Inferring influenza dynamics and control in households

Rapid Detection of Influenza Outbreaks in Long-Term Care Facilities Reduces Emergency Room Visits and Hospitalization: A Randomized Trial

OBJECTIVES

To assess whether the use of rapid influenza diagnostic tests (RIDTs) for long-term care facility (LTCF) residents with acute respiratory infection is associated with increased antiviral use and decreased health care utilization.

DESIGN

Nonblinded, pragmatic, randomized controlled trial evaluating a 2-part intervention with modified case identification criteria and nursing staff-initiated collection of nasal swab specimen for on-site RIDT.

SETTING AND PARTICIPANTS

Residents of 20 LTCFs in Wisconsin matched by bed capacity and geographic location and then randomized.

METHODS

Primary outcome measures, expressed as events per 1000 resident-weeks, included antiviral treatment courses, antiviral prophylaxis courses, total emergency department (ED) visits, ED visits for respiratory illness, total hospitalizations, hospitalizations for respiratory illness, hospital length of stay, total deaths, and deaths due to respiratory illness over 3 influenza seasons.

RESULTS

Oseltamivir use for prophylaxis was higher at intervention LTCFs [2.6 vs 1.9 courses per 1000 person-weeks; rate ratio (RR) 1.38, 95% CI 1.24-1.54; P < .001]; rates of oseltamivir use for influenza treatment were not different. Rates of total ED visits (7.6 vs 9.8/1000 person-weeks; RR 0.78, 95% CI 0.64-0.92; P = .004), total hospitalizations (8.6 vs 11.0/1000 person-weeks; RR 0.79, 95% CI 0.67-0.93; P = .004), and hospital length of stay (35.6 days vs 55.5 days/1000 person-weeks; RR 0.64, 95% CI 0.0.59-0.69; P < .001) were lower at intervention as compared to control LTCFs. No significant differences were noted for respiratory-related ED visits or hospitalizations or in rates for all-cause or respiratory-associated mortality.

CONCLUSIONS AND IMPLICATIONS

The use of low threshold criteria to trigger nursing staff-initiated testing for influenza with RIDT resulted in increased prophylactic use of oseltamivir. There were significant reductions in the rates of all-cause ED visits (22% decline), hospitalizations (21% decline), and hospital length of stay (36% decline) across 3 combined influenza seasons. No significant differences were noted in respiratory-associated and all-cause deaths between intervention and control sites.

Environmental factors related to differences in the microbiota in the upper respiratory tract in young children: Focusing on the impact of early nursery attendance

BACKGROUND

Microbial colonization of the upper respiratory tract (URT) during the first years of life differs significantly according to environmental factors. We investigated the association between early nursery attendance, URT infection (URTI) and drugs used for its treatment and the differences in the URT microbiota.

METHODS

This prospective study included 33 young children (11 and 22 with and without nursery attendance during their infancy, respectively). URT secretions were collected from the nasopharynx of these children at 2, 4, 6, 12, 18 and 24 months old. Clinical information after the latest sampling, including histories of URTI and the uses of antibiotics or cold medicines, was collected from all children. URT bacteria were identified by a clone library analysis of the 16S rRNA gene.

RESULTS

In the diversity of URT microbiota using the Shannon index, we did not detect any associations between variations in the URT microbiota and environmental factors (nursery attendance, development of URTIs, or the uses of antibiotics or cold medicines). However, in a clustering analysis, the proportion of the samples classified as Corynebacterium propinquum-dominant cluster was significantly lower in children ≥6 months old with nursery attendance than in those without nursery attendance. In addition, the URT microbiota was significantly different between samples from children ≥6 months old with and without a history of ≥3 URTI episodes after the first sampling. Furthermore, the URT microbiota was also significantly different between samples from these children with and without antibiotic use between the previous and present samplings.

CONCLUSION

Early nursery attendance and its related factors, including the frequency of URTI and antibiotic treatment, may be associated with the differences in the URT flora in young children.

Detection of Influenza in Managed Quarantine in Australia and the Estimated Risk of Importation

BACKGROUND

Influenza circulated at historically low levels during 2020/2021 due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic travel restrictions. In Australia, international arrivals were required to undergo a 14-day hotel quarantine to limit new introduction of SARS-CoV-2.

METHODS

We usedtesting data for travelers arriving on repatriation flights to Darwin, Australia, from 3 January 2021 to 11 October 2021 to identify importations of influenza virus into Australia. We used this information to estimate the risk of a case exiting quarantine while still infectious. Influenza-positive samples were sequenced, and cases were followed up to identify transmission clusters. Data on the number of cases and total passengers were used to infer the risk of influenza cases exiting quarantine while infectious.

RESULTS

Despite very low circulation of influenza globally, 42 cases were identified among 15 026 returned travelers, of which 30 were A(H3N2), 2 were A(H1N1)pdm09, and 10 were B/Victoria. Virus sequencing data identified potential in-flight transmission, as well as independent infections prior to travel. Under the quarantine strategy in place at the time, the probability that these cases could initiate influenza outbreaks in Australia neared 0. However, this probability rose as quarantine requirements relaxed.

CONCLUSIONS

Detection of influenza virus infections in repatriated travelers provided a source of influenza viruses otherwise unavailable and enabled development of the A(H3N2) vaccine seed viruses included in the 2022 Southern Hemisphere influenza vaccine. Failure to test quarantined returned travelers for influenza represents a missed opportunity for enhanced surveillance to better inform public health preparedness.

Household transmission of influenza A and B within a prospective cohort during the 2013-2014 and 2014-2015 seasons

People living within the same household as someone ill with influenza are at increased risk of infection. Here, we use Markov chain Monte Carlo methods to partition the hazard of influenza illness within a cohort into the hazard from the community and the hazard from the household. During the 2013‐2014 influenza season, 49 (4.7%) of the 1044 people enrolled in a community surveillance cohort had an acute respiratory illness (ARI) attributable to influenza. During the 2014‐2015 influenza season, 50 (4.7%) of the 1063 people in the cohort had an ARI attributable to influenza. The secondary attack rate from a household member was 2.3% for influenza A (H1) during 2013‐2014, 5.3% for influenza B during 2013‐2014, and 7.6% for influenza A (H3) during 2014‐2015. Living in a household with a person ill with influenza increased the risk of an ARI attributable to influenza up to 350%, depending on the season and the influenza virus circulating within the household.

The Oregon Child Absenteeism Due to Respiratory Disease Study (ORCHARDS): Rationale, objectives, and design

BACKGROUND

Influenza viruses pose significant disease burdens through seasonal outbreaks and unpredictable pandemics. Existing surveillance programs rely heavily on reporting of medically attended influenza (MAI). Continuously monitoring cause-specific school absenteeism may identify local acceleration of seasonal influenza activity. The Oregon Child Absenteeism Due to Respiratory Disease Study (ORCHARDS; Oregon, WI) implements daily school-based monitoring of influenza-like illness-specific student absenteeism (a-ILI) in kindergarten through Grade 12 schools and assesses this approach for early detection of accelerated influenza and other respiratory pathogen transmission in schools and surrounding communities.

METHODS

Starting in September 2014, ORCHARDS combines automated reporting of daily absenteeism within six schools and home visits to school children with acute respiratory infection (ARI). Demographic, epidemiological, and symptom data are collected along with respiratory specimens. Specimens are tested for influenza and other respiratory viruses. Household members can opt into a supplementary household transmission study. Community comparisons are possible using a pre-existing and highly effective influenza surveillance program, based on MAI at five family medicine clinics in the same geographical area.

RESULTS

Over the first 5 years, a-ILI occurred on 6634 (0.20%) of 3,260,461 student school days. Viral pathogens were detected in 64.5% of 1728 children with ARI who received a home visit. Influenza was the most commonly detected virus, noted in 23.3% of ill students.

CONCLUSION

ORCHARDS uses a community-based design to detect influenza trends over multiple seasons and to evaluate the utility of absenteeism for early detection of accelerated influenza and other respiratory pathogen transmission in schools and surrounding communities.

Expiratory aerosol particle escape from surgical masks due to imperfect sealing

Wearing surgical masks or other similar face coverings can reduce the emission of expiratory particles produced via breathing, talking, coughing, or sneezing. Although it is well established that some fraction of the expiratory airflow leaks around the edges of the mask, it is unclear how these leakage airflows affect the overall efficiency with which masks block emission of expiratory aerosol particles. Here, we show experimentally that the aerosol particle concentrations in the leakage airflows around a surgical mask are reduced compared to no mask wearing, with the magnitude of reduction dependent on the direction of escape (out the top, the sides, or the bottom). Because the actual leakage flowrate in each direction is difficult to measure, we use a Monte Carlo approach to estimate flow-corrected particle emission rates for particles having diameters in the range 0.5-20 μm. in all orientations. From these, we derive a flow-weighted overall number-based particle removal efficiency for the mask. The overall mask efficiency, accounting both for air that passes through the mask and for leakage flows, is reduced compared to the through-mask filtration efficiency, from 93 to 70% for talking, but from only 94-90% for coughing. These results demonstrate that leakage flows due to imperfect sealing do decrease mask efficiencies for reducing emission of expiratory particles, but even with such leakage surgical masks provide substantial control.

"We didn't get much schooling because we were fishing all the time": Potential impacts of irregular school attendance on the spread of epidemics

Objectives

Especially in traditional, rural, and low‐income areas, children attend school irregularly. School‐based interventions are common mitigation strategies for infectious disease epidemics, but if daily attendance is not the norm, the impact of schools on disease spread might be overestimated.

Methods

We use an agent‐based model of an early 20th century Newfoundland community to compare epidemic size and duration in three scenarios: (1) all school‐aged children attend school each weekday, (2) students aged 10–15 have a chance of engaging in adult activities each day, and (3) students aged 10–15 have a chance of being reassigned to adult roles at the start of each simulation and thus never attend school.

Results

As the probability of not attending school increases, epidemics become smaller and peak earlier. The change in final size is larger with permanent reassignment (35% at baseline, 18% at maximum reassignment) than with daily nonattendance (35% vs. 22%). For both scenarios, the peak occurs 3 days earlier with maximum absence compared to the baseline. Benefits extend beyond the reassigned agents, as all school‐aged agents are more likely to escape infection with increasing reassignment, and on average, 3–6 additional agents (2.6%–5.3%) escape infection compared to the baseline.

Conclusions

This study demonstrates that absenteeism can have important impacts on epidemic outcomes. Thus, socioeconomic and other reasons for nonattendance of school, as well as how rates vary in different contexts, must be considered in models predicting epidemic outcomes or evaluating public health interventions in the face of major pandemics.

Estimating and interpreting secondary attack risk: Binomial considered biased

The household secondary attack risk (SAR), often called the secondary attack rate or secondary infection risk, is the probability of infectious contact from an infectious household member A to a given household member B, where we define infectious contact to be a contact sufficient to infect B if he or she is susceptible. Estimation of the SAR is an important part of understanding and controlling the transmission of infectious diseases. In practice, it is most often estimated using binomial models such as logistic regression, which implicitly attribute all secondary infections in a household to the primary case. In the simplest case, the number of secondary infections in a household with m susceptibles and a single primary case is modeled as a binomial(m, p) random variable where p is the SAR. Although it has long been understood that transmission within households is not binomial, it is thought that multiple generations of transmission can be neglected safely when p is small. We use probability generating functions and simulations to show that this is a mistake. The proportion of susceptible household members infected can be substantially larger than the SAR even when p is small. As a result, binomial estimates of the SAR are biased upward and their confidence intervals have poor coverage probabilities even if adjusted for clustering. Accurate point and interval estimates of the SAR can be obtained using longitudinal chain binomial models or pairwise survival analysis, which account for multiple generations of transmission within households, the ongoing risk of infection from outside the household, and incomplete follow-up. We illustrate the practical implications of these results in an analysis of household surveillance data collected by the Los Angeles County Department of Public Health during the 2009 influenza A (H1N1) pandemic.

The household secondary attack risk (SAR), often called the secondary attack rate or secondary infection risk, is the probability of infectious contact from an infectious household member A to a given household member B, where we define infectious contact to be a contact sufficient to infect B if he or she is susceptible. The most common statistical models used to estimate the SAR are binomial models such as logistic regression, which implicitly assume that all secondary infections in a household are infected by the primary case. Here, we use analytical calculations and simulations to show that estimation of the SAR must account for multiple generations of transmission within households. As an example, we show that binomial models and statistical models that account for multiple generations of within-household transmission reach different conclusions about the household SAR for 2009 influenza A (H1N1) in Los Angeles County, with the latter models fitting the data better. In an epidemic, accurate estimation of the SAR allows rigorous evaluation of the effectiveness of public health interventions such as social distancing, prophylaxis or treatment, and vaccination.

Transmissibility and transmission of respiratory viruses

Human respiratory virus infections lead to a spectrum of respiratory symptoms and disease severity, contributing to substantial morbidity, mortality and economic losses worldwide, as seen in the COVID-19 pandemic. Belonging to diverse families, respiratory viruses differ in how easy they spread (transmissibility) and the mechanism (modes) of transmission. Transmissibility as estimated by the basic reproduction number (R₀) or secondary attack rate is heterogeneous for the same virus. Respiratory viruses can be transmitted via four major modes of transmission: direct (physical) contact, indirect contact (fomite), (large) droplets and (fine) aerosols. We know little about the relative contribution of each mode to the transmission of a particular virus in different settings, and how its variation affects transmissibility and transmission dynamics. Discussion on the particle size threshold between droplets and aerosols and the importance of aerosol transmission for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza virus is ongoing. Mechanistic evidence supports the efficacies of non-pharmaceutical interventions with regard to virus reduction; however, more data are needed on their effectiveness in reducing transmission. Understanding the relative contribution of different modes to transmission is crucial to inform the effectiveness of non-pharmaceutical interventions in the population. Intervening against multiple modes of transmission should be more effective than acting on a single mode.

Efficacy of masks and face coverings in controlling outward aerosol particle emission from expiratory activities

The COVID-19 pandemic triggered a surge in demand for facemasks to protect against disease transmission. In response to shortages, many public health authorities have recommended homemade masks as acceptable alternatives to surgical masks and N95 respirators. Although mask wearing is intended, in part, to protect others from exhaled, virus-containing particles, few studies have examined particle emission by mask-wearers into the surrounding air. Here, we measured outward emissions of micron-scale aerosol particles by healthy humans performing various expiratory activities while wearing different types of medical-grade or homemade masks. Both surgical masks and unvented KN95 respirators, even without fit-testing, reduce the outward particle emission rates by 90% and 74% on average during speaking and coughing, respectively, compared to wearing no mask, corroborating their effectiveness at reducing outward emission. These masks similarly decreased the outward particle emission of a coughing superemitter, who for unclear reasons emitted up to two orders of magnitude more expiratory particles via coughing than average. In contrast, shedding of non-expiratory micron-scale particulates from friable cellulosic fibers in homemade cotton-fabric masks confounded explicit determination of their efficacy at reducing expiratory particle emission. Audio analysis of the speech and coughing intensity confirmed that people speak more loudly, but do not cough more loudly, when wearing a mask. Further work is needed to establish the efficacy of cloth masks at blocking expiratory particles for speech and coughing at varied intensity and to assess whether virus-contaminated fabrics can generate aerosolized fomites, but the results strongly corroborate the efficacy of medical-grade masks and highlight the importance of regular washing of homemade masks.

Efficacy of masks and face coverings in controlling outward aerosol particle emission from expiratory activities

The COVID-19 pandemic triggered a surge in demand for facemasks to protect against disease transmission. In response to shortages, many public health authorities have recommended homemade masks as acceptable alternatives to surgical masks and N95 respirators. Although mask wearing is intended, in part, to protect others from exhaled, virus-containing particles, few studies have examined particle emission by mask-wearers into the surrounding air. Here, we measured outward emissions of micron-scale aerosol particles by healthy humans performing various expiratory activities while wearing different types of medical-grade or homemade masks. Both surgical masks and unvented KN95 respirators, even without fit-testing, reduce the outward particle emission rates by 90% and 74% on average during speaking and coughing, respectively, compared to wearing no mask, corroborating their effectiveness at reducing outward emission. These masks similarly decreased the outward particle emission of a coughing superemitter, who for unclear reasons emitted up to two orders of magnitude more expiratory particles via coughing than average. In contrast, shedding of non-expiratory micron-scale particulates from friable cellulosic fibers in homemade cotton-fabric masks confounded explicit determination of their efficacy at reducing expiratory particle emission. Audio analysis of the speech and coughing intensity confirmed that people speak more loudly, but do not cough more loudly, when wearing a mask. Further work is needed to establish the efficacy of cloth masks at blocking expiratory particles for speech and coughing at varied intensity and to assess whether virus-contaminated fabrics can generate aerosolized fomites, but the results strongly corroborate the efficacy of medical-grade masks and highlight the importance of regular washing of homemade masks.

An open source tool to infer epidemiological and immunological dynamics from serological data: serosolver

We present a flexible, open source R package designed to obtain biological and epidemiological insights from serological datasets. Characterising past exposures for multi-strain pathogens poses a specific statistical challenge: observed antibody responses measured in serological assays depend on multiple unobserved prior infections that produce cross-reactive antibody responses. We provide a general modelling framework to jointly infer infection histories and describe immune responses generated by these infections using antibody titres against current and historical strains. We do this by linking latent infection dynamics with a mechanistic model of antibody kinetics that generates expected antibody titres over time. Our aim is to provide a flexible package to identify infection histories that can be applied to a range of pathogens. We present two case studies to illustrate how our model can infer key immunological parameters, such as antibody titre boosting, waning and cross-reaction, as well as latent epidemiological processes such as attack rates and age-stratified infection risk.

Precipitation regime change in Western North America: The role of Atmospheric Rivers

Daily precipitation in California has been projected to become less frequent even as precipitation extremes intensify, leading to uncertainty in the overall response to climate warming. Precipitation extremes are historically associated with Atmospheric Rivers (ARs). Sixteen global climate models are evaluated for realism in modeled historical AR behavior and contribution of the resulting daily precipitation to annual total precipitation over Western North America. The five most realistic models display consistent changes in future AR behavior, constraining the spread of the full ensemble. They, moreover, project increasing year-to-year variability of total annual precipitation, particularly over California, where change in total annual precipitation is not projected with confidence. Focusing on three representative river basins along the West Coast, we show that, while the decrease in precipitation frequency is mostly due to non-AR events, the increase in heavy and extreme precipitation is almost entirely due to ARs. This research demonstrates that examining meteorological causes of precipitation regime change can lead to better and more nuanced understanding of climate projections. It highlights the critical role of future changes in ARs to Western water resources, especially over California.

Assessing Zika Virus Transmission Within Households During an Outbreak in Martinique, 2015-2016

Since 2015, Zika virus (ZIKV) has caused large epidemics in the Americas. Households are natural targets for control interventions, but quantification of the contribution of household transmission to overall spread is needed to guide policy. We developed a modeling framework to evaluate this contribution and key epidemic features of the ZIKV epidemic in Martinique in 2015-2016 from the joint analysis of a household transmission study (n = 68 households), a study among symptomatic pregnant women (n = 281), and seroprevalence surveys of blood donors (n = 457). We estimated that the probability of mosquito-mediated within-household transmission (from an infected member to a susceptible one) was 21% (95% credible interval (CrI): 5, 51), and the overall probability of infection from outside the household (i.e., in the community) was 39% (95% CrI: 27, 50). Overall, 50% (95% CrI: 43, 58) of the population was infected, with 22% (95% CrI: 5, 46) of infections acquired in households and 40% (95% CrI: 23, 56) being asymptomatic. The probability of presenting with Zika-like symptoms due to another cause was 16% (95% CrI: 10, 23). This study characterized the contribution of household transmission in ZIKV epidemics, demonstrating the benefits of integrating multiple data sets to gain more insight into epidemic dynamics.

Common cold in Team Finland during 2018 Winter Olympic Games (PyeongChang): epidemiology, diagnosis including molecular point-of-care testing (POCT) and treatment

Objectives

The common cold is the main cause of medical time loss in elite sport. Rapid diagnosis has been a challenge that may be amenable to molecular point-of-care testing (POCT).

Methods

We performed a prospective observational study of the common cold in Team Finland during the 2018 Winter Olympic Games. There were 44 elite athletes and 68 staff members. The chief physician recorded the symptoms of the common cold daily on a standardised form. Two nasal swabs were taken at the onset of symptoms. One swab was analysed within 45 min using a molecular POCT for respiratory syncytial virus and influenza A and B viruses. After the Games, the other swab was tested for 16 possible causative respiratory viruses using PCR in laboratory-based testing.

Results

20 out of 44 (45%) athletes and 22 out of 68 (32%) staff members experienced symptoms of the common cold during a median stay of 21 days. Eleven (26%) samples tested virus-positive using POCT. All subjects with influenza (n=6) and 32 close contacts were treated with oseltamivir. The aetiology of the common cold was finally detected in 75% of the athletes and 68 % of the staff members. Seven virus clusters were identified. They were caused by coronaviruses 229E, NL63 and OC43, influenza B virus, respiratory syncytial virus A, rhinovirus and human metapneumovirus. The virus infections spread readily within the team, most commonly within the same sport discipline.

Conclusions

The cold was indeed a common illness in Team Finland during the Winter Olympic Games. POCT proved to be clinically valuable, especially for influenza. The aetiology of the common cold was identified in most cases.

Influenza Transmission Dynamics in Urban Households, Managua, Nicaragua, 2012-2014

In this low-income country setting, ≈16% of household contacts acquired infections from index patients, despite high oseltamivir use.

During August 2012–November 2014, we conducted a case ascertainment study to investigate household transmission of influenza virus in Managua, Nicaragua. We collected up to 5 respiratory swab samples from each of 536 household contacts of 133 influenza virus–infected persons and assessed for evidence of influenza virus transmission. The overall risk for influenza virus infection of household contacts was 15.7% (95% CI 12.7%–19.0%). Oseltamivir treatment of index patients did not appear to reduce household transmission. The mean serial interval for within-household transmission was 3.1 (95% CI 1.6–8.4) days. We found the transmissibility of influenza B virus to be higher than that of influenza A virus among children. Compared with households with <4 household contacts, those with >4 household contacts appeared to have a reduced risk for infection. Further research is needed to model household influenza virus transmission and design interventions for these settings.

Regulation of atmospheric circulation controlling the tropical Pacific precipitation change in response to CO2 increases

The spatial pattern of precipitation responses to CO₂ concentration increases significantly influences global weather and climate variability by altering the location of tropical heating in a warmer climate. In this study, we analyze the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model projections of tropical Pacific rainfall response to quadrupled increase of CO₂. We found that the precipitation changes to the CO₂ concentration increase cannot be interpreted by a weakening or strengthening of large-scale east-west coupling across the tropical Pacific basin, i.e., Walker circulation. By calculating the water vapor transport, we suggest instead that different responses of the Walker and Hadley circulations to the increasing CO₂ concentration shape the details of the spatial pattern of precipitation in the tropical Pacific. Therefore, more regionally perturbed circulations over the tropical Pacific, which is influenced by the mean state change in the tropical Pacific and the enhanced precipitation outside the tropical Pacific, lead to greater increases in precipitation in the western equatorial Pacific as compared to the eastern tropical Pacific in a warmer climate.

Assessing the Dynamics and Control of Droplet- and Aerosol-Transmitted Influenza Using an Indoor Positioning System

There is increasing evidence that aerosol transmission is a major contributor to the spread of influenza. Despite this, virtually all studies assessing the dynamics and control of influenza assume that it is transmitted solely through direct contact and large droplets, requiring close physical proximity. Here, we use wireless sensors to measure simultaneously both the location and close proximity contacts in the population of a US high school. This dataset, highly resolved in space and time, allows us to model both droplet and aerosol transmission either in isolation or in combination. In particular, it allows us to computationally quantify the potential effectiveness of overlooked mitigation strategies such as improved ventilation that are available in the case of aerosol transmission. Our model suggests that recommendation-abiding ventilation could be as effective in mitigating outbreaks as vaccinating approximately half of the population. In simulations using empirical transmission levels observed in households, we find that bringing ventilation to recommended levels had the same mitigating effect as a vaccination coverage of 50% to 60%. Ventilation is an easy-to-implement strategy that has the potential to support vaccination efforts for effective control of influenza spread.

Model-based estimates of transmission of respiratory syncytial virus within households

Introduction

Respiratory syncytial virus (RSV) causes a significant respiratory disease burden in the under 5 population. The transmission pathway to young children is not fully quantified in low-income settings, and this information is required to design interventions.

Methods

We used an individual level transmission model to infer transmission parameters using data collected from 493 individuals distributed across 47 households over a period of 6 months spanning the 2009/2010 RSV season. A total of 208 episodes of RSV were observed from 179 individuals. We model competing transmission risk from within household exposure and community exposure while making a distinction between RSV groups A and B.

Results

We find that 32–53% of all RSV transmissions are between members of the same household; the rate of pair-wise transmission is 58% (95% CrI: 30–74%) lower in larger households (≥8 occupants) than smaller households; symptomatic individuals are 2–7 times more infectious than asymptomatic individuals i.e. 2.48 (95% CrI: 1.22–5.57) among symptomatic individuals with low viral load and 6.7(95% CrI: 2.56–16) among symptomatic individuals with high viral load; previous infection reduces susceptibility to re-infection within the same epidemic by 47% (95% CrI: 17%–68%) for homologous RSV group and 39% (95%CrI: -8%-69%) for heterologous group; RSV B is more frequently introduced into the household, and RSV A is more rapidly transmitted once in the household.

Discussion

Our analysis presents the first transmission modelling of cohort data for RSV and we find that it is important to consider the household social structuring and household size when modelling transmission. The increased infectiousness of symptomatic individuals implies that a vaccine against RSV related disease would also have an impact on infection transmission. Together, the weak cross immunity between RSV groups and the possibility of different transmission niches could form part of the explanation for the group co-existence.

Detecting a Surprisingly Low Transmission Distance in the Early Phase of the 2009 Influenza Pandemic

The spread of the 2009 H1N1 influenza pandemic in England was characterized by two major waves of infections: the first one was highly spatially localized (mainly in the London area), while the second one spread homogeneously through the entire country. The reasons behind this complex spatiotemporal dynamics have yet to be clarified. In this study, we perform a Bayesian analysis of five models entailing different hypotheses on the possible determinants of the observed pattern. We find a consensus among all models in showing a surprisingly low transmission distance (defined as the geographic distance between the place of residence of the infectors and her/his infectees) during the first wave: about 1.5 km (2.2 km if infections linked to household and school transmission are excluded). The best-fitting model entails a change in human activity regarding contacts not related to household and school. By using this model we estimate that the transmission distance sharply increased to 5.3 km (10 km when excluding infections linked to household and school transmission) during the second wave. Our study reveals a possible explanation for the observed pattern and highlights the need of better understanding human mobility and activity patterns under the pressure posed by a pandemic threat.

Tightening of tropical ascent and high clouds key to precipitation change in a warmer climate

The change of global-mean precipitation under global warming and interannual variability is predominantly controlled by the change of atmospheric longwave radiative cooling. Here we show that tightening of the ascending branch of the Hadley Circulation coupled with a decrease in tropical high cloud fraction is key in modulating precipitation response to surface warming. The magnitude of high cloud shrinkage is a primary contributor to the intermodel spread in the changes of tropical-mean outgoing longwave radiation (OLR) and global-mean precipitation per unit surface warming (dP/dTs) for both interannual variability and global warming. Compared to observations, most Coupled Model Inter-comparison Project Phase 5 models underestimate the rates of interannual tropical-mean dOLR/dTs and global-mean dP/dTs, consistent with the muted tropical high cloud shrinkage. We find that the five models that agree with the observation-based interannual dP/dTs all predict dP/dTs under global warming higher than the ensemble mean dP/dTs from the ∼20 models analysed in this study.

Characterising pandemic severity and transmissibility from data collected during first few hundred studies

Early estimation of the probable impact of a pandemic influenza outbreak can assist public health authorities to ensure that response measures are proportionate to the scale of the threat. Recently, frameworks based on transmissibility and severity have been proposed for initial characterization of pandemic impact. Data requirements to inform this assessment may be provided by "First Few Hundred" (FF100) studies, which involve surveillance-possibly in person, or via telephone-of household members of confirmed cases. This process of enhanced case finding enables detection of cases across the full spectrum of clinical severity, including the date of symptom onset. Such surveillance is continued until data for a few hundred cases, or satisfactory characterization of the pandemic strain, has been achieved. We present a method for analysing these data, at the household level, to provide a posterior distribution for the parameters of a model that can be interpreted in terms of severity and transmissibility of a pandemic strain. We account for imperfect case detection, where individuals are only observed with some probability that can increase after a first case is detected. Furthermore, we test this methodology using simulated data generated by an independent model, developed for a different purpose and incorporating more complex disease and social dynamics. Our method recovers transmissibility and severity parameters to a high degree of accuracy and provides a computationally efficient approach to estimating the impact of an outbreak in its early stages.

How social structures, space, and behaviors shape the spread of infectious diseases using chikungunya as a case study

Whether an individual becomes infected in an infectious disease outbreak depends on many interconnected risk factors, which may relate to characteristics of the individual (e.g., age, sex), his or her close relatives (e.g., household members), or the wider community. Studies monitoring individuals in households or schools have helped elucidate the determinants of transmission in small social structures due to advances in statistical modeling; but such an approach has so far largely failed to consider individuals in the wider context they live in. Here, we used an outbreak of chikungunya in a rural community in Bangladesh as a case study to obtain a more comprehensive characterization of risk factors in disease spread. We developed Bayesian data augmentation approaches to account for uncertainty in the source of infection, recall uncertainty, and unobserved infection dates. We found that the probability of chikungunya transmission was 12% [95% credible interval (CI): 8-17%] between household members but dropped to 0.3% for those living 50 m away (95% CI: 0.2-0.5%). Overall, the mean transmission distance was 95 m (95% CI: 77-113 m). Females were 1.5 times more likely to become infected than males (95% CI: 1.2-1.8), which was virtually identical to the relative risk of being at home estimated from an independent human movement study in the country. Reported daily use of antimosquito coils had no detectable impact on transmission. This study shows how the complex interplay between the characteristics of an individual and his or her close and wider environment contributes to the shaping of infectious disease epidemics.

Review of the clinical significance of respiratory virus infections in newborn infants

Respiratory viruses have been recognised as causative agents for a wide spectrum of clinical manifestations and severe respiratory compromise in neonates during birth hospitalisation. Early‐life respiratory virus infections have also been shown to be associated with adverse long‐term consequences.

Conclusion

Preventing virus infections by intensifying hygiene measures and cohorting infected infants should be a major goal for neonatal intensive care units, as well as more common use of virus diagnostics. Active virus surveillance and long‐term follow‐up are needed to ascertain the causality and exact underlying mechanisms for adverse long‐term consequences.

Trends in the Mechanistic and Dynamic Modeling of Infectious Diseases

The dynamics of infectious disease epidemics are driven by interactions between individuals with differing disease status (e.g., susceptible, infected, immune). Mechanistic models that capture the dynamics of such “dependent happenings” are a fundamental tool of infectious disease epidemiology. Recent methodological advances combined with access to new data sources and computational power have resulted in an explosion in the use of dynamic models in the analysis of emerging and established infectious diseases. Increasing use of models to inform practical public health decision making has challenged the field to develop new methods to exploit available data and appropriately characterize the uncertainty in the results. Here, we discuss recent advances and areas of active research in the mechanistic and dynamic modeling of infectious disease. We highlight how a growing emphasis on data and inference, novel forecasting methods, and increasing access to “big data” are changing the field of infectious disease dynamics. We showcase the application of these methods in phylodynamic research, which combines mechanistic models with rich sources of molecular data to tie genetic data to population-level disease dynamics. As dynamics and mechanistic modeling methods mature and are increasingly tied to principled statistical approaches, the historic separation between the infectious disease dynamics and “traditional” epidemiologic methods is beginning to erode; this presents new opportunities for cross pollination between fields and novel applications.

A Systematic Bayesian Integration of Epidemiological and Genetic Data

Genetic sequence data on pathogens have great potential to inform inference of their transmission dynamics ultimately leading to better disease control. Where genetic change and disease transmission occur on comparable timescales additional information can be inferred via the joint analysis of such genetic sequence data and epidemiological observations based on clinical symptoms and diagnostic tests. Although recently introduced approaches represent substantial progress, for computational reasons they approximate genuine joint inference of disease dynamics and genetic change in the pathogen population, capturing partially the joint epidemiological-evolutionary dynamics. Improved methods are needed to fully integrate such genetic data with epidemiological observations, for achieving a more robust inference of the transmission tree and other key epidemiological parameters such as latent periods. Here, building on current literature, a novel Bayesian framework is proposed that infers simultaneously and explicitly the transmission tree and unobserved transmitted pathogen sequences. Our framework facilitates the use of realistic likelihood functions and enables systematic and genuine joint inference of the epidemiological-evolutionary process from partially observed outbreaks. Using simulated data it is shown that this approach is able to infer accurately joint epidemiological-evolutionary dynamics, even when pathogen sequences and epidemiological data are incomplete, and when sequences are available for only a fraction of exposures. These results also characterise and quantify the value of incomplete and partial sequence data, which has important implications for sampling design, and demonstrate the abilities of the introduced method to identify multiple clusters within an outbreak. The framework is used to analyse an outbreak of foot-and-mouth disease in the UK, enhancing current understanding of its transmission dynamics and evolutionary process.

In the midst of increasingly available sequence data of pathogens, a key challenge is to better integrate these data with traditional epidemiological data, with the proximate goal of reliable prediction and the ultimate aim of effective management of disease outbreaks. Although substantial advances have been made for such an integration, and they have improved our understandings of many disease dynamics which are not available otherwise, current methods have relied on fast algorithms, rather than achieving a systematic integration and accurate inference of the joint epidemiological-evolutionary process. Building on methods in current literature, this paper describes a novel Bayesian approach for systematically integrating these two streams of data. We propose a computationally tractable Bayesian inferential algorithm which takes the full joint epidemiological-evolutionary process into account. Using this algorithm, we study systematically the value of genetic data, providing valuable insights into future sampling designs. The algorithm is subsequently applied to real-world dataset describing the spread of animal foot-and-mouth disease in the UK, demonstrating the importance of such a systematic integration achieved with our methodology.