Association between meteorological variations and activities of influenza A and B across different climate zones: a multi-region modelling analysis across the globe

Chong, Ka Chun; Lee, Tsz Cheung; Bialasiewicz, Seweryn; Chen, Jian; Smith, David W.; Choy, Wisely S.C.; Krajden, Mel; Jalal, Hamid; Jennings, Lance C.; Alexander, Burmaa; Lee, Hong Kai; Fraaij, Pieter L. A.; Levy, Avram; Yeung, Albert T.; Tozer, Sarah; Lau, Shun; Jia, Katherine Min; Tang, Julian W.; Hui, David Shu-Cheong; Chan, Paul Kay Sheung

doi:10.1016/j.jinf.2019.09.013

Cited by 65 publications

(68 citation statements)

References 46 publications

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“…Except for the negative relationships, we also observed that during some time periods, changes in temperature lead the prevalence of A(H1N1)pdm09 and influenza virus B by 2-3 weeks (2015-2016) and 1-2 weeks (2013-2014), respectively. Therefore, temperature may be a good predictor of influenza virus epidemics, not only influenza virus A, but also influenza virus B, which was consistent with the results of Chong et al (2019) and Peci et al (2019). Indeed, most of the studies demonstrated that temperature was a dominant determinant of influenza virus A and B in temperate regions, which was observed in a guinea pig model (Lowen & Steel, 2014).…”

Section: Discussionsupporting

confidence: 81%

“…The color code for power ranges from pink (low power) to white (high power). Azziz Baumgartner et al, 2012;Chong et al, 2019). Through WTC analysis and MWC, we concluded that there were statistically significant associations between atmospheric pressure, temperature and humidity (RH and AH) and influenza seasonality.…”

Section: Discussionmentioning

confidence: 79%

“…Tamerius et al (2013) and Bloom-Feshbach et al (2013) showed that the onset of influenza epidemics is associated with the cold-dry and humid-rainy condition in temperate and tropical areas, respectively. After analyzing the relative reports of 11 locations in different climate regions, Chong et al (2019) found that temperature and AH were the major determinant to influenza A and influenza B and RH was less consistent with influenza B activity, while Peci et al (2019) reported the negative relationship of both AH and temperature with influenza virus A and B and a controversial effect of RH on influenza virus A and B. Although influenza patterns have been well described in developed temperate regions, those involving developing temperate areas are limited.…”

Section: Introductionmentioning

confidence: 99%

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Seasonal pattern of influenza and the association with meteorological factors based on wavelet analysis in Jinan City, Eastern China, 2013–2016

Liu

Geng

et al. 2020

PeerJ

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Background Influenza is a disease under surveillance worldwide with different seasonal patterns in temperate and tropical regions. Previous studies have conducted modeling of influenza seasonality using climate variables. This study aimed to identify potential meteorological factors that are associated with influenza seasonality in Jinan, China. Methods Data from three influenza sentinel hospitals and respective climate factors (average temperature, relatively humidity (RH), absolute humidity (AH), sunshine duration, accumulated rainfall and speed of wind), from 2013 to 2016, were collected. Statistical and wavelet analyses were used to explore the epidemiological characteristics of influenza virus and its potential association with climate factors. Results The dynamic of influenza was characterized by annual cycle, with remarkable winter epidemic peaks from December to February. Spearman’s correlation and wavelet coherence analysis illuminated that temperature, AH and atmospheric pressure were main influencing factors. Multiple wavelet coherence analysis showed that temperature and atmospheric pressure might be the main influencing factors of influenza virus A(H3N2) and influenza virus B, whereas temperature and AH might best shape the seasonality of influenza virus A(H1N1)pdm09. During the epidemic season, the prevalence of influenza virus lagged behind the change of temperature by 1–8 weeks and atmospheric pressure by 0.5–3 weeks for different influenza viruses. Conclusion Climate factors were significantly associated with influenza seasonality in Jinan during the influenza epidemic season and the optional time for influenza vaccination is before November. These finding should be considered in influenza planning of control and prevention.

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Section: Discussionsupporting

confidence: 81%

Section: Discussionmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

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Seasonal pattern of influenza and the association with meteorological factors based on wavelet analysis in Jinan City, Eastern China, 2013–2016

Liu

Geng

et al. 2020

PeerJ

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“…Su et al [17] observed a negative association between average temperature and three influenza virus subtype by using of Spearman's correlation and WTC. Chong et al [49] verified that the temperature may be connected with the influenza virus epidemic, not only influenza virus A, but also influenza virus B. In this study, we also observed that minimum temperature is negatively associated with influenza cases, which was consistent with the finding of related studies of Lowen and Steel [17].…”

Section: Resultssupporting

confidence: 92%

Identifying the Dynamic Pattern and Influencing Factors of Influenza in Northwest China from 2013 to 2020, Based on Dynamic Regression Model and Wavelet Analysis

Wang

et al. 2020

Preprint

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Background: Influenza remains a serious global public health problem and a substantial economic burden. The dynamic pattern of influenza differs considerably among geographic and climatological areas, however, the factors underlying these differences are still uncertain. The aim of this paper is to characterize the dynamic pattern of influenza and its potential influencing factors in Northwest China. Methods: Influenza cases in Ningxia China from Nov. 2013 to Jun. 2020 were served as influenza proxy. Firstly, the baseline seasonal ARIMA model of influenza cases and seasonal pattern were analyzed. Then, the dynamic regression model was used to identifying the potential influencing factors of influenza. In addition, the wavelet analysis was further used to explore the coherence between influenza cases and these significant influencing factors.Results: The high risk periods of influenza in Ningxia presented a winter cycle outbreaks pattern and the fastigium came in January. The seasonal ARIMA(0,0,1)(1,1,0)12 was the optimal baseline forecast model. The dynamic regression models and wavelet analysis indicated that PM2.5 and public awareness are significantly positively associated with influenza, as well as minimum temperature is negatively associated. Conclusion: Meteorological (minimum temperature), pollution (PM2.5) and social (public awareness) factors may significantly associated with influenza in Northwest China. Decreasing PM2.5 concentration or increasing the public awareness prior to the fastigium of influenza may be the serviceable methods to reduce the disease risk of influenza, which have an important implication for policy-makers to choose an optimal time for influenza prevention campaign.

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“…Research on other respiratory diseases, such as influenza and severe acute respiratory syndrome (SARS) [30] have highlighted the importance of climatic variables in the transmission of these diseases. In addition, the marked change in ambient temperature was associated with a greater transmission capacity of SARS [31,32].…”

Section: Discussionmentioning

confidence: 99%

How Climate Variables Influence the Spread of SARS-CoV-19 in the United States

et al. 2020

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During the 2020 Coronavirus pandemic, several scientific types of research investigated the causes of high transmissibility and deaths caused by SARS-CoV-2. Among the spreading factors of the disease, it is known that there is an association between temperature and infected people. However, the studies that identified this phenomenon explored an association relationship, which is weaker and does not allow the identification of which variable would be the cause. This study aimed to analyze the impact of temperature variations and other climatic variables on the infection rate of COVID-19. Data were extracted from weather stations in the United States, which were segregated by county and day. Daily COVID-19 infections and deaths per county were also collected. Two models were used: the first model to analyze the temperature and the number of infected cases and the second model to evaluate the variables of temperature, precipitation, and snow in relation to COVID-19 infection. Model 1 shows that an increase in temperature at time zero caused a decrease in the number of infected cases. Meanwhile, a decrease in temperature after the temperature shock was associated with an increase in the number of cases, which tended to zero overall. A 1% increase in temperature caused a 0.002% decrease in the number of cases. The results suggested a causal relationship between the average temperature and number of CODIV-19 cases. Model 2, which includes temperature, precipitation, and snow shows that an increase in temperature resulted in a 0.00154% decrease response. There was no significant effect of increased precipitation and snow on the infection rate with COVID-19.

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Association between meteorological variations and activities of influenza A and B across different climate zones: a multi-region modelling analysis across the globe

Cited by 65 publications

References 46 publications

Seasonal pattern of influenza and the association with meteorological factors based on wavelet analysis in Jinan City, Eastern China, 2013–2016

Seasonal pattern of influenza and the association with meteorological factors based on wavelet analysis in Jinan City, Eastern China, 2013–2016

Identifying the Dynamic Pattern and Influencing Factors of Influenza in Northwest China from 2013 to 2020, Based on Dynamic Regression Model and Wavelet Analysis

How Climate Variables Influence the Spread of SARS-CoV-19 in the United States

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