A four year seasonal survey of the relationship between outdoor climate and epidemiology of viral respiratory tract infections in a temperate climate

Sundell, Nicklas; Andersson, Lars‐Magnus; Brittain-Long, Robin; Lindh, Magnus; Westin, Johan

doi:10.1016/j.jcv.2016.10.005

Cited by 76 publications

(76 citation statements)

References 45 publications

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“…As we have reported earlier, RV is the most frequently detected virus in patients with respiratory symptoms, without marked seasonality in a temperate climate (32). In the present study, RV was the major finding in asymptomatic and in symptomatic adults.…”

Section: Discussionsupporting

confidence: 82%

PCR Detection of Respiratory Pathogens in Asymptomatic and Symptomatic Adults

et al. 2019

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The frequency of viral respiratory pathogens in asymptomatic subjects is poorly defined. The aim of this study was to explore the prevalence of respiratory pathogens in the upper airways of asymptomatic adults, compared with a reference population of symptomatic patients sampled in the same centers during the same period. Nasopharyngeal (NP) swab samples were prospectively collected from adults with and without ongoing symptoms of respiratory tract infection (RTI) during 12 consecutive months, in primary care centers and hospital emergency departments, and analyzed for respiratory pathogens by a PCR panel detecting 16 viruses and four bacteria. Altogether, 444 asymptomatic and 75 symptomatic subjects completed sampling and follow-up (FU) at day 7. In the asymptomatic subjects, the detection rate of viruses was low (4.3%), and the most common virus detected was rhinovirus (3.2%). Streptococcus pneumoniae was found in 5.6% of the asymptomatic subjects and Haemophilus influenzae in 1.4%. The only factor independently associated with low viral detection rate in asymptomatic subjects was age Ն65 years (P ϭ 0.04). An increased detection rate of bacteria was seen in asymptomatic subjects who were currently smoking (P Ͻ 0.01) and who had any chronic condition (P Ͻ 0.01). We conclude that detection of respiratory viruses in asymptomatic adults is uncommon, suggesting that a positive PCR result from a symptomatic patient likely is relevant for ongoing respiratory symptoms. Age influences the likelihood of virus detection among asymptomatic adults, and smoking and comorbidities may increase the prevalence of bacterial pathogens in the upper airways.

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Section: Discussionsupporting

confidence: 82%

PCR Detection of Respiratory Pathogens in Asymptomatic and Symptomatic Adults

et al. 2019

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“…Our study indicated that decline in RH promoted influenza activity, and the result is consistent with previous studies. Several previous studies indicated that low RH can favour the transmission of influenza (Hemmes et al, 1960;Lowen et al, 2007;Schaffer et al, 1976;Sundell et al, 2016). Low RH can allow influenza virus particles to remain in the air for longer time because of the smaller size and lower velocity of settling (Yang and Marr, 2011).…”

Section: N=100%mentioning

confidence: 99%

The complex associations of climate variability with seasonal influenza A and B virus transmission in subtropical Shanghai, China

Zhang

et al. 2020

Science of The Total Environment

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h i g h l i g h t sHigh temperature only associated with influenza A occurrence. High diurnal temperature range (DTR) causes more influenza A cases. Low DTR causes more influenza B cases. High DTR and low temperature were the key drivers for influenza A and B separately. g r a p h i c a l a b s t r a c t X-axis: The value of climate variable; Y-axis: 2-weeks lagged cumulative relative risk (RR), indicating the number of times more likely to have influenza compared to reference value (Ref); Solid line: RR value; Grey shadow: 95% confidence interval (95% CI). a b s t r a c tMost previous studies focused on the association between climate variables and seasonal influenza activity in tropical or temperate zones, little is known about the associations in different influenza types in subtropical China. The study aimed to explore the associations of multiple climate variables with influenza A (Flu-A) and B virus (Flu-B) transmissions in Shanghai, China. Weekly influenza virus and climate data (mean temperature (MeanT), diurnal temperature range (DTR), relative humidity (RH) and wind velocity (Wv)) were collected between June 2012 and December 2018. Generalized linear models (GLMs), distributed lag non-linear models (DLNMs) and regression tree models were developed to assess such associations. MeanT exerted the peaking risk of Flu-A at 1.4°C (2-weeks' cumulative relative risk (RR): 14.88, 95% confidence interval (CI): 8.67-23.31) and 25.8°C (RR: 12.21, 95%CI: 6.64-19.83), Flu-B had the peak at 1. 4°C (RR: 26.44,). The highest RR of Flu-A was 23.05 (95%CI: 5.12-88.45) at DTR of 15.8°C, that of Flu-B was 38.25 (95%CI: 15.82-87.61) at 3.2°C. RH of 51.5% had the highest RR of Flu-A (9.98, 95%CI: 4.03-26.28) and Flu-B (4.63, 95%CI: 1.95-11.27). Wv of 3.5 m/s exerted the peaking RR of Flu-A (7.48, 95%CI: 2.73-30.04) and . DTR 12°C and MeanT <22°C were the key drivers for Flu-A and Flu-B, separately. The study found j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l oc a t e / s c i t o t e n v complex non-linear relationships between climate variability and different influenza types in Shanghai. We suggest the careful use of meteorological variables in influenza prediction in subtropical regions, considering such complex associations, which may facilitate government and health authorities to better minimize the impacts of seasonal influenza.

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“…A further limitation is that, for simplicity, only weather information from the capitals were used in the study, but for countries like Japan, Thailand and Taiwan, there are great variation between climatic conditions in the North and the South. Prediction accuracy might improve if [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] incidence and weather information can be collected at a finer resolution. We suspect that even the accuracy of short term forecasts may be reduced should new epidemiological conditions replace those that the model was trained on.…”

Section: Limitationsmentioning

confidence: 99%

“…Existing approaches to real-time forecasting include generalized linear regression, seasonal autoregressive integrated moving average (SARIMA) model or a simpler ARIMA form of it, phenomenological models like the logistic growth model and Richards model, and mechanistic models like the SIR models [19][20][21][22][23][24]. Often such approaches involve the challenge of integrating environmental factors including temperature, humidity and rainfall, which may influence pathogen transmission directly or affect the vector activities (for vector borne diseases), especially in temperate regions [25][26][27][28][29]. For instance, influenza virus is more transmissible in low temperature and low humidity conditions [30,31], while the primary vector of dengue, the yellow fever mosquito Aedes aegypti, favors higher temperature [32,33].…”

Section: Introductionmentioning

confidence: 99%