Using mathematical transmission modelling to investigate drivers of respiratory syncytial virus seasonality in children in the Philippines

Modelling respiratory syncytial virus age-specific risk of hospitalisation in term and preterm infants

BACKGROUND

Respiratory syncytial virus (RSV) is the most common cause of acute lower respiratory infections in children worldwide. The highest incidence of severe disease is in the first 6 months of life, with infants born preterm at greatest risk for severe RSV infections. The licensure of new RSV therapeutics (a long-acting monoclonal antibody and a maternal vaccine) in Europe, USA, UK and most recently in Australia, has driven the need for strategic decision making on the implementation of RSV immunisation programs. Data driven approaches, considering the local RSV epidemiology, are critical to advise on the optimal use of these therapeutics for effective RSV control.

METHODS

We developed a dynamic compartmental model of RSV transmission fitted to individually-linked population-based laboratory, perinatal and hospitalisation data for 2000-2012 from metropolitan Western Australia (WA), stratified by age and prior exposure. We account for the differential risk of RSV-hospitalisation in full-term and preterm infants (defined as < 37 weeks gestation). We formulated a function relating age, RSV exposure history, and preterm status to the risk of RSV-hospitalisation given infection.

RESULTS

The age-to-risk function shows that risk of hospitalisation, given RSV infection, declines quickly in the first 12 months of life for all infants and is 2.6 times higher in preterm compared with term infants. The hospitalisation risk, given infection, declines to < 10% of the risk at birth by age 7 months for term infants and by 9 months for preterm infants.

CONCLUSIONS

The dynamic model, using the age-to-risk function, characterises RSV epidemiology for metropolitan WA and can now be extended to predict the impact of prevention measures. The stratification of the model by preterm status will enable the comparative assessment of potential strategies in the extended model that target this RSV risk group relative to all-population approaches. Furthermore, the age-to-risk function developed in this work has wider relevance to the epidemiological characterisation of RSV.

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.

Reconstructing the impact of COVID-19 on the immunity gap and transmission of respiratory syncytial virus in Lombardy, Italy

BACKGROUND

Respiratory syncytial virus (RSV) is a leading cause of hospitalisation and mortality in young children globally. The social distancing measures implemented against COVID-19 in Lombardy (Italy) disrupted the typically seasonal RSV circulation during 2019-2021 and caused substantially more hospitalisations during 2021-2022. The primary aim of this study is to quantify the immunity gap-defined as the increased proportion of the population naïve to RSV infection following the relaxation of COVID-19 restrictions in Lombardy, which has been hypothesised to be a potential cause of the increased RSV burden in 2021-2022.

METHODS

We developed a catalytic model to reconstruct changes in the age-dependent susceptibility profile of the Lombardy population throughout the COVID-19 pandemic. The model is calibrated to routinely collected hospitalisation, syndromic, and virological surveillance data and tested for alternative assumptions on age-dependencies in the risk of RSV infection throughout the pandemic.

FINDINGS

We estimate that the proportion of the Lombardy population naïve to RSV infection increased by 60.8% (95% CrI: 55.2-65.4%) during the COVID-19 pandemic: from 1.4% (95% CrI: 1.3-1.6%) in 2018-2019 to 2.3% (95% CrI: 2.2-2.5%) before the 2021-2022 season, corresponding to an immunity gap of 0.87% (95% CrI: 0.87-0.88%). We found evidence of heterogeneity in RSV transmission by age, suggesting that the COVID-19 restrictions had variable impact on the contact patterns and risk of RSV infection across ages.

INTERPRETATION

We estimate a substantial increase in the population-level susceptibility to RSV in Lombardy during 2019-2021, which contributed to an increase in primary RSV infections in 2021-2022.

FUNDING

UK Medical Research Council (MRC), UK Foreign, Commonwealth & Development Office (FCDO), EDCTP2 programme, European Union, Wellcome Trust, Royal Society, EU-MUR PNRR INF-ACT.

Etiological and clinical characteristics of severe pneumonia in pediatric intensive care unit (PICU)

OBJECTIVE

To analyze the etiological distribution characteristics of pediatric patients with severe pneumonia admitted to the Pediatric Intensive Care Unit (PICU), in order to provide a reference for the rational use of clinical antimicrobial drugs.

METHODS

A retrospective analysis of pediatric patients admitted to PICU with a diagnosis of severe pneumonia from January 2018 to December 2021 was performed and statistical analysis of pathogenic characteristics was performed.

RESULTS

A total of 649 pathogens were detected in 515 children, with a positive detection rate of 77.48%. Bacteria were detected at the highest rate (40.52%), followed by viruses (34.35%), atypical pathogens (19.72%) and fungal (4.31%). Gram-positive infections were dominated by Staphylococcus aureus (39.56%) and Streptococcus pneumoniae (32.97%), and Gram-negative infections were dominated by Acinetobacter Bahmani (16.28%) and Haemophilus influenzae (15.12%), followed by Klebsiella pneumoniae (13.95%) and Pseudomonas aeruginosa (12.21%). Viral infections were dominated by respiratory syncytial virus (25.65%) and EB virus (20.43%), fungal infections were dominated by Candida albicans (50.0%). The proportion of children infected with single pathogen (49.62%) was comparable to that of those with mixed infections (50.38%). There were statistically significant differences in the distribution of children with single pathogen infection by gender (P < 0.05). The age distribution of children with single bacterial, single viral and single fungal infections was statistically different (P < 0.05). There was no significant difference in the distribution of onset season in children with single pathogen infections (P > 0.05), but the number of children with single viral infections was significantly higher in winter and spring than that in summer and autumn, and the difference was statistically significant (P < 0.05). A mixture of 2 pathogens (77.61%) accounted for the majority of mixed infections, there were statistical differences in the distribution of bacterial + viral infection in terms of gender, age, and onset season (P < 0.05), children with viral + mycoplasma infection in terms of gender and age (P < 0.05), and children with viral + fungal infection in terms of gender (P < 0.05), and children with bacterial + mycoplasma infection in terms of age and onset season (P < 0.05). Among the children infected with 3 pathogens, there were statistically significant differences in the distribution of bacterial + viral + fungal and viral + mycoplasma + fungal infections in terms of gender (P < 0.05), and children with bacterial + viral + mycoplasma infection in terms of age (P < 0.05), while there was no significant difference in the distribution of onset season (P > 0.05). There were no significant differences in the distribution of children infected with 4 pathogens in terms of gender, age and onset season (P > 0.05).

CONCLUSION

The pathogens of pediatric patients with severe pneumonia in PICU commonly involves bacteria and viruses. As the age of children grows, the detection rate of bacteria shows a decreasing trend, and the pathogenic spectrum gradually changes from bacteria to mycoplasma and viruses, and the number of mixed infections gradually increase. Rational selection of antimicrobial drugs needs to consider pathogenic characteristics of different age, gender, and onset season in clinical practice.

Burden of Respiratory Syncytial Virus Related Acute Lower Respiratory Tract Infection in Hospitalized Thai Children: A 6-Year National Data Analysis

Objectives: This study sought to determine the epidemiology, seasonal variations, morbidity, and mortality of respiratory syncytial virus (RSV) infection among hospitalized children with lower respiratory tract infection in Thailand. In addition, we assessed the risk factors associated with severe RSV lower respiratory tract infection (LRTI)-related morbidity and mortality. Methods: The data were reviewed retrospectively from the National Health Security Office for hospitalized children younger than 18 years old diagnosed with RSV-related LRTI in Thailand, between the fiscal years of 2015 to 2020. The RSV-related LRTIs were identified using the International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Thai Modification. ICD-10-TM codes J12.1, J20.5, and J21.0, which represent respiratory syncytial virus pneumonia, acute bronchitis due to respiratory syncytial virus, and acute bronchiolitis due to respiratory syncytial virus, respectively, were studied. Results: During the study period, RSV-related LRTI accounted for 19,340 of the 1,610,160 hospital admissions due to LRTI. RSV pneumonia was the leading cause of hospitalization (13,684/19,340; 70.76%), followed by bronchiolitis (2849/19,340; 14.73%) and bronchitis (2807/19,340; 14.51%), respectively. The highest peak incidence of 73.55 percent occurred during Thailand’s rainy season, from August to October. The mortality rate of RSV-related LRTI in infants younger than 1 year of age was 1.75 per 100,000 person years, which was significantly higher than that of children 1 to younger than 5 years old and children 5 to younger than 18 years old (0.21 per 100,000 person years and 0.01 per 100,000 person years, respectively, p-value < 0.001). Factors associated with mortality were congenital heart disease, hematologic malignancy, malnutrition, and neurological disease. Conclusions: In children with RSV LRTI, pneumonia was the leading cause of hospitalization. The admission rate was highest during the rainy season. Mortality from RSV-related LRTI was higher in children under 1 year old and in children with underlying illnesses; future preventive interventions should target these groups of patients.

Drivers of respiratory syncytial virus seasonal epidemics in children under 5 years in Kilifi, coastal Kenya

Respiratory syncytial virus (RSV) causes significant childhood morbidity and mortality in the developing world. The determinants of RSV seasonality are of importance in designing interventions. They are poorly understood in tropical and sub-tropical regions in low- and middle-income countries. Our study utilized long-term surveillance data on cases of RSV associated with severe or very severe pneumonia in children aged 1 day to 59 months admitted to the Kilifi County Hospital. A generalized additive model was used to investigate the association between RSV admissions and meteorological variables (maximum temperature, rainfall, absolute humidity); weekly number of births within the catchment population; and school term dates. Furthermore, a time-series-susceptible-infected-recovered (TSIR) model was used to reconstruct an empirical transmission rate which was used as a dependent variable in linear regression and generalized additive models with meteorological variables and school term dates. Maximum temperature, absolute humidity, and weekly number of births were significantly associated with RSV activity in the generalized additive model. Results from the TSIR model indicated that maximum temperature and absolute humidity were significant factors. Rainfall and school term did not yield significant relationships. Our study indicates that meteorological parameters and weekly number of births potentially play a role in the RSV seasonality in this region. More research is required to explore the underlying mechanisms underpinning the observed relationships.

Mathematical Modeling: Global Stability Analysis of Super Spreading Transmission of Respiratory Syncytial Virus (RSV) Disease

In this paper, a model for the transmission of respiratory syncytial virus (RSV) in a constant human population in which there exist super spreading infected individuals (who infect many people during a single encounter) is considered. It has been observed in the epidemiological data for the diseases caused by this virus that there are cases where some individuals are super-spreaders of the virus. We formulate a simply SEIrIsR (susceptible–exposed–regular infected–super-spreading infected–recovered) mathematical model to describe the dynamics of the transmission of this disease. The proposed model is analyzed using the standard stability method by using Routh-Hurwitz criteria. We obtain the basic reproductive number (R0) using the next generation method. We establish that when R0<1, the disease-free state is locally asymptotically stable and the disease endemic state is unstable. The reverse is true when R0>1, the disease endemic state becomes the locally asymptotically stable state and the disease-free state becomes unstable. It is also established that the two equilibrium states are globally asymptotically stable. The numerical simulations show how the dynamics of the disease change as values of the parameters in the SEIrIsR are varied.

Use of mathematical modelling to assess respiratory syncytial virus epidemiology and interventions: a literature review

Respiratory syncytial virus (RSV) is a leading cause of acute lower respiratory tract infection worldwide, resulting in approximately sixty thousand annual hospitalizations of< 5-year-olds in the United States alone and three million annual hospitalizations globally. The development of over 40 vaccines and immunoprophylactic interventions targeting RSV has the potential to significantly reduce the disease burden from RSV infection in the near future. In the context of RSV, a highly contagious pathogen, dynamic transmission models (DTMs) are valuable tools in the evaluation and comparison of the effectiveness of different interventions. This review, the first of its kind for RSV DTMs, provides a valuable foundation for future modelling efforts and highlights important gaps in our understanding of RSV epidemics. Specifically, we have searched the literature using Web of Science, Scopus, Embase, and PubMed to identify all published manuscripts reporting the development of DTMs focused on the population transmission of RSV. We reviewed the resulting studies and summarized the structure, parameterization, and results of the models developed therein. We anticipate that future RSV DTMs, combined with cost-effectiveness evaluations, will play a significant role in shaping decision making in the development and implementation of intervention programs.

Epidemiology and Seasonality of Childhood Respiratory Syncytial Virus Infections in the Tropics

Infections caused by respiratory syncytial virus (RSV) are a major cause of morbidity and mortality in young children worldwide. Understanding seasonal patterns of region-specific RSV activity is important to guide resource allocation for existing and future treatment and prevention strategies. The decades of excellent RSV surveillance data that are available from the developed countries of the world are incredibly instructive in advancing public health initiatives in those regions. With few exceptions, these developed nations are positioned geographically across temperate regions of the world. RSV surveillance across tropical regions of the world has improved in recent years, but remains spotty, and where available, still lacks the necessary longitudinal data to determine the amount of seasonal variation expected over time. However, existing and emerging data collected across tropical regions of the world do indicate that patterns of infection are often quite different from those so well described in temperate areas. Here, we provide a brief summary regarding what is known about general patterns of RSV disease activity across tropical Asia, Africa and South America, then offer additional country-specific details using examples where multiple reports and/or more robust surveillance data have become available.

Mathematical modelling of respiratory syncytial virus (RSV) in low- and middle-income countries: A systematic review

Background:

Due to high burden of respiratory syncytial virus (RSV) in low- and middle-income countries (LMIC), international funding organizations have prioritized the development of RSV vaccines. Mathematical models of RSV will play an important role in assessing the relative value of these interventions. Our objectives were to provide an overview of the existing RSV modelling literature in LMIC and summarize available results on population-level effectiveness and cost-effectiveness.

Methods:

We searched MEDLINE from 2000 to 2020 for English language publications that employed a mathematical model of RSV calibrated to LMIC. Qualitative data were extracted on study and model characteristics. Quantitative data were collected on key model input assumptions and base case effectiveness and cost-effectiveness estimates for various immunization strategies.

Findings:

Of the 283 articles reviewed, 15 met inclusion criteria. Ten studies used modelling techniques to explore RSV transmission and/or natural history, while eight studies evaluated RSV vaccines and/or monoclonal antibodies, three of which included cost-effectiveness analyses. Six studies employed deterministic compartmental models, five studies employed individual transmission models, and four studies used different types of cohort models. Nearly every model was calibrated to at least one middle-income country, while four were calibrated to low-income countries.

Interpretation:

The mathematical modelling literature in LMIC has demonstrated the potential effectiveness of RSV vaccines and monoclonal antibodies. This review has demonstrated the importance of accounting for seasonality, social contact rates, immunity from prior infection and maternal antibody transfer. Future models should consider incorporating individual-level risk factors, subtype-specific effects, long-term sequelae of RSV infections, and out-of-hospital mortality.

Correlation between Children Respiratory Virus Infections and Climate Factors

Objective Respiratory viruses are the most important cause of lower respiratory tract infections (LRTI) in children. Meteorological factors can influence viral outbreaks. The objective of this study was to determine the association between climate variables and respiratory virus detection.

Methods Multicenter prospective 1-year surveillance was conducted among children hospitalized for LRTI in Tunisia. Nasopharyngeal aspirates were tested by direct immunofluorescence assay (DIFA) for the detection of respiratory syncytial virus (RSV); adenovirus (AdV); influenza virus (IFV) A and B; and parainfluenza virus 1, 2, and 3 (PIV1/2/3). Samples were further analyzed by reverse-transcription polymerase chain reaction for the detection of human metapneumovirus (hMPV). Monthly meteorological data were determined by consulting the National Institute of Meteorology and the World Weather Online Meteorological Company websites. Pearson's correlation tests were used to determine the statistical association between the detection of respiratory viruses and climatic characteristics.

Results Among 572 patients, 243 (42.5%) were positive for at least one virus. The most frequently detected viruses by DIFA were RSV (30.0%), followed by IFVA (3.8%), IFVB (3.5%), PIV (0.9%), and AdV (0.9%). HMPV was detected in 13 RSV-negative samples (3.3%). Dual infections were detected in seven cases (1.2%). Monthly global respiratory viruses and RSV detections correlated significantly with temperature, rainfall, cloud cover, wind speed, wind temperature, and duration of sunshine. Monthly IFV detection significantly correlated with rainfall, wind speed, wind temperature, and duration of sunshine. HMPV detection significantly correlated with temperature and wind temperature.

Conclusion Respiratory viral outbreaks are clearly related to meteorological factors in Tunisia.

Scoping Review of Climate Change and Health Research in the Philippines: A Complementary Tool in Research Agenda-Setting

The impacts of climate change on human health have been observed and projected in the Philippines as vector-borne and heat-related diseases have and continue to increase. As a response, the Philippine government has given priority to climate change and health as one of the main research funding topics. To guide in identifying more specific research topics, a scoping review was done to complement the agenda-setting process by mapping out the extent of climate change and health research done in the country. Research articles and grey literature published from 1980 to 2017 were searched from online databases and search engines, and a total of 34 quantitative studies were selected. Fifty-three percent of the health topics studied were about mosquito-borne diseases, particularly dengue fever. Seventy-nine percent of the studies reported evidence of positive associations between climate factors and health outcomes. Recommended broad research themes for funding were health vulnerability, health adaptation, and co-benefits. Other notable recommendations were the development of open data and reproducible modeling schemes. In conclusion, the scoping review was useful in providing a background for research agenda-setting; however, additional analyses or consultations should be complementary for added depth.

Timing of First Respiratory Virus Detections in Infants: A Community-Based Birth Cohort Study

Background

Determining timing of first virus detection episodes (fVDEs) for different respiratory viruses in infants identifies risk periods and informs preventive interventions, including vaccination. We describe the ages and nature of fVDEs in an infant birth cohort and explore factors associated with increased odds of symptomatic fVDEs.

Methods

The Observational Research in Childhood Infectious Diseases (ORChID) study is a community-based birth cohort describing acute respiratory infections in infants until their second birthday. Parents recorded daily symptoms and collected nose swabs weekly, which were batch-tested using polymerase chain reaction assays for 17 respiratory viruses.

Results

One hundred fifty-eight infants participated in ORChID. The median age for fVDEs was 2.9 months for human rhinovirus (HRV) but was ≥13.9 months for other respiratory viruses. Overall, 52% of HRV fVDEs were symptomatic, compared with 57%–83% of other fVDEs. Respiratory syncytial virus and human metapneumovirus fVDEs were more severe than HRV fVDEs. Older age and the winter season were associated with symptomatic episodes.

Conclusions

Infants do not always experience respiratory symptoms with their fVDE. Predominance of early HRV detections highlights the need for timing any intervention early in life. fVDEs from other respiratory viruses most commonly occur when maternal vaccines may no longer provide protection.

Respiratory syncytial virus tracking using internet search engine data

Background

Respiratory Syncytial Virus (RSV) is the leading cause of hospitalization in children less than 1 year of age in the United States. Internet search engine queries may provide high resolution temporal and spatial data to estimate and predict disease activity.

Methods

After filtering an initial list of 613 symptoms using high-resolution Bing search logs, we used Google Trends data between 2004 and 2016 for a smaller list of 50 terms to build predictive models of RSV incidence for five states where long-term surveillance data was available. We then used domain adaptation to model RSV incidence for the 45 remaining US states.

Results

Surveillance data sources (hospitalization and laboratory reports) were highly correlated, as were laboratory reports with search engine data. The four terms which were most often statistically significantly correlated as time series with the surveillance data in the five state models were RSV, flu, pneumonia, and bronchiolitis. Using our models, we tracked the spread of RSV by observing the time of peak use of the search term in different states. In general, the RSV peak moved from south-east (Florida) to the north-west US.

Conclusions

Our study represents the first time that RSV has been tracked using Internet data results and highlights successful use of search filters and domain adaptation techniques, using data at multiple resolutions. Our approach may assist in identifying spread of both local and more widespread RSV transmission and may be applicable to other seasonal conditions where comprehensive epidemiological data is difficult to collect or obtain.

Meteorological factors and respiratory syncytial virus seasonality in subtropical Australia

Evidence is emerging regarding the influence of meteorological factors on seasonal respiratory syncytial virus outbreaks. Data however, are limited for subtropical regions, especially in the southern hemisphere. We examined whether meteorological data (daily minimum and maximum temperatures, rainfall, relative humidity, dew point, daily global solar exposure) and tourist numbers were associated with the incidence of RSV in children aged <5 years for the Gold Coast region of South-East Queensland, Australia (latitude 28.0°S). RSV cases between 1 July 2007 and 30 June 2016 were identified from the Pathology Queensland Gold Coast Laboratory database. Time-series methods were used to identify seasonal patterns. RSV activity peaked in mid-to-late autumn (April–May), tapering in winter (June–August). While most meteorological variables measured were associated with RSV incidence, rainfall (ρ = 0.40, 95% confidence interval (CI) 0.32–0.48) and humidity (ρ = 0.38, 95% CI 0.29–0.46) 8 weeks earlier had the nearest temporal relationship. Tourist numbers were not correlated with RSV activity. Identifying meteorological conditions associated with seasonal RSV epidemics can improve understanding of virus transmission and assist planning for their impact upon the health sector, including timing of passive RSV immunoprophylaxis for high-risk infants and future public health interventions, such as maternal immunisation with RSV vaccines.

Identifying climate drivers of infectious disease dynamics: recent advances and challenges ahead

Climate change is likely to profoundly modulate the burden of infectious diseases. However, attributing health impacts to a changing climate requires being able to associate changes in infectious disease incidence with the potentially complex influences of climate. This aim is further complicated by nonlinear feedbacks inherent in the dynamics of many infections, driven by the processes of immunity and transmission. Here, we detail the mechanisms by which climate drivers can shape infectious disease incidence, from direct effects on vector life history to indirect effects on human susceptibility, and detail the scope of variation available with which to probe these mechanisms. We review approaches used to evaluate and quantify associations between climate and infectious disease incidence, discuss the array of data available to tackle this question, and detail remaining challenges in understanding the implications of climate change for infectious disease incidence. We point to areas where synthesis between approaches used in climate science and infectious disease biology provide potential for progress.

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.

Unexpected Infection Spikes in a Model of Respiratory Syncytial Virus Vaccination

Respiratory Syncytial Virus (RSV) is an acute respiratory infection that infects millions of children and infants worldwide. Recent research has shown promise for the development of a vaccine, with a range of vaccine types now in clinical trials or preclinical development. We extend an existing mathematical model with seasonal transmission to include vaccination. We model vaccination both as a continuous process, applying the vaccine during pregnancy, and as a discrete one, using impulsive differential equations, applying pulse vaccination. We develop conditions for the stability of the disease-free equilibrium and show that this equilibrium can be destabilised under certain extreme conditions, even with 100% coverage using an (unrealistic) vaccine. Using impulsive differential equations and introducing a new quantity, the impulsive reproduction number, we showed that eradication could be acheived with 75% coverage, while 50% coverage resulted in low-level oscillations. A vaccine that targets RSV infection has the potential to significantly reduce the overall prevalence of the disease, but appropriate coverage is critical.

Seasonality of Influenza and Respiratory Syncytial Viruses and the Effect of Climate Factors in Subtropical-Tropical Asia Using Influenza-Like Illness Surveillance Data, 2010 -2012

INTRODUCTION

The seasonality of influenza and respiratory syncytial virus (RSV) is well known, and many analyses have been conducted in temperate countries; however, this is still not well understood in tropical countries. Previous studies suggest that climate factors are involved in the seasonality of these viruses. However, the extent of the effect of each climate variable is yet to be defined.

MATERIALS AND METHODS

We investigated the pattern of seasonality and the effect of climate variables on influenza and RSV at three sites of different latitudes: the Eastern Visayas region and Baguio City in the Philippines, and Okinawa Prefecture in Japan. Wavelet analysis and the dynamic linear regression model were applied. Climate variables used in the analysis included mean temperature, relative and specific humidity, precipitation, and number of rainy days. The Akaike Information Criterion estimated in each model was used to test the improvement of fit in comparison with the baseline model.

RESULTS

At all three study sites, annual seasonal peaks were observed in influenza A and RSV; peaks were unclear for influenza B. Ranges of climate variables at the two Philippine sites were narrower and mean variables were significantly different among the three sites. Whereas all climate variables except the number of rainy days improved model fit to the local trend model, their contributions were modest. Mean temperature and specific humidity were positively associated with influenza and RSV at the Philippine sites and negatively associated with influenza A in Okinawa. Precipitation also improved model fit for influenza and RSV at both Philippine sites, except for the influenza A model in the Eastern Visayas.

CONCLUSIONS

Annual seasonal peaks were observed for influenza A and RSV but were less clear for influenza B at all three study sites. Including additional data from subsequent more years would help to ascertain these findings. Annual amplitude and variation in climate variables are more important than their absolute values for determining their effect on the seasonality of influenza and RSV.

Respiratory syncytial virus seasonality in tropical Australia

OBJECTIVE

Respiratory syncytial virus (RSV) is most common during the rainy season in a number of low- to middle-income tropical settings, a pattern driven by seasonal changes in climate and nutrition. We investigated the seasonality of RSV in the high-income tropical setting of North Queensland, Australia.

METHODS

We used RSV hospital admissions data from Cairns and Townsville to assess the seasonality of RSV. We examined the seasonal scale associations between selected meteorological exposures and RSV admissions using cross-correlation of weekly data.

RESULTS

In both Cairns and Townsville, RSV admissions were highest in the latter half of the rainy season. In Cairns, RSV admissions were most strongly correlated with rainfall four weeks previously. In Townsville, RSV admissions were most strongly correlated with rainfall six weeks previously.

CONCLUSIONS

The seasonality of RSV in the tropical setting of North Queensland appears to be driven by seasonal variations in rainfall. Further research is needed to assess the impact of climate on RSV incidence in the tropics.

Time series analysis of RSV and bronchiolitis seasonality in temperate and tropical Western Australia

Respiratory syncytial virus (RSV) causes respiratory illness in young children and is most commonly associated with bronchiolitis. RSV typically occurs as annual or biennial winter epidemics in temperate regions, with less pronounced seasonality in the tropics. We sought to characterise and compare the seasonality of RSV and bronchiolitis in temperate and tropical Western Australia. We examined over 13 years of RSV laboratory identifications and bronchiolitis hospitalisations in children, using an extensive linked dataset from Western Australia. We applied mathematical time series analyses to identify the dominant seasonal cycle, and changes in epidemic size and timing over this period. Both the RSV and bronchiolitis data showed clear winter epidemic peaks in July or August in the southern Western Australia regions, but less identifiable seasonality in the northern regions. Use of complex demodulation proved very effective at comparing disease epidemics. The timing of RSV and bronchiolitis epidemics coincided well, but the size of the epidemics differed, with more consistent peak sizes for bronchiolitis than for RSV. Our results show that bronchiolitis hospitalisations are a reasonable proxy for the timing of RSV detections, but may not fully capture the magnitude of RSV epidemics.

Exploring the dynamics of respiratory syncytial virus (RSV) transmission in children

Respiratory syncytial virus (RSV) is the main cause of lower respiratory tract infections in children. Whilst highly seasonal, RSV dynamics can have either one-year (annual) or two-year (biennial) cycles. Furthermore, some countries show a 'delayed biennial' pattern, where the epidemic peak in low incidence years is delayed. We develop a compartmental model for RSV infection, driven by a seasonal forcing function, and conduct parameter space and bifurcation analyses to document parameter ranges that give rise to these different seasonal patterns. The model is sensitive to the birth rate, transmission rate, and seasonality parameters, and can replicate RSV dynamics observed in different countries. The seasonality parameter must exceed a threshold for the model to produce biennial cycles. Intermediate values of the birth rate produce the greatest delay in these biennial cycles, while the model reverts to annual cycles if the duration of immunity is too short. Finally, the existence of period doubling and period halving bifurcations suggests robust model dynamics, in agreement with the known regularity of RSV outbreaks. These findings help explain observed RSV data, such as regular biennial dynamics in Western Australia, and delayed biennial dynamics in Finland. From a public health perspective, our findings provide insight into the drivers of RSV transmission, and a foundation for exploring RSV interventions.

Incorporating Transmission Into Causal Models of Infectious Diseases for Improved Understanding of the Effect and Impact of Risk Factors

Conventional measures of causality (which compare risks between exposed and unexposed individuals) do not factor in the population-scale dynamics of infectious disease transmission. We used mathematical models of 2 childhood infections (respiratory syncytial virus and rotavirus) to illustrate this problem. These models incorporated 3 causal pathways whereby malnutrition could act to increase the incidence of severe infection: increasing the proportion of infected children who develop severe infection, increasing the children's susceptibility to infection, and increasing infectiousness. For risk factors that increased the proportion of infected children who developed severe infection, the population attributable fraction (PAF) calculated conventionally was the same as the PAF calculated directly from the models. However, for risk factors that increased transmission (by either increasing susceptibility to infection or increasing infectiousness), the PAF calculated directly from the models was much larger than that predicted by the conventional PAF calculation. The models also showed that even when conventional studies find no association between a risk factor and an outcome, risk factors that increase transmission can still have a large impact on disease burden. For a complete picture of infectious disease causality, transmission effects must be incorporated into causal models.

Analyzing Seasonal Variations in Suicide With Fourier Poisson Time-Series Regression: A Registry-Based Study From Norway, 1969-2007

Seasonal variation in the number of suicides has long been acknowledged. It has been suggested that this seasonality has declined in recent years, but studies have generally used statistical methods incapable of confirming this. We examined all suicides occurring in Norway during 1969-2007 (more than 20,000 suicides in total) to establish whether seasonality decreased over time. Fitting of additive Fourier Poisson time-series regression models allowed for formal testing of a possible linear decrease in seasonality, or a reduction at a specific point in time, while adjusting for a possible smooth nonlinear long-term change without having to categorize time into discrete yearly units. The models were compared using Akaike's Information Criterion and analysis of variance. A model with a seasonal pattern was significantly superior to a model without one. There was a reduction in seasonality during the period. Both the model assuming a linear decrease in seasonality and the model assuming a change at a specific point in time were both superior to a model assuming constant seasonality, thus confirming by formal statistical testing that the magnitude of the seasonality in suicides has diminished. The additive Fourier Poisson time-series regression model would also be useful for studying other temporal phenomena with seasonal components.

Environmental drivers of the spatiotemporal dynamics of respiratory syncytial virus in the United States

Epidemics of respiratory syncytial virus (RSV) are known to occur in wintertime in temperate countries including the United States, but there is a limited understanding of the importance of climatic drivers in determining the seasonality of RSV. In the United States, RSV activity is highly spatially structured, with seasonal peaks beginning in Florida in November through December and ending in the upper Midwest in February-March, and prolonged disease activity in the southeastern US. Using data on both age-specific hospitalizations and laboratory reports of RSV in the US, and employing a combination of statistical and mechanistic epidemic modeling, we examined the association between environmental variables and state-specific measures of RSV seasonality. Temperature, vapor pressure, precipitation, and potential evapotranspiration (PET) were significantly associated with the timing of RSV activity across states in univariate exploratory analyses. The amplitude and timing of seasonality in the transmission rate was significantly correlated with seasonal fluctuations in PET, and negatively correlated with mean vapor pressure, minimum temperature, and precipitation. States with low mean vapor pressure and the largest seasonal variation in PET tended to experience biennial patterns of RSV activity, with alternating years of “early-big” and “late-small” epidemics. Our model for the transmission dynamics of RSV was able to replicate these biennial transitions at higher amplitudes of seasonality in the transmission rate. This successfully connects environmental drivers to the epidemic dynamics of RSV; however, it does not fully explain why RSV activity begins in Florida, one of the warmest states, when RSV is a winter-seasonal pathogen. Understanding and predicting the seasonality of RSV is essential in determining the optimal timing of immunoprophylaxis.

Respiratory syncytial virus (RSV) causes annual outbreaks of respiratory disease every winter in temperate climates, which can be severe particularly among infants. In the United States, RSV activity begins each autumn in Florida and appears to spread from the southeast to the northwest. Using data on hospitalizations and laboratory tests for RSV, we show that the timing of epidemics is associated with a variety of climatic factors, including temperature, vapor pressure, precipitation, and potential evapotranspiration (PET). Furthermore, using a dynamic model, we show that seasonal variation in the transmission rate of RSV can be correlated with the amplitude and timing of variation in PET, which is a measure of demand for water from the atmosphere. States with colder, drier weather and a large seasonal swing in PET tended to experience an alternating pattern of “early-big” RSV epidemics one year followed by a “late-small” epidemic the next year, which our model was able to reproduce based on the interaction between susceptible and infectious individuals. However, we cannot fully explain why epidemics begin in Florida. Being able to understand and predict the timing of RSV activity is important for optimizing the delivery of immunoprophylaxis to high-risk individuals.

The importance of the local environment in the transmission of respiratory syncytial virus

The role of the environment in the spread of respiratory infections is poorly understood, and consequently probably underappreciated. To improve our understanding of the environmental drivers of respiratory syncytial virus (RSV) transmission, we examined RSV seasonality in two settings with unusual seasonal patterns: The Gambia (where RSV epidemics occur at different times of the year) and Southeast Florida (where RSV seasonality differs from the rest of mainland USA). We used published data to correlate the seasonality of RSV with rainfall and child nutrition in the Gambia, and with rainfall and temperature in Florida. In the Gambia, RSV incidence was more strongly and more consistently correlated with child nutrition (r = -0.73 [95%CI -0.90 to -0.38]) than with rainfall (r = 0.37 [95%CI 0.20 to 0.52]). In Southeast Florida RSV incidence was strongly correlated with rainfall two months previously (r = 0.65 [95%CI 0.40 to 0.81]) compared to North Florida where RSV incidence was strongly correlated with temperature (r = -0.75 [95%CI -0.87 to -0.56]). We propose that nutrition is the dominant environmental driver of RSV seasonality in the Gambia, while rainfall is the dominant driver of RSV seasonality in Southeast Florida. This reinforces the importance of an ecological scale understanding of disease transmission: only with such an evidence base can setting-specific recommendations be made for public health interventions that are targeted for maximum efficacy.