Numerical Prediction for Spreading Novel Coronavirus Disease in India Using Logistic Growth and SIR Models

Sandip Saha; Pankaj Biswas; Sujit Nath

doi:10.30935/ejmets/10848

EUROPEAN JOURNAL OF MEDICAL AND EDUCATIONAL TECHNOLOGIES

Numerical Prediction for Spreading Novel Coronavirus Disease in India Using Logistic Growth and SIR Models

Sandip Saha ¹ ^* , Pankaj Biswas ¹, Sujit Nath ²

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EUROPEAN J MED ED TE, Volume 14, Issue 2, Article No: em2106

https://doi.org/10.30935/ejmets/10848

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Abstract

At present, Novel COVID-19 has become the greatest issue in the world which was first detected in the city of Wuhan of Hubei province in China in the month of December 2019. SARS-COV-2 is responsible for the spreading of corona virus disease. Within a very short time period, it has spread very fast throughout the world. Beyond all the boundaries of medical science, nowadays COVID-19 has become a main interesting topic in many research fields such as Applied Mathematics, economy, politics, up to the living room. The aim of this study is to investigate the dynamic behavior of pandemic COVID-19 which based on real-time data. The logistic growth model and SIR model has been employed to study the different four characteristics of COVID-19, such as low growth state, moderate growth state, transition state, and steady-state. The models have been validated with the results of real-time data. Moreover, the model presents a rapid change due to the unavailability of precautions. Furthermore, some parameters have been implemented to predict the COVID-19 status up to 5^th Jan 2021. From these models, it is predicted that the total number of infected peoples reaches 10M up to 5^th Jan 2021. It has also been revealed that with the support of lockdown, social alertness, increasing testing facility, and social distancing recovery growth rate of infected persons increases with the increase of time.

Keywords: COVID-19, logistic growth model, SIR model, Novel coronavirus

References

Paul A, Chatterjee S, Bairagi N. Prediction on Covid-19 epidemic for different countries: Focusing on South Asia under various precautionary measures. medRxiv. 2020. (doi: 10.1101/2020.04.08.20055095).
Lin Q, Zhao S, Gao D, Lou Y, Yang S, Musa SS, He D. A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action. International journal of infectious diseases. 2020; 93: 211-6. (doi: 10.1016/j.ijid.2020.02.058).
COVID-19 pandemic in India. Available at: https://en.wikipedia.org/wiki/COVID-19_pandemic_in_India
India Fights Corona COVID-19. Available at: https://www.mygov.in/covid-19/
Coronavirus Outbreak in India. Available at: https://www.covid19india.org/7
Demographics of India. Available at: https://en.wikipedia.org/wiki/Demographics_of_India#cite_note-CIAWFIN-1
“South Asia: India”. CIA.gov. Central Intelligence Agency. Available at: https://www.cia.gov/ (Accessed: 7 February 2020).
Central Intelligence Agency. The world factbook. Available at: https://www.cia.gov/library/publications/the-world-factbook/geos/in.html
Total Coronavirus Cases in India. Available at: https://www.worldometers.info/coronavirus/country/india/
World Health Organization. “Coronavirus disease 2019”. Available at: www.who.int/emergencies/diseases/novel-coronavirus-2019
Fang Y, Nie Y, Penny M. Transmission dynamics of the COVID‐19 outbreak and effectiveness of government interventions: A data‐driven analysis. Journal of medical virology. 2020; 92(6): 645-59. (doi: 10.1002/jmv.25750).
Bajardi P, Poletto C, Ramasco JJ, Tizzoni M, Colizza V, Vespignani A. Human mobility networks, travel restrictions, and the global spread of 2009 H1N1 pandemic. PloS one, 2011; 6(1): e16591. (doi: 10.1371/journal.pone.0016591).
Charu V, Zeger S, Gog J, Bjørnstad ON, Kissler S, Simonsen L, et al. Human mobility and the spatial transmission of influenza in the United States. PLoS computational biology, 2017; 13(2): e1005382. (doi: 10.1371/journal.pcbi.1005382).
Mandal S, Bhatnagar T, Arinaminpathy N, Agarwal A, Chowdhury A, Murhekar M, et al. Prudent public health intervention strategies to control the coronavirus disease 2019 transmission in India: A mathematical model-based approach. Indian J Med Res, 2020; 151(2&3): 190-9. (doi: 10.4103/ijmr.IJMR_504_20).
Al-Tawfiq JA, Al-Homoud AH, Memish ZA. Remdesivir as a possible therapeutic option for the COVID-19. Travel medicine and infectious disease. 2020: 34: 101615. (doi: 10.1016/j.tmaid.2020.101615).
Wölfel R, Corman VM, Guggemos W, Seilmaier M, Zange S, Müller MA, et al. Virological assessment of hospitalized patients with COVID-2019. Nature, 2020; 581: 465-9. (doi: 10.1038/s41586-020-2196-x).
Hall Jr DC, Ji HF. A search for medications to treat COVID-19 via in silico molecular docking models of the SARS-CoV-2 spike glycoprotein and 3CL protease. Travel Medicine and Infectious Disease, 2020: 35: 101646. (doi: 10.1016/j.tmaid.2020.101646).
Chowell G, Simonsen L, Viboud C, Kuang Y. Is West Africa approaching a catastrophic phase or is the 2014 Ebola epidemic slowing down? Different models yield different answers for Liberia. PLoS currents, 2014; 6: ecurrents.outbreaks.b4690859d91684da963dc40e00f3da81. (doi: 10.1371/currents.outbreaks.b4690859d91684d‌a963dc40e00f3da81).
Pell B, Kuang Y, Viboud C, Chowell G. Using phenomenological models for forecasting the 2015 Ebola challenge. Epidemics, 2018; 22: 62-70. (doi: 10.1016/j.epidem.2016.11.002).
Khaleque A, Sen P. An empirical analysis of the Ebola outbreak in West Africa. Scientific reports, 2017; 7: 42594. (doi: 10.1038/srep42594).
Zakary O, Larrache A, Rachik M, Elmouki I. Effect of awareness programs and travel-blocking operations in the control of HIV/AIDS outbreaks: a multi-domains SIR model. Advances in Difference Equations, 2016; 2016: 169. (doi: 10.1186/s13662-016-0900-9).
Berger DW, Herkenhoff KF, Mongey S. An seir infectious disease model with testing and conditional quarantine (No. w26901). National Bureau of Economic Research. 2020: Working Paper 26901. (doi: 10.3386/w26901).
Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proceedings of the royal society of london. Series A, Containing papers of a mathematical and physical character, 1927; 115(772): 700-21. (doi: 10.1098/rspa.1927.0118).
Batista M. fitVirusCOVID19 MATLAB Central File Exchange. 2020. Available at: https://www.mathworks.com/matlabcentral/fileexchange/74658-fitviruscovid19

Citation

Saha S, Biswas P, Nath S. Numerical Prediction for Spreading Novel Coronavirus Disease in India Using Logistic Growth and SIR Models. EUROPEAN J MED ED TE. 2021;14(2):em2106. https://doi.org/10.30935/ejmets/10848

Saha, S., Biswas, P., & Nath, S. (2021). Numerical Prediction for Spreading Novel Coronavirus Disease in India Using Logistic Growth and SIR Models. European Journal of Medical and Educational Technologies, 14(2), em2106. https://doi.org/10.30935/ejmets/10848

Saha, S., Biswas, P., and Nath, S. (2021). Numerical Prediction for Spreading Novel Coronavirus Disease in India Using Logistic Growth and SIR Models. European Journal of Medical and Educational Technologies, 14(2), em2106. https://doi.org/10.30935/ejmets/10848

Saha S, Biswas P, Nath S. Numerical Prediction for Spreading Novel Coronavirus Disease in India Using Logistic Growth and SIR Models. EUROPEAN J MED ED TE. 2021;14(2), em2106. https://doi.org/10.30935/ejmets/10848

Saha, Sandip, Pankaj Biswas, and Sujit Nath. "Numerical Prediction for Spreading Novel Coronavirus Disease in India Using Logistic Growth and SIR Models". European Journal of Medical and Educational Technologies 2021 14 no. 2 (2021): em2106. https://doi.org/10.30935/ejmets/10848

Saha, Sandip et al. "Numerical Prediction for Spreading Novel Coronavirus Disease in India Using Logistic Growth and SIR Models". European Journal of Medical and Educational Technologies, vol. 14, no. 2, 2021, em2106. https://doi.org/10.30935/ejmets/10848