Effect of meteorological factors on the activity of influenza in Chongqing, China, 2012–2019

Li, Qi; Liu, Tian; Gao, Yuan; Tian, Dechao; Li, Qin; Su, Kun; Tang, Wenge; Feng, Luzhao; Liu, Qiyong

doi:10.21203/rs.3.rs-36868/v2

Cited by 4 publications

(7 citation statements)

References 26 publications

(30 reference statements)

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“…In this study, it was found that temp, DTR, RH and SD were significantly associated with Flu-A and Flu-B based on different time lags which were similar to previous studies conducted in neighboring cities ( Zhang et al., 2020 ; Ma et al., 2021 ; Qi et al., 2021 ).…”

Section: Discussionsupporting

confidence: 89%

“…The model was used for each meteorological variable including temp, DTR, RH, and SD. The maximum lag day was defined as 27 days, which was based on the incubation period of influenza and potential lagged effects revealed by previous studies ( Guo et al., 2019 ; Qi et al., 2021 ). Akaike information criterion (AIC) was used to select the dfs for the meteorological variable in each model and the lowest AIC score was the most fitted model.…”

Section: Methodsmentioning

confidence: 99%

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Association between multiple meteorological variables and seasonal influenza A and B virus transmission in Macau

Zhang

et al. 2022

Heliyon

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Section: Discussionsupporting

confidence: 89%

Section: Methodsmentioning

confidence: 99%

Association between multiple meteorological variables and seasonal influenza A and B virus transmission in Macau

Zhang

et al. 2022

Heliyon

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“…This founding was similar with previous studies. (9,22) One hypothesis was that the lack of sunshine reduced the synthesis of melatonin and vitamin D in the body, which decreased human immunity. (37) Melatonin could activate intracellular signalling pathways and transcription factors, thereby inhibiting in ammatory activity.…”

Section: Discussionmentioning

confidence: 99%

“…The maximum lag day was de ned as 27 days, which was based on the incubation period of in uenza and infectious period of in uenza virus from the previous studies. (12,22) Akaike Information criterion (AIC) was used to select the dfs for meteorological variable in each model and the lowest AIC score was the most tted model. The AIC values were summarized in Table S1.…”

Section: Discussionmentioning

confidence: 99%

The association between multiple meteorological factors and seasonal influenza A and B virus transmission in Macau

Zhang

et al. 2022

Preprint

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Background and objectives: Few studies evaluated the influence of meteorological factors on different influenza types activity in subtropical regions. The study aimed to explore the association between meteorological factors and the onset of influenza A (Flu-A) and B (Flu-B) in Macau.Methods: Daily influenza cases data were collected from Kiang Wu Hospital from January 1st, 2014 to December 31st, 2018 in Macau. Daily meteorological data were obtained from Macau Meteorological Service. The distributed lag non-linear model (DLNM) was used to estimate the effects of meteorological factors on the seasonal influenza outbreak.Results：Mean temperature had the peaking risks of both types at 4.0°C and 28.0°C. For the diurnal temperature range (DTR), the peaks of the cumulative relative risk (RR) of Flu-A were at 1.0°C and 5.0°C, while the cumulative RR of Flu-B increased as the DTR decreased. The association between influenza and relative humidity were showed a U-shape curve. The risk of influenza increased when the relative humidity was below 50% or above 90%. Both types increased significantly when the sunshine duration was below about 3.5 hours. Taking the median value as the reference, a significant cold effect was observed along 16-24 lag days in Flu-A. The lag effects were found in low-DTR, humid and short sunshine conditions in both types.Conclusions：This study revealed the complex non-linear association between meteorological factors and the different influenza types activity in Macau. The finding helped to develop an early warning system to improve the response capacity to influenza epidemics.

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“…More interestingly, at an AT of-5.35°C, the risk of in uenza incidence was highest, and AT was negatively correlated with in uenza incidence in all age groups. Several studies have shown that temperature changes signi cantly impact in uenza transmission and that the relative risks (RRs) of in uenza activity increase with the decrease in weekly AT, and the in uenza infection rate decreases by 1.1% for every 1°C increase [24,25]. Another study has shown that for in uenza A or B, the temperature and the incidence of in uenza have a non-linear negative correlation.…”

Section: Discussionmentioning

confidence: 99%

Analysis of meteorological factors influencing the incidence of influenza in Fujian Province based on a neural network model

Yuan

Lan

et al. 2022

Preprint

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Objective: This study aimed to assess and compare the predictive effects of meteorological factors on the incidence of influenza in Fujian Province, China,using four different deep learning network models.Methods: From 2016 to 2020,weekly meteorological and influenza surveillance data in Fujian Province were collected. Using four different deep learning network models, including ordinary neural network (ANN), deep neural network (DNN), recurrent neural network (RNN), and gated recurrent unit (GRU), the prediction model of the weekly average temperature, influenza lag and influenza incidence were determined, and the predictive effects from each different models were compared.Results: The incidence of influenza in Fujian Province showed obvious seasonality, with a high incidence in winter, especially from November to March, during which influenza incidence reached the highest value each year. A non-linear negative correlation between temperature and incidence of influenza was obtained. Compared with the prediction model that only considers “temperature” as a factor, the model that includes both temperature and lag had a better predictive effect. Overall, the GRU model, with three hidden layers (constructed from temperature, influenza lag of one week and two weeks), had the best prediction ability, followed by RNN, DNN, and ANN, respectively.Conclusion: Temperature and influenza incidence showed a non-linear negative correlation. Furthermore, the GRU model provides a better prediction of the influenza incidence and, therefore, can be used to develop an influenza risk early warning system based on temperature and influenza lag, to prevent the incidence and spread of influenza.

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Effect of meteorological factors on the activity of influenza in Chongqing, China, 2012–2019

Cited by 4 publications

References 26 publications

Association between multiple meteorological variables and seasonal influenza A and B virus transmission in Macau

Association between multiple meteorological variables and seasonal influenza A and B virus transmission in Macau

The association between multiple meteorological factors and seasonal influenza A and B virus transmission in Macau

Analysis of meteorological factors influencing the incidence of influenza in Fujian Province based on a neural network model

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