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Influence of the environmental factors and genome diversity on COVID-19 cumulative infection in highland region of China: Comparative correlational study (Preprint)

Deji et al., Interactive Journal of Medical Research, doi:10.2196/43585
Mar 2024  
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Retrospective 24,826 COVID-19 cases in China showing lower COVID-19 cases with increasing sunlight hours in high altitude regions. Higher sunlight hours was also associated with fewer mutations.
Deji et al., 25 Mar 2024, China, peer-reviewed, 8 authors, study period 23 January, 2020 - 7 July, 2022. Contact: nadger_wang@139.com.
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Impact of altitude on COVID-19 aggregation infection in China: evidence from linkage of environmental factors, COVID-19, and genome diversity (Preprint)
Zhuoga Deji, Yuantao Tong, Zeyu Zhang, Meng Fang, James C Crabble, Ying Wang, Xiaoyan Zhang
doi:10.2196/preprints.43585
Background: The novel coronavirus disease due to SARS-Cov-2 (COVID-19) has been declared as a global pandemic and is still ongoing worldwide. While based on the government report and genome diversity study, the overall COVID-19 mortality and infection cases are significantly low in high altitude place. Several factors which may effect the development tendency of COVID-19 were investigated. However, there is limited information on the evidence of low infection cases in altitude area, and how underlying biological mechanism effect the aggregation infection of COVID-19 in altitude area of China. Objective: This study is aiming to investigate the impacts of altitude on COVID-19 aggregation infection in China by analyzing the correlation between environmental factors with COVID-19 confirmed cases and viral mutations. Methods: We first examined the correlation between altitude and COVID-19 infection through linear regression, and further identified several major factors correlated to COVID-19 through text mining in the zero-shot model. Spearman correlation was conducted to analyze the correlation between selected meteorological factors and air quality with COVID-19 confirmed cases in high altitude regions of China. We further identified different virus mutations at both high and low altitudes, and the relationship between our environmental factors and mutation frequency using the same correlation technique. Results: The linear regression analysis revealed a negative correlation between altitude and COVID-19 confirmed case in China. The Spearman correlation analysis indicated that the average temperature, sun hours, UV index, wind speed and average concentration of NO2 were negatively correlated to daily new confirmed cases at high altitude regions in China, while air pressure, average concentration of PM2.5 were positively correlated. Further research of mutation diversity in low-and highaltitude groups showed lower genetic diversity among nucleotides for each SARS-CoV-2 genome (p < .001) and three open reading frames (ORFs) (p < .001) compared to 300 sequences of low altitude. Further correlation analysis investigated positive correlations between altitude, wind speed, atmospheric pressure with some key nonsynonymous mutations, and negative correlation with temperature, UV index, sun hours. Conclusions: The characteristics of environmental factors including meteorology and air quality parameters largely reflect the evidence of low COVID-19 confirmed cases in high altitude region of China. Besides, environmental factors on virus mutation also adds knowledge of altitude impact on aggregation infection of COVID-19 and provides novel suggestions for preventive intervention.
test was used to evaluate the significance of mutation frequency in each of the ORFs (Figure 5A ). The amino acid mutation frequency values for the three ORFs known as ORF1b, ORF7a, and ORF7b were significantly lower in the high-altitude group than in the low-altitude group (p < .001), but higher for the S and N genes (p < .001). Among these, differences between high-and low-altitude groups showed 44 nonsynonymous mutations and 32 synonymous mutations (Figure 5B Correlation analysis between mutation frequency and environmental factors The impact of 7 environmental factors on nonsynonymous mutation frequency was analyzed through all sequences (Figure 6 and Multimedia Appendix 8). Most of the nonsynonymous mutations had a positive correlation with altitude, wind speed, and atmospheric pressure, whereas UV index, relative humidity, and sun hour (p < .01 or p < .05) showed a negative correlation. The highest correlation between mutation frequency and environmental factors was found for altitude with N_G29427A, ORF1b_C20320T, and ORF1a _C1191T (r = 0.53, p < .001), followed by UV index with ORF8_C28093T, S_A23063T (r = -0.42, p < .001), and temperature with ORF8_C28093T, S_C23604A, S_A23063T, ORF1a_G6671A. Some significant nonsynonymous Conflicts of Interest The authors disclose no conflicts of interest. Author's Contributions
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