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Association between climatic and nonclimatic parameters and transmission of SARS-CoV-2 infection in Nepal
Sarmila Tandukar1, Dinesh Bhandari2, Rajani Ghaju Shrestha3, Samendra P Sherchan4, Anil Aryal5
1 nterdisciplinary Center for River Basin Environment (ICRE), University of Yamanashi, Kofu, Yamanashi, 400-8510, Japan; Policy Research Institute, Sano Gaucharan, Kathmandu, Nepal
2 The University of Adelaide, School of Public Health, Adelaide, South Australia, Australia
3 Division of Sustainable Energy and Environmental Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
4 Global Health Environmental Sciences, Tulane University, 1140 Canal Street, New Orleans, LA 70112, USA
5 Interdisciplinary Center for River Basin Environment (ICRE), University of Yamanashi, Kofu, Yamanashi, 400-8510, Japan
Correspondence Address:
Dr. Anil Aryal
nterdisciplinary Centre for River Basin Environment (ICRE), University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511
Japan
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/ed.ed_2_21
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Background: Preliminary evidence suggests that environmental factors may modify the transmission of SARS-CoV-2 infection. Although the role of non-pharmaceutical interventions (NPIs) on the reduction of SARS-CoV-2 transmission rate is well explored, the role of local climate across different geographical transects on the onset and transmission of SARS-CoV-two remains unclear.
Aims and Objectives: In this study, we explored the potential association among climatic factors, non-climatic factors and COVID-19 burden, via Pearson correlation analysis. We also investigated the association between COVID-19 cases and cumulative effect of NPIs or behavioral changes during lockdown as non-climatic factors in our analysis.
Setting and Design: The research was carried out in the COVID-19 impacted districts across Nepal.
Material and Methods: The meteorological/climatic factors consisting of temperature and rainfall as predictor variables and total laboratory confirmed COVID-19 cases reported between January and May 2020 were considered in the study.
Statistical Analysis Used: The statistical tests were carried out using R programming language.
Results: Of the total 375 confirmed positive cases until May 19, 2020, clusters of the cases were diagnosed from the Terai region, which was associated with comparatively higher temperature and open border to India. Upon time series and spatial analysis, the number of positive cases increased after the end of April, possibly due to expansion of diagnostic tests throughout the country. We found a positive correlation betweenCOVID-19, and temperature indices (mean and minimum) (P < 0.05).
Conclusions: In the absence of an effective vaccine, these findings can inform infection control division of Nepal on the implementation of effective NPIs based on the observed variability in meteorological factors, especially in prevention of possible second wave of infection during winter.
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