Future trends of COVID-19 disease outbreak in different states in India: a periodic regression analysis

Bharath Prasad Cholanayakanahalli Thyaga, Srikantha Gowda, Sharanagouda Patil, Chandrashekar Srikantiah, Kuralayanapalya Puttahonnappa Suresh

Abstract


COVID-19 (Coronavirus disease 19) is the deadliest pandemic, and by August 2, >18.2 million population worldwide were infected with SARS-CoV-2 virus causing burden on human life and economic loss. Disease outbreak analysis has become a priority for the Indian government to initiate necessary healthcare measures in lowering the impact of this deadly pandemic viral disease. In this study, time series data for COVID-19  disease was extracted from the website www.covid19india.org, analysed by using periodic regression model, the expected number of cases till 02 October 2020 was predicted and to develop a stochastic models using periodic regression in the top 15 highly infected states in India. The analysis reported increasing pattern at initial days of prediction and showed a decreasing trend for the number of reporting cases, which may reduce in future days for states like West Bengal, Karnataka, Uttar Pradesh, Bihar, Telangana, Assam and Odisha. However, for the states of Maharashtra, Tamil Nadu, Gujarat, Rajasthan, Haryana and Madhya Pradesh, showed a rapid phase of increase in disease outbreak that is likely to infect more population and indicates the pandemic nature of this disease over a period. Presently, Delhi shows a drastic reduction in the number of cases, that may increase in the future, which can be controlled if appropriate preventive measures are followed strictly and effectively. Our model highlights that continuous and constant efforts are needed for the prevention of new infections of the disease in all states that helps to effectively mitigate the disease and to allocate scarce resources effectively in the future that could improve the economic wealth in India.

Keywords


Trend, COVID, disease, Outbreaks, Periodic regression

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References


Report of the WHO – world joint mission on Coronavirus Disease 2019 (COVID-19) [Internet]. World Health Organization; 2020. [cited, 2020 August 02]. Available from: https://covid19.who.int/.

Singh RK, Rani M, Bhagavathula AS, Sah R, Rodriguez-Morales AJ, Kalita H, et al. Prediction of the COVID-19 pandemic for the top 15 affected countries: Advanced autoregressive integrated moving average (ARIMA) model. JMIR Public Health and Surveillance. 2020;6(2): e19115.

Data on number of Covid-19 cases reported in India [Internet]. A cloud source data; 2020. [cited 2020 August 02]. Available from: https://www.covid19india.org/.

Data on confirmed cases, recoveries and death of Covid-19 in India [Internet]. Ministry of Health and Family Welfare; New Delhi; 2020. [cited 2020 August 03]. Available from: https://www.mohfw.gov.in

India most infected by Covid-19 among Asian countries, leaves Turkey behind [Internet]. Hindustan Times, New Delhi; 2020 [cited 2020 May 30]. Available from: https://www.hindustantimes.com/india-news/.

Sagar K. India becomes third worst affected country by coronavirus, overtakes Russia [Internet]. Deccan Herald. New Delhi; 2020 [cited 2020 July 5]. Available from: https://m.dailyhunt.in/news/india/english/deccan+herald-epaper-deccan/.

Another grim milestone: India No.3 in total coronavirus cases [Internet]. Hindustan Times, New Delhi; 2020 [cited 2020 July 6]. Available from: https://www.hindustantimes.com/india-news/.

Infections over 1 lakh, five cities with half the cases: India's coronavirus story so far [Internet]. The Week; 2020 [cited 2020 May 20]. Available from: https://www.theweek.in/news/india/.

Kumar S. Covid-19: Number of recoveries exceed active cases for first time. [Internet]. Hindustan Times, New Delhi; 2020 [cited 2020 June 11]. Available from: https://www.hindustantimes.com/india-news/

Bliss CI. Statistics in Biology. McGraw-Hill Book Company, New York, USA, 1970.

Little TM, Hills FJ. Agricultural experimentation: Design and Analysis. John Wiley and Sons, Inc, New York, USA; 1978.

Yan P. Distribution theory, stochastic processes and infectious disease modelling. In Mathematical epidemiology 2008 (pp. 229-293). Springer, Berlin, Heidelberg.

Krishnamoorthy P, Kurli R, Patil SS, Roy P, Suresh KP. Trends and future prediction of livestock diseases outbreaks by periodic regression analysis. Indian Journal of Animal Sciences. 2019; 89(4): 369–76.

Ye QH, Qin LX, Forgues M, He P, Kim JW, Peng AC, et al. Predicting hepatitis B virus–positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning. Nature Medicine 2003; 9(4): 416-23.

Mai MV, Krauthammer M. Controlling testing volume for respiratory viruses using machine learning and text mining. In: AMIA annual symposium proceedings, vol 2016. American Medical Informatics Association. 2016;p 1910

Zhao J, Yuan Q, Wang H, Liu W, Liao X, Su Y, et al. Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019. Clinical Infectious Diseases. 2020;ciaa344.




DOI: https://doi.org/10.36462/H.BioSci.20224

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