| Literature DB >> 32672064 |
Malay Pramanik1,2, Parmeshwar Udmale1, Praffulit Bisht2, Koushik Chowdhury3, Sylvia Szabo1, Indrajit Pal4.
Abstract
The study is the first attempt to assess the role of climatic predictors in the rise of COVID-19 intensity in the Russian climatic region. The study used the Random Forest algorithm to understand the underlying associations and monthly scenarios. The results show that temperature seasonality (29.2 ± 0.9%) has the highest contribution for COVID-19 transmission in the humid continental region. In comparison, the diurnal temperature range (26.8 ± 0.4%) and temperature seasonality (14.6 ± 0.8%) had the highest impacts in the sub-arctic region. Our results also show that September and October have favorable climatic conditions for the COVID-19 spread in the sub-arctic and humid continental regions, respectively. From June to August, the high favorable zone for the spread of the disease will shift towards the sub-arctic region from the humid continental region. The study suggests that the government should implement strict measures for these months to prevent the second wave of COVID-19 outbreak in Russia.Entities:
Keywords: COVID-19; Russia; community transmission; random forest model; transmission wave
Mesh:
Year: 2020 PMID: 32672064 DOI: 10.1080/09603123.2020.1793921
Source DB: PubMed Journal: Int J Environ Health Res ISSN: 0960-3123 Impact factor: 3.411