Relative frequency, Possible Risk Factors, Viral Codetection Rates, and Seasonality of Respiratory Syncytial Virus Among Children With Lower Respiratory Tract Infection in Northeastern Brazil

Gurgel, Ricardo Queiróz; Bezerra, Patrícia Gomes de Matos; Duarte, Mariano; Moura, Adriana Ávila; Souza, Edna Lúcia; Silva, Luciana Sobral da Silveira; Suzuki, Claudia Eiko; Peixoto, Rodrigo Buzzatti

doi:10.1097/md.0000000000003090

Cited by 25 publications

(34 citation statements)

References 35 publications

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“…In temperate climates, RSV detection rates were positively associated with relative humidity . However, some studies undertaken in tropical and subtropical areas have found a positive association with relative humidity, while others have found a negative association . Based on the LASSO model, we found that the RSV detection rate was higher when average relative humidity was low.…”

Section: Discussionmentioning

confidence: 47%

“…Previous studies have shown that temperature is the meteorological factor most commonly associated with respiratory viral infections. Several studies have reported that RSV epidemics have been found to be strongly associated with changes in temperature . The incidence of RSV infection has been reported to decrease as the average temperature increases .…”

Section: Discussionmentioning

confidence: 99%

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Meteorological factors affecting respiratory syncytial virus infection: A time‐series analysis

Zhang

Wen

Zheng

et al. 2020

Pediatric Pulmonology

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Introduction Respiratory syncytial virus (RSV) infection is a major cause of hospitalization in children. Meteorological factors are known to influence seasonal RSV epidemics, but the relationship between meteorological factors and RSV infection in children is not well understood. We aimed to explore the relationship between meteorological factors and RSV infections among hospitalized children, using different statistical models. Methods We conducted a retrospective review concerning children with RSV infections admitted to a tertiary pediatric hospital in Wenzhou, China, between January 2008 and December 2017. The relationship between meteorological factors (average daily temperatures, average daily relative humidity, rainfall, rainfall days, and wind speed) and the incidence of RSV in hospitalized children was analyzed using three time‐series models, namely an autoregressive integrated moving average (ARIMA) model, a generalized additive model (GAM), and a least absolute shrinkage and selection operator (LASSO)‐based model. Results In total, 15 858 (17.6%) children tested positive for RSV infection. The ARIMA model revealed a marked seasonal pattern in the RSV detection rate, which peaked in winter and spring. The model was a good predictor of RSV incidence (R2: 83.5%). The GAM revealed that a lower temperature and higher wind speed preceded increases in RSV detection. The LASSO‐based model revealed that temperature and relative humidity were negatively correlated with RSV detection. Conclusions Seasonality of RSV infection in hospitalized children correlated strongly with temperature. The LASSO‐based model can be used to predict annual RSV epidemics using weather forecast data.

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Section: Discussionmentioning

confidence: 47%

Section: Discussionmentioning

confidence: 99%

Meteorological factors affecting respiratory syncytial virus infection: A time‐series analysis

Zhang

Wen

Zheng

et al. 2020

Pediatric Pulmonology

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“…Because the 2019-nCoV is a sudden infectious disease, time series analysis cannot be considered (Gurgel et al, 2016;Yusef et al, 2007).…”

Section: Regression Analysis Of Incremental Cases and Meteorological mentioning

confidence: 99%

“…Previous studies have found that, in tropical, subtropical and temperate regions, RSV infections are related to meteorological factors such as temperature, RH, atmospheric pressure and rainfall Darniot et al,2018;Gurgel et al, 2016;Tian et al,2017;Sirimi et al,2016;Liu et al,2016;Chen et al,2014;du Prel et al,2009). The increase of RSV cases is negatively correlated with RH (Gurgel et al, 2016;Tian et al, 2017;Liu et al, 2016). Similarly, due to the high incidence rate of influenza virus, it brings heavy medical burden and has been fully studied.…”

Section: Relationship Between Covid-19 Epidemic and Climate Charactermentioning

confidence: 99%

“…GLM was also used in the studies of Onozuka et al and Souza et al (Souza et al, 2012;Onozuka et al, 2009). For seasonal viral infectious diseases, Gurgel et al and Yusuf et al used time series analysis to reduce model deviations (Gurgel et al, 2016;Yusury et al, 2007). Chan et al, du Prel et al, Lin et al, Varela et al use the autoregressive integral moving average (ARIMA) model to control the autocorrelation of time series data (Chan et al ., 2002;du Prel et al, 2009;Lin et al, 2009;Varela et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

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Meteorological factors correlate with transmission of 2019-nCoV: Proof of incidence of novel coronavirus pneumonia in Hubei Province, China

Zhang

et al. 2020

Preprint

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Objective: many potential factors contribute to the outbreak of COVID-19. The aim of this study was to explore the effects of various meteorological factors on the incidence of COVID-19. Methods:Taking Hubei province of China as an example, where COVID-19 was first reported and there were the most cases, we collected 53 days of cases up to March 10(total 67773 confirmed cases).COVID-19 confirmed cases were retrieved from the official website of Hubei Health Commission. Ten meteorological parameters were provided by China meteorological administration, including average pressure (hPa), average temperature ( ), maximum temperature, minimum temperature ( ), average water vapor pressure (hPa), average relative humidity (%),etc.Cross correlation analysis and linear regression were used to judge the relationship of . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) : medRxiv preprint meteorological factors and increment of COVID-19 confirmed cases. Results:Under 95% CI, the increment of confirmed cases in Hubei were significantly correlated with four meteorological parameters of average pressure, average temperature, minimum temperature and average water vapor pressure (equivalent to absolute humidity).The average pressure was positively correlated with the increment ( r=+0.358,p=0.010).The negative correlations included average temperature (r=-0.306,p=0.029), minimum air temperature (r=-0.347,p=0.013), average water vapor pressure (r=-0.326,p=0.020). The linear regression results show if minimum temperature increases by 1 , the incremental confirmed cases in Hubei decreases by 72.470 units on average. Conclusion:The incidence of COVID-19 was significantly correlated with average pressure, average temperature, minimum temperature and average water vapor pressure. It is positively correlated with the average pressure and negatively correlated with the other three parameters. Compared with relative humidity, 2019-nCov is more sensitive to water vapor pressure. The reason why the epidemic situation in Hubei expanded rapidly is significantly related to the climate characteristics of low temperature and dryness of Hubei in winter.

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Metagenomic analysis of viral diversity in respiratory samples from patients with respiratory tract infections in Kuwait

Madi

Al‐Nakib

Mustafa

et al. 2017

Journal of Medical Virology

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A metagenomic approach based on target independent next-generation sequencing has become a known method for the detection of both known and novel viruses in clinical samples. This study aimed to use the metagenomic sequencing approach to characterize the viral diversity in respiratory samples from patients with respiratory tract infections. We have investigated 86 respiratory samples received from various hospitals in Kuwait between 2015 and 2016 for the diagnosis of respiratory tract infections. A metagenomic approach using the next-generation sequencer to characterize viruses was used. According to the metagenomic analysis, an average of 145, 019 reads were identified, and 2% of these reads were of viral origin. Also, metagenomic analysis of the viral sequences revealed many known respiratory viruses, which were detected in 30.2% of the clinical samples. Also, sequences of non-respiratory viruses were detected in 14% of the clinical samples, while sequences of non-human viruses were detected in 55.8% of the clinical samples. The average genome coverage of the viruses was 12% with the highest genome coverage of 99.2% for respiratory syncytial virus, and the lowest was 1% for torque teno midi virus 2. Our results showed 47.7% agreement between multiplex Real-Time PCR and metagenomics sequencing in the detection of respiratory viruses in the clinical samples. Though there are some difficulties in using this method to clinical samples such as specimen quality, these observations are indicative of the promising utility of the metagenomic sequencing approach for the identification of respiratory viruses in patients with respiratory tract infections.

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Relative frequency, Possible Risk Factors, Viral Codetection Rates, and Seasonality of Respiratory Syncytial Virus Among Children With Lower Respiratory Tract Infection in Northeastern Brazil

Cited by 25 publications

References 35 publications

Meteorological factors affecting respiratory syncytial virus infection: A time‐series analysis

Meteorological factors affecting respiratory syncytial virus infection: A time‐series analysis

Meteorological factors correlate with transmission of 2019-nCoV: Proof of incidence of novel coronavirus pneumonia in Hubei Province, China

Metagenomic analysis of viral diversity in respiratory samples from patients with respiratory tract infections in Kuwait

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