Estimating the Impact of Daily Weather on the Temporal Pattern of COVID-19 Outbreak in India
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ORIGINAL ARTICLE
Estimating the Impact of Daily Weather on the Temporal Pattern of COVID‑19 Outbreak in India Amitesh Gupta1 · Biswajeet Pradhan2,3 · Khairul Nizam Abdul Maulud3,4 Received: 18 August 2020 / Accepted: 5 September 2020 © The Author(s) 2020
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. Keywords COVID-19 · Weather · Temporal trend · India
1 Introduction In human history, it is apparent that pathogens have caused devastating consequences in social wellbeing and economies (Briz-Redón and Serrano-Aroca 2020). The recent novel
* Biswajeet Pradhan [email protected]; [email protected] 1
Remote Sensing and GIS Department, JIS University, Agarpara, Kolkata, India
2
Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Information, Systems and Modelling, Faculty of Engineering and IT, University of Technology Sydney (UTS), Sydney, Australia
3
Earth Observation Centre, Institute of Climate Change (IPI), Universiti Kebangsaan Malaysia (UKM), 43600 UKM Bangi, Selangor, Malaysia
4
Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
coronavirus disease (COVID-19) is one prominent example of such a disastrous event that has grasped the world. The earliest outbreak of COVID-19 caused by Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) happened in Wuhan, Hubei Province, China during the
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