Detection of transmission change points during unlock-3 and unlock-4 measures controlling COVID-19 in India

  • Manisha Mandal Department of Physiology, MGM Medical College, Kishanganj-855107, India
  • Shyamapada Mandal Laboratory of Microbiology and Experimental Medicine, Department of Zoology, University of Gour Banga, Malda-732103, India

Abstract

Objective: To evaluate the efficiency of unlock-3 and unlock-4 measure related to COVID-19 transmission change points in India, for projecting the infected population, to help in prospective planning of suitable measures related to future interventions and lifting of restrictions so that the economic settings are not damaged beyond repair.


Methods: The SIR model and Bayesian approach combined with Monte Carlo Markov algorithms were applied on the Indian COVID-19 daily new infected cases from 1 August 2020 to 30 September 2020. The effectiveness of unlock-3 and unlock-4 measure were quantified as the change in both effective transmission rates and the basic reproduction number (R0).


Results: The study demonstrated that the COVID-19 epidemic declined after implementing unlock-4 measure and the identified change-points were consistent with the timelines of announced unlock-3 and unlock-4 measure, on 1 August 2020 and 1 September 2020, respectively.


Conclusions: Changes in the transmission rates with 100% reduction as well as the R0 attaining 1 during unlock-3 and unlock-4 indicated that the measures adopted to control and mitigate the COVID-19 epidemic in India were effective in flattening and receding the epidemic curve.


Keywords: COVID-19 in India, epidemiological parameters, unlock-3 and unlock-4, SIR model, Bayesian inference, Monte Carlo Markov sampling

Keywords: COVID-19 in India, epidemiological parameters, unlock-3 and unlock-4, SIR model, Bayesian inference, Monte Carlo Markov sampling

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Author Biographies

Manisha Mandal, Department of Physiology, MGM Medical College, Kishanganj-855107, India

Department of Physiology, MGM Medical College, Kishanganj-855107, India

Shyamapada Mandal, Laboratory of Microbiology and Experimental Medicine, Department of Zoology, University of Gour Banga, Malda-732103, India

Laboratory of Microbiology and Experimental Medicine, Department of Zoology, University of Gour Banga, Malda-732103, India

References

1. Institute for Health Metrics and Evaluation (IHME). COVID-19 mortality, infection, testing, hospital resource use, and social distancing projections. University of Washington, USA, 2020.
2. COVID19India, 2020. Available at: https://www.covid19india.org/ (last accessed: October 1, 2020).
3. Varghese GM, John R. COVID-19 in India: Moving from containment to mitigation. Indian J Med Res 2020; 151:136-139.
4. Ministry of Health and Family Welfare (MHFW). Government of India, 2020. Available at: https://www.mohfw.gov.in/ (last accessed: October 1, 2020).
5. Dehning J, Zierenberg J, Spitzner FP, Wibral M, Neto JP, Wilczek M., et al. Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions. Science 2020; 369:161-171.
6. Priesemann-Group, 2020. Available at: https://github.com/Priesemann-Group/covid19_inference_forecast (last accessed: October 1, 2020).
7. Center for Systems Science and Engineering (CSSE). COVID-19 dashboard. Johns Hopkins University, 2020. Available at: https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6 (last accessed: October 1, 2020).
8. Kucukelbir A, Tran D, Ranganath R, Gelman A, Blei DM. Automatic differentiation variational inference. J Mach Learn Res 2017; 18: 1-45.
9. Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proc R Soc Lond 1927; 115:700-721.
10. Carcione JM. In wave Fields in Real Media: Theory and numerical simulation of wave propagation in anisotropic, anelastic, porous and electromagnetic media (3rd edn) Elsevier Science, Amsterdam, 2014; 38:1-690.
11. Hethcote HW. The mathematics of infectious diseases. SIAM Rev 2000; 42:599-653.
12. Bayes T. Bayesian inference of phylogeny. Phil Trans Roy Soc 1763; 330.
13. Box GEP, Tiao GC. Bayesian inference in statistical analysis. Wiley-Interscience, USA, 1992; 1-608.
14. Hoffman MD, Gelman A. The No-U-Turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. J Machine Learning Res 2014; 30:1351-1381.
15. Vehtari A, Gelman A, Simpson D, Carpenter B, Burkner PC. Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC. Bayesian Anal 2020. DOI: https://doi.org/10.1214/20-BA1221
16. Vehtari A, Gelman A, Gabry J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics Computing 2017; 27:1413-1432.
17. Baruah HK. A Numerical Study of the Current COVID-19 Spread Patterns in India, the USA and the World. MedRxiv 2020. DOI: https://doi.org/10.1101/2020.10.05.20206839.
18. Yadav S, Yadav PK. The peak of COVID-19 in India. MedRxiv 2020. DOI: https://doi.org/10.1101/2020.09.17.20197087
19. Ferguson N, Laydon D, Nedjati-Gilani G, Imai N, Ainslie K, Baguelin M, Report 9: Impact of non-pharmaceutical interventions (NPIS) to reduce COVID-19 mortality and healthcare demand. Imperial College London, 2020. DOI: https://doi.org/10.25561/77482
20. Mandal M, Mandal S. COVID-19 pandemic scenario in India compared to China and rest of the world: a data driven and model analysis. MedRxiv 2020. DOI: https://doi.org/10.1101/2020.04.20.20072744
21. Pan A, Liu L, Wang C, Guo H, Hao X, Wang Q, et al. Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China. JAMA 2020; 323:1889-1890.
22. Mbuvha R, Marwala T. Bayesian inference of COVID-19 spreading rates in South Africa. PLoS One 2020; 15:e0237126. DOI: https://doi.org/10.1371/journal.pone.0237126
23. Jiang S, Zhou Q, Zhan X, Li Q. BayesSMILES: Bayesian segmentation modeling for longitudinal epidemiological studies. MedRxiv 2020. DOI: https://doi.org/10.1101/2020.10.06.20208132.
24. Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, et al. The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: Estimation and application. Ann Intern Med 2020; 172:577-582
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1.
Mandal M, Mandal S. Detection of transmission change points during unlock-3 and unlock-4 measures controlling COVID-19 in India. JDDT [Internet]. 15Mar.2021 [cited 29Mar.2024];11(2):76-. Available from: https://jddtonline.info/index.php/jddt/article/view/4600