tetano
Editor, Senior Moderator
Sci Total Environ
. 2022 Sep 5;158525.
doi: 10.1016/j.scitotenv.2022.158525. Online ahead of print.
Subtypes specified environmental dependence of seasonal influenza virus
Bing Zhang[SUP] 1 [/SUP], Tao Chen[SUP] 2 [/SUP], Shiwen Liang[SUP] 3 [/SUP], Wei Shen[SUP] 4 [/SUP], Qianru Sun[SUP] 5 [/SUP], Daoze Wang[SUP] 5 [/SUP], Gang Wang[SUP] 5 [/SUP], Jing Yang[SUP] 2 [/SUP], Lei Yang[SUP] 2 [/SUP], Dayan Wang[SUP] 6 [/SUP], Yuelong Shu[SUP] 7 [/SUP], Xiangjun Du[SUP] 8 [/SUP]
Affiliations
Abstract
Understanding the role of meteorological factors in the transmission dynamics of respiratory infectious diseases remains challenging. Our study was to comprehensively investigate the nonlinear effects of environmental factors on influenza transmission, based on multi-region surveillance data from mainland China. An approach related to time-varying reproduction number (R[SUB]t[/SUB]) was proposed, which extracts the environment-related components from R[SUB]t[/SUB] to estimate the relationship between environmental factors and influenza transmission based on a mixed-effects regression model. Nonlinear relationships for absolute humidity (the lowest transmission was observed at absolute humidity of 12 g/m[SUP]3[/SUP]) and mean temperature (the lowest transmission was observed at the mean temperature of 18 [SUP]°[/SUP]C) with influenza transmission were observed. Influenza transmission holds almost constant with the average precipitation below 1 mm or sunshine hour below 9 h/day, but increases for the precipitation and decreases for the sunshine hour afterward. The environmental dependence varies across subtypes: A(H3N2) maintains relatively higher transmission in high temperature and humidity conditions, compared with other influenza subtypes. Overall, the subtypes specified environmental dependence of influenza transmission could explain 23.1 %, 29.2 % and 27.1 % of the variations for A(H1N1)pdm09, A(H3N2) and B-lineage in China. The projected seasonal transmission rates based on our approach could be used as a valuable seasonal proxy to model the influenza dynamics under various meteorological spaces. Finally, our approach is also applicable to obtain novel insights into the impact of environmental factors on other respiratory infectious diseases.
Keywords: Environmental dependence; Mixed-effects regression; Nonlinear relationship; Reproduction number; Seasonal influenza.
. 2022 Sep 5;158525.
doi: 10.1016/j.scitotenv.2022.158525. Online ahead of print.
Subtypes specified environmental dependence of seasonal influenza virus
Bing Zhang[SUP] 1 [/SUP], Tao Chen[SUP] 2 [/SUP], Shiwen Liang[SUP] 3 [/SUP], Wei Shen[SUP] 4 [/SUP], Qianru Sun[SUP] 5 [/SUP], Daoze Wang[SUP] 5 [/SUP], Gang Wang[SUP] 5 [/SUP], Jing Yang[SUP] 2 [/SUP], Lei Yang[SUP] 2 [/SUP], Dayan Wang[SUP] 6 [/SUP], Yuelong Shu[SUP] 7 [/SUP], Xiangjun Du[SUP] 8 [/SUP]
Affiliations
- PMID: 36075410
- DOI: 10.1016/j.scitotenv.2022.158525
Abstract
Understanding the role of meteorological factors in the transmission dynamics of respiratory infectious diseases remains challenging. Our study was to comprehensively investigate the nonlinear effects of environmental factors on influenza transmission, based on multi-region surveillance data from mainland China. An approach related to time-varying reproduction number (R[SUB]t[/SUB]) was proposed, which extracts the environment-related components from R[SUB]t[/SUB] to estimate the relationship between environmental factors and influenza transmission based on a mixed-effects regression model. Nonlinear relationships for absolute humidity (the lowest transmission was observed at absolute humidity of 12 g/m[SUP]3[/SUP]) and mean temperature (the lowest transmission was observed at the mean temperature of 18 [SUP]°[/SUP]C) with influenza transmission were observed. Influenza transmission holds almost constant with the average precipitation below 1 mm or sunshine hour below 9 h/day, but increases for the precipitation and decreases for the sunshine hour afterward. The environmental dependence varies across subtypes: A(H3N2) maintains relatively higher transmission in high temperature and humidity conditions, compared with other influenza subtypes. Overall, the subtypes specified environmental dependence of influenza transmission could explain 23.1 %, 29.2 % and 27.1 % of the variations for A(H1N1)pdm09, A(H3N2) and B-lineage in China. The projected seasonal transmission rates based on our approach could be used as a valuable seasonal proxy to model the influenza dynamics under various meteorological spaces. Finally, our approach is also applicable to obtain novel insights into the impact of environmental factors on other respiratory infectious diseases.
Keywords: Environmental dependence; Mixed-effects regression; Nonlinear relationship; Reproduction number; Seasonal influenza.