How infections propagate after point-source outbreaks: an analysis of secondary norovirus transmission

The Statewide Economic Impact of Child Care-Associated Viral Acute Gastroenteritis Infections

INTRODUCTION

More than 65% of children aged ≤5 years in the United States require out-of-home child care. Child care attendance has been associated with an elevated risk of respiratory illness and acute gastroenteritis (AGE). While child care-associated respiratory disease cases are more numerous, AGE is associated with more severe symptoms and more than double the number of absences from child care. In addition, viral pathogens such as norovirus, rotavirus, and adenovirus are highly infectious and may be spread to parents and other household members. As a result, child care-associated viral AGE may incur substantial economic costs due to healthcare service usage and lost productivity.

METHODS

We used surveillance data from a network of child care centers in Washtenaw County, Michigan, as well as a household transmission model to estimate the annual cost of child care-associated viral AGE in the state of Michigan.

RESULTS

We estimated that child care-associated viral AGE in Michigan costs between $15 million and $31 million annually, primarily due to lost productivity.

CONCLUSIONS

The economic burden of child care-associated infections is considerable. Effective targeted interventions are needed to mitigate this impact.

Modeling the Potential Impact of Norovirus Vaccination Among DoD Forces

INTRODUCTION

Norovirus, a contagious disease that spreads rapidly in close-quartered communities, has a debilitating effect in military settings, affecting troops' health, productivity, and mission-readiness. This research presents a model of norovirus transmission, testing the vaccination's effectiveness in military training centers.

METHODS

Transmission was modeled using structured ordinary differential equations, including symptomatic and asymptomatic infection, genetic resistance, vaccination, and herd-immunity effects, within a hypothetical cohort of trainees and support staff. The modeled vaccine had an efficacy of 72%, 4 weeks after a single dose in phase 2 clinical trials. The transmission model was calibrated against data from a norovirus outbreak in a university setting. Sensitivity and uncertainty analyses were performed on 22 parameters.

RESULTS

The greatest reduction in norovirus cases resulted from prophylactic environmental decontamination and vaccination of trainee and staff populations. These combined interventions prevented more than 6,800 cases of norovirus over the 10-year simulated period-a 15% reduction over the baseline scenario of no interventions. Implementing vaccination and environmental decontamination with an outbreak response threshold of 0.1%, prevented more than 5,300 infections; raising the threshold to 0.2% to 0.5% significantly reduced effectiveness. Environmental decontamination and contact reduction alone had little impact on overall norovirus cases.

CONCLUSIONS

Given vaccine characteristics, the model predicted that up to 15% of norovirus cases occurring in training settings over a 10-year period could be prevented by vaccinating all trainees and staff members immediately upon arrival on-base combined with continuous environmental decontamination. There was an impact on morbidity from implementing vaccination of trainees, alone and in combination with staff members. However, vaccinating staff alone prevented few cases over the simulation period, indicating the importance of trainees in norovirus transmission. Likewise, the negligible impact of environmental decontamination or contact reduction alone highlights the importance of addressing both person-to-person and environmental transmission together to minimize illnesses and training downtime.

Understanding the Importance of Contact Heterogeneity and Variable Infectiousness in the Dynamics of a Large Norovirus Outbreak

BACKGROUND

Large norovirus (NoV) outbreaks are explosive in nature and vary widely in final size and duration, suggesting that superspreading combined with heterogeneous contact may explain these dynamics. Modeling tools that can capture heterogeneity in infectiousness and contact are important for NoV outbreak prevention and control, yet they remain limited.

METHODS

Data from a large NoV outbreak at a Dutch scout jamboree, which resulted in illness among 326 (of 4500 total) individuals from 7 separate camps, were used to examine the contributions of individual variation in infectiousness and clustered contact patterns to the transmission dynamics. A Bayesian hierarchical model of heterogeneous, clustered outbreak transmission was applied to represent (1) between-individual heterogeneity in infectiousness and (2) heterogeneous patterns of contact.

RESULTS

We found wide heterogeneity in infectiousness across individuals, suggestive of superspreading. Nearly 50% of individual infectiousness was concentrated in the individual's subcamp of residence, with the remainder distributed over other subcamps. This suggests a source-and-sink dynamic in which subcamps with greater average infectiousness fed cases to those with a lower transmission rate. Although the per capita transmission rate within camps was significantly greater than that between camps, the large pool of susceptible individuals across camps enabled similar numbers of secondary cases generated between versus within camps.

CONCLUSIONS

The consideration of clustered transmission and heterogeneous infectiousness is important for understanding NoV transmission dynamics. Models including these mechanisms may be useful for providing early warning and guiding outbreak response.

Confirming the need for virus disinfection in municipal subsurface drinking water supplies

Enteric viruses pose the greatest acute human health risks associated with subsurface drinking water supplies, yet quantitative risk assessment tools have rarely been used to develop health-based targets for virus treatment in drinking water sourced from these supplies. Such efforts have previously been hampered by a lack of consensus concerning a suitable viral reference pathogen and dose-response model as well as difficulties in quantifying pathogenic viruses in water. A reverse quantitative microbial risk assessment (QMRA) framework and quantitative polymerase chain reaction data for norovirus genogroup I in subsurface drinking water supplies were used herein to evaluate treatment needs for such water supplies. Norovirus was not detected in over 90% of samples, which emphasizes the need to consider the spatially and/or temporally intermittent patterns of enteric pathogen contamination in subsurface water supplies. Collectively, this analysis reinforces existing recommendations that a minimum 4-log treatment goal is needed for enteric viruses in groundwater in absence of well-specific monitoring information. This result is sensitive to the virus dose-response model used as there is approximately a 3-log discrepancy among virus dose-response models in the existing literature. This emphasizes the need to address the uncertainties and lack of consensus related to various QMRA modelling approaches and the analytical limitations that preclude more accurate description of virus risks.

Quantifying the relative effects of environmental and direct transmission of norovirus

Norovirus is a common cause of outbreaks of acute gastroenteritis in health- and child-care settings, with serial outbreaks also frequently observed aboard cruise ships. The relative contributions of environmental and direct person-to-person transmission of norovirus have hitherto not been quantified. We employ a novel mathematical model of norovirus transmission, and fit the model to daily incidence data from a major norovirus outbreak on a cruise ship, and examine the relative efficacy of potential control strategies aimed at reducing environmental and/or direct transmission. The reproduction number for environmental and direct transmission combined is [Formula: see text] [6.1,9.5], and of environmental transmission alone is [Formula: see text] [0.9,2.6]. Direct transmission is overwhelmingly due to passenger-to-passenger contacts, but crew can act as a reservoir of infection from cruise to cruise. This is the first quantification of the relative roles of environmental and direct transmission of norovirus. While environmental transmission has the potential to maintain a sustained series of outbreaks aboard a cruise ship in the absence of strict sanitation practices, direct transmission dominates. We find that intensive promotion of good hand washing practices may prevent outbreaks. Isolation of ill passengers and cleaning are beneficial, but appear to be less efficacious at outbreak control.

Randomised controlled trial of Lactobacillus rhamnosus (LGG) versus placebo in children presenting to the emergency department with acute gastroenteritis: the PECARN probiotic study protocol

INTRODUCTION

Acute gastroenteritis (AGE) is a common and burdensome condition that affects millions of children worldwide each year. Currently available strategies are limited to symptomatic management, treatment and prevention of dehydration and infection control; no disease-modifying interventions exist. Probiotics, defined as live microorganisms beneficial to the host, have shown promise in improving AGE outcomes, but existing studies have sufficient limitations such that the use of probiotics cannot currently be recommended with confidence. Here we present the methods of a large, rigorous, randomised, double-blind placebo-controlled study to assess the effectiveness and side effect profile of Lactobacillus rhamnosus GG (LGG) (ATCC 53103) in children with AGE.

METHODS AND ANALYSIS

The study is being conducted in 10 US paediatric emergency departments (EDs) within the federally funded Pediatric Emergency Care Applied Research Network, in accordance with current SPIRIT and CONSORT statement recommendations. We will randomise 970 children presenting to participating EDs with AGE to either 5 days of treatment with LGG (10¹⁰colony-forming unit twice a day) or placebo between July 2014 to December 2017. The main outcome is the occurrence of moderate-to-severe disease over time, as defined by the Modified Vesikari Scale. We also record adverse events and side effects related to the intervention. We will conduct intention-to-treat analyses and use an enrichment design to restore the statistical power in case the presence of a subpopulation with a substantially low treatment effect is identified.

ETHICS AND DISSEMINATION

Institutional review board approval has been obtained at all sites, and data and material use agreements have been established between the participating sites. The results of the trial will be published in peer-reviewed journals. A deidentified public data set will be made available after the completion of all study procedures.

TRIAL REGISTRATION NUMBER

NCT01773967.

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.

Fitting outbreak models to data from many small norovirus outbreaks

Infectious disease often occurs in small, independent outbreaks in populations with varying characteristics. Each outbreak by itself may provide too little information for accurate estimation of epidemic model parameters. Here we show that using standard stochastic epidemic models for each outbreak and allowing parameters to vary between outbreaks according to a linear predictor leads to a generalized linear model that accurately estimates parameters from many small and diverse outbreaks. By estimating initial growth rates in addition to transmission rates, we are able to characterize variation in numbers of initially susceptible individuals or contact patterns between outbreaks. With simulation, we find that the estimates are fairly robust to the data being collected at discrete intervals and imputation of about half of all infectious periods. We apply the method by fitting data from 75 norovirus outbreaks in health-care settings. Our baseline regression estimates are 0.0037 transmissions per infective-susceptible day, an initial growth rate of 0.27 transmissions per infective day, and a symptomatic period of 3.35 days. Outbreaks in long-term-care facilities had significantly higher transmission and initial growth rates than outbreaks in hospitals.

Linking time-varying symptomatology and intensity of infectiousness to patterns of norovirus transmission

BACKGROUND

Norovirus (NoV) transmission may be impacted by changes in symptom intensity. Sudden onset of vomiting, which may cause an initial period of hyper-infectiousness, often marks the beginning of symptoms. This is often followed by: a 1-3 day period of milder symptoms, environmental contamination following vomiting, and post-symptomatic shedding that may result in transmission at progressively lower rates. Existing models have not included time-varying infectiousness, though representing these features could add utility to models of NoV transmission.

METHODS

We address this by comparing the fit of three models (Models 1-3) of NoV infection to household transmission data from a 2009 point-source outbreak of GII.12 norovirus in North Carolina. Model 1 is an SEIR compartmental model, modified to allow Gamma-distributed sojourn times in the latent and infectious classes, where symptomatic cases are uniformly infectious over time. Model 2 assumes infectiousness decays exponentially as a function of time since onset, while Model 3 is discontinuous, with a spike concentrating 50% of transmissibility at onset. We use Bayesian data augmentation techniques to estimate transmission parameters for each model, and compare their goodness of fit using qualitative and quantitative model comparison. We also assess the robustness of our findings to asymptomatic infections.

RESULTS

We find that Model 3 (initial spike in shedding) best explains the household transmission data, using both quantitative and qualitative model comparisons. We also show that these results are robust to the presence of asymptomatic infections.

CONCLUSIONS

Explicitly representing explosive NoV infectiousness at onset should be considered when developing models and interventions to interrupt and prevent outbreaks of norovirus in the community. The methods presented here are generally applicable to the transmission of pathogens that exhibit large variation in transmissibility over an infection.

Heterogeneity in norovirus shedding duration affects community risk

Norovirus is a common cause of gastroenteritis in all ages. Typical infections cause viral shedding periods of days to weeks, but some individuals can shed for months or years. Most norovirus risk models do not include these long-shedding individuals, and may therefore underestimate risk. We reviewed the literature for norovirus-shedding duration data and stratified these data into two distributions: regular shedding (mean 14-16 days) and long shedding (mean 105-136 days). These distributions were used to inform a norovirus transmission model that predicts the impact of long shedders. Our transmission model predicts that this subpopulation increases the outbreak potential (measured by the reproductive number) by 50-80%, the probability of an outbreak by 33%, the severity of transmission (measured by the attack rate) by 20%, and transmission duration by 100%. Characterizing and understanding shedding duration heterogeneity can provide insights into community transmission that can be useful in mitigating norovirus risk.

Malaria transmission modelling: a network perspective

Malaria transmission can be affected by multiple or even hidden factors, making it difficult to timely and accurately predict the impact of elimination and eradication programs that have been undertaken and the potential resurgence and spread that may continue to emerge. One approach at the moment is to develop and deploy surveillance systems in an attempt to identify them as timely as possible and thus to enable policy makers to modify and implement strategies for further preventing the transmission. Most of the surveillance data will be of temporal and spatial nature. From an interdisciplinary point of view, it would be interesting to ask the following important as well as challenging question: Based on the available surveillance data in temporal and spatial forms, how can we build a more effective surveillance mechanism for monitoring and early detecting the relative prevalence and transmission patterns of malaria? What we can note from the existing clustering-based surveillance software systems is that they do not infer the underlying transmission networks of malaria. However, such networks can be quite informative and insightful as they characterize how malaria transmits from one place to another. They can also in turn allow public health policy makers and researchers to uncover the hidden and interacting factors such as environment, genetics and ecology and to discover/predict malaria transmission patterns/trends. The network perspective further extends the present approaches to modelling malaria transmission based on a set of chosen factors. In this article, we survey the related work on transmission network inference, discuss how such an approach can be utilized in developing an effective computational means for inferring malaria transmission networks based on partial surveillance data, and what methodological steps and issues may be involved in its formulation and validation.

Characterisation of Clostridium difficile hospital ward-based transmission using extensive epidemiological data and molecular typing

BACKGROUND

Clostridium difficile infection (CDI) is a leading cause of antibiotic-associated diarrhoea and is endemic in hospitals, hindering the identification of sources and routes of transmission based on shared time and space alone. This may compromise rational control despite costly prevention strategies. This study aimed to investigate ward-based transmission of C. difficile, by subdividing outbreaks into distinct lineages defined by multi-locus sequence typing (MLST).

METHODS AND FINDINGS

All C. difficile toxin enzyme-immunoassay-positive and culture-positive samples over 2.5 y from a geographically defined population of ~600,000 persons underwent MLST. Sequence types (STs) were combined with admission and ward movement data from an integrated comprehensive healthcare system incorporating three hospitals (1,700 beds) providing all acute care for the defined geographical population. Networks of cases and potential transmission events were constructed for each ST. Potential infection sources for each case and transmission timescales were defined by prior ward-based contact with other cases sharing the same ST. From 1 September 2007 to 31 March 2010, there were means of 102 tests and 9.4 CDIs per 10,000 overnight stays in inpatients, and 238 tests and 15.7 CDIs per month in outpatients/primary care. In total, 1,276 C. difficile isolates of 69 STs were studied. From MLST, no more than 25% of cases could be linked to a potential ward-based inpatient source, ranging from 37% in renal/transplant, 29% in haematology/oncology, and 28% in acute/elderly medicine to 6% in specialist surgery. Most of the putative transmissions identified occurred shortly (≤ 1 wk) after the onset of symptoms (141/218, 65%), with few >8 wk (21/218, 10%). Most incubation periods were ≤ 4 wk (132/218, 61%), with few >12 wk (28/218, 13%). Allowing for persistent ward contamination following ward discharge of a CDI case did not increase the proportion of linked cases after allowing for random meeting of matched controls.

CONCLUSIONS

In an endemic setting with well-implemented infection control measures, ward-based contact with symptomatic enzyme-immunoassay-positive patients cannot account for most new CDI cases.