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Estimation of Reproduction Number of COVID-19 for the Northeastern States of India Using SIR Model

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Mathematical Modeling, Computational Intelligence Techniques and Renewable Energy

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1405))

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Abstract

Coronavirus disease (COVID-19) has been the cause of over a million deaths across the globe. The pandemic has affected the social, economic, and psychological facets of human life. India currently ranks second in the total number of cases in the world. There is an emergent need to understand the severity of the prevalence of the disease in India. In the present work, an SIR model in conjunction with daily case count has been implemented to analyze the transmission dynamics of COVID-19 across the eight northeastern states of India. The parameters associated with this model, namely the infection, recovery, and death rates, have been estimated for the northeastern region of India. The infection rate is found to be in the range from 0.18 to 0.49, which is observed to be the least in Sikkim and the highest in Mizoram. The basic reproduction number for COVID-19 is found to vary between 1.1 and 1.3.

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References

  1. World Health Organisation COVID-19 Q&A. https://www.who.int/news-room/q-a-detail/coronavirus-disease-covid-19-how-is-it-transmitted. Accessed 28 Nov 2020

  2. Chan, J.F.-W., 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 395(10223), 14–23 (2020). https://doi.org/10.1016/S0140-6736(20)30154-9

  3. Ghinai, I., McPherson, T.D., Hunter, J.C., Kirking, H.L., Christiansen, D., Joshi, K., et al.: First known person-to-person transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the USA. Lancet 395(10230), 1137–1144 (2020). https://doi.org/10.1016/S0140-6736(20)30607-3

    Article  Google Scholar 

  4. Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan China. Lancet 395(10223), 497–506 (2020). https://doi.org/10.1016/S0140-6736(20)30183-5

    Article  Google Scholar 

  5. Stadnytskyi, V., Bax, C.E., Bax, A., Anfinrud, P.: The airborne lifetime of small speech droplets and their potential importance in SARS-CoV-2 transmission. Proc. Natl. Acad. Sci. USA 117(22), 11875–11877 (2020). https://doi.org/10.1073/pnas.2006874117

    Article  Google Scholar 

  6. Somsen, G.A., van Rijn, C., Kooij, S., Bem, R.A., Bonn, D.: Small droplet aerosols in poorly ventilated spaces and SARS-CoV-2 transmission. Lancet Respir. Med. 8(7), 658–659 (2020). https://doi.org/10.1016/S2213-2600(20)30245-9

    Article  Google Scholar 

  7. Fears, A.C., et al.: Persistence of severe acute respiratory syndrome coronavirus 2 in aerosol suspensions. Emerg. Infect. Dis. 26(9), 2168–2171 (2020). https://doi.org/10.3201/eid2609.201806

    Article  Google Scholar 

  8. Ong, S.W.X., Tan, Y.K., Chia, P.Y., Lee, T.H., Ng, O.T., Wong, M.S.Y.: Air, surface environmental, and personal protective equipment contamination by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from a symptomatic patient. JAMA 323(16), 1610–1612 (2020). https://doi.org/10.1001/jama.2020.3227

  9. Chia, P.Y., Coleman, K.K., Tan, Y.K.: Detection of air and surface contamination by SARS-CoV-2 in hospital rooms of infected patients. Nat. Commun. 11(1), 2800 (2020). https://doi.org/10.1038/s41467-020-16670-2

    Article  Google Scholar 

  10. Guo, Z., Wang, Z., Zhang, S., Li, X., Li, L., Li, C.: Aerosol and surface distribution of severe acute respiratory syndrome coronavirus 2 in hospital wards, Wuhan China. Emerg. Infect. Dis. 26(7), 1583–1591 (2020). https://doi.org/10.3201/eid2607.200885

    Article  Google Scholar 

  11. Wu, Y.C., Chen, C.S., Chan, Y.J.: The outbreak of COVID-19: an overview. J. Chin. Med. Assoc. 83(3), 217–220 (2020). https://doi.org/10.1097/JCMA.0000000000000270

    Article  Google Scholar 

  12. La Rosa, G., et al.: SARS-CoV-2 has been circulating in northern Italy since December 2019: evidence from environmental monitoring. Sci. Total Environ. 750 (2021). https://doi.org/10.1016/j.scitotenv.2020.141711

  13. World Health Organisation Coronavirus Disease (COVID-19) Dashboard. https://covid19.who.int/table. Accessed 28 Nov 2020

  14. Ministry of Health and Family Welfare Press Release. https://pib.gov.in/PressReleaseIframePage.aspx?PRID=1601095. Accessed 28 Nov 2020

  15. Ministry of Health and Family Welfare COVID-19 Statewise Status. https://www.mohfw.gov.in/. Accessed 28 Nov 2020

  16. World Health Organisation COVID-19 Media Statement. https://www.who.int/indonesia/news/detail/08-03-2020-knowing-the-risk-for-covid-19. Accessed 28 Nov 2020

  17. Suryavanshi, N., Kadam, A., Dhumal, G., et al.: Mental health and quality of life among healthcare professionals during the COVID-19 pandemic in India. Brain Behav. 10(01837) (2020). https://doi.org/10.1002/brb3.1837

  18. Lyngdoh, L.M.: Inter-state variations in rural healthcare infrastructure in north-east India. NEHU J. 8(2) (2015)

    Google Scholar 

  19. Hethcote, H.W.: The mathematics of infectious diseases. SIAM Rev. 42(4), 599–653 (2000). https://doi.org/10.1137/S0036144500371907

  20. Keeling, M.J., Danon, L.: Mathematical modelling of infectious diseases. Br. Med. Bull. 92(1), 33–42 (2009). https://doi.org/10.1093/bmb/ldp038

  21. Al-Raeei, M.: The basic reproduction number of the new coronavirus pandemic with mortality for India, the Syrian Arab Republic, the United States, Yemen, China, France, Nigeria and Russia with different rate of cases. Clin. Epidemiol. Glob. Health (2020). https://doi.org/10.1016/j.cegh.2020.08.005

    Article  Google Scholar 

  22. Dharmaratne, S., Sudaraka, S., Abeyagunawardena, I., et al.: Estimation of the basic reproduction number (R0) for the novel coronavirus disease in Sri Lanka. Virol. J. 17(144) (2020). https://doi.org/10.1186/s12985-020-01411-0

  23. D'Arienzo, M., Coniglio, A.: Assessment of the SARS-CoV-2 basic reproduction number, R0, based on the early phase of COVID-19 outbreak in Italy. Biosafety Health 2(2), 57–59 (2020). https://doi.org/10.1016/j.bsheal.2020.03.004

  24. Nuno, C.: Modeling the early evolution of the COVID-19 in Brazil: results from a Susceptible–Infectious–Quarantined–Recovered (SIQR) model. IJMPC 31(10), 1–7 (2020). https://doi.org/10.1142/S0129183120501351

  25. Arino, J., Portet, S.: A simple model for COVID-19. Infect. Dis. Model. 5, 309–315 (2020). https://doi.org/10.1016/j.idm.2020.04.002

  26. Chen, Y.-C., Lu, P.-E., Chang, C.-S., Liu, T.-H.: A time-dependent SIR model for COVID-19 with undetectable infected persons. IEEE Trans. Netw. Sci. Eng. (2020). https://doi.org/10.1109/TNSE.2020.3024723

  27. Cooper, I., Mondal, A., Antonopoulos, C.G.: A SIR model assumption for the spread of COVID-19 in different communities. Chaos Solitons Fractals 139 (2020). https://doi.org/10.1016/j.chaos.2020.110057

  28. Zhao, Z.Y., Zhu, Y.Z., Xu, J.W., et al.: A five-compartment model of age-specific transmissibility of SARS-CoV-2. Infect. Dis. Poverty 9(117) (2020). https://doi.org/10.1186/s40249-020-00735-x

  29. Roda, W.C., Varughese, M.B., Han, D., Li, M.Y.: Why is it difficult to accurately predict the COVID-19 epidemic? Infect. Dis. Model. 5, 271–281 (2020). https://doi.org/10.1016/j.idm.2020.03.001

    Article  Google Scholar 

  30. COVID19-India Homepage. https://www.covid19india.org/. Accessed 29 Nov 2020

  31. Hilton, J., Keeling, M.J.: Estimation of country-level basic reproductive ratios for novel Coronavirus (SARS-CoV-2/COVID-19) using synthetic contact matrices. PLOS Comput. Biol. 16(7) (2020). https://doi.org/10.1371/journal.pcbi.1008031

  32. Patrikar, S.R., Kotwal, A., Bhatti, V.K., et al.: Incubation period and reproduction number for novel coronavirus 2019 (COVID-19) infections in India. Asia Pacific J. Public Health (2020). https://doi.org/10.1177/1010539520956427

    Article  Google Scholar 

  33. Sardar, T., Nadim, S.S., Rana, S., Chattopadhyay, J.: Assessment of lockdown effect in some states and overall India: a predictive mathematical study on COVID-19 outbreak. Chaos Solitons Fractals 139 (2020). https://doi.org/10.1016/j.chaos.2020.110078

  34. Marimuthu, S., Joy, M., Malavika, B., Nadaraj, A., Asirvatham, E.S., Jeyaseelan, L.: Modelling of reproduction number for COVID-19 in India and high incidence states. Clin. Epidemiol. Global Health (2020). https://doi.org/10.1016/j.cegh.2020.06.012

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India

    Prabhdeep Singh & Arindam Sharma

  2. Department of Mathematics, DIT University, Dehradun, Uttarakhand, 248009, India

    Sandeep Sharma

  3. School of Mathematics, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India

    Pankaj Narula

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  1. Prabhdeep Singh
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  4. Pankaj Narula
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Correspondence to Pankaj Narula .

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Editors and Affiliations

  1. Department of Mathematics, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India

    Manoj Sahni

  2. School of Information, Systems and Modelling, University of Technology Sydney, Broadway, NSW, Australia

    José M. Merigó

  3. Department of Mathematics, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India

    Ritu Sahni

  4. Department of Management Control and Info System, University of Chile, Santiago, Chile

    Rajkumar Verma

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Singh, P., Sharma, A., Sharma, S., Narula, P. (2022). Estimation of Reproduction Number of COVID-19 for the Northeastern States of India Using SIR Model. In: Sahni, M., Merigó, J.M., Sahni, R., Verma, R. (eds) Mathematical Modeling, Computational Intelligence Techniques and Renewable Energy. Advances in Intelligent Systems and Computing, vol 1405. Springer, Singapore. https://doi.org/10.1007/978-981-16-5952-2_16

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