Analyzing the Effect of Environmental and Demographic Factors on COVID-19 Spread in India Using Statistical Methods: A Case Study

Veena Ghuriani^1, and Jyotsna Talreja Wassan^1,

¹Department of Computer Science, Maitreyi College, University of Delhi, India

Cite this paper:
Veena Ghuriani and Jyotsna Talreja Wassan. Analyzing the Effect of Environmental and Demographic Factors on COVID-19 Spread in India Using Statistical Methods: A Case Study. Applied Ecology and Environmental Sciences. 2021; 9(2):177-185. doi: 10.12691/aees-9-2-9

Abstract

The study focuses on analyzing the factors associated with novel coronavirus (COVID-19) which is reported as a pandemic by the World Health Organization (WHO) using the statistical methods. The main objective of this work is twofold: i) predicting the spread of coronavirus across regions by analyzing the growth rate, death rate, and recovery rate in different states of INDIA and ii) correlating the coronavirus and environmental metadata conditions. This study aims to analyze how environmental metadata such as temperature, humidity and AQI and population demographics can affect the COVID-19 cases across INDIA, a land of natural variety. The study considers COVID-19 cases, recovery, and deaths in March-July 2020 along with the data collected regarding environmental conditions of temperature, humidity, and air quality. The role of environmental data in spreading of COVID-19 in the highly populated country INDIA has potential of being studied for making policies to curb COVID-19. In this paper, we propose use of statistical methods of Spearman correlation and Linear regression to conduct different analyses related to the coronavirus. Our results indicate that varied temperatures, high humidity, and the air quality could serve as drivers for COVID-19 transmission and recoveries. For example, a high correlation of 0.98 was observed between humidity and COVID spread in southern Indian regions of Karnataka and West Bengal with tropical semi-arid steppe climate. Recovery rate found to be related to the environmental conditions such as air quality. Additionally, the population density proved to be one of the major factors affecting COVID-19 spread in INDIA with rural and urban populations. Keeping this in viewpoint, prevention measures accordingly be adopted in Indian States and Union territories for reducing the COVID-19 spread and prepare INDIA as medically and socially a better country amidst this global pandemic.

Keywords:
COVID-19 temperature humidity regression analysis spearman correlation

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References:

[1]	M. A. Andrews et al., “First confirmed case of COVID-19 infection in India: A case report,” Indian Journal of Medical Research. 2020.
[2]	Z. Y. Zu et al., “a Coronavirus Disease 2019 (COVID-19): A Perspective from China. Radiology”, 200490. Radiology, 2020.
[3]	R. B. Bhagat, R. R.S., H. Sahoo, A. K. Roy, and D. Govil, “The COVID-19 , Migration and Livelihood in India,” Int. Inst. Popul. Sci. Mumbai, 2020.
[4]	W. Luo et al., “The role of absolute humidity on transmission rates of the COVID-19 outbreak,” pp. 1-7, 2020.
[5]	M. F. Bashir et al., “Correlation between climate indicators and COVID-19 pandemic in New York, USA,” Sci. Total Environ., 2020.
[6]	S. Gupta, G. S. Raghuwanshi, and A. Chanda, “Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020,” Sci. Total Environ., 2020.
[7]	B. Oliveiros, L. Caramelo, N. C. Ferreira, and F. Caramelo, “Oliveiros et al 2020 Temperature and humidity in doubling time of cases,” 2020.
[8]	J. Wang, K. Tang, K. Feng, and W. Lv, “High Temperature and High Humidity Reduce the Transmission of COVID-19,” SSRN Electron. J., 2020.
[9]	A. C. Auler, F. A. M. Cássaro, V. O. da Silva, and L. F. Pires, “Evidence that high temperatures and intermediate relative humidity might favor the spread of COVID-19 in tropical climate: A case study for the most affected Brazilian cities,” Sci. Total Environ., 2020.
[10]	M. M. Sajadi, P. Habibzadeh, A. Vintzileos, S. Shokouhi, F. Miralles-Wilhelm, and A. Amoroso, “Temperature and Latitude Analysis to Predict Potential Spread and Seasonality for COVID-19,” SSRN Electron. J., 2020.
[11]	P. Sedgwick, “Spearman’s rank correlation coefficient,” BMJ (Online). 2014.
[12]	C. Angelini, “Regression analysis,” in Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 2018.
[13]	P. Humphrey, “What is a p-value?” Teach. Stat., 2012.
[14]	J. T. Kilmer and R. L. Rodríguez, “Ordinary least squares regression is indicated for studies of allometry,” Journal of Evolutionary Biology. 2017.
[15]	P. Mecenas, R. T. da Rosa Moreira Bastos, A. C. Rosário Vallinoto, and D. Normando, “Effects of temperature and humidity on the spread of COVID-19: A systematic review,” PLoS One, 2020.
[16]	L. D. Martins, I. da Silva, W. V. Batista, M. de F. Andrade, E. D. de Freitas, and J. A. Martins, “How socio-economic and atmospheric variables impact COVID-19 and influenza outbreaks in tropical and subtropical regions of Brazil,” Environ. Res., 2020.
[17]	S. Gautam, “The Influence of COVID-19 on Air Quality in India: A Boon or Inutile,” Bull. Environ. Contam. Toxicol., 2020.
[18]	J. W. Gooch, “Coefficient of Determination,” in Encyclopedic Dictionary of Polymers, 2011.
[19]	M. Palacios Cruz, E. Santos, M. A. Velázquez Cervantes, and M. León Juárez, “COVID-19, a worldwide public health emergency,” Revista Clinica Espanola. 2020.