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

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

doi:10.1371/journal.pone.0246023

Cited by 18 publications

(18 citation statements)

References 30 publications

(40 reference statements)

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“…However, conflicting views have been reported regarding the impact of RH on influenza. The findings of the current study on RH were not in line with those reported by studies in Poland, Zhejiang, and Chongqing, in which the influenza incidence was moderately positively correlated with RH, and higher RH could increase the risk of influenza incidence (42)(43)(44). The difference in the results may be because of the difference in the latitude, climate type, demographic characteristics of the study area, and the statistical methods and models used.…”

Section: Discussioncontrasting

confidence: 99%

Effects and Interaction of Meteorological Parameters on Influenza Incidence During 2010–2019 in Lanzhou, China

Wang

Zhang

Lei

et al. 2022

Front. Public Health

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BackgroundInfluenza is a seasonal infectious disease, and meteorological parameters critically influence the incidence of influenza. However, the meteorological parameters linked to influenza occurrence in semi-arid areas are not studied in detail. This study aimed to clarify the impact of meteorological parameters on influenza incidence during 2010–2019 in Lanzhou. The results are expected to facilitate the optimization of influenza-related public health policies by the local healthcare departments.MethodsDescriptive data related to influenza incidence and meteorology during 2010–2019 in Lanzhou were analyzed. The exposure-response relationship between the risk of influenza occurrence and meteorological parameters was explored according to the distributed lag no-linear model (DLNM) with Poisson distribution. The response surface model and stratified model were used to estimate the interactive effect between relative humidity (RH) and other meteorological parameters on influenza incidence.ResultsA total of 6701 cases of influenza were reported during 2010–2019. DLNM results showed that the risk of influenza would gradually increase as the weekly mean average ambient temperature (AT), RH, and absolute humidity (AH) decrease at lag 3 weeks when they were lower than 12.16°C, 51.38%, and 5.24 g/m3, respectively. The low Tem (at 5th percentile, P5) had the greatest effect on influenza incidence; the greatest estimated relative risk (RR) was 4.54 (95%CI: 3.19–6.46) at cumulative lag 2 weeks. The largest estimates of RRs for low RH (P5) and AH (P5) were 4.81 (95%CI: 3.82–6.05) and 4.17 (95%CI: 3.30–5.28) at cumulative lag 3 weeks, respectively. An increase in AT by 1°C led to an estimates of percent change (95%CI) of 3.12% (−4.75% to −1.46%) decrease in the weekly influenza case counts in a low RH environment. In addition, RH showed significant interaction with AT and AP on influenza incidence but not with wind speed.ConclusionThis study indicated that low AT, low humidity (RH and AH), and high air pressure (AP) increased the risk of influenza. Moreover, the interactive effect of low RH with low AT and high AP can aggravate the incidence of influenza.

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Section: Discussioncontrasting

confidence: 99%

Effects and Interaction of Meteorological Parameters on Influenza Incidence During 2010–2019 in Lanzhou, China

Wang

Zhang

Lei

et al. 2022

Front. Public Health

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“…The seemingly contradictory results further emphasize the complexity of the influence of climate factors and geographical environment on influenza activity. Air pressure was positively correlated with influenza activity, while influenza was negatively correlated with sunshine hours and daily mean wind speed, which was consistent with the results of Qi et al [ 65 ] and previous research in Lanzhou from 2005 to 2010 [ 66 ]. When the air pressure is higher, the diffused virus in the air and is not easy to dissipate, resulting in an increase in influenza patients.…”

Section: Discussionsupporting

confidence: 91%

Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014–2017

Zhu

Yin

et al. 2022

PLoS ONE

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Humans are susceptible to influenza. The influenza virus spreads quickly and behave seasonally. The seasonality and spread of influenza are often associated with meteorological factors and have spatio-temporal differences. Based on the influenza cases and daily average meteorological factors in Lanzhou from 2014 to 2017, this study firstly aimed to analyze the characteristics of influenza incidence in Lanzhou and the impact of meteorological factors on influenza activities. Then, SARIMA(X) models for the prediction were established. The influenza cases in Lanzhou from 2014 to 2017 was more male than female, and the younger the age, the higher the susceptibility; the epidemic characteristics showed that there is a peak in winter, a secondary peak in spring, and a trough in summer and autumn. The influenza cases in Lanzhou increased with increasing daily pressure, decreasing precipitation, average relative humidity, hours of sunshine, average daily temperature and average daily wind speed. Low temperature was a significant driving factor for the increase of transmission intensity of seasonal influenza. The SARIMAX (1,0,0)(1,0,1)[12] multivariable model with average temperature has better prediction performance than the university model. This model is helpful to establish an early warning system, and provide important evidence for the development of influenza control policies and public health interventions.

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“…Our study showed higher test‐positive rates for RSV‐A, RSV‐B, and HRV among children in Chongqing compared with the other three cities, consistent with results from a study at a children's hospital in Chongqing during 2013–2018 31 . Meteorological factors in Chongqing have been shown to be associated with influenza and pertussis 32,33 …”

Section: Discussionmentioning

confidence: 99%

Interactions among acute respiratory viruses in Beijing, Chongqing, Guangzhou, and Shanghai, China, 2009–2019

Madewell,

Wang,

Dean

et al. 2023

Influenza Resp Viruses

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BackgroundA viral infection can modify the risk to subsequent viral infections via cross‐protective immunity, increased immunopathology, or disease‐driven behavioral change. There is limited understanding of virus–virus interactions due to lack of long‐term population‐level data.MethodsOur study leverages passive surveillance data of 10 human acute respiratory viruses from Beijing, Chongqing, Guangzhou, and Shanghai collected during 2009 to 2019: influenza A and B viruses; respiratory syncytial virus A and B; human parainfluenza virus (HPIV), adenovirus, metapneumovirus (HMPV), coronavirus, bocavirus (HBoV), and rhinovirus (HRV). We used a multivariate Bayesian hierarchical model to evaluate correlations in monthly prevalence of test‐positive samples between virus pairs, adjusting for potential confounders.ResultsOf 101,643 lab‐tested patients, 33,650 tested positive for any acute respiratory virus, and 4,113 were co‐infected with multiple viruses. After adjusting for intrinsic seasonality, long‐term trends and multiple comparisons, Bayesian multivariate modeling found positive correlations for HPIV/HRV in all cities and for HBoV/HRV and HBoV/HMPV in three cities. Models restricted to children further revealed statistically significant associations for another ten pairs in three of the four cities. In contrast, no consistent correlation across cities was found among adults. Most virus–virus interactions exhibited substantial spatial heterogeneity.ConclusionsThere was strong evidence for interactions among common respiratory viruses in highly populated urban settings. Consistent positive interactions across multiple cities were observed in viruses known to typically infect children. Future intervention programs such as development of combination vaccines may consider spatially consistent virus–virus interactions for more effective control.

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

Cited by 18 publications

References 30 publications

Effects and Interaction of Meteorological Parameters on Influenza Incidence During 2010–2019 in Lanzhou, China

Effects and Interaction of Meteorological Parameters on Influenza Incidence During 2010–2019 in Lanzhou, China

Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014–2017

Interactions among acute respiratory viruses in Beijing, Chongqing, Guangzhou, and Shanghai, China, 2009–2019

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