Impact of pollution, climate, and sociodemographic factors on spatiotemporal dynamics of seasonal respiratory viruses

Impact of COVID-19 control measures on respiratory syncytial virus and hand-foot-and-mouth disease transmission in Hong Kong and South Korea

The public health and social measures (PHSMs) for mitigation/control of COVID-19 pandemic influenced the transmission dynamics of many other infectious diseases, including respiratory syncytial virus (RSV) infection, and hand, foot and mouth disease (HFMD) and their disease-burden. This study aimed to infer the transmission dynamics of these respiratory viruses and assess the impact of COVID-19 PHSMs on their community activity. We developed a compartmental framework to infer the transmission dynamics of RSV and HFMD in Hong Kong and South Korea from January 2014 to May 2024. We assessed the impact of PHSMs by comparing the change in virus transmissibility, reproduction number and population susceptibility before, during, and after the COVID-19 pandemic period. A significant reduction in RSV and HFMD activity was observed starting in January 2020, with a resurgence since late 2021. Transmissibility of both diseases decreased by 46 % - 95 % during the lull, while population susceptibility was estimated to increase by maximum of 19 %. On relaxation of the PHSMs, the transmissibility were recovered up to 70 % in Hong Kong and nearly 100 % in South Korea in 2023 with significant epidemics for these viruses. Strict implementation of COVID-19 PHSMs led to low RSV and HFMD activity, but the absence of community infection resulted in reductions in population immunity, and slightly larger epidemics when these diseases re-emerged following the COVID-19 pandemic.

Respiratory pathogen dynamics in community fever cases: Jiangsu Province, China (2023-2024)

BACKGROUND

Respiratory infectious diseases have the highest incidence among infectious diseases worldwide. Currently, global monitoring of respiratory pathogens primarily focuses on influenza and coronaviruses. This study included influenza and other common respiratory pathogens to establish a local respiratory pathogen spectrum. We investigated and analyzed the co-infection patterns of these pathogens and explored the impact of lifting non-pharmaceutical interventions (NPIs) on the transmission of influenza and other respiratory pathogens. Additionally, we used a predictive model for infectious diseases, utilizing the commonly used An autoregressive comprehensive moving average model (ARIMA), which can effectively forecast disease incidence.

METHODS

From June 2023 to February 2024, we collected influenza-like illness (ILI) cases weekly from the community in Xuanwu District, Nanjing, and obtained 2046 samples. We established a spectrum of respiratory pathogens in Nanjing and analysed the age distribution and clinical symptom distribution of various pathogens. We compared age, gender, symptom counts, and viral loads between individuals with co-infections and those with single infections. An autoregressive comprehensive moving average model (ARIMA) was constructed to predict the incidence of respiratory infectious diseases.

RESULTS

Among 2046 samples, the total detection rate of respiratory pathogen nucleic acids was 53.37% (1092/2046), with influenza A virus 479 cases (23.41%), influenza B virus 224 cases (10.95%), and HCoV 95 cases (4.64%) being predominant. Some pathogens were statistically significant in age and number of symptoms. The positive rate of mixed infections was 6.11% (125/2046). There was no significant difference in age or number of symptoms between co-infection and simple infection. After multiple iterative analyses, an ARIMA model (0,1,4), (0,0,0) was established as the optimal model, with an R2 value of 0.930, indicating good predictive performance.

CONCLUSIONS

The spectrum of respiratory pathogens in Nanjing, Jiangsu Province, was complex in the past. The primary age groups of different viruses were different, causing various symptoms, and the co-infection of viruses did not correlate with the age and gender of patients. The ARIMA model estimated future incidence, which plateaued in subsequent months.

Multi-faceted analysis and prediction for the outbreak of pediatric respiratory syncytial virus

OBJECTIVES

Respiratory syncytial virus (RSV) is a significant cause of pediatric hospitalizations. This article aims to utilize multisource data and leverage the tensor methods to uncover distinct RSV geographic clusters and develop an accurate RSV prediction model for future seasons.

MATERIALS AND METHODS

This study utilizes 5-year RSV data from sources, including medical claims, CDC surveillance data, and Google search trends. We conduct spatiotemporal tensor analysis and prediction for pediatric RSV in the United States by designing (i) a nonnegative tensor factorization model for pediatric RSV diseases and location clustering; (ii) and a recurrent neural network tensor regression model for county-level trend prediction using the disease and location features.

RESULTS

We identify a clustering hierarchy of pediatric diseases: Three common geographic clusters of RSV outbreaks were identified from independent sources, showing an annual RSV trend shifting across different US regions, from the South and Southeast regions to the Central and Northeast regions and then to the West and Northwest regions, while precipitation and temperature were found as correlative factors with the coefficient of determination R2≈0.5, respectively. Our regression model accurately predicted the 2022-2023 RSV season at the county level, achieving R2≈0.3 mean absolute error MAE < 0.4 and a Pearson correlation greater than 0.75, which significantly outperforms the baselines with P-values <.05.

CONCLUSION

Our proposed framework provides a thorough analysis of RSV disease in the United States, which enables healthcare providers to better prepare for potential outbreaks, anticipate increased demand for services and supplies, and save more lives with timely interventions.

The changing pattern of common respiratory viruses among children from 2018 to 2021 in Wuhan, China

BACKGROUND

Acute respiratory infections in children are a global public health challenge. Owing to the coronavirus disease (COVID-19) pandemic, non-pharmaceutical interventions, including patient isolation, social distancing, hand washing, and mask wearing, have been widely implemented, impacting the transmission of common respiratory viruses. The aim of this study was to clarify the epidemiological features of respiratory viruses in children less than 14 years of age in Wuhan before and after COVID-19.

METHODS

Respiratory specimens were collected from patients aged < 14 years at two hospitals in Wuhan, China, from January 2018 to December 2021. Seven respiratory viruses were identified using an immunofluorescence assay. Pathogen profiles and seasonality were analysed.

RESULTS

The number of visits and virus detection rate decreased dramatically after February 2020. The respiratory virus detection rate peaked in January and December and decreased dramatically in February and August. The detection rate was lower in 2021 than in 2018 and 2019. Respiratory syncytial virus (RSV) was identified as the leading pathogen in children aged < 1 year and 1-4 years before and after the COVID-19 pandemic. In children aged 5-14 years, influenza virus was detected at the highest rate before, and RSV after, the COVID-19 pandemic. RSV was the most common virus in coinfections.

CONCLUSIONS

This study revealed the epidemiological patterns of common respiratory viruses from 2018 to 2021. The spectrum of pathogens involved in paediatric respiratory infections had partly changed. Non-pharmaceutical interventions resulted in fewer opportunities for the spread of common viruses but also in an "immunity debt" that could have negative consequences when the pandemic is under control in Wuhan.

Mapping spatial morbidity patterns for bronchiolitis related to socioeconomic estimators: A spatial epidemiology approach to identify health disparities in Puerto Madryn, Argentina

OBJECTIVES

To describe the frequency of hospitalizations of infants under 1 year of age with bronchiolitis in Puerto Madryn, Argentina, and to study the spatial distribution of cases throughout the city in relation to socioeconomic indicators. To visualize and better understand the underlying processes behind the local manifestation of the disease by creating a vulnerability map of the city.

METHODS

We performed a cross-sectional study of all patients discharged for bronchiolitis from the local public Hospital in 2017, considering length of hospital stay, readmission rate, patient age, home address and socioeconomic indicators (household overcrowding). To understand the local spatial distribution of the disease and its relationship to overcrowding, we used GIS and Moran's global and local spatial autocorrelation indices.

RESULTS

The spatial distribution of bronchiolitis cases was not random, but significantly aggregated. Of the 120 hospitalized children, 100 infants (83.33%) live in areas identified as having at least one unsatisfied basic need (UBN). We found a positive and statistically significant relationship between frequency of cases and percentage of overcrowded housing by census radius.

CONCLUSIONS

A clear association was found between bronchiolitis and neighborhoods with UBNs, and overcrowding is likely to be a particularly important explanatory factor in this association. By combining GIS tools, spatial statistics, geo-referenced epidemiological data, and population-level information, vulnerability maps can be created to facilitate visualization of priority areas for development and implementation of more effective health interventions. Incorporating the spatial and syndemic perspective into health studies makes important contributions to the understanding of local health-disease processes.

Nonlinear and Multidelayed Effects of Meteorological Drivers on Human Respiratory Syncytial Virus Infection in Japan

In this study, we aimed to characterize the nonlinear and multidelayed effects of multiple meteorological drivers on human respiratory syncytial virus (HRSV) infection epidemics in Japan. The prefecture-specific weekly time-series of the number of newly confirmed HRSV infection cases and multiple meteorological variables were collected for 47 Japanese prefectures from 1 January 2014 to 31 December 2019. We combined standard time-series generalized linear models with distributed lag nonlinear models to determine the exposure-lag-response association between the incidence relative risks (IRRs) of HRSV infection and its meteorological drivers. Pooling the 2-week cumulative estimates showed that overall high ambient temperatures (22.7 °C at the 75th percentile compared to 16.3 °C) and high relative humidity (76.4% at the 75th percentile compared to 70.4%) were associated with higher HRSV infection incidence (IRR for ambient temperature 1.068, 95% confidence interval [CI], 1.056-1.079; IRR for relative humidity 1.045, 95% CI, 1.032-1.059). Precipitation revealed a positive association trend, and for wind speed, clear evidence of a negative association was found. Our findings provide a basic picture of the seasonality of HRSV transmission and its nonlinear association with multiple meteorological drivers in the pre-HRSV-vaccination and pre-coronavirus disease 2019 (COVID-19) era in Japan.

Spatiotemporal effects of meteorological conditions on global influenza peaks

BACKGROUND

Numerous studies have suggested that meteorological conditions such as temperature and absolute humidity are highly indicative of influenza outbreaks. However, the explanatory power of meteorological factors on the seasonal influenza peaks varied widely between countries at different latitudes.

OBJECTIVES

We aimed to explore the modification effects of meteorological factors on the seasonal influenza peaks in multi-countries.

METHODS

Data on influenza positive rate (IPR) were collected across 57 countries and data on meteorological factors were collected from ECMWF Reanalysis v5 (ERA5). We used linear regression and generalized additive models to investigate the spatiotemporal associations between meteorological conditions and influenza peaks in cold and warm seasons.

RESULTS

Influenza peaks were significantly correlated with months with both lower and higher temperatures. In temperate countries, the average intensity of cold season peaks was stronger than that of warm season peaks. However, the average intensity of warm season peaks was stronfger than of cold season peaks in tropical countries. Temperature and specific humidity had synergistic effects on influenza peaks at different latitudes, stronger in temperate countries (cold season: R2=0.90; warm season: R2=0.84) and weaker in tropical countries (cold season: R2=0.64; warm season: R2=0.03). Furthermore, the effects could be divided into cold-dry and warm-humid modes. The temperature transition threshold between the two modes was 16.5-19.5 °C. During the transition from cold-dry mode to warm-humid mode, the average 2 m specific humidity increased by 2.15 times, illustrating that transporting a large amount of water vapor may compensate for the negative effect of rising temperatures on the spread of the influenza virus.

CONCLUSION

Differences in the global influenza peaks were related to the synergistic influence of temperature and specific humidity. The global influenza peaks could be divided into cold-dry and warm-humid modes, and specific thresholds of meteorological conditions were needed for the transition of the two modes.

Cold exposure impairs extracellular vesicle swarm-mediated nasal antiviral immunity

BACKGROUND

The human upper respiratory tract is the first site of contact for inhaled respiratory viruses and elaborates an array of innate immune responses. Seasonal variation in respiratory viral infections and the importance of ambient temperature in modulating immune responses to infections have been well recognized; however, the underlying biological mechanisms remain understudied.

OBJECTIVE

We investigated the role of nasal epithelium-derived extracellular vesicles (EVs) in innate Toll-like receptor 3 (TLR3)-dependent antiviral immunity.

METHODS

We evaluated the secretion and composition of nasal epithelial EVs after TLR3 stimulation in human autologous cells and fresh human nasal mucosal surgical specimens. We also explored the antiviral activity and mechanisms of TLR3-stimulated EVs against respiratory viruses as well as the effect of cool ambient temperature on TLR3-dependent antiviral immunity.

RESULTS

We found that polyinosinic:polycytidylic acid, aka poly(I:C), exposure induced a swarm-like increase in the secretion of nasal epithelial EVs via the TLR3 signaling. EVs participated in TLR3-dependent antiviral immunity, protecting the host from viral infections through both EV-mediated functional delivery of miR-17 and direct virion neutralization after binding to virus ligands via surface receptors, including LDLR and ICAM-1. These potent antiviral immune defense functions mediated by TLR3-stimulated EVs were impaired by cold exposure via a decrease in total EV secretion as well as diminished microRNA packaging and antiviral binding affinity of individual EV.

CONCLUSION

TLR3-dependent nasal epithelial EVs exhibit multiple innate antiviral mechanisms to suppress respiratory viral infections. Furthermore, our study provides a direct quantitative mechanistic explanation for seasonal variation in upper respiratory tract infection prevalence.

Temperature and influenza transmission: Risk assessment and attributable burden estimation among 30 cities in China

BACKGROUND

Many studies have explored the epidemiological characteristics of influenza. However, most previous studies were conducted in a specific region without a national picture which is important to develop targeted strategies and measures on influenza control and prevention.

OBJECTIVES

To explore the association between ambient temperature and incidence of influenza, to estimate the attributable risk from temperature in 30 Chinese cities with different climatic characteristics for a national picture, and to identify the vulnerable populations for national preventative policy development.

METHODS

Daily meteorological and influenza incidence data from the 30 Chinese cities over the period 2016-19 were collected. We estimated the city-specific association between daily mean temperature and influenza incidence using a distributed lag non-linear model and evaluated the pooled effects using multivariate meta-analysis. The attributable fractions compared with reference temperature were calculated. Stratified analyses were performed by region, sex and age.

RESULTS

Overall, an N-shape relationship between temperature and influenza incidence was found in China. The cumulative relative risk of the peak risk temperature (5.1 °C) was 2.13 (95%CI: 1.41, 3.22). And 60% (95%eCI: 54.3%, 64.3%) of influenza incidence was attributed to ambient temperature during the days with sensitive temperatures (1.6°C-14.4 °C). The ranges of sensitive temperatures and the attributable disease burden due to temperatures varied for different populations and regions. The residents in South China and the children aged ≤5 and 6-17 years had higher fractions attributable to sensitive temperatures.

CONCLUSIONS

Tailored preventions targeting on most vulnerable populations and regions should be developed to reduce influenza burden from sensitive temperatures.

Epidemiological Comparison of Four COVID-19 Waves in the Democratic Republic of the Congo, March 2020-January 2022

PURPOSE

Nationwide analyses are required to optimise and tailor activities to control future COVID-19 waves of resurgence continent-wide. We compared epidemiological and clinical outcomes of the four COVID-19 waves in the Democratic Republic of Congo (DRC).

METHODS

This retrospective descriptive epidemiological analysis included data from the national line list of confirmed COVID-19 cases in all provinces for all waves between 9 March 2020 and 2 January 2022. Descriptive statistical measures (frequencies, percentages, case fatality rates [CFR], test positivity rates [TPR], and characteristics) were compared using chi-squared or the Fisher-Irwin test.

RESULTS

During the study period, 72,108/445,084 (16.2%) tests were positive, with 9,641/56,637 (17.0%), 16,643/66,560 (25.0%), 24,172/157,945 (15.3%), and 21,652/163,942 (13.2%) cases during the first, second, third, and fourth waves, respectively. TPR significantly decreased from 17.0% in the first wave to 13.2% in the fourth wave as did infection of frontline health workers (5.2% vs. 0.9%). CFR decreased from 5.1 to 0.9% from the first to fourth wave. No sex- or age-related differences in distributions across different waves were observed. The majority of cases were asymptomatic in the first (73.1%) and second (86.6%) waves, in contrast to that in the third (11.1%) and fourth (31.3%) waves.

CONCLUSION

Despite fewer reported cases, the primary waves (first and second) of the COVID-19 pandemic in the DRC were more severe than the third and fourth waves, with each wave being associated with a new SARS-CoV-2 variant. Tailored public health and social measures, and resurgence monitoring are needed to control future waves of COVID-19.

Clinical risk, sociodemographic factors, and SARS-CoV-2 infection over time in Ontario, Canada

We aimed to determine whether early public health interventions in 2020 mitigated the association of sociodemographic and clinical risk factors with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We conducted a population-based cohort study of all adults in Ontario, Canada who underwent testing for SARS-CoV-2 through December 31, 2020. The outcome was laboratory-confirmed SARS-CoV-2 infection, determined by reverse transcription polymerase chain reaction testing. Adjusted odds ratios (ORs) were determined for sociodemographic and clinical risk factors before and after the first-wave peak of the pandemic to assess for changes in effect sizes. Among 3,167,753 community-dwelling individuals, 142,814 (4.5%) tested positive. The association between age and SARS-CoV-2 infection risk varied over time (P-interaction < 0.0001). Prior to the first-wave peak, SARS-CoV-2 infection increased with age whereas this association reversed thereafter. Risk factors that persisted included male sex, residing in lower income neighborhoods, residing in more racially/ethnically diverse communities, immigration to Canada, hypertension, and diabetes. While there was a reduction in infection rates after mid-April 2020, there was less impact in regions with higher racial/ethnic diversity. Immediately following the initial peak, individuals living in the most racially/ethnically diverse communities with 2, 3, or ≥ 4 risk factors had ORs of 1.89, 3.07, and 4.73-fold higher for SARS-CoV-2 infection compared to lower risk individuals in their community (all P < 0.0001). In the latter half of 2020, this disparity persisted with corresponding ORs of 1.66, 2.48, and 3.70-fold higher, respectively. In the least racially/ethnically diverse communities, there was little/no gradient in infection rates across risk strata. Further efforts are necessary to reduce the risk of SARS-CoV-2 infection among the highest risk individuals residing in the most racially/ethnically diverse communities.

The Influence of Environmental Factors on the Spread of COVID-19 in Italy

The aim of this work is to investigate possible relationships between air quality and the spread of the pandemic. We evaluate the performance of machine learning techniques in predicting new cases. Specifically, we describe a cross-correlation analysis on daily COVID-19 cases and environmental factors, such as temperature, relative humidity, and atmospheric pollutants. Our analysis confirms a significant association of some environmental parameters with the spread of the virus. This suggests that machine learning models trained using environmental parameters might provide accurate predictions about the number of infected cases. Our empirical evaluation shows that temperature and ozone are negatively correlated with confirmed cases (therefore, the higher the values of these parameters, the lower the number of infected cases), whereas atmospheric particulate matter and nitrogen dioxide are positively correlated. We developed and compared three different predictive models to test whether these technologies can be useful to estimate the evolution of the pandemic.

Implementation of a Vaccination Program Based on Epidemic Geospatial Attributes: COVID-19 Pandemic in Ohio as a Case Study and Proof of Concept

Geospatial vaccine uptake is a critical factor in designing strategies that maximize the population-level impact of a vaccination program. This study uses an innovative spatiotemporal model to assess the impact of vaccination distribution strategies based on disease geospatial attributes and population-level risk assessment. For proof of concept, we adapted a spatially explicit COVID-19 model to investigate a hypothetical geospatial targeting of COVID-19 vaccine rollout in Ohio, United States, at the early phase of COVID-19 pandemic. The population-level deterministic compartmental model, incorporating spatial-geographic components at the county level, was formulated using a set of differential equations stratifying the population according to vaccination status and disease epidemiological characteristics. Three different hypothetical scenarios focusing on geographical subpopulation targeting (areas with high versus low infection intensity) were investigated. Our results suggest that a vaccine program that distributes vaccines equally across the entire state effectively averts infections and hospitalizations (2954 and 165 cases, respectively). However, in a context with equitable vaccine allocation, the number of COVID-19 cases in high infection intensity areas will remain high; the cumulative number of cases remained >30,000 cases. A vaccine program that initially targets high infection intensity areas has the most significant impact in reducing new COVID-19 cases and infection-related hospitalizations (3756 and 213 infections, respectively). Our approach demonstrates the importance of factoring geospatial attributes to the design and implementation of vaccination programs in a context with limited resources during the early stage of the vaccine rollout.

SARS-CoV-2: Origin, Intermediate Host and Allergenicity Features and Hypotheses

The goal of this study is to investigate the probable intermediate hosts and the allergenicity of the notorious virus SARS-CoV-2 to understand how this virus emerged. The phylogenetic analysis of the virus spike proteins indicates that SARS-CoV-2 falls into various small subclades that include a bat coronavirus RaTG13, suggesting bats as a likely natural origin. Refined alignment of the spike protein in NCBI found several fragments that are specific to SARS-CoV-2 and/or SARS-CoV are specific to Rattus norvegicus and/or Mus musculus, suggesting that rodents are the intermediate reservoir of SARS-CoV-2 and SARS-CoV. To evaluate the allergenicity values, the binding affinities of human leukocyte antigen (HLA) class I or II molecules with the spike proteins were calculated, and the results showed that both SARS-CoV-2 and SARS-CoV are predicted to bind to fourteen HLA class I and II molecules with super-high HLA allele-peptide affinities. The infection rate of individuals who have HLA alleles with very high binding affinities who might become infected and develop into refractory patients if there were no medical or non-medical interventions is about 7.36% and 4.78% of Chinese and Americans, respectively. Extremely high temperature and exceptionally low precipitation, the common climate factors between the outbreak sites of COVID-19 in Wuhan in 2019 and SARS in Guangdong in 2002, might have promoted coronavirus evolution into more virulent forms. Our hypothesis suggests that early immunization with an allergenically-engineered virus, in combination with continued surveillance of meteorological factors and viral mutations, may be one of the most powerful prophylactic modalities to fight this virus.

Research and effectiveness of anti-viral drugs against COVID-19; global public intervention to prevent coronavirus and to improve human health

Since the first case report on COVID-19, its transmission took place rapidly across the globe. Currently, it is reported to be spread into a total of 216 countries and territories. The suppression on industrial and gasoline burning activities accompanied after COVID-19 lock down favorably boosted the nature to restore its deteriorated sections such as in air with the improved Air quality index and in water bodies with the natural tone of purity. Increased use of anti-viral drugs along with herbal therapies has been observed at mass scale as global intervention to prevent the disease. The use of personal protective equipment and disinfection strategies for the control of pandemic has dramatically increased the pollution of plastic and medical waste. This article aims to forecast and highlight the evidence-based impact/changes (+ive and -ive) of coronavirus on the environment, global interventions to prevent the disease along with the levels of effectiveness of personal used protective equipment to stop the spread of coronavirus.

Spatiotemporal Analysis of COVID-19 Pandemic and Predictive Models based on Artificial Intelligence for different States of India

Geographical and spatial diversities play important roles in dynamics of spread of COVID-19 virus. These phenomena are not properly addressed in the literature yet. In this paper, COVID data of various states of India are collected. The data had been processed and analysed using an open-source software. A framework based on Susceptible, Infectious, Hospitalised, Recovered and Deaths model to determine the effects of geographical diversities of Indian states on COVID-19 pandemic has been developed. The confirmed, cured and death cases due to the virus have been analysed for different state. Reasons behind the differences in number of cases in different states are identified. An improved Long-Short-Term-Memory algorithm has been developed to forecast the virus spread and recovery of patients for the next one month. Numerical results along with discussions are given.

Understanding the cycles of COVID-19 incidence: Principal Component Analysis and interaction of biological and socio-economic factors

The incidence curve of coronavirus disease 19 (COVID-19) shows cyclical patterns over time. We examine the cyclical properties of the incidence curves in various countries and use principal components analysis to shed light on the underlying dynamics that are common to all countries. We find that the cyclical series of 37 countries can be summarized in four principal components which explain over 90% of the variation. We also discuss the influence of complex interactions between biological viral natural history and socio-political reactions and measures adopted by different countries on the cyclical patterns exhibited by COVID-19 around the globe.

A chronicle of SARS-CoV-2: Seasonality, environmental fate, transport, inactivation, and antiviral drug resistance

In this review, we present the environmental perspectives of the viruses and antiviral drugs related to SARS-CoV-2. The present review paper discusses occurrence, fate, transport, susceptibility, and inactivation mechanisms of viruses in the environment as well as environmental occurrence and fate of antiviral drugs, and prospects (prevalence and occurrence) of antiviral drug resistance (both antiviral drug resistant viruses and antiviral resistance in the human). During winter, the number of viral disease cases and environmental occurrence of antiviral drug surge due to various biotic and abiotic factors such as transmission pathways, human behaviour, susceptibility, and immunity as well as cold climatic conditions. Adsorption and persistence critically determine the fate and transport of viruses in the environment. Inactivation and disinfection of virus include UV, alcohol, and other chemical-base methods but the susceptibility of virus against these methods varies. Wastewater treatment plants (WWTPs) are major reserviors of antiviral drugs and their metabolites and transformation products. Ecotoxicity of antiviral drug residues against aquatic organisms have been reported, however more threatening is the development of antiviral resistance, both in humans and in wild animal reservoirs. In particular, emergence of antiviral drug-resistant viruses via exposure of wild animals to high loads of antiviral residues during the current pandemic needs further evaluation.

Public Perception of the First Major SARS-Cov-2 Outbreak in the Suceava County, Romania

The first months of 2020 were marked by the rapid spread of the acute respiratory disease, which swiftly reached the proportions of a pandemic. The city and county of Suceava, Romania, faced an unprecedented crisis in March and April 2020, triggered not only by the highest number of infections nationwide but also by the highest number of infected health professionals (47.1% of the infected medical staff nationwide, in April 2020). Why did Suceava reach the peak number of COVID-19 cases in Romania? What were the vulnerability factors that led to the outbreak, the closure of the city of Suceava and neighboring localities, and the impossibility of managing the crisis with local resources? What is the relationship between the population's lack of confidence in the authorities' ability to solve the crisis, and their attitude towards the imposed measures? The present article aims to provide answers to the above questions by examining the attitudes of the public towards the causes that have led to the outbreak of an epidemiological crisis, systemic health problems, and the capacity of decision makers to intervene both at local and national level. The research is based on an online survey, conducted between April and May 2020, resulting in a sample of 1231 people from Suceava County. The results highlight that the development of the largest COVID-19 outbreak in Romania is, without a doubt, the result of a combination of factors, related to the medical field, decision makers, and the particularities of the population's behavior.

Jennings C, Parekh SH. Effect of ambient temperature on respiratory tract cells exposed to SARS-CoV-2 viral mimicking nanospheres-An experimental study

The novel coronavirus caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has reached more than 160 countries and has been declared a pandemic. SARS-CoV-2 infects host cells by binding to the angiotensin-converting enzyme 2 (ACE-2) surface receptor via the spike (S) receptor-binding protein (RBD) on the virus envelope. Global data on a similar infectious disease spread by SARS-CoV-1 in 2002 indicated improved stability of the virus at lower temperatures facilitating its high transmission in the community during colder months (December-February). Seasonal viral transmissions are strongly modulated by temperatures, which can impact viral trafficking into host cells; however, an experimental study of temperature-dependent activity of SARS-CoV-2 is still lacking. We mimicked SARS-CoV-2 with polymer beads coated with the SARS-CoV-2 S protein to study the effect of seasonal temperatures on the binding of virus-mimicking nanospheres to lung epithelia. The presence of the S protein RBD on nanosphere surfaces led to binding by Calu-3 airway epithelial cells via the ACE-2 receptor. Calu-3 and control fibroblast cells with S-RBD-coated nanospheres were incubated at 33 and 37 °C to mimic temperature fluctuations in the host respiratory tract, and we found no temperature dependence in contrast to nonspecific binding of bovine serum ablumin-coated nanospheres. Moreover, the ambient temperature changes from 4 to 40 °C had no effect on S-RBD-ACE-2 ligand-receptor binding and minimal effect on the S-RBD protein structure (up to 40 °C), though protein denaturing occurred at 51 °C. Our results suggest that ambient temperatures from 4 to 40 °C have little effect on the SARS-CoV-2-ACE-2 interaction in agreement with the infection data currently reported.

Influenza activity during the outbreak of coronavirus disease 2019 in Chinese mainland

Since coronavirus disease 2019 (COVID-19) might circulate in the following seasons, it is essential to understand how COVID-19 influences other respiratory diseases, especially influenza. In this study, we analyzed the influenza activity from mid-November 2019 to March 2020 in Chinese mainland and found that the influenza season ended much earlier than previous seasons for all subtypes and lineages, which may have resulted from the circulation of COVID-19 and measures such as travel control and personal protection. These findings provide rudimentary knowledge of the co-circulation patterns of the two types of viruses.

Meteorological drivers of respiratory syncytial virus infections in Singapore

Meteorological drivers are known to affect transmissibility of respiratory viruses including respiratory syncytial virus (RSV), but there are few studies quantifying the role of these drivers. We used daily RSV hospitalization data to estimate the daily effective reproduction number (Rt), a real-time measure of transmissibility, and examined its relationship with environmental drivers in Singapore from 2005 through 2015. We used multivariable regression models to quantify the proportion of the variance in Rt explained by each meteorological driver. After constructing a basic model for RSV seasonality, we found that by adding meteorological variables into this model we were able to explain a further 15% of the variance in RSV transmissibility. Lower and higher value of mean temperature, diurnal temperature range (DTR), precipitation and relative humidity were associated with increased RSV transmissibility, while higher value of maximum wind speed was correlated with decreased RSV transmissibility. We found that a number of meteorological drivers were associated with RSV transmissibility. While indoor conditions may differ from ambient outdoor conditions, our findings are indicative of a role of ambient temperature, humidity and wind speed in affecting RSV transmission that could be biological or could reflect indirect effects via the consequences on time spent indoors.

Effects of air pollutants on the transmission and severity of respiratory viral infections

Particulate matter, sulfur dioxide, nitrogen oxides, ozone, carbon monoxide, volatile organic compounds (VOCs) and polycyclic aromatic hydrocarbons (PAHs) are among the outdoor air pollutants that are major factors in diseases, causing especially adverse respiratory effects in humans. On the other hand, the role of respiratory viruses in the pathogenesis of severe respiratory infections is an issue of great importance. The present literature review was aimed at assessing the potential effects of air pollutants on the transmission and severity of respiratory viral infections. We have reviewed the scientific literature regarding the association of outdoor air pollution and respiratory viruses on respiratory diseases. Evidence supports a clear association between air concentrations of some pollutants and human respiratory viruses interacting to adversely affect the respiratory system. Given the undoubted importance and topicality of the subject, we have paid special attention to the association between air pollutants and the transmission and severity of the effects caused by the coronavirus named SARS-CoV-2, which causes the COVID-19. Although to date, and by obvious reasons, the number of studies on this issue are still scarce, most results indicate that chronic exposure to air pollutants delays/complicates recovery of patients of COVID-19 and leads to more severe and lethal forms of this disease. This deserves immediate and in-depth experimental investigations.

Spatiotemporal transmission dynamics of the COVID-19 pandemic and its impact on critical healthcare capacity

The role of geospatial disparities in the dynamics of the COVID-19 pandemic is poorly understood. We developed a spatially-explicit mathematical model to simulate transmission dynamics of COVID-19 disease infection in relation with the uneven distribution of the healthcare capacity in Ohio, U.S. The results showed substantial spatial variation in the spread of the disease, with localized areas showing marked differences in disease attack rates. Higher COVID-19 attack rates experienced in some highly connected and urbanized areas (274 cases per 100,000 people) could substantially impact the critical health care response of these areas regardless of their potentially high healthcare capacity compared to more rural and less connected counterparts (85 cases per 100,000). Accounting for the spatially uneven disease diffusion linked to the geographical distribution of the critical care resources is essential in designing effective prevention and control programmes aimed at reducing the impact of COVID-19 pandemic.

Seasonality of Respiratory Viral Infections

The seasonal cycle of respiratory viral diseases has been widely recognized for thousands of years, as annual epidemics of the common cold and influenza disease hit the human population like clockwork in the winter season in temperate regions. Moreover, epidemics caused by viruses such as severe acute respiratory syndrome coronavirus (SARS-CoV) and the newly emerging SARS-CoV-2 occur during the winter months. The mechanisms underlying the seasonal nature of respiratory viral infections have been examined and debated for many years. The two major contributing factors are the changes in environmental parameters and human behavior. Studies have revealed the effect of temperature and humidity on respiratory virus stability and transmission rates. More recent research highlights the importance of the environmental factors, especially temperature and humidity, in modulating host intrinsic, innate, and adaptive immune responses to viral infections in the respiratory tract. Here we review evidence of how outdoor and indoor climates are linked to the seasonality of viral respiratory infections. We further discuss determinants of host response in the seasonality of respiratory viruses by highlighting recent studies in the field.

Human respiratory syncytial virus and influenza seasonality patterns-Early findings from the WHO global respiratory syncytial virus surveillance

Background

Human respiratory syncytial virus (RSV) causes illnesses among all age groups and presents a burden to healthcare services. To better understand the epidemiology and seasonality of RSV in different geographical areas, the World Health Organization (WHO) coordinated a pilot initiative to access the feasibility of establishing RSV surveillance using the existing Global Influenza Surveillance and Response System (GISRS) platform.

Objectives

To describe and compare RSV and influenza seasonality in countries in the northern andsouthern temperate, and tropics during the period January 2017 to April 2019.

Methods

Fourteen countries in six WHO regions participating in the GISRS were invited for the pilot. Hospitalized patients presenting with severe acute respiratory illness (SARI), SARI without fever and outpatients presenting with acute respiratory illness (ARI) were enrolled from January 2017 to April 2019. The expected minimum sample size was 20 samples per week, year‐round, per country. Real‐time RT‐PCR was used to detect RSV and influenza viruses. Results were uploaded to the WHO FluMart platform.

Results

Annual seasonality of RSV was observed in all countries, which overlapped to a large extent with the influenza activity. In countries, in temperate regions RSV peaked in the autumn/winter months. In Egypt, a subtropical country, RSV activity peaked in the cooler season. In the tropical regions, RSV peaked during the rainy seasons.

Conclusion

Early findings from the WHO RSV surveillance pilot based on the GISRS suggest annual seasonal patterns for of RSV circulation that overlap with influenza. RSV surveillance needs to be continued for several more seasons to establish seasonality patterns to inform prevention and control strategies.

Comparative Seasonal Respiratory Virus Epidemic Timing in Utah

Previous studies have found evidence of viral interference between seasonal respiratory viruses. Using laboratory-confirmed data from a Utah-based healthcare provider, Intermountain Health Care, we analyzed the time-specific patterns of respiratory syncytial virus (RSV), influenza A, influenza B, human metapneumovirus, rhinovirus, and enterovirus circulation from 2004 to 2018, using descriptive methods and wavelet analysis (n = 89,462) on a local level. The results showed that RSV virus dynamics in Utah were the most consistent of any of the viruses studied, and that the other seasonal viruses were generally in synchrony with RSV, except for enterovirus (which mostly occurs late summer to early fall) and influenza A and B during pandemic years.

Respiratory syncytial virus infection: why does disease severity vary among individuals?

Introduction: Respiratory syncytial virus (RSV) is the most common cause of acute lower respiratory tract infections in infancy. While many infants are infected with RSV, the nature and severity of the disease vary among individuals. RSV causes bronchiolitis, pneumonia, and asthma exacerbation. However, most children infected with RSV have only mild upper airways disease and may be asymptomatic.Areas covered: Despite efforts to elucidate mechanisms for the various clinical responses to RSV infection, they remain largely unknown, suggesting that susceptibility and disease are influenced by multiple intrinsic and extrinsic factors. This article reviews the available literature on the field of RSV disease severity and discusses important factors associated to susceptibility and different disease outcome.Expert opinion: The severity of RSV-induced illness is a phenomenon that depends on a variety of graded mechanisms of interaction between the host, virus, and environment. This may lead to differences in the intensity of immune response in the lung and different courses of the disease. By characterizing, classifying, and grading the affecting factors in high-risk patients versus those who do not fall ill by RSV, we may find therapies or point to disease-limiting medications.

Six-monthly palivizumab prophylaxis effectively reduced RSV-associated hospitalization rates of preterm infants in a subtropical area: a population-based cohort study

BACKGROUND

To evaluate the effects of 6-monthly palivizumab on respiratory syncytial virus-associated hospitalization (RSVH) in preterm infants in an area without RSV seasonality.

METHODS

RSV prophylaxis with 6-monthly palivizumab in infants born at gestational age (GA) ≤28 weeks or those born at GA 29-35 weeks with bronchopulmonary dysplasia (BPD) was implemented in Taiwan since 2010. RSVH, use of mechanical ventilation (MV), admission to intensive care unit (ICU), length of hospital stay, and annual mortality were compared between the historical control group (no prophylaxis, 2008-2009) and the prophylaxis group (2011-2013).

RESULTS

The annual RSVH rates decreased in the target population and in subgroups of infants who received prophylaxis (all target infants: odds ratio [OR], 0.43; 95% confidence interval [CI], 0.29-0.65). No difference was observed in MV and ICU usage and 1-year mortality in the ≤28 weeks subgroup. In the GA 29-35 weeks with BPD subgroup, ICU usage and 1-year mortality rates were significantly reduced with palivizumab prophylaxis regimen. A significant decrease was noted in the annual mortality and ICU admission rates of infants who received prophylactic treatment.

CONCLUSION

Six-monthly palivizumab treatment reduced the RSVH rate, ICU usage, and annual mortality rates of target infants in an area without RSV seasonality.

Viruses associated with acute respiratory infection in a community-based cohort of healthy New Zealand children

Acute respiratory infections (ARIs) are a major cause of morbidity among children. Respiratory viruses are commonly detected in both symptomatic and asymptomatic periods. The rates of infection and community epidemiology of respiratory viruses in healthy children needs further definition to assist interpretation of molecular diagnostic assays in this population. Children otherwise healthy aged 1 to 8 years were prospectively enrolled in the study during two consecutive winters, when ARIs peak in New Zealand. Parents completed a daily symptom diary for 8 weeks, during which time they collected a nasal swab from the child for each clinical ARI episode. A further nasal swab was collected by research staff during a clinic visit at the conclusion of the study. All samples were tested for 15 respiratory viruses commonly causing ARI using molecular multiplex polymerase chain reaction assays. There were 575 ARIs identified from 301 children completing the study, at a rate of 1.04 per child‐month. Swabs collected during an ARI were positive for a respiratory virus in 76.8% (307 of 400), compared with 37.3% (79 of 212) of swabs collected during asymptomatic periods. The most common viruses detected were human rhinovirus, coronavirus, parainfluenza viruses, influenzavirus, respiratory syncytial virus, and human metapneumovirus. All of these were significantly more likely to be detected during ARIs than asymptomatic periods. Parent‐administered surveillance is a useful mechanism for understanding infectious disease in healthy children in the community. Interpretation of molecular diagnostic assays for viruses must be informed by understanding of local rates of asymptomatic infection by such viruses.

During winter, children experienced acute respiratory infections at a rate of 1.04/month.

A virus was detected in 76.8% of acute respiratory infections.

Specific viruses are more likely to be associated with respiratory symptoms.

The epidemiology and severity of respiratory viral infections in a tropical country: Ecuador, 2009-2016

BACKGROUND

Respiratory viral infections (RVI) are a leading cause of mortality worldwide. We compared the epidemiology and severity of RVI in Ecuador during 2009-2016.

METHODS

Respiratory specimens collected within the national surveillance system were tested for influenza viruses, respiratory syncytial virus (RSV), adenovirus, parainfluenza virus, and human metapneumovirus. Overall and virus-specific positive detection rate (PDR) were calculated and compared the timing of epidemics caused by the different viruses. Logistic regression models were used to compare the age distribution and risk of death across respiratory viruses.

RESULTS

A total of 41,172 specimens were analyzed: influenza (PDR=14.3%) and respiratory syncytial virus (RSV) (PDR=9.5%) were the most frequently detected viruses. Influenza epidemics typically peaked in December-January and RSV epidemics in March; seasonality was less evident for the other viruses. Compared to adults, children were more frequently infected with RSV, adenovirus, parainfluenza, and influenza B, while the elderly were less frequently infected with influenza A(H1N1)p. The age-adjusted risk of death was highest for A(H1N1)p (odds ratio [OR] 1.73, 95% confidence intervals [CI] 1.38-2.17), and lowest for RSV (OR 0.75, 95%CI 0.57-0.98).

CONCLUSIONS

Whilst influenza and RSV were the most frequently detected pathogens, the risk of death differed by RVI, being highest for pandemic influenza and lowest for RSV.

Prevalence of Common Respiratory Viral Infections and Identification of Adenovirus in Hospitalized Adults in Harbin, China 2014 to 2017

Background: Respiratory infections pose a great challenge in global health, and the prevalence of viral infection in adult patients has been poorly understood in northeast China. Harbin is one of the major cities in northeast China, and more than half of any given year in Harbin is occupied by winter. To reveal the viral etiology and seasonality in adult patients from Harbin, a 4-year consecutive survey was conducted in Harbin, China.

Methods: From January 2014 to December 2017, specimens were obtained from adult patients admitted to the Second Affiliated Hospital of Harbin Medical University with lower respiratory tract infections. Sputum samples were examined by direct immunofluorescence assays to detect seven common respiratory viruses, including influenza virus (type A and B), parainfluenza virus (type 1 to 3), respiratory syncytial virus and adenovirus. Adenovirus positive samples were seeded onto A549 cells to isolate viral strains. Phylogenetic analysis was conducted on the highly variable region of adenoviral hexon gene.

Results: A total of 1,300 hospitalized adult patients with lower respiratory tract infections were enrolled, in which 189 patients (14.5%) were detected as having at least one viral infection. The co-infection rate in this study was 25.9% (49/189). The dominant viral pathogen from 2014 to 2017 was parainfluenza virus, with a detection rate of 7.2%, followed by influenza virus, respiratory syncytial virus and adenovirus. Based on the climate seasons determined by daily average temperature, the highest overall viral detection rate was detected in spring (22.0%, 52/236), followed by winter (13.4%, 109/813), autumn (11.4%, 13/114) and summer (10.9%, 15/137). Adenovirus type 3 strains with slight variations were isolated from positive cases, which were closely related to the GB strain from the United States, as well as the Harbin04B strain isolated locally.

Conclusion: This study demonstrated that common respiratory viruses were partially responsible for hospitalized lower respiratory tract infections in adult patients from Harbin, China, with parainfluenza virus as the dominant viral pathogen. Climate seasons could be rational indicators for the seasonality analysis of airborne viral infections. Future surveillance on viral mutations would be necessary to reveal the evolutionary history of respiratory viruses.

Design Choices for Automated Disease Surveillance in the Social Web

The social web has emerged as a dominant information architecture accelerating technology innovation on an unprecedented scale. The utility of these developments to public health use cases like disease surveillance, information dissemination, outbreak prediction and so forth has been widely investigated and variously demonstrated in work spanning several published experimental studies and deployed systems. In this paper we provide an overview of automated disease surveillance efforts based on the social web characterized by their different high level design choices regarding functional aspects like user participation and language parsing approaches. We briefly discuss the technical rationale and practical implications of these different choices in addition to the key limitations associated with these systems within the context of operable disease surveillance. We hope this can offer some technical guidance to multi-disciplinary teams on how best to implement, interpret and evaluate disease surveillance programs based on the social web.

Superensemble forecast of respiratory syncytial virus outbreaks at national, regional, and state levels in the United States

Respiratory syncytial virus (RSV) infections peak during the winter months in the United States, yet the timing, intensity, and onset of these outbreaks vary each year. An RSV vaccine is on the cusp of being released; precise models and accurate forecasts of RSV epidemics may prove vital for planning where and when the vaccine should be deployed. Accurate forecasts with sufficient spatial and temporal resolution could also be used to support the prevention or treatment of RSV infections. Previously, we developed and validated an RSV forecast system at the regional scale in the United States. This model-inference system had considerable forecast skill, relative to the historical expectance, for outbreak peak intensity, total outbreak size, and onset, but only marginal skill for predicting the timing of the outbreak peak. Here, we use a superensemble approach to combine three forecasting methods for RSV prediction in the US at three different spatial resolutions: national, regional, and state. At the regional and state levels, we find a substantial improvement of forecast skill, relative to historical expectance, for peak intensity, timing, and onset outbreak up to two months in advance of the predicted outbreak peak. Moreover, due to the greater variability of RSV outbreaks at finer spatial scales, we find that improvement of forecast skill at the state level exceeds that at the regional and national levels. Such finer scale superensemble forecasts may be more relevant for effecting local-scale interventions, particularly in communities with a high burden of RSV infection.

Analysis of multi-level spatial data reveals strong synchrony in seasonal influenza epidemics across Norway, Sweden, and Denmark

Population structure, spatial diffusion, and climatic conditions mediate the spatiotemporal spread of seasonal influenza in temperate regions. However, much of our knowledge of these dynamics stems from a few well-studied countries, such as the United States (US), and the extent to which this applies in different demographic and climatic environments is not fully understood. Using novel data from Norway, Sweden, and Denmark, we applied wavelet analysis and non-parametric spatial statistics to explore the spatiotemporal dynamics of influenza transmission at regional and international scales. We found the timing and amplitude of epidemics were highly synchronized both within and between countries, despite the geographical isolation of many areas in our study. Within Norway, this synchrony was most strongly modulated by population size, confirming previous findings that hierarchical spread between larger populations underlies seasonal influenza dynamics at regional levels. However, we found no such association when comparing across countries, suggesting that other factors become important at the international scale. Finally, to frame our results within a wider global context, we compared our findings from Norway to those from the US. After correcting for differences in geographic scale, we unexpectedly found higher levels of synchrony in Norway, despite its smaller population size. We hypothesize that this greater synchrony may be driven by more favorable and spatially uniform climatic conditions, although there are other likely factors we were unable to consider (such as reduced variation in school term times and differences in population movements). Overall, our results highlight the importance of comparing influenza spread at different spatial scales and across diverse geographic regions in order to better understand the complex mechanisms underlying disease dynamics.

The relationship of climate variables in the prevalence of acute respiratory infection in children under two years old in Rondonópolis-MT, Brazil

It is estimated that approximately 30% of childhood diseases can be attributed to environmental factors and 40% involve children under the age of five years old, representing about 10% of world population. This study aimed to analyze the relationship of climate variables in the prevalence of acute respiratory infection (ARI) in children under two years old, in Rondonopolis-MT, from 1999 to 2014. It was used a cross-sectional study with a quantitative and a descriptive approach with meteorological teaching and research data from the database from the health information system. For statistical analysis, it adjusted the negative binomial model belonging to the class of generalized linear models, adopting a significance level of 5%, based on the statistical platform R. The average number of cases of ARI decreases at approximately by 7.9% per degree centigrade increase above the average air temperature and decrease about 1.65% per 1% increase over the average air relative humidity. Already, the rainfall not associated with these cases. It is the interdisciplinary team refocus practical actions to assist in the control and reduction of ARI significant numbers in primary health care, related climate issues in children.

Adaptive diversification in heterogeneous environments

The role of environmental heterogeneity in the evolution of biological diversity has been studied only for simple types of heterogeneities and dispersals. This article broadens previous results by considering heterogeneities and dispersals that are structured by several environmental factors. It studies the evolution of a metapopulation, living in a network of patches connected by dispersal, under the effects of mutation, selection and migration. First, it is assumed that patches are equally connected and that they carry habitats characterized by several factors exerting selection pressures on several individual traits. Habitat factors may vary in the environment independently or they may be correlated. It is shown that correlations between habitat factors promote adaptive diversification and that this effect may be modified by trait interactions on survival. Then, it is assumed that patches are structured by two crossed factors, called the row and column factors, such that patches are more connected when they occur in the same row or in the same column. Environmental patterns in which each habitat appears in each row the same number of times and appears in each column the same number of times are found to hinder adaptive diversification.

Influence of meteorological conditions on RSV infection in Portugal

Acute viral bronchiolitis is a common cause for infant hospital admissions. Of all etiological agents, respiratory syncytial virus (RSV) is commonly the most frequent. The present study assesses relationships between atmospheric factors and RSV infections in under 3-year-old patients admitted to the Inpatient Paediatric Service of Vila Real (North of Portugal). For this purpose, (1) clinical files of children admitted with a diagnosis of acute bronchiolitis from September 2005 to December 2015 (>10 years) were scrutinised and (2) local daily temperature/precipitation series, as well as six weather types controlling meteorological conditions in Portugal, were used. Fifty-five percent of all 770 admitted children were effectively infected with a given virus, whilst 48 % (367) were RSV+, i.e. 87 % of virus-infected children were RSV+. The bulk of incidence is verified in the first year of age (82 %, 302), slightly higher in males. RSV outbreaks are typically from December to March, but important inter-annual variability is found in both magnitude and shape. Although no clear connections were found between monthly temperatures/precipitation and RSV outbreaks apart from seasonality, a linkage to wintertime cold spells is apparent on a daily basis. Anomalously low minimum temperatures from the day of admittance back to 10 days before are observed. This relationship is supported by anomalously high occurrences of the E and AA weather types over the same period, which usually trigger dry and cold weather. These findings highlight some predictability in the RSV occurrences, revealing potential for modelling and risk assessments.

Rhinovirus Infections and Associated Respiratory Morbidity in Infants: A Prospective Cohort Study

BACKGROUND

Risk factors promoting rhinovirus (RV) infections are inadequately described in healthy populations, especially infants.

OBJECTIVES

To determine the frequency of symptomatic and asymptomatic RV infections and identify possible risk factors from host and environment among otherwise healthy infants.

METHODS

In a prospective birth cohort, respiratory health was assessed in 41 term-born infants by weekly telephonic interviews during the first year of life, and weekly nasal swabs were collected to determine RV prevalence. In a multilevel logistic regression model, associations between prevalence and respiratory symptoms during RV infections and host/environmental factors were determined.

RESULTS

Twenty-seven percent of nasal swabs in 41 infants tested positive for RVs. Risk factors for RV prevalence were autumn months [odds ratio (OR) = 1.71, P = 0.01, 95% confidence interval (CI): 1.13-2.61], outdoor temperatures between 5 and 10°C (OR = 2.33, P = 0.001, 95% CI: 1.41-3.86), older siblings (OR = 2.60, P = 0.001, 95% CI: 1.50-4.51) and childcare attendance (OR = 1.53, P = 0.07, 95% CI: 0.96-2.44). Fifty-one percent of RV-positive samples were asymptomatic. Respiratory symptoms during RV infections were less likely during the first 3 months of life (OR = 0.34, P = 0.003, 95% CI: 0.17-0.69) and in infants with atopic mothers (OR = 0.44, P = 0.008, 95% CI: 0.24-0.80). Increased tidal volume (OR = 1.67, P = 0.03, 95% CI: 1.04-2.68) and outdoor temperatures between 2 and 5°C (OR = 2.79, P = 0.02, 95% CI: 1.17-6.61) were associated with more symptoms.

CONCLUSIONS

RVs are highly prevalent during the first year of life, and most infections are asymptomatic. Frequency of RV infections is associated with environmental factors, while respiratory symptoms during RV infections are linked to host determinants like infant age, maternal atopy or premorbid lung function.

Respiratory syncytial virus a and b display different temporal patterns in a 4-year prospective cross-sectional study among children with acute respiratory infection in a tropical city

Respiratory syncytial virus (RSV) is one of the most common etiological agents of childhood respiratory infections globally. Information on seasonality of different antigenic groups is scarce. We aimed to describe the frequency, seasonality, and age of children infected by RSV antigenic groups A (RSVA) and B (RSVB) among children with ARI in a 4-year period.Children (6-23 months old) with respiratory infection for ≤7 days were enrolled in a prospective cross-sectional study, from September, 2009 to October, 2013, in Salvador, in a tropical region of Brazil. Upon recruitment, demographic, clinical data, and nasopharyngeal aspirates (NPA) were collected. A multiplex quantitative real-time polymerase chain reaction (RT-PCR) with a group-specific primer and probeset for RSVA and RSVB was used. Seasonal distribution of infection by RSV different antigenic groups was evaluated by Prais-Wisten regression.Of 560 cases, the mean age was 11.4 ± 4.5 months and there were 287 (51.3%) girls. Overall, RSV was detected in 139 (24.8%; 95% CI: 21.4%-28.5%) cases, RSVA in 74 (13.2%; 95% CI: 10.6%-16.2%) cases, and RSVB in 67 (12.0%; 95% CI: 9.5%-14.9%) cases. Two (0.4%; 95% CI: 0.06%-1.2%) cases had coinfection. RSVA frequency was 9.6%, 18.4%, 21.6%, and 3.1% in 2010, 2011, 2012, and 2013, respectively. RSVB frequency was 19.2%, 0.7%, 1.4%, and 35.4% in the same years. RSVA was more frequently found from August to January than February to July (18.2% vs. 6.4%, P < 0.001). RSVB was more frequently found (P < 0.001) between March and June (36.0%) than July to October (1.0%) or November to February (1.6%). RSVB infection showed seasonal distribution and positive association with humidity (P = 0.02) whereas RSVA did not. RSVA was more common among children ≥1-year-old (17.8% vs. 1.8%; P = 0.02), as opposed to RSVB (11.5% vs. 12.2%; P = 0.8).One quarter of patients had RSV infection. RSVA compromised more frequently children aged ≥1 year. RSVA predominated in 2011 and 2012 whereas RSVB predominated in 2010 and 2013. In regard to months, RSVA was more frequent from August to January whereas RSVB was more often detected between March and June. Markedly different monthly as well as yearly patterns for RSVA and RSVB reveal independent RSV antigenic groups' epidemics.

A Decrease in Temperature and Humidity Precedes Human Rhinovirus Infections in a Cold Climate

Both temperature and humidity may independently or jointly contribute to the risk of human rhinovirus (HRV) infections, either through altered survival and spread of viruses in the environment or due to changes in host susceptibility. This study examined the relationship between short-term variations in temperature and humidity and the risk of HRV infections in a subarctic climate. We conducted a case-crossover study among conscripts (n = 892) seeking medical attention due to respiratory symptoms during their military training and identified 147 HRV cases by real-time PCR. An average temperature, a decline in daily ambient temperature and absolute humidity (AH) during the three preceding days of the onset (hazard period) and two reference periods (a week prior and after the onset) were obtained. The average daily temperature preceding HRV infections was −9.9 ± 4.9 °C and the average AH was 2.2 ± 0.9 g/m³. An average (odds ratios (OR) 1.07 (95% confidence interval (CI) 1.00–1.15)) and maximal (OR 1.08 (1.01–1.17)) change in temperature increased the risk of HRV infections by 8% per 1 °C decrease. An average (OR 1.20 (CI 1.03–1.40)) and maximal decrease (OR 1.13 (CI 0.96–1.34)) in AH increased the risk of HRV infection by 13% and 20% per 0.5 g/m³ decrease. A higher average temperature during the three preceding days was positively associated with HRV infections (OR 1.07 (CI 1.00–1.15)). A decrease rather than low temperature and humidity per se during the preceding few days increases the risk of HRV infections in a cold climate. The information is applicable to populations residing in cold climates for appropriate personal protection and prevention of adverse health effects.

Google Flu Trends Spatial Variability Validated Against Emergency Department Influenza-Related Visits

Background

Influenza is a deadly and costly public health problem. Variations in its seasonal patterns cause dangerous surges in emergency department (ED) patient volume. Google Flu Trends (GFT) can provide faster influenza surveillance information than traditional CDC methods, potentially leading to improved public health preparedness. GFT has been found to correlate well with reported influenza and to improve influenza prediction models. However, previous validation studies have focused on isolated clinical locations.

Objective

The purpose of the study was to measure GFT surveillance effectiveness by correlating GFT with influenza-related ED visits in 19 US cities across seven influenza seasons, and to explore which city characteristics lead to better or worse GFT effectiveness.

Methods

Using Healthcare Cost and Utilization Project data, we collected weekly counts of ED visits for all patients with diagnosis (International Statistical Classification of Diseases 9) codes for influenza-related visits from 2005-2011 in 19 different US cities. We measured the correlation between weekly volume of GFT searches and influenza-related ED visits (ie, GFT ED surveillance effectiveness) per city. We evaluated the relationship between 15 publically available city indicators (11 sociodemographic, two health care utilization, and two climate) and GFT surveillance effectiveness using univariate linear regression.

Results

Correlation between city-level GFT and influenza-related ED visits had a median of .84, ranging from .67 to .93 across 19 cities. Temporal variability was observed, with median correlation ranging from .78 in 2009 to .94 in 2005. City indicators significantly associated (P<.10) with improved GFT surveillance include higher proportion of female population, higher proportion with Medicare coverage, higher ED visits per capita, and lower socioeconomic status.

Conclusions

GFT is strongly correlated with ED influenza-related visits at the city level, but unexplained variation over geographic location and time limits its utility as standalone surveillance. GFT is likely most useful as an early signal used in conjunction with other more comprehensive surveillance techniques. City indicators associated with improved GFT surveillance provide some insight into the variability of GFT effectiveness. For example, populations with lower socioeconomic status may have a greater tendency to initially turn to the Internet for health questions, thus leading to increased GFT effectiveness. GFT has the potential to provide valuable information to ED providers for patient care and to administrators for ED surge preparedness.

Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance

The 2009 influenza A(H1N1)pdm09 pandemic highlighted the need for improved scientific knowledge to support better pandemic preparedness and seasonal influenza control. The Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS) project, a 5-year (2012–2016) multiagency and multidisciplinary collaboration, aimed to measure disease burden, epidemiology, aetiology, risk factors, immunology, effectiveness of vaccination and other prevention strategies for influenza and other respiratory infectious diseases of public health importance. Two active, prospective, population-based surveillance systems were established for monitoring influenza and other respiratory pathogens among those hospitalized patients with acute respiratory illness and those enrolled patients seeking consultations at sentinel general practices. In 2015, a sero-epidemiological study will use a sample of patients from the same practices. These data will provide a full picture of the disease burden and risk factors from asymptomatic infections to severe hospitalized disease and deaths and related economic burden. The results during the first 2 years (2012–2013) provided scientific evidence to (a) support a change to NZ's vaccination policy for young children due to high influenza hospitalizations in these children; (b) contribute to the revision of the World Health Organization's case definition for severe acute respiratory illness for global influenza surveillance; and (c) contribute in part to vaccine strain selection using vaccine effectiveness assessment in the prevention of influenza-related consultations and hospitalizations. In summary, SHIVERS provides valuable international platforms for supporting seasonal influenza control and pandemic preparedness, and responding to other emerging/endemic respiratory-related infections.

Absence of back to school peaks in human rhinovirus detections and respiratory symptoms in a cohort of children with asthma

Much of what is known about the seasonality of human rhinovirus (hRV) infections has been learned from the study of acute asthma exacerbations presenting to emergency care, including those among children at the start of the school term. Much less is known about the patterns of hRVs in the community. In this study, viruses and day-to-day symptoms of asthma and colds were monitored twice weekly in 67 children with asthma aged 5-12 years, over a 15 month period in Sydney, Australia. Overall hRV was detected in 314/1232 (25.5%) of nasal wash samples and 142/1231 (11.5%) of exhaled breath samples; of these, 231 and 24 respectively were genotyped. HRVs were detected with similar prevalence rate throughout the year, including no peak in hRV prevalence following return to school. No peaks were seen in asthma and cold symptoms using twice-weekly diary records. However, over the same period in the community, there were peaks in asthma emergency visits both at a large local hospital and in state-wide hospitalizations, following both return to school (February) and in late autumn (May) in children of the same age. This study suggests that hRV infections are common throughout the year among children, and differences in virus prevalence alone may not account for peaks in asthma symptoms.

Eighteen Years of Respiratory Syncytial Virus Surveillance: Changes in Seasonality and Hospitalization Rates in Southwestern Alaska Native Children

BACKGROUND

Alaska Native infants from the Yukon-Kuskokwim Delta (YKD) experienced respiratory syncytial virus (RSV) hospitalization rates 5 times higher and an RSV season twice as long as the general US infant population. We describe trends in hospitalization rates and seasonality during 18 years of continuous RSV surveillance in this population and explore contributions of climate and sociodemographic factors.

METHODS

We abstracted clinical and RSV test information from computerized medical records at YKD Regional Hospital and Alaska Native Medical Center from 1994 to 2012 to determine hospitalization rates and RSV season timing. Descriptive village and weather data were acquired through the US Census and Alaska Climate Research Center, University of Alaska, Fairbanks, respectively.

RESULTS

During 1994-2012, YKD infant RSV hospitalization rates declined nearly 3-fold, from 177 to 65 per 1000 infants/yr. RSV season onset shifted later, from mid October to late December, contributing to a significantly decreased season duration, from 30 to 11 weeks. In a multivariate analysis, children from villages with more crowded households and lacking plumbed water had higher rates of RSV hospitalizations (relative rate, 1.17; P = 0.0005 and relative rate, 1.45; P = 0.0003). No association of temperature or dew point was found with the timing or severity of RSV season.

CONCLUSIONS

Although the RSV hospitalization rate decreased 3-fold, YKD infants still experience a hospitalization rate 3-fold higher than the general US infant population. Overcrowding and lack of plumbed water were associated with RSV hospitalization. Dramatic changes occurred in RSV seasonality, not explained by changes in climate.

Age-specific epidemic waves of influenza and respiratory syncytial virus in a subtropical city

Both influenza and respiratory syncytial virus (RSV) are active throughout the year in subtropical or tropical regions, but few studies have reported on age-specific seasonal patterns of these viruses. We examined the age-specific epidemic curves of laboratory-confirmed cases of influenza A (subtyped into seasonal A(H1N1), A(H3N2), and pandemic virus A(H1N1)pdm09), influenza B and respiratory syncytial virus (RSV), in subtropical city Hong Kong from 2004 to 2013. We found that different types and subtypes of influenza showed similar two-peak patterns across age groups, with one peak in winter and another in spring/summer. Age differences were found in epidemic onset time and duration, but none could reach statistical significance (p > 0.05). Age synchrony was found in epidemic peak time for both cool and warm seasons. RSV showed less clear seasonal patterns and non-synchronized epidemic curves across age. In conclusion, age synchrony was found in influenza seasonal epidemics and the 2009 pandemic, but not in RSV. None of the age groups consistently appear as the driving force for seasonal epidemics of influenza and RSV in Hong Kong.

Seasonality, ambient temperatures and hospitalizations for acute exacerbation of COPD: a population-based study in a metropolitan area

BACKGROUND

Excluding the tropics, exacerbations of chronic obstructive pulmonary disease (COPD) are more frequent in winter. However, studies that directly relate hospitalizations for exacerbation of COPD to ambient temperature are lacking. The aim of this study was to assess the influence of temperature on the number of hospitalizations for COPD.

METHODS

This was a population-based study in a metropolitan area. All hospital discharges for acute exacerbation of COPD during 2009 in Barcelona and its metropolitan area were analyzed. The relationship between the number of hospitalizations for COPD and the mean, minimum, and maximum temperatures alongside comorbidity, humidity, influenza rate, and environmental pollution were studied.

RESULTS

A total of 9,804 hospitalization discharges coded with COPD exacerbation as a primary diagnosis were included; 75.4% of cases were male with a mean age of 74.9±10.5 years and an average length of stay of 6.5±6.1 days. The highest number of admissions (3,644 [37.2%]) occurred during winter, followed by autumn with 2,367 (24.1%), spring with 2,347 (23.9%), and summer with 1,446 (14.7%; P<0.001). The maximum, minimum, and mean temperatures were associated similarly with the number of hospitalizations. On average, we found that for each degree Celsius decrease in mean weekly temperature, hospital admissions increased by 5.04% (r(2)=0.591; P<0.001). After adjustment for humidity, comorbidity, air pollution, and influenza-like illness, only mean temperatures retained statistical significance, with a mean increase of 4.7% in weekly admissions for each degree Celsius of temperature (r(2)=0.599, P<0.001).

CONCLUSION

Mean temperatures are closely and independently related to the number of hospitalizations for COPD.

Do pollution and climate influence respiratory tract infections in children?

To review if pollution and climate changes can influence respiratory tract infections in children.

DATA SOURCE

Articles published on the subject in PubMed, SciELO, Bireme, EBSCO and UpTodate were reviewed. The following inclusion criteria were considered: scientific papers between 2002 and 2012, study design, the pediatric population, reference documents such as the CETESB and World Health Organization Summary of the data: We analyzed research that correlated respiratory viruses and climate and/or pollution changes. Respiratory syncytial virus has been the virus related most to changes in climate and humidity. Other "old and new" respiratory viruses such as Human Bocavirus, Metapneumovirus, Parechovirus and Parainfuenza would need to be investigated owing to their clinical importance. Although much has been studied with regard to the relationship between climate change and public health, specific studies about its influence on children's health remain scarce.

The potential impact of climate change and ultraviolet radiation on vaccine-preventable infectious diseases and immunization service delivery system

Climate change and solar ultraviolet radiation may affect vaccine-preventable infectious diseases (VPID), the human immune response process and the immunization service delivery system. We systematically reviewed the scientific literature and identified 37 relevant publications. Our study shows that climate variability and ultraviolet radiation may potentially affect VPID and the immunization delivery system through modulating vector reproduction and vaccination effectiveness, possibly influencing human immune response systems to the vaccination, and disturbing immunization service delivery. Further research is needed to determine these affects on climate-sensitive VPID and on human immune response to common vaccines. Such research will facilitate the development and delivery of optimal vaccination programs for target populations, to meet the goal of disease control and elimination.