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The outbreak of 2019 novel coronavirus disease (COVID-19) is a public health emergency of international concern. • The ability to detect disease in their early stages is a key component of efficient disease control and prevention. • The research focuses to develop a transparent user-friendly method to simulate spatial-temporal disease outbreak data. • GIS techniques, resources, and methods can be used in Pakistan for more effective investigation of disease. • It is quite easy for authorities to locate the highly effected area and take appropriate actions in that particular areas.

Abstract:This study uses the data from 157 countries from 1960 to 2014 to analyze the relationship between economic growth, electricity consumption, oil prices, capital, and labor. The economic growth of developing countries with industrial infrastructure has a more significant association with electricity consumption than oil prices. We use oil prices and electricity consumption jointly to study highly predictive observations for economic growth. The data are categorized by income, OECD and regional levels. The panel cointegration, long-run parameter estimation, and Pool Mean Group testsare used to analyze the cointegration and short-run and long-run relationships between the variables. The empirical results indicate the presence of cointegration between the variables. The presence of feedback effects between electricity consumption and economic growth, oil prices and economic growth is valid. These findings confirm that inspite of the oil prices, developing countries rely heavily on electricity consumption for economic growth. In the short run, growth and feedback effects suggest that more vigorous electricity policies should be implemented to attain sustainable economic growth for the long-term.

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