Abstract
COVID-19 disease is an infectious disease caused by Coronavirus. Studying COVID-19 influencing factors has become essential by many researchers in various scientific disciplines. This study analyzes factors that may impact the total COVID-19 infection and death cases in selected countries. The factors we study are population density, age, healthcare factors, temperature, humidity, wind speed, and government responses. We utilize exploratory data analysis techniques and statistical measures to understand the relationships between variables. The results show that the population density factor, age, and government measures are strongly correlated with the total infection and death cases, whereas, temperature, humidity, and wind speed factors are not correlated with cases. This study may help in containing the disease and limiting its spread.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Coronavirus Update (Live) (2021) 84,087,380 Cases and 1,830,453 Deaths from COVID-19 Virus Pandemic-Worldometer.Worldometers.info. https://www.worldometers.info/coronavirus/?mc_cid=5fc7bf99f2&mc_eid=39f59cd803. Accessed 1 Jan (2021)
Coronavirus Source Data (2021) Our world in data. https://ourworldindata.org/coronavirus-source-data. Accessed 1 Jan 2021
COVID19_Global_Weather_Data.Kaggle.com. https://www.kaggle.com/winterpierre91/covid19-global-weather-data. Accessed 1 Jan 2021
Dowd J, Andriano L, Brazel D et al (2020) Demographic science aids in understanding the spread and fatality rates of COVID-19. Proc Natl Acad Sci 117(18):9696–9698
Gandhi KRR, Murthy KVR, Prasada Rao SSP, Casella F (2020) Non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality. SSRN Electron J
Gong X, Wang Z, Chen R, Li T, Zeng D, Li M (2020) Clinical features of familial clustering in patients infected with 2019 novel coronavirus in Wuhan, China. Virus Res 286:198043
Hale T, Webster S, Petherick A, Phillips T, Kira B (2020) Coronavirus government response tracker. University of Oxford, Blavatnik School of Government
Huang Y, Lin C, Wang P, Xu Z (2020) Saving China from the Coronavirus and economic meltdown: experiences and lessons. SSRN Electron J
Liu W, Tao Z, Wang L et al (2020) Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease. Chin Med J (Engl) 133(9):1032–1038
Qiu Y, Chen X, Shi W (2020) Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China. J Popul Econ 33(4):1127–1172
Remuzzi A, Remuzzi G (2020) COVID-19 and Italy: what next? The Lancet 395(10231):1225–1228
World Bank WDI 2.12 - Health Systems. Kaggle.com. https://www.kaggle.com/danevans/world-bank-wdi-212-health-systems. Accessed 1 Jan 2021
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Awawdeh, R., Melhem, S., Alqudah, N., Al-smadi, T., Mustafa, A. (2022). Analyzing the Most Influencing Factors in Limiting the Spread of COVID-19 Disease. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 216. Springer, Singapore. https://doi.org/10.1007/978-981-16-1781-2_8
Download citation
DOI: https://doi.org/10.1007/978-981-16-1781-2_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1780-5
Online ISBN: 978-981-16-1781-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)