A Spatial Econometric Analysis on the Impact of COVID-19 on Mortality Outcome
DOI:
https://doi.org/10.2478/eb-2020-0013Keywords:
COVID19, confirmed cases, mortality, System GMMAbstract
The study develops and examines the spatial distribution of the Corona Virus Disease (COVID) on mortality outcomes using a global panel dataset of 79 countries. The empirical evidence is based on Fixed Effect (FE) and System Generalized Method of Moment (SGMM) estimator. The predicted variable is proxy with daily mortality outcomes, while the predictor variable is proxy with spatial COVID spread while controlling for social tension and average temperature. The global and regional findings of the study established that spatial variation in COVID spread had positive and significant relationships with mortality outcomes. Further results also indicate that social tension is a contributing factor to the rising daily mortality outcome from the COVD outbreak, whereas temperature variation reduces mortality outcome. Thus, the study recommends the use of statistical modelling to predict and manage the epidemic. Also, there is an urgent demand to deploy essential social need to the vulnerable proportion of the population to reduce the level of social unrest, while strengthening collaborative research among scientists to develop, produce and distribute vaccines that will put an end to the pandemic.References
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Yeo, C., Kaushal, S., Yeo, D. (2020). Enteric involvement of coronaviruses: Is facial oral transmission of SARS-CoV-2 possible? The Lancet Gastroenterol. Hepatol. 5(4), 335–337. https://doi.org/10.1016/s2468-1253(20)30048-0.
Ahmadi, A., Fadaei, Y., Shirani, M., Rahmani, F. (2020). Modelling and forecasting trend of COVID19 epidemic in Iran. MedRxiv. https://doi.org/10.1101/2020.03.17.20037671
Araujo, M. B., & Naimi, B. (2020). Spread of SARS-CoV-2 Coronavirus likely to be constrained by climate. MedRxiv. https://doi.org/10.1101/2020.03.12.20034728
Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error components models. J. Econ., 68(1), 29–51. https://doi.org./10.1016/0304-4076(94)01642-D.
Bun, M. J. G, & Sarafidis, V. (2015). Dynamic panel data model. The Oxford Handbook of Panel Data. (Eds.) Badi H. Baltagi. [Online] https://doi.org/10.1093/oxfordhb/9780199940042.013.0003
Barreca, A. I. (2020). Climate change, humidity, and mortality in the United States. J. Environ Econ Manag. 63(1), 19–34. https://doi.org/10.1016/j.jeem.2011.07.004
Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. J. Econ. 87(1), 115–143. https://doi.org/10.1016/s0304-4076(98)0009-8.
Blundell, R., & Bond, S. (2000). GMM estimation with persistent panel data; an application to production functions. Econ. Rev. 19(3), 321–340. https://doi.org/10.1080/07474900088475.
Etienne, J., Skalli, A., & Theodossious, I. (2020). Do inequalities harm health? Evidence from Europe. Journal of Income Distribution, 20(3-4), 57–74.
Gasparrini, A., et al. (2015). Mortality risk attributable to high and low ambient temperature: a multicountry observational study. The Lancet, 386(9991), 369–375. https://doi.org/10.1016/S0140-6736(14)62114-0
Gautam, S., & Hens, L. (2020). SARS-CoV-2 pandemic in India: what might we expect? Environ Dev Sustain, 22, 3867–3869. https://doi.org/10.1007/s10668-020-00739-5
Grossman, M. (1972). On the concept of health capital and the demand for health. Journal of Political Economy, 80(2), 223–255. https://doi.org/10.1086/259880
Hansen, L. P. (1982). Large sample properties of the generalised method of moments estimators. Econometrica, 50(4), 1029–1054. https://doi.org/10.2307/1912775.
Kawachi, I., & Kennedy, B. P. (1997a). Socioeconomic determinants of health: Health and social cohesion: Why care about income inequality? British Medical Journal, 314, 1037–1040. https://doi.org/10.1136/bmj.314.7086.1037
Kawachi, I., Kennedy, B. P., K. Lochner, K., & Prothrow-Stith., D. (1997b). Social capital, income inequality and mortality. American Journal of Public Health, 87, 1491–1498. https://doi.org/10.2105/AJPH.87.9.1491
Ma, Y., et al. (2020). Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China. Science of The Total Environment, 724, 138226. https://doi.org/10.1016/j.scitotenv.2020.138226
Mellor, J. M., & Milyo, D. (2002). Income inequality and health status in the United States: Evidence from the current population survey. Journal of Human Resources, 37(3), 510–539. https://doi.org/10.2307/3069680
Petra, Z., & Nigel, C. (2020). Coronavirus infections in children, including COVID-19. The Pediatric Infectious Disease Journal, March 12, 2020. https://doi.org/10.1097/INF.0000000000002660
Rodgers, G. B. (1979). Income and inequality as determinants of mortality: an international cross-section analysis. Population Studies, 33, 343–51. https://doi.org/10.1080/00324728.1979.10410449
Sarkodie, S. A., & Owusu, P. A. (2020). Investigating the cases of novel coronavirus disease (COV19) in China using statistical techniques. Heliyon 6(4), 03747. https://doi.org/10.1016/j.heliyon.2020.e03747.
Shereen, M.A., Khan, S., Kazmi, A., & Siddique, R. (2020). COVID-19 infection: Origin, transmission, and characteristics of human coronavirus. J. Adv. Res. 24, 91–98. https://doi.org/10.1016/j.jare.2020.03.005.
Shobande, O. A. (2019). Effects of Energy Use on Socioeconomic Predictors in Africa: Synthesizing Evidence. Studia Universitatis „Vasile Goldis” Arad – Economics Series, 29(4), 21–40. https://doi.org/10.2478/sues-2019-0016
Shobande, O. A. (2020). Effects of energy use on the mortality rate in Africa. Environmental and Sustainability Indicators, 5, 100015, 1–11. https://doi.org/10.1016/j.indic.2019.100015.
WHO (2020). Coronavirus Disease 2019 (COVID-19) Situation Reports. World Health Organisation.
Yeo, C., Kaushal, S., Yeo, D. (2020). Enteric involvement of coronaviruses: Is facial oral transmission of SARS-CoV-2 possible? The Lancet Gastroenterol. Hepatol. 5(4), 335–337. https://doi.org/10.1016/s2468-1253(20)30048-0.
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10.10.2020
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Shobande, O., & Ogbeifun, L. (2020). A Spatial Econometric Analysis on the Impact of COVID-19 on Mortality Outcome. Economics and Business, 34, 179-200. https://doi.org/10.2478/eb-2020-0013
