Epidemic features of seasonal influenza transmission among eight different climate zones in Gansu, China

Liu, Haixia; Zhang, Yuzhou; Yuan, Tian; Zheng, Yan; Gou, Faxiang; Yang, Xiaoting; He, Jian; Liu, Xinfeng; Meng, Lu

doi:10.1016/j.envres.2020.109189

Cited by 15 publications

(17 citation statements)

References 35 publications

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“…Our results are in good agreement with other studies, concluding that mean temperature and relative humidity parameters play an important role in the COVID-19 epidemic ( Liu et al, 2020 ; Liu et al, 2020 ; Xie and Zhu, 2020 ; Tobías and Molina, 2020 ; Tantrakarnapa et al, 2020 ; Pani et al, 2020 ). Regarding weather temperature, although most findings suggested a negative correlation between temperature and the COVID-19 epidemic ( Zoran et al, 2022 ; Nevels et al, 2021 ; Hossain et al, 2021 ; Pahuja et al, 2021 ; Meyer et al, 2020 ; Shi et al, 2020 ; Liu et al, 2020 ; Tobías and Molina, 2020 ; Menebo, 2020 ; Kumar & Kumar, 2020 ; Li et al, 2020 ; Menebo, 2020 , 2020 ; Prata et al, 2020 ), some research indicated that temperature positively affects epidemics ( Xie and Zhu, 2020 ; Iqbal et al, 2020 ; Islam et al, 2021 ; Pani et al, 2020 ; Tosepu et al, 2020 ), and a few found no relationship ( Jahangiri et al, 2020 ; Briz-Redón and Serrano-Aroca, 2020 ).…”

Section: Resultssupporting

confidence: 93%

“…Although there have been several studies that explored the impact of weather conditions on the COVID-19 epidemic in China ( Shi et al, 2020 ; Liu et al, 2020 ; Xie and Zhu, 2020 ; Ma et al, 2020 ), these studies only analyzed and discussed the relationship between weather conditions and outbreaks without further indicating which cities in China were more likely to have outbreaks based on the results. Our study filled in the void.…”

Section: Resultsmentioning

confidence: 99%

“…Much of the literature on this discussion suggests a correlation between temperature and the spread of COVID-19. Although most literature believes that temperature negatively affects COVID-19 spread, with higher temperatures resulting in fewer confirmed cases ( Nevels et al, 2021 ; Hossain et al, 2021 ; Meyer et al, 2020 ; Shi et al, 2020 ; Liu et al, 2020 ), some literature suggests that temperature is positively correlated with COVID-19 transmission ( Sangkham et al, 2021 ; Iqbal et al, 2020 ; Islam et al, 2021 ; Pani et al, 2020 ). Only a few studies denies any correlation between temperature and outbreaks ( Jahangiri et al, 2020 ; Briz-Redón and Serrano-Aroca, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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The influence of weather conditions on the COVID-19 epidemic

Lin

Wang

Huang

2022

Environmental Research

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Studying the influence of weather conditions on the COVID-19 epidemic is an emerging field. However, existing studies in this area tend to utilize time-series data, which have certain limitations and fail to consider individual, social, and economic factors. Therefore, this study aimed to fill this gap. In this paper, we explored the influence of weather conditions on the COVID-19 epidemic using COVID-19-related prefecture-daily panel data collected in mainland China between January 1, 2020, and February 19, 2020. A two-way fixed effect model was applied taking into account factors including public health measures, effective distance to Wuhan, population density, economic development level, health, and medical conditions. We also used a piecewise linear regression to determine the relationship in detail. We found that there is a conditional negative relationship between weather conditions and the epidemic. Each 1 °C rise in mean temperature led to a 0.49% increase in the confirmed cases growth rate when mean temperature was above −7 °C. Similarly, when the relative humidity was greater than 46%, it was negatively correlated with the epidemic, where a 1% increase in relative humidity decreased the rate of confirmed cases by 0.19%. Furthermore, prefecture-level administrative regions, such as Chifeng (included as “warning cities”) have more days of “dangerous weather”, which is favorable for outbreaks. In addition, we found that the impact of mean temperature is greatest in the east, the influence of relative humidity is most pronounced in the central region, and the significance of weather conditions is more important in the coastal region. Finally, we found that rising diurnal temperatures decreased the negative impact of weather conditions on the spread of COVID-19. We also observed that strict public health measures and high social concern can mitigate the adverse effects of cold and dry weather on the spread of the epidemic. To the best of our knowledge, this is the first study which applies the two-way fixed effect model to investigate the influence of weather conditions on the COVID-19 epidemic, takes into account socio-economic factors and draws new conclusions.

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Section: Resultssupporting

confidence: 93%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The influence of weather conditions on the COVID-19 epidemic

Lin

Wang

Huang

2022

Environmental Research

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“…The COVID associated to human beings with the seasonal flu and common cold was testified to remain workable only for a short duration of 3 h on the different surfaces in an arid environment, though it remains workable for more duration in humid environment Influenza and RSV viruses reported viable for 2 and 6 h respectively after drying on surfaces (Artika et al, 2020;Beeler and Eichelberger, 2013;Crisci et al, 2013;Liu et al, 2020d;Zhang et al, 2020). Human coronavirus (229E) on aerosolized shape is mostly less stable in high humidity and temperature (Altamimi and Ahmed, 2019;Brugger and Rubel, 2009;Chan et al, 2011;Gould, 2009;Hoogeveen, 2020;Li et al, 2018;Liu et al, 2020a;Liu et al, 2020c;Lyth and Holbrook, 2015;Ma et al, 2020;Mas-Coma et al, 2020;Morin et al, 2018;Nicholson and Munoz, 2018;Otter et al, 2016;Schwartz, 2020;Sirisena and Noordeen, 2014).…”

Section: Resultsmentioning

confidence: 99%

“…Optimum climatic parameters such as temperature, wind speed, relative humidity, and precipitation can be considered as leading variable activating the existence and diffusion of the viruses. Furthermore, weather changes and regional climate are also verily associated with the pneumonia fatalities (Bhowmick et al, 2020;Hoogeveen, 2020;Liu et al, 2020a;Liu et al, 2020b;Ma et al, 2020;Qi et al, 2020;Schwartz, 2020;Tobías and Molina, 2020;Wang et al, 2020;Zambrano-Monserrate et al, 2020;Zhang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

The effects of regional climatic condition on the spread of COVID-19 at global scale

Iqbal

Abid

Hussain

et al. 2020

Science of The Total Environment

109

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Does ambient particulate matter 1 increase the risk of gastric cancer in the northwest of China?

Liu,

Gan,

et al. 2024

Intl Journal of Cancer

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Gastric cancer (GC) remains a significant health concern in Gansu province, China, with morbidity and mortality rates surpassing national averages. Despite the recognized health risks associated with ambient particulate matter with an aerodynamic diameter <1 μm (PM1), the relationship between PM1 exposure and GC incidence has not been extensively studied. Data on GC cases from 2013 to 2021 were gathered from 262 hospitals in Gansu, China. Concurrently, data on the normalized vegetation index (NDVI), gross domestic product (GDP), drinking and smoking behavioral index (DSBI), PM1, PM2.5, and PM2.5–1 were collected. Utilizing a Bayesian conditional autoregressive (CAR) combined generalized linear model (GLM) with quasi‐Poisson regression, we evaluated the impact of PM1, PM2.5, PM2.5–1, NDVI, DSBI, and GDP on GC morbidity while adjusting for potential confounders. Our analysis indicated that exposure to PM1 (μg/m3) is significantly positively correlated with GC incidence in regions with an overall age‐standardized incidence rate (ASIR) >40 (relative risks [RR]: 1.023, 95% confidence intervals [CI, 1.007, 1.039]), male ASIR >50 (RR: 1.014, 95% CI [1.009, 1.019]), and female ASIR >20 (RR: 1.010, 95% CI [1.002, 1.018]). PM2.5, PM2.5–1, DSBI, and GDP were positively correlated with GC incidence, while NDVI was negatively correlated in the study regions. Our findings provided evidence of a positive correlation between PM1 exposure and GC incidence in high‐risk areas of GC within arid regions. Further research is warranted to elucidate the complex nonlinear relationships between environmental factors and GC. These insights could inform strategies for improving the control and prevention of GC in Gansu and similar regions.

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Epidemic features of seasonal influenza transmission among eight different climate zones in Gansu, China

Cited by 15 publications

References 35 publications

The influence of weather conditions on the COVID-19 epidemic

The influence of weather conditions on the COVID-19 epidemic

The effects of regional climatic condition on the spread of COVID-19 at global scale

Does ambient particulate matter 1 increase the risk of gastric cancer in the northwest of China?

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