ArticleTechnological forecasting and social change2021
A novel analysis of COVID 19 risk in India incorporating climatic and socioeconomic Factors.
Article in Technological forecasting and social change, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review.The Science of the total environment · 2022Pooled it
- Data driven contagion risk management in low-income countries using machine learning applications with COVID-19 in South Asia.Scientific reports · 2023Article
- Decision-making framework for identifying regions vulnerable to transmission of COVID-19 pandemic.Computers & industrial engineering · 2022Article
- Experimental study on rubber concrete filled steel tube members under pure bending.Scientific reports · 2022Article
- Compound impact of drought and COVID-19 on agriculture yield in the USA.The Science of the total environment · 2022Article
- COVID-19 pandemic and unemployment rate prediction for developing countries of Asia: A hybrid approach.PloS one · 2022Article
- Influences of climatic and non-climatic factors on COVID-19 outbreak: A review of existing literature.Environmental challenges (Amsterdam, Netherlands) · 2021Review
- A diary study of psychological effects of misinformation and COVID-19 Threat on work engagement of working from home employees.Technological forecasting and social change · 2021Article
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4 authors.
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Abstract
This study investigates the influence of climate variables (pressure, relative humidity, temperature and wind speed) in inducing risk due to COVID 19 at rural, urban and total (rural and urban) population scale in 623 pandemic affected districts of India incorporating the socioeconomic vulnerability factors. We employed nonstationary extreme value analysis to model the different quantiles of cumulative COVID 19 cases in the districts by using climatic factors as covariates. Wind speed was the most dominating climatic factor followed by relative humidity, pressure, and temperature in the evolution of the cases. The results reveal that stationarity, i.e., the COVID 19 cases which are independent of pressure, relative humidity, temperature and wind speed, existed only in 148 (23.7%) out of 623 districts. Whereas, strong nonstationarity, i.e., climate dependence, was detected in the cases of 474 (76.08%) districts. 334 (53.6%), 200 (32.1%) and 336 (53.9%) districts out of 623 districts were at high risk (or above) at rural, urban and total population scales respectively. 19 out of 35 states were observed to be under high (or above) Kerala, Maharashtra, Goa and Delhi being the most risked ones. The study provides high-risk maps of COVID 19 pandemic at the district level and is aimed at supporting the decision-makers to identify climatic and socioeconomic factors in augmenting the risks.
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