English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

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 ( T  Max ), minimum ( T  Min ), mean ( T  Mean ) and dew point temperature ( T  Dew ), 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, T  Max , T  Min , T  Mean , T  Dew , but is comparatively better with a lag of 14 days. Support Vector regression successfully estimated the count of confirmed cases ( R  2  &gt; 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 T  Max , T  Mean , T  Min , T  Dew , 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.

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