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

Abstract

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.

Impact of Weather Parameters and Population Density on the COVID-19 Transmission: Evidence from 81 Provinces of Turkey

“…Researchers have done a lot of studies to determine which factors affect to transmission of COVID-19 by evaluating these data together with various parameters. The weather variables are good indicators to forecast COVID-19 cases in following days (Gupta et al 2020a , b ; Li et al 2020a , b ; Rohrer et al 2020 ; Wu et al 2020 ). Table 1 shows the results of existing literature.…”

“…Iqbal et al ( 2020 ), Liu et al ( 2020 ), Ma et al ( 2020 ), Shi et al ( 2020 ) have studied these variables for China which is the first centre of the pandemic. Bashir et al ( 2020 ), Gupta et al ( 2020a , b ), Runkle et al ( 2020 ) have focused on US and they have employed similar variables.…”

Investigation of the Importance of Climatic Factors in COVID-19 Worldwide Intensity

“…More recently, after the onset of the new pandemic, a number of studies have been carried out to investigate the possible effect of weather on the spread of COVID-19. Some of these studies have focused on specific regions and countries, such as 122 cities in China [10], Wuhan, China [11], Mainland China [12], Jakarta, Indonesia [13], India [14,15], Iran [16], Brazil [17], Europe and United States [18], and Russia [19], while others have examined the effect of weather conditions on the pandemic at a global scale . Smit et al [42], who presented a comprehensive critical review of the relevant literature published in the first half of 2020, concluded that it is important for studies to include the effects of socio-economic factors and non-pharmaceutical interventions (such as government response measures) in their analysis.…”

Estimation of particulate matter (PM2.5, PM10) concentration and its variation over urban sites in Bangladesh

“…https://doi.org/10.1007/s42452-020-03829-1 with a view to make it linear [101]. Usually linear and polynomial kernels are less time-consuming to perform but provide less accuracy than radial kernel [102,103].…”