Significance of geographical factors to the COVID-19 outbreak in India

Gupta, Amitesh; Banerjee, Sreejita; Das, Sumit

doi:10.1007/s40808-020-00838-2

Cited by 122 publications

(138 citation statements)

References 25 publications

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“…In particular, below 3 °C, the number of COVID-19 infections in China is found to have a positive linear association with average temperature (Zhu and Xie 2020). A similar correlation between temperature and Covid-19 cases is also found in several Countries and cities, like India (Gupta et al 2020), Indonesia (Tosepu et al 2020), Turkey (Şahin 2020), New York (USA) (Bashir et al 2020), as well as on a worldwide scale (Chen et al 2020a).…”

Section: Introductionmentioning

confidence: 80%

Impact of population density on Covid-19 infected and mortality rate in India

Bhadra

Mukherjee

Sarkar

2020

Model. Earth Syst. Environ.

291

166

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The Covid-19 is a highly contagious disease which becomes a serious global health concern. The residents living in areas with high population density, such as big or metropolitan cities, have a higher probability to come into close contact with others and consequently any contagious disease is expected to spread rapidly in dense areas. However, recently, after analyzing Covid-19 cases in the USA researchers at the

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Section: Introductionmentioning

confidence: 80%

Impact of population density on Covid-19 infected and mortality rate in India

Bhadra

Mukherjee

Sarkar

2020

Model. Earth Syst. Environ.

291

166

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“…Based on the USA model, a reduction of transmission in warmer conditions had been predicted for India (Gupta et al 2020a). However, in view of the long-term climate record, it was found that comparatively hot areas in India are possibly going to be more affected by this disease (Gupta et al 2020b). On the basis of regional data for several provinces in India, Goswami et al (2020) reported on the inconsistency of the weather-infection interrelationship in India.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Impact of Daily Weather on the Temporal Pattern of COVID-19 Outbreak in India

Gupta¹,

Pradhan

Maulud

2020

Earth Syst Environ

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The COVID-19 pandemic has spread obstreperously in India. The increase in daily confirmed cases accelerated significantly from ~ 5 additional new cases (ANC)/day during early March up to ~ 249 ANC/day during early June. An abrupt change in this temporal pattern was noticed during mid-April, from which can be inferred a much reduced impact of the nationwide lockdown in India. Daily maximum (TMax), minimum (TMin), mean (TMean) and dew point temperature (TDew), wind speed (WS), relative humidity, and diurnal range in temperature and relative humidity during March 01 to June 04, 2020 over 9 major affected cities are analyzed to look into the impact of daily weather on COVID-19 infections on that day and 7, 10, 12, 14, 16 days before those cases were detected (i.e., on the likely transmission days). Spearman’s correlation exhibits significantly lower association with WS, TMax, TMin, TMean, TDew, but is comparatively better with a lag of 14 days. Support Vector regression successfully estimated the count of confirmed cases (R2 > 0.8) at a lag of 12–16 days, thus reflecting a probable incubation period of 14 ± 02 days in India. Approximately 75% of total cases were registered when TMax, TMean, TMin, TDew, and WS at 12–16 days previously were varying within the range of 33.6–41.3 °C, 29.8–36.5 °C, 24.8–30.4 °C, 18.7–23.6 °C, and 4.2–5.75 m/s, respectively. Thus, we conclude that coronavirus transmission is not well correlated (linearly) with any individual weather parameter; rather, transmission is susceptible to a certain weather pattern. Hence multivariate non-linear approach must be employed instead.

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“…In past, many studies have established that the emergence and transmission of several infectious disease like pneumonia or influenza depend on climatic conditions, for example, temperature, rainfall, humidity, wind speed, pressure, sunshine, and evaporation (Dalziel et al 2020 ). Likewise, many researchers have studied the association between different climatic variables and COVID-19 pandemic transmission and revealed a significant positive relationships, resembling with the occurrence of past transmissible diseases (Tosepu et al 2020 ; Bashir et al 2020 ; Gupta et al 2020a ; Liu et al 2020a , b ; Ma et al 2020 ; Sahin 2020 ; Xie and Zhu 2020 ). Remarkably, Shi et al ( 2020 ) have reported a strong association among diurnal temperature and diurnal count of COVID-19 pandemic confirmed patients in China and suggested that temperature above 8–10 °C would lead to a decline in confirmed cases.…”

Section: Introductionmentioning

confidence: 99%

Association between climatic variables and COVID-19 pandemic in National Capital Territory of Delhi, India

Singh

Bhardwaj²,

Kumar³

2020

Environ Dev Sustain

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Globally, since the end of December 2019, coronavirus disease (COVID-19) has been recognized as a severe infectious disease. Therefore, this study has been attempted to examine the linkage between climatic variables and COVID-19 particularly in National Capital Territory of Delhi (NCT of Delhi), India. For this, daily data of COVID-19 has been used for the period March 14 to June 11, 2020, (90 days). Eight climatic variables such as maximum, minimum and mean temperature (°C), relative humidity (%), bright sunshine hours, wind speed (km/h), evaporation (mm), and rainfall (mm) have been analyzed in relation to COVID-19. To study the relationship among different climatic variables and COVID-19 spread, Karl Pearson's correlation analysis has been performed. The Mann-Kendall method and Sen's slope estimator have been used to detect the direction and magnitude of COVID-19 trends, respectively. The results have shown that out of eight selected climatic variables, six variables, viz. maximum temperature, minimum temperature, mean temperature, relative humidity, evaporation, and wind speed are positively associated with coronavirus disease cases (statistically significant at 95 and 99% confidence levels). No association of coronavirus disease has been found with bright sunshine hours and rainfall. Besides, COVID-19 cases and deaths have shown increasing trends, significant at 99% confidence level. The results of this study suggest that climatic conditions in NCT of Delhi are favorable for COVID-19 and the disease may spread further with the increasing temperature, relative humidity, evaporation and wind speed. This is the only study which has presented the analysis of COVID-19 spread in relation to several climatic variables for the most densely populated and rapidly growing city of India. Thus, considering the results obtained, effective policies and actions are necessary especially by identifying the areas where the spread rate is increasing rapidly in this megacity. The prevention and protection measures should be adopted aiming at to reduce the further transmission of disease in the city.

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Significance of geographical factors to the COVID-19 outbreak in India

Cited by 122 publications

References 25 publications

Impact of population density on Covid-19 infected and mortality rate in India

Impact of population density on Covid-19 infected and mortality rate in India

Estimating the Impact of Daily Weather on the Temporal Pattern of COVID-19 Outbreak in India

Association between climatic variables and COVID-19 pandemic in National Capital Territory of Delhi, India

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