLoSMed. 8(7), e1001051 (2011)
Meltzer, M.I., Atkins, C.Y., Santibanez, S., Estimating the future number of cases in the Ebola epidemic—liberia and sierra leone. MMWR Suppl. 63(3), 1–14 (2014–2015)
Influenza Forecasting. http://predict.phiresearchlab.org/flu/index.html (2017)
Dengue Forecasting. http://dengueforecasting.noaa.gov/. (2017)
Chikungunya Forecasting. http://www.darpa.mil/news-events/ (2017)
Liu, Y. et al.: The reproductive number of COVID-19 is higher compared to SARS coronavirus. J. Travel. Med. (2020)
Zhang, S. et al.: Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the Diamond Princess cruise ship: a data-driven analysis. Int. J. Infect. Dis. 93, 201–204 (2020)
Li, Q. et al.: Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. New England J. Med. (2020)
Nishiura, H., Linton, N.M., Akhmetzhanov, A.R.: Serial interval of novel coronavirus (COVID-19) infections. Int. J. Infect. Dis. (2020)
Du, Z. et al.: The serial interval of COVID-19 from publicly reported confirmed cases. medRxiv (2020)
Jombart, T. et al.: earlyr: Estimation of transmissibility in the early stages of a disease outbreak. Available from: https://cran.r-project.org/package=earlyR
Boelle, P.-Y., Obadia, T.: R0: Estimation of R0 and Real-Time Reproduction Number from Epidemics. Available from: https://cran.r-project.org/package=R0
Obadia, T., Haneef, R., Boëlle, P.Y.: The R0 package: a toolbox to estimate reproduction numbers for epidemic outbreaks. BMC Med. Inf. Decis. Making. 12(1), 147 (2012)
Cori, et al.: A new framework and software to estimate time-varying reproduction numbers during epidemics. Am. J. Epidemiol. 178(9), 1505–1512 (2013)
Forsberg White, L., Pagano, M.: A likelihood-based method for real-time estimation of the serial interval and reproductive number of an epidemic. Stat. Med. 27(16), 2999–3016 (2008)
Wallinga, J., Lipsitch, M.: How generation intervals shape the relationship between growth rates and reproductive numbers. Proc. Roy. Soc. B: Biol. Sci. 274(1609), 599 (2007)
Wallinga, J., Teunis, P.: Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am. J. Epidemiol. 160(6), 509–516 (2004)
Nouvellet, et al.: A simple approach to measure transmissibility and forecast incidence. Epidemics 22, 29–35 (2018)
Thibaut, et al.: Projections: project future case incidence. Available from: https://cran.r-project.org/package=projections
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Laha, S.K., Ghosh, D., Ghosh, D., Swarnakar, B. (2020). Transmission Dynamics and Estimation of Basic Reproduction Number (R0) from Early Outbreak of Novel Coronavirus (COVID-19) in India. In: Chakraborty, C., Banerjee, A., Garg, L., Rodrigues, J.J.P.C. (eds) Internet of Medical Things for Smart Healthcare. Studies in Big Data, vol 80. Springer, Singapore. https://doi.org/10.1007/978-981-15-8097-0_1
Download citation
DOI: https://doi.org/10.1007/978-981-15-8097-0_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8096-3
Online ISBN: 978-981-15-8097-0
eBook Packages: Computer ScienceComputer Science (R0)