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10.1016/j.ijheh.2020.113610 http://scihub22266oqcxt.onion/10.1016/j.ijheh.2020.113610 32896785!7448770!32896785     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32896785&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Hyg+Environ+Health 2020 ; 230 (ä): 113610 Nephropedia Template TP {{tp|p=32896785|t=2020. Impacts of transportation and meteorological factors on the transmission of COVID-19 |pdf=|usr=}}{{32896785}} gab.com Text Impacts of transportation and meteorological factors on the transmission of COVID-19 JO: Int J Hyg Environ Health http://www.kidney.de/pget.php?&tw=1&pmid=32896785 #FOAvip The ongoing pandemic of 2019 novel coronavirus disease (COVID-19) is challenging global public health response system. We aim to identify the risk factors for the transmission of COVID-19 using data on mainland China. We estimated attack rate (AR) at county level. Logistic regression was used to explore the role of transportation in the nationwide spread. Generalized additive model and stratified linear mixed-effects model were developed to identify the effects of multiple meteorological factors on local transmission. The ARs in affected counties ranged from 0.6 to 9750.4 per million persons, with a median of 8.8. The counties being intersected by railways, freeways, national highways or having airports had significantly higher risk for COVID-19 with adjusted odds ratios (ORs) of 1.40 (p = 0.001), 2.07 (p < 0.001), 1.31 (p = 0.04), and 1.70 (p < 0.001), respectively. The higher AR of COVID-19 was significantly associated with lower average temperature, moderate cumulative precipitation and higher wind speed. Significant pairwise interactions were found among above three meteorological factors with higher risk of COVID-19 under low temperature and moderate precipitation. Warm areas can also be in higher risk of the disease with the increasing wind speed. In conclusion, transportation and meteorological factors may play important roles in the transmission of COVID-19 in mainland China, and could be integrated in consideration by public health alarm systems to better prevent the disease. Twit Text FOAVip Impacts of transportation and meteorological factors on the transmission of COVID-19 ????Int J Hyg Environ Health http://www.kidney.de/pget.php?&tw=1&pmid=32896785 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32896785 #FOAvip English Wikipedia <ref>{{Cite pmid|32896785}}</ref> Impacts of transportation and meteorological factors on the transmission of COVID-19 #MMPMID32896785 Wei JT; Liu YX; Zhu YC; Qian J; Ye RZ; Li CY; Ji XK; Li HK; Qi C; Wang Y; Yang F; Zhou YH; Yan R; Cui XM; Liu YL; Jia N; Li SX; Li XJ; Xue FZ; Zhao L; Cao WC Int J Hyg Environ Health 2020[Sep]; 230 (ä): 113610 PMID32896785show ga The ongoing pandemic of 2019 novel coronavirus disease (COVID-19) is challenging global public health response system. We aim to identify the risk factors for the transmission of COVID-19 using data on mainland China. We estimated attack rate (AR) at county level. Logistic regression was used to explore the role of transportation in the nationwide spread. Generalized additive model and stratified linear mixed-effects model were developed to identify the effects of multiple meteorological factors on local transmission. The ARs in affected counties ranged from 0.6 to 9750.4 per million persons, with a median of 8.8. The counties being intersected by railways, freeways, national highways or having airports had significantly higher risk for COVID-19 with adjusted odds ratios (ORs) of 1.40 (p = 0.001), 2.07 (p < 0.001), 1.31 (p = 0.04), and 1.70 (p < 0.001), respectively. The higher AR of COVID-19 was significantly associated with lower average temperature, moderate cumulative precipitation and higher wind speed. Significant pairwise interactions were found among above three meteorological factors with higher risk of COVID-19 under low temperature and moderate precipitation. Warm areas can also be in higher risk of the disease with the increasing wind speed. In conclusion, transportation and meteorological factors may play important roles in the transmission of COVID-19 in mainland China, and could be integrated in consideration by public health alarm systems to better prevent the disease. |*COVID-19[MESH] |Humans[MESH] |Meteorological Concepts[MESH] |Pandemics[MESH] |SARS-CoV-2[MESH]Impacts of transportation and meteorological factors on the transmissi(epmc).pdf DeepDyve Pubget Overpricing