The impact of illness and the impact of school closure on social contact patterns

Parasite-induced shifts in host movement may explain the transient coexistence of high- and low-pathogenic disease strains

Many parasites induce decreased host movement, known as lethargy, which can impact disease spread and the evolution of virulence. Mathematical models have investigated virulence evolution when parasites cause host death, but disease-induced decreased host movement has received relatively less attention. Here, we consider a model where, due to the within-host parasite replication rate, an infected host can become lethargic and shift from a moving to a resting state, where it can die. We find that when the lethargy and disease-induced mortality costs to the parasites are not high, then evolutionary bistability can arise, and either moderate or high virulence can evolve depending on the initial virulence and the magnitude of mutation. These results suggest, firstly, the coexistence of strains with different virulence, which may explain the transient coexistence of low- and high-pathogenic strains of avian influenza viruses, and secondly, that medical interventions to treat the symptoms of lethargy or prevent disease-induced host deaths can result in a large jump in virulence and the rapid evolution of high virulence. In complement to existing results that show bistability when hosts are heterogeneous at the population level, we show that evolutionary bistability may arise due to transmission heterogeneity at the individual host level.

School Closures in the United States and Severe Respiratory Illnesses in Children: A Normalized Nationwide Sample

OBJECTIVES

To determine the association between nationwide school closures and prevalence of common admission diagnoses in the pediatric critical care unit.

DESIGN

Retrospective cohort study.

SETTING

National database evaluation using the Virtual Pediatric Systems LLC database.

PATIENTS

All patients admitted to the PICU in 81 contributing hospitals in the United States.

MEASUREMENTS AND MAIN RESULTS

Diagnosis categories were determined for all 110,418 patients admitted during the 20-week study period in each year (2018, 2019, and 2020). Admission data were normalized relative to statewide school closure dates for each patient using geographic data. The "before school closure" epoch was defined as 8 weeks prior to school closure, and the "after school closure" epoch was defined as 12 weeks following school closure. For each diagnosis, admission ratios for each study day were calculated by dividing 2020 admissions by 2018-2019 admissions. The 10 most common diagnosis categories were examined. Significant changes in admission ratios were identified for bronchiolitis, pneumonia, and asthma. These changes occurred at 2, 8, and 35 days following school closure, respectively. PICU admissions decreased by 82% for bronchiolitis, 76% for pneumonia, and 76% for asthma. Nonrespiratory diseases such as diabetic ketoacidosis, status epilepticus, traumatic injury, and poisoning/ingestion did not show significant changes following school closure.

CONCLUSIONS

School closures are associated with a dramatic reduction in the prevalence of severe respiratory disease requiring PICU admission. School closure may be an effective tool to mitigate future pandemics but should be balanced with potential academic, economic, mental health, and social consequences.

Networks, cultures, and institutions: Toward a social immunology

This paper calls for increased attention to the ways in which immune function – including its behavioral aspects – are responsive to social contexts at multiple levels. Psychoneuroimmunology has demonstrated that the quantity and quality of social connections can affect immune responses, while newer research is finding that sickness temporarily affects these same social networks and that some aspects of culture can potentially “get under the skin” to affect inflammatory responses. Social immunology, the research framework proposed here, unifies these findings and also considers the effects of structural factors – that is, a society's economic, political, and environmental landscape – on exposure to pathogens and subsequent immune responses. As the COVID-19 pandemic has highlighted, a holistic understanding of the effects of social contexts on the patterning of morbidity and mortality is critically important. Social immunology provides such a framework and can highlight important risk factors related to impaired immune function.

Effect of Acute Illness on Contact Patterns, Malawi, 2017

The way persons interact when ill could profoundly affect transmission of infectious agents. To obtain data on these patterns in Africa, we recorded self-reported named contacts and opportunities for casual contact in rural northern Malawi. We interviewed 384 patients and 257 caregivers about contacts over three 24-hour periods: day of the clinic visit for acute illness, the next day, and 2 weeks later when well. For participants of all ages, the number of adult contacts and the proportion using public transportation was higher on the day of the clinic visit than later when well. Compared with the day after the clinic visit, well participants (2 weeks later) named a mean of 0.4 extra contacts; the increase was larger for indoor or prolonged contacts. When well, participants were more likely to visit other houses and congregate settings. When ill, they had more visitors at home. These findings could help refine models of infection spread.

Quantifying population contact patterns in the United States during the COVID-19 pandemic

SARS-CoV-2 is transmitted primarily through close, person-to-person interactions. Physical distancing policies can control the spread of SARS-CoV-2 by reducing the amount of these interactions in a population. Here, we report results from four waves of contact surveys designed to quantify the impact of these policies during the COVID-19 pandemic in the United States. We surveyed 9,743 respondents between March 22 and September 26, 2020. We find that interpersonal contact has been dramatically reduced in the US, with an 82% (95%CI: 80%-83%) reduction in the average number of daily contacts observed during the first wave compared to pre-pandemic levels. However, we find increases in contact rates over the subsequent waves. We also find that certain demographic groups, including people under 45 and males, have significantly higher contact rates than the rest of the population. Tracking these changes can provide rapid assessments of the impact of physical distancing policies and help to identify at-risk populations.

Mapping Instructional Barriers during COVID-19 Outbreak: Islamic Education Context

Coronavirus disease 2019 (COVID-19) is currently the most potent threat to educational systems, a crisis that may become disastrous. For the current study, a qualitative design within a case study tradition was implemented to investigate instructional barriers during COVID-19 faced by Indonesian teachers in Islamic boarding schools (Pesantren). Within this study, we applied a purposeful convenient sampling in which the access was obtained through communication with the principals of two Pesantren. Seven invited participants with more than ten years of teaching experience agreed to participate. Semi-structured interviews were addressed for data collection; each interview lasted from 40 to 50 min. The interviews were conducted in the participants’ mother tongue to provide an in-depth understanding of their perceptions, ideas, and arguments regarding instructional barriers during the COVID-19 outbreak. The thematic analysis revealed three major findings regarding the barriers; technological barriers, financial barriers, and pedagogical barriers affecting instructional activities in the two Pesantren. Based on the three themes, the development of a qualitative conceptual map of teachers’ instructional barriers was finalized. Recommendations are also proposed by the participants and the study for the betterment of Indonesian Islamic education facing future similar outbreaks.

Barriers to distance learning during the COVID-19 outbreak: A qualitative review from parents' perspective

AIMS

The goal of this study was to review the content posted in available local Jordanian Facebook groups to explore the perceptions of parents regarding the challenges of distance learning faced by their children during the coronavirus outbreak in Jordan.

METHOD

The Facebook search engine was used to identify local Facebook groups. The search keywords included distance learning, parents, and Jordan. Several faculty professors reviewed the posts and discussion flow on distance learning posted in Facebook groups from March 15th to April 25th 2020.

RESULTS

The study identified a total of 248 posts and threads which categorized thematically for further analysis. The selected threads and answers revealed four underlying themes: (1) personal barriers (2) technical barriers (3) logistical barriers and (4) financial barriers.

CONCLUSION

Overall, parents were not limited to their daily routines during the pandemic. They performed the responsibility of helping school in teaching students. Many parents faced many types of barriers in their endeavors to assist their children with distance learning during the pandemic.

A Systematic Review of Social Contact Surveys to Inform Transmission Models of Close-contact Infections

BACKGROUND

Researchers increasingly use social contact data to inform models for infectious disease spread with the aim of guiding effective policies about disease prevention and control. In this article, we undertake a systematic review of the study design, statistical analyses, and outcomes of the many social contact surveys that have been published.

METHODS

We systematically searched PubMed and Web of Science for articles regarding social contact surveys. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines as closely as possible.

RESULTS

In total, we identified 64 social contact surveys, with more than 80% of the surveys conducted in high-income countries. Study settings included general population (58%), schools or universities (37%), and health care/conference/research institutes (5%). The largest number of studies did not focus on a specific age group (38%), whereas others focused on adults (32%) or children (19%). Retrospective (45%) and prospective (41%) designs were used most often with 6% using both for comparison purposes. The definition of a contact varied among surveys, e.g., a nonphysical contact may require conversation, close proximity, or both. We identified age, time schedule (e.g., weekday/weekend), and household size as relevant determinants of contact patterns across a large number of studies.

CONCLUSIONS

We found that the overall features of the contact patterns were remarkably robust across several countries, and irrespective of the study details. By considering the most common approach in each aspect of design (e.g., sampling schemes, data collection, definition of contact), we could identify recommendations for future contact data surveys that may be used to facilitate comparison between studies.

Statewide Interventions and Covid-19 Mortality in the United States: An Observational Study

Background

Social distancing is encouraged to mitigate viral spreading during outbreaks. However, the association between distancing and patient-centered outcomes in coronavirus disease 2019 (COVID-19) has not been demonstrated. In the United States, social distancing orders are implemented at the state level with variable timing of onset. Emergency declarations and school closures were 2 early statewide interventions.

Methods

To determine whether later distancing interventions were associated with higher mortality, we performed a state-level analysis in 55 146 COVID-19 nonsurvivors. We tested the association between timing of emergency declarations and school closures with 28-day mortality using multivariable negative binomial regression. Day 1 for each state was set to when they recorded ≥ 10 deaths. We performed sensitivity analyses to test model assumptions.

Results

At time of analysis, 37 of 50 states had ≥ 10 deaths and 28 follow-up days. Both later emergency declaration (adjusted mortality rate ratio [aMRR] 1.05 per day delay; 95% confidence interval [CI], 1.00–1.09; P = .040) and later school closure (aMRR 1.05; 95% CI, 1.01–1.09; P = .008) were associated with more deaths. When assessing all 50 states and setting day 1 to the day a state recorded its first death, delays in declaring an emergency (aMRR 1.05; 95% CI, 1.01–1.09; P = .020) or closing schools (aMRR 1.06; 95% CI, 1.03–1.09; P < .001) were associated with more deaths. Results were unchanged when excluding New York and New Jersey.

Conclusions

Later statewide emergency declarations and school closure were associated with higher Covid-19 mortality. Each day of delay increased mortality risk 5 to 6%.

SOCRATES: an online tool leveraging a social contact data sharing initiative to assess mitigation strategies for COVID-19

Objective

Establishing a social contact data sharing initiative and an interactive tool to assess mitigation strategies for COVID-19.

Results

We organized data sharing of published social contact surveys via online repositories and formatting guidelines. We analyzed this social contact data in terms of weighted social contact matrices, next generation matrices, relative incidence and R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{0}$$\end{document}0. We incorporated location-specific physical distancing measures (e.g. school closure or at work) and capture their effect on transmission dynamics. All methods have been implemented in an online application based on R Shiny and applied to COVID-19 with age-specific susceptibility and infectiousness. Using our online tool with the available social contact data, we illustrate that physical distancing could have a considerable impact on reducing transmission for COVID-19. The effect itself depends on assumptions made about disease-specific characteristics and the choice of intervention(s).

Virulence-mediated infectiousness and activity trade-offs and their impact on transmission potential of influenza patients

Communicable diseases are often virulent, i.e. they cause morbidity symptoms in those infected. While some symptoms may be transmission-enhancing, other symptoms are likely to reduce transmission potential. For human diseases, the reduction in transmission opportunities is commonly caused by reduced activity. There is limited data regarding the potential impact of virulence on transmission potential. We performed an exploratory data analysis of 324 influenza patients at a university health centre during the 2016/2017 influenza season. We classified symptoms as infectiousness-related or morbidity-related and calculated two scores. The scores were used to explore the relationship between infectiousness, morbidity (virulence), and activity level. We found a decrease in the activity level with increasing morbidity scores. There was no consistent pattern between an activity level and an infectiousness score. We also found a positive correlation between morbidity and infectiousness scores. Overall, we find that increasing virulence leads to increased infectiousness and reduced activity, suggesting a trade-off that can impact overall transmission potential. Our findings indicate that a reduction of systemic symptoms may increase host activity without reducing infectiousness. Therefore, interventions should target both systemic- and infectiousness-related symptoms to reduce overall transmission potential. Our findings can also inform simulation models that investigate the impact of different interventions on transmission.

School closure and management practices during coronavirus outbreaks including COVID-19: a rapid systematic review

In response to the coronavirus disease 2019 (COVID-19) pandemic, 107 countries had implemented national school closures by March 18, 2020. It is unknown whether school measures are effective in coronavirus outbreaks (eg, due to severe acute respiratory syndrome [SARS], Middle East respiratory syndrome, or COVID-19). We undertook a systematic review by searching three electronic databases to identify what is known about the effectiveness of school closures and other school social distancing practices during coronavirus outbreaks. We included 16 of 616 identified articles. School closures were deployed rapidly across mainland China and Hong Kong for COVID-19. However, there are no data on the relative contribution of school closures to transmission control. Data from the SARS outbreak in mainland China, Hong Kong, and Singapore suggest that school closures did not contribute to the control of the epidemic. Modelling studies of SARS produced conflicting results. Recent modelling studies of COVID-19 predict that school closures alone would prevent only 2-4% of deaths, much less than other social distancing interventions. Policy makers need to be aware of the equivocal evidence when considering school closures for COVID-19, and that combinations of social distancing measures should be considered. Other less disruptive social distancing interventions in schools require further consideration if restrictive social distancing policies are implemented for long periods.

Dengue illness impacts daily human mobility patterns in Iquitos, Peru

Background

Human mobility plays a central role in shaping pathogen transmission by generating spatial and/or individual variability in potential pathogen-transmitting contacts. Recent research has shown that symptomatic infection can influence human mobility and pathogen transmission dynamics. Better understanding the complex relationship between symptom severity, infectiousness, and human mobility requires quantification of movement patterns throughout infectiousness. For dengue virus (DENV), human infectiousness peaks 0–2 days after symptom onset, making it paramount to understand human movement patterns from the beginning of illness.

Methodology and principal findings

Through community-based febrile surveillance and RT-PCR assays, we identified a cohort of DENV+ residents of the city of Iquitos, Peru (n = 63). Using retrospective interviews, we measured the movements of these individuals when healthy and during each day of symptomatic illness. The most dramatic changes in mobility occurred during the first three days after symptom onset; individuals visited significantly fewer locations (Wilcoxon test, p = 0.017) and spent significantly more time at home (Wilcoxon test, p = 0.005), compared to when healthy. By 7–9 days after symptom onset, mobility measures had returned to healthy levels. Throughout an individual’s symptomatic period, the day of illness and their subjective sense of well-being were the most significant predictors for the number of locations and houses they visited.

Conclusions/Significance

Our study is one of the first to collect and analyze human mobility data at a daily scale during symptomatic infection. Accounting for the observed changes in human mobility throughout illness will improve understanding of the impact of disease on DENV transmission dynamics and the interpretation of public health-based surveillance data.

Dengue is the most important mosquito-borne viral disease of humans worldwide. Due to the limited mobility of the mosquitoes that transmit dengue virus, human mobility can be a key to both understanding an individual’s exposure to the virus and explaining the spread of dengue throughout a population. Accurate disease models should include human mobility; however, changes in human movement patterns due to the presence of symptoms need to be taken into account. We quantified the impact of symptom presence on human mobility throughout the infectious period by analyzing a dataset on the daily movements of dengue virus infected individuals. Accounting for these changing patterns of mobility will improve understanding of the complex relationship between symptom severity, human movement, and dengue virus transmission. Furthermore, dengue transmission models that incorporate symptom-driven mobility changes can be used to evaluate scenarios and strategies for disease prevention.

Incorporating calendar effects to predict influenza seasonality in Milwaukee, Wisconsin

Social outings can trigger influenza transmission, especially in children and elderly. In contrast, school closures are associated with reduced influenza incidence in school-aged children. While influenza surveillance modelling studies typically account for holidays and mass gatherings, age-specific effects of school breaks, sporting events and commonly celebrated observances are not fully explored. We examined the impact of school holidays, social events and religious observances for six age groups (all ages, ⩽4, 5–24, 25–44, 45–64, ⩾65 years) on four influenza outcomes (tests, positives, influenza A and influenza B) as reported by the City of Milwaukee Health Department Laboratory, Milwaukee, Wisconsin from 2004 to 2009. We characterised holiday effects by analysing average weekly counts in negative binomial regression models controlling for weather and seasonal incidence fluctuations. We estimated age-specific annual peak timing and compared influenza outcomes before, during and after school breaks. During the 118 university holiday weeks, average weekly tests were lower than in 140 school term weeks (5.93 vs. 11.99 cases/week, P < 0.005). The dampening of tests during Winter Break was evident in all ages and in those 5–24 years (RR = 0.31; 95% CI 0.22–0.41 vs. RR = 0.14; 95% CI 0.09–0.22, respectively). A significant increase in tests was observed during Spring Break in 45–64 years old adults (RR = 2.12; 95% CI 1.14–3.96). Milwaukee Public Schools holiday breaks showed similar amplification and dampening effects. Overall, calendar effects depend on the proximity and alignment of an individual holiday to age-specific and influenza outcome-specific peak timing. Better quantification of individual holiday effects, tailored to specific age groups, should improve influenza prevention measures.

The impact of behavioral interventions on co-infection dynamics: An exploration of the effects of home isolation

Behavioral epidemiology, the field aiming to determine the impact of individual behavior on the spread of an epidemic, has gained increased recognition during the last few decades. Behavioral changes due to the development of symptoms have been studied in mono-infections. However, in reality, multiple infections are circulating within the same time period and behavioral changes resulting from contraction of one of the diseases affect the dynamics of the other. The present study aims at assessing the effect of home isolation on the joint dynamics of two infectious diseases, including co-infection, assuming that the two diseases do not confer cross-immunity. We use an age- and time- structured co-infection model based on partial differential equations. Social contact matrices, describing different mixing patterns of symptomatic and asymptomatic individuals are incorporated into the calculation of the age- and time-specific marginal forces of infection. Two scenarios are simulated, assuming that one of the diseases has more severe symptoms than the other. In the first scenario, people stay only at home when having symptoms of the most severe disease. In the second scenario, twice as many people stay at home when having symptoms of the most severe disease than when having symptoms of the other disease. The results show that the impact of home isolation on the joint dynamics of two infectious diseases depends on the epidemiological parameters and properties of the diseases (e.g., basic reproduction number, symptom severity). In case both diseases have a low to moderate basic reproduction number, and there is no home isolation for the less severe disease, the final size of the less severe disease increases with the proportion of symptomatic cases of the most severe disease staying at home, after an initial decrease. This counterintuitive result could be explained by a shift in the peak time of infection of the disease with the most severe symptoms, resulting in a smaller number of people with less contacts at the peak time of the other infection. When twice as many people stay at home when having symptoms of the most severe disease than when having symptoms of the other disease, increasing the proportion staying at home always reduces the final size of both diseases, and the number of co-infections. In conclusion, when providing advise if people should stay at home in the context of two or more co-circulating diseases, one has to take into account epidemiological parameters and symptom severity.

Real-time modelling of a pandemic influenza outbreak

BACKGROUND

Real-time modelling is an essential component of the public health response to an outbreak of pandemic influenza in the UK. A model for epidemic reconstruction based on realistic epidemic surveillance data has been developed, but this model needs enhancing to provide spatially disaggregated epidemic estimates while ensuring that real-time implementation is feasible.

OBJECTIVES

To advance state-of-the-art real-time pandemic modelling by (1) developing an existing epidemic model to capture spatial variation in transmission, (2) devising efficient computational algorithms for the provision of timely statistical analysis and (3) incorporating the above into freely available software.

METHODS

Markov chain Monte Carlo (MCMC) sampling was used to derive Bayesian statistical inference using 2009 pandemic data from two candidate modelling approaches: (1) a parallel-region (PR) approach, splitting the pandemic into non-interacting epidemics occurring in spatially disjoint regions; and (2) a meta-region (MR) approach, treating the country as a single meta-population with long-range contact rates informed by census data on commuting. Model discrimination is performed through posterior mean deviance statistics alongside more practical considerations. In a real-time context, the use of sequential Monte Carlo (SMC) algorithms to carry out real-time analyses is investigated as an alternative to MCMC using simulated data designed to sternly test both algorithms. SMC-derived analyses are compared with 'gold-standard' MCMC-derived inferences in terms of estimation quality and computational burden.

RESULTS

The PR approach provides a better and more timely fit to the epidemic data. Estimates of pandemic quantities of interest are consistent across approaches and, in the PR approach, across regions (e.g. R₀ is consistently estimated to be 1.76-1.80, dropping by 43-50% during an over-summer school holiday). A SMC approach was developed, which required some tailoring to tackle a sudden 'shock' in the data resulting from a pandemic intervention. This semi-automated SMC algorithm outperforms MCMC, in terms of both precision of estimates and their timely provision. Software implementing all findings has been developed and installed within Public Health England (PHE), with key staff trained in its use.

LIMITATIONS

The PR model lacks the predictive power to forecast the spread of infection in the early stages of a pandemic, whereas the MR model may be limited by its dependence on commuting data to describe transmission routes. As demand for resources increases in a severe pandemic, data from general practices and on hospitalisations may become unreliable or biased. The SMC algorithm developed is semi-automated; therefore, some statistical literacy is required to achieve optimal performance.

CONCLUSIONS

Following the objectives, this study found that timely, spatially disaggregate, real-time pandemic inference is feasible, and a system that assumes data as per pandemic preparedness plans has been developed for rapid implementation.

FUTURE WORK RECOMMENDATIONS

Modelling studies investigating the impact of pandemic interventions (e.g. vaccination and school closure); the utility of alternative data sources (e.g. internet searches) to augment traditional surveillance; and the correct handling of test sensitivity and specificity in serological data, propagating this uncertainty into the real-time modelling.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN40334843.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology programme and will be published in full in Health Technology Assessment; Vol. 21, No. 58. See the NIHR Journals Library website for further project information. Daniela De Angelis was supported by the UK Medical Research Council (Unit Programme Number U105260566) and by PHE. She received funding under the NIHR grant for 10% of her time. The rest of her salary was provided by the MRC and PHE jointly.

The impact of regular school closure on seasonal influenza epidemics: a data-driven spatial transmission model for Belgium

BACKGROUND

School closure is often considered as an option to mitigate influenza epidemics because of its potential to reduce transmission in children and then in the community. The policy is still however highly debated because of controversial evidence. Moreover, the specific mechanisms leading to mitigation are not clearly identified.

METHODS

We introduced a stochastic spatial age-specific metapopulation model to assess the role of holiday-associated behavioral changes and how they affect seasonal influenza dynamics. The model is applied to Belgium, parameterized with country-specific data on social mixing and travel, and calibrated to the 2008/2009 influenza season. It includes behavioral changes occurring during weekend vs. weekday, and holiday vs. school-term. Several experimental scenarios are explored to identify the relevant social and behavioral mechanisms.

RESULTS

Stochastic numerical simulations show that holidays considerably delay the peak of the season and mitigate its impact. Changes in mixing patterns are responsible for the observed effects, whereas changes in travel behavior do not alter the epidemic. Weekends are important in slowing down the season by periodically dampening transmission. Christmas holidays have the largest impact on the epidemic, however later school breaks may help in reducing the epidemic size, stressing the importance of considering the full calendar. An extension of the Christmas holiday of 1 week may further mitigate the epidemic.

CONCLUSION

Changes in the way individuals establish contacts during holidays are the key ingredient explaining the mitigating effect of regular school closure. Our findings highlight the need to quantify these changes in different demographic and epidemic contexts in order to provide accurate and reliable evaluations of closure effectiveness. They also suggest strategic policies in the distribution of holiday periods to minimize the epidemic impact.

Calling in sick: impacts of fever on intra-urban human mobility

Pathogens inflict a wide variety of disease manifestations on their hosts, yet the impacts of disease on the behaviour of infected hosts are rarely studied empirically and are seldom accounted for in mathematical models of transmission dynamics. We explored the potential impacts of one of the most common disease manifestations, fever, on a key determinant of pathogen transmission, host mobility, in residents of the Amazonian city of Iquitos, Peru. We did so by comparing two groups of febrile individuals (dengue-positive and dengue-negative) with an afebrile control group. A retrospective, semi-structured interview allowed us to quantify multiple aspects of mobility during the two-week period preceding each interview. We fitted nested models of each aspect of mobility to data from interviews and compared models using likelihood ratio tests to determine whether there were statistically distinguishable differences in mobility attributable to fever or its aetiology. Compared with afebrile individuals, febrile study participants spent more time at home, visited fewer locations, and, in some cases, visited locations closer to home and spent less time at certain types of locations. These multifaceted impacts are consistent with the possibility that disease-mediated changes in host mobility generate dynamic and complex changes in host contact network structure.

Community Mitigation Guidelines to Prevent Pandemic Influenza - United States, 2017

When a novel influenza A virus with pandemic potential emerges, nonpharmaceutical interventions (NPIs) often are the most readily available interventions to help slow transmission of the virus in communities, which is especially important before a pandemic vaccine becomes widely available. NPIs, also known as community mitigation measures, are actions that persons and communities can take to help slow the spread of respiratory virus infections, including seasonal and pandemic influenza viruses.These guidelines replace the 2007 Interim Pre-pandemic Planning Guidance: Community Strategy for Pandemic Influenza Mitigation in the United States - Early, Targeted, Layered Use of Nonpharmaceutical Interventions (https://stacks.cdc.gov/view/cdc/11425). Several elements remain unchanged from the 2007 guidance, which described recommended NPIs and the supporting rationale and key concepts for the use of these interventions during influenza pandemics. NPIs can be phased in, or layered, on the basis of pandemic severity and local transmission patterns over time. Categories of NPIs include personal protective measures for everyday use (e.g., voluntary home isolation of ill persons, respiratory etiquette, and hand hygiene); personal protective measures reserved for influenza pandemics (e.g., voluntary home quarantine of exposed household members and use of face masks in community settings when ill); community measures aimed at increasing social distancing (e.g., school closures and dismissals, social distancing in workplaces, and postponing or cancelling mass gatherings); and environmental measures (e.g., routine cleaning of frequently touched surfaces).Several new elements have been incorporated into the 2017 guidelines. First, to support updated recommendations on the use of NPIs, the latest scientific evidence available since the influenza A (H1N1)pdm09 pandemic has been added. Second, a summary of lessons learned from the 2009 H1N1 pandemic response is presented to underscore the importance of broad and flexible prepandemic planning. Third, a new section on community engagement has been included to highlight that the timely and effective use of NPIs depends on community acceptance and active participation. Fourth, to provide new or updated pandemic assessment and planning tools, the novel influenza virus pandemic intervals tool, the Influenza Risk Assessment Tool, the Pandemic Severity Assessment Framework, and a set of prepandemic planning scenarios are described. Finally, to facilitate implementation of the updated guidelines and to assist states and localities with prepandemic planning and decision-making, this report links to six supplemental prepandemic NPI planning guides for different community settings that are available online (https://www.cdc.gov/nonpharmaceutical-interventions).

Community Mitigation Guidelines to Prevent Pandemic Influenza - United States, 2017

When a novel influenza A virus with pandemic potential emerges, nonpharmaceutical interventions (NPIs) often are the most readily available interventions to help slow transmission of the virus in communities, which is especially important before a pandemic vaccine becomes widely available. NPIs, also known as community mitigation measures, are actions that persons and communities can take to help slow the spread of respiratory virus infections, including seasonal and pandemic influenza viruses.These guidelines replace the 2007 Interim Pre-pandemic Planning Guidance: Community Strategy for Pandemic Influenza Mitigation in the United States - Early, Targeted, Layered Use of Nonpharmaceutical Interventions (https://stacks.cdc.gov/view/cdc/11425). Several elements remain unchanged from the 2007 guidance, which described recommended NPIs and the supporting rationale and key concepts for the use of these interventions during influenza pandemics. NPIs can be phased in, or layered, on the basis of pandemic severity and local transmission patterns over time. Categories of NPIs include personal protective measures for everyday use (e.g., voluntary home isolation of ill persons, respiratory etiquette, and hand hygiene); personal protective measures reserved for influenza pandemics (e.g., voluntary home quarantine of exposed household members and use of face masks in community settings when ill); community measures aimed at increasing social distancing (e.g., school closures and dismissals, social distancing in workplaces, and postponing or cancelling mass gatherings); and environmental measures (e.g., routine cleaning of frequently touched surfaces).Several new elements have been incorporated into the 2017 guidelines. First, to support updated recommendations on the use of NPIs, the latest scientific evidence available since the influenza A (H1N1)pdm09 pandemic has been added. Second, a summary of lessons learned from the 2009 H1N1 pandemic response is presented to underscore the importance of broad and flexible prepandemic planning. Third, a new section on community engagement has been included to highlight that the timely and effective use of NPIs depends on community acceptance and active participation. Fourth, to provide new or updated pandemic assessment and planning tools, the novel influenza virus pandemic intervals tool, the Influenza Risk Assessment Tool, the Pandemic Severity Assessment Framework, and a set of prepandemic planning scenarios are described. Finally, to facilitate implementation of the updated guidelines and to assist states and localities with prepandemic planning and decision-making, this report links to six supplemental prepandemic NPI planning guides for different community settings that are available online (https://www.cdc.gov/nonpharmaceutical-interventions).

Evidence synthesis to inform model-based cost-effectiveness evaluations of diagnostic tests: a methodological review of health technology assessments

BACKGROUND

Evaluations of diagnostic tests are challenging because of the indirect nature of their impact on patient outcomes. Model-based health economic evaluations of tests allow different types of evidence from various sources to be incorporated and enable cost-effectiveness estimates to be made beyond the duration of available study data. To parameterize a health-economic model fully, all the ways a test impacts on patient health must be quantified, including but not limited to diagnostic test accuracy.

METHODS

We assessed all UK NIHR HTA reports published May 2009-July 2015. Reports were included if they evaluated a diagnostic test, included a model-based health economic evaluation and included a systematic review and meta-analysis of test accuracy. From each eligible report we extracted information on the following topics: 1) what evidence aside from test accuracy was searched for and synthesised, 2) which methods were used to synthesise test accuracy evidence and how did the results inform the economic model, 3) how/whether threshold effects were explored, 4) how the potential dependency between multiple tests in a pathway was accounted for, and 5) for evaluations of tests targeted at the primary care setting, how evidence from differing healthcare settings was incorporated.

RESULTS

The bivariate or HSROC model was implemented in 20/22 reports that met all inclusion criteria. Test accuracy data for health economic modelling was obtained from meta-analyses completely in four reports, partially in fourteen reports and not at all in four reports. Only 2/7 reports that used a quantitative test gave clear threshold recommendations. All 22 reports explored the effect of uncertainty in accuracy parameters but most of those that used multiple tests did not allow for dependence between test results. 7/22 tests were potentially suitable for primary care but the majority found limited evidence on test accuracy in primary care settings.

CONCLUSIONS

The uptake of appropriate meta-analysis methods for synthesising evidence on diagnostic test accuracy in UK NIHR HTAs has improved in recent years. Future research should focus on other evidence requirements for cost-effectiveness assessment, threshold effects for quantitative tests and the impact of multiple diagnostic tests.

Is the impact of childhood influenza vaccination less than expected: a transmission modelling study

BACKGROUND

To reduce the burden of severe influenza, most industrialized countries target specific risk-groups with influenza vaccines, e.g. the elderly or individuals with comorbidities. Since children are the main spreaders, some countries have recently implemented childhood vaccination programs to reduce overall virus transmission and thereby influenza disease in the whole population. The introduction of childhood vaccination programs was often supported by modelling studies that predicted substantial incidence reductions. We developed a mathematical transmission model to examine the potential impact of childhood influenza vaccination in Germany, while also challenging established modelling assumptions.

METHODS

We developed an age-stratified SEIR-type transmission model to reproduce the epidemic influenza seasons between 2003/04 and 2013/14. The model was built upon German population counts, contact patterns, and vaccination history and was fitted to seasonal data on influenza-attributable medically attended acute respiratory infections (I-MAARI) and strain distribution using Bayesian methods. As novelties we (i) implemented a stratified model structure enabling seasonal variability and (ii) deviated from the commonly assumed mass-action-principle by employing a phenomenological transmission rate.

RESULTS

According to the model, by vaccinating primarily the elderly over ten seasons 4 million (95% prediction interval: 3.84 - 4.19) I-MAARI were prevented which corresponds to an 8.6% (8.3% - 8.9%) reduction compared to a no-vaccination scenario and a number-needed-to-vaccinate (NNV) to prevent one I-MAARI of 37.1 (35.5 - 38.7). Additional vaccination of 2-10 year-old children at 40% coverage would have led to an overall I-MAARI reduction of 17.8% (17.1 - 18.7%) mostly due to indirect effects with a NNV of 20.7 (19.6 - 21.6). When employing the traditional mass-action-principle, the model predicted a more than 3-fold higher I-MAARI reduction (55.6%) due to childhood vaccination.

CONCLUSION

In Germany, the introduction of routine childhood influenza vaccination could considerably reduce I-MAARI among all age-groups and improve the NNV. However, the predicted impact is much lower compared to previous studies, which is primarily caused by our phenomenological approach to modelling influenza virus transmission.

Social and Economic Impacts of School Influenza Outbreaks in England: Survey of Caregivers

BACKGROUND

Influenza is a cause of considerable morbidity in England, particularly among children. A total of 39% of all influenza-attributable general practitioner consultations and 37% of all influenza-attributable hospital admissions occur in those aged under 15 years. Few studies have quantified the impact of influenza outbreaks on families. We assessed this impact during 2 influenza seasons.

METHODS

We used questionnaires to obtain data in primary schools that reported an outbreak of an influenza-like-illness (ILI). We sought data on the loss of productivity, costs borne by families and loss in health-related quality of life (HRQoL). ILIs were identified using the symptoms criteria from the European Centre for Disease Prevention and Control and the UK Flusurvey.

RESULTS

For each child reporting ILI, mean school absence was 3.8 days (95% confidence interval [CI]): 3.0-4.8) with mean work absence for caregivers reported as 3.7 days (95% CI: 2.7-4.8). The mean loss in HRQoL was 2.1 quality-adjusted life days (95% CI: 1.5-2.7). The estimated total pediatric burden of disease for reported school-based outbreaks during the 2 influenza seasons was 105.3 QALYs (95% CI: 77.7-139.0).

CONCLUSIONS

This study shows the potential social and economic benefit of vaccination of children during mild influenza seasons.

Social mixing in Fiji: Who-eats-with-whom contact patterns and the implications of age and ethnic heterogeneity for disease dynamics in the Pacific Islands

Empirical data on contact patterns can inform dynamic models of infectious disease transmission. Such information has not been widely reported from Pacific islands, nor strongly multi-ethnic settings, and few attempts have been made to quantify contact patterns relevant for the spread of gastrointestinal infections. As part of enteric fever investigations, we conducted a cross-sectional survey of the general public in Fiji, finding that within the 9,650 mealtime contacts reported by 1,814 participants, there was strong like-with-like mixing by age and ethnicity, with higher contact rates amongst iTaukei than non-iTaukei Fijians. Extra-domiciliary lunchtime contacts follow these mixing patterns, indicating the overall data do not simply reflect household structures. Inter-ethnic mixing was most common amongst school-age children. Serological responses indicative of recent Salmonella Typhi infection were found to be associated, after adjusting for age, with increased contact rates between meal-sharing iTaukei, with no association observed for other contact groups. Animal ownership and travel within the geographical division were common. These are novel data that identify ethnicity as an important social mixing variable, and use retrospective mealtime contacts as a socially acceptable metric of relevance to enteric, contact and respiratory diseases that can be collected in a single visit to participants. Application of these data to other island settings will enable communicable disease models to incorporate locally relevant mixing patterns in parameterisation.

Structural differences in mixing behavior informing the role of asymptomatic infection and testing symptom heritability

Most infectious disease data is obtained from disease surveillance which is based on observations of symptomatic cases only. However, many infectious diseases are transmitted before the onset of symptoms or without developing symptoms at all throughout the entire disease course, referred to as asymptomatic transmission. Fraser and colleagues [1] showed that this type of transmission plays a key role in assessing the feasibility of intervention measures in controlling an epidemic outbreak. To account for asymptomatic transmission in epidemic models, methods often rely on assumptions that cannot be verified given the data at hand. The present study aims at assessing the contribution of social contact data from asymptomatic and symptomatic individuals in quantifying the contribution of (a)symptomatic infections. We use a mathematical model based on ordinary differential equations (ODE) and a likelihood-based approach followed by Markov Chain Monte Carlo (MCMC) to estimate the model parameters and their uncertainty. Incidence data on influenza-like illness in the initial phase of the 2009 A/H1N1pdm epidemic is used to illustrate that it is possible to estimate either the proportion of asymptomatic infections or the relative infectiousness of symptomatic versus asymptomatic infectives. Further, we introduce a model in which the chance of developing symptoms depends on the disease state of the person that transmitted the infection. In conclusion, incorporating social contact data from both asymptomatic and symptomatic individuals allows inferring on parameters associated with asymptomatic infection based on disease data from symptomatic cases only.

Coupled Heterogeneities and Their Impact on Parasite Transmission and Control

Most host-parasite systems exhibit remarkable heterogeneity in the contribution to transmission of certain individuals, locations, host infectious states, or parasite strains. While significant advancements have been made in the understanding of the impact of transmission heterogeneity in epidemic dynamics and parasite persistence and evolution, the knowledge base of the factors contributing to transmission heterogeneity is limited. We argue that research efforts should move beyond considering the impact of single sources of heterogeneity and account for complex couplings between conditions with potential synergistic impacts on parasite transmission. Using theoretical approaches and empirical evidence from various host-parasite systems, we investigate the ecological and epidemiological significance of couplings between heterogeneities and discuss their potential role in transmission dynamics and the impact of control.

Crossing the scale from within-host infection dynamics to between-host transmission fitness: a discussion of current assumptions and knowledge

The progression of an infection within a host determines the ability of a pathogen to transmit to new hosts and to maintain itself in the population. While the general connection between the infection dynamics within a host and the population-level transmission dynamics of pathogens is widely acknowledged, a comprehensive and quantitative understanding that would allow full integration of the two scales is still lacking. Here, we provide a brief discussion of both models and data that have attempted to provide quantitative mappings from within-host infection dynamics to transmission fitness. We present a conceptual framework and provide examples of studies that have taken first steps towards development of a quantitative framework that scales from within-host infections to population-level fitness of different pathogens. We hope to illustrate some general themes, summarize some of the recent advances and-maybe most importantly-discuss gaps in our ability to bridge these scales, and to stimulate future research on this important topic.

Estimates of Social Contact in a Middle School Based on Self-Report and Wireless Sensor Data

Estimates of contact among children, used for infectious disease transmission models and understanding social patterns, historically rely on self-report logs. Recently, wireless sensor technology has enabled objective measurement of proximal contact and comparison of data from the two methods. These are mostly small-scale studies, and knowledge gaps remain in understanding contact and mixing patterns and also in the advantages and disadvantages of data collection methods. We collected contact data from a middle school, with 7th and 8th grades, for one day using self-report contact logs and wireless sensors. The data were linked for students with unique initials, gender, and grade within the school. This paper presents the results of a comparison of two approaches to characterize school contact networks, wireless proximity sensors and self-report logs. Accounting for incomplete capture and lack of participation, we estimate that "sensor-detectable", proximal contacts longer than 20 seconds during lunch and class-time occurred at 2 fold higher frequency than "self-reportable" talk/touch contacts. Overall, 55% of estimated talk-touch contacts were also sensor-detectable whereas only 15% of estimated sensor-detectable contacts were also talk-touch. Contacts detected by sensors and also in self-report logs had longer mean duration than contacts detected only by sensors (6.3 vs 2.4 minutes). During both lunch and class-time, sensor-detectable contacts demonstrated substantially less gender and grade assortativity than talk-touch contacts. Hallway contacts, which were ascertainable only by proximity sensors, were characterized by extremely high degree and short duration. We conclude that the use of wireless sensors and self-report logs provide complementary insight on in-school mixing patterns and contact frequency.

Social Contact Networks and Mixing among Students in K-12 Schools in Pittsburgh, PA

Students attending schools play an important role in the transmission of influenza. In this study, we present a social network analysis of contacts among 1,828 students in eight different schools in urban and suburban areas in and near Pittsburgh, Pennsylvania, United States of America, including elementary, elementary-middle, middle, and high schools. We collected social contact information of students who wore wireless sensor devices that regularly recorded other devices if they are within a distance of 3 meters. We analyzed these networks to identify patterns of proximal student interactions in different classes and grades, to describe community structure within the schools, and to assess the impact of the physical environment of schools on proximal contacts. In the elementary and middle schools, we observed a high number of intra-grade and intra-classroom contacts and a relatively low number of inter-grade contacts. However, in high schools, contact networks were well connected and mixed across grades. High modularity of lower grades suggests that assumptions of homogeneous mixing in epidemic models may be inappropriate; whereas lower modularity in high schools suggests that homogenous mixing assumptions may be more acceptable in these settings. The results suggest that interventions targeting subsets of classrooms may work better in elementary schools than high schools. Our work presents quantitative measures of age-specific, school-based contacts that can be used as the basis for constructing models of the transmission of infections in schools.

Design and methods of a social network isolation study for reducing respiratory infection transmission: The eX-FLU cluster randomized trial

BACKGROUND

Social networks are increasingly recognized as important points of intervention, yet relatively few intervention studies of respiratory infection transmission have utilized a network design. Here we describe the design, methods, and social network structure of a randomized intervention for isolating respiratory infection cases in a university setting over a 10-week period.

METHODOLOGY/PRINCIPAL FINDINGS

590 students in six residence halls enrolled in the eX-FLU study during a chain-referral recruitment process from September 2012-January 2013. Of these, 262 joined as "seed" participants, who nominated their social contacts to join the study, of which 328 "nominees" enrolled. Participants were cluster-randomized by 117 residence halls. Participants were asked to respond to weekly surveys on health behaviors, social interactions, and influenza-like illness (ILI) symptoms. Participants were randomized to either a 3-Day dorm room isolation intervention or a control group (no isolation) upon illness onset. ILI cases reported on their isolation behavior during illness and provided throat and nasal swab specimens at onset, day-three, and day-six of illness. A subsample of individuals (N=103) participated in a sub-study using a novel smartphone application, iEpi, which collected sensor and contextually-dependent survey data on social interactions. Within the social network, participants were significantly positively assortative by intervention group, enrollment type, residence hall, iEpi participation, age, gender, race, and alcohol use (all P<0.002).

CONCLUSIONS/SIGNIFICANCE

We identified a feasible study design for testing the impact of isolation from social networks in a university setting. These data provide an unparalleled opportunity to address questions about isolation and infection transmission, as well as insights into social networks and behaviors among college-aged students. Several important lessons were learned over the course of this project, including feasible isolation durations, the need for extensive organizational efforts, as well as the need for specialized programmers and server space for managing survey and smartphone data.

Targeted vaccination in healthy school children - Can primary school vaccination alone control influenza?

BACKGROUND

The UK commenced an extension to the seasonal influenza vaccination policy in autumn 2014 that will eventually see all healthy children between the ages of 2-16 years offered annual influenza vaccination. Models suggest that the new policy will be both highly effective at reducing the burden of influenza as well as cost-effective. We explore whether targeting vaccination at either primary or secondary schools would be more effective and/or cost-effective than the current strategy.

METHODS

An age-structured deterministic transmission dynamic SEIR-type mathematical model was used to simulate a national influenza outbreak in England. Costs including GP consultations, hospitalisations due to influenza and vaccinations were compared to potential gains in quality-adjusted life years achieved through vaccinating healthy children. Costs and benefits of the new JCVI vaccination policy were estimated over a single season, and compared to the hypothesised new policies of targeted and heterogeneous vaccination.

FINDINGS AND CONCLUSION

All potential vaccination policies were highly cost-effective. Influenza transmission can be eliminated for a particular season by vaccinating both primary and secondary school children, but not by vaccinating only one group. The most cost-effective policy overall is heterogeneous vaccination coverage with 48% uptake in primary schools and 34% in secondary schools. The Joint Committee on Vaccination and Immunisation can consider a modification to their policy of offering seasonal influenza vaccinations to all healthy children of ages 2-16 years.

Evidence compendium and advice on social distancing and other related measures for response to an influenza pandemic

The role of social distancing measures in mitigating pandemic influenza is not precisely understood. To this end, we have conducted a systematised review, particularly in light of the 2009 pandemic influenza, to better inform the role of social distancing measures against pandemic influenza. Articles were identified from relevant databases and the data were synthesised to provide evidence on the role of school or work place-based interventions, case-based distancing (self-isolation, quarantine), and restriction of mobility and mass gatherings. School closure, whether proactive or reactive, appears to be moderately effective and acceptable in reducing the transmission of influenza and in delaying the peak of an epidemic but is associated with very high secondary costs. Voluntary home isolation and quarantine are also effective and acceptable measures but there is an increased risk of intra-household transmission from index cases to contacts. Work place-related interventions like work closure and home working are also modestly effective and are acceptable, but likely to be economically disruptive. Internal mobility restriction is effective only if prohibitively high (50% of travel) restrictions are applied and mass gatherings occurring within 10 days before the epidemic peak are likely to increase the risk of transmission of influenza.

Inferring a district-based hierarchical structure of social contacts from census data

Researchers have recently paid attention to social contact patterns among individuals due to their useful applications in such areas as epidemic evaluation and control, public health decisions, chronic disease research and social network research. Although some studies have estimated social contact patterns from social networks and surveys, few have considered how to infer the hierarchical structure of social contacts directly from census data. In this paper, we focus on inferring an individual’s social contact patterns from detailed census data, and generate various types of social contact patterns such as hierarchical-district-structure-based, cross-district and age-district-based patterns. We evaluate newly generated contact patterns derived from detailed 2011 Hong Kong census data by incorporating them into a model and simulation of the 2009 Hong Kong H1N1 epidemic. We then compare the newly generated social contact patterns with the mixing patterns that are often used in the literature, and draw the following conclusions. First, the generation of social contact patterns based on a hierarchical district structure allows for simulations at different district levels. Second, the newly generated social contact patterns reflect individuals social contacts. Third, the newly generated social contact patterns improve the accuracy of the SEIR-based epidemic model.

School closures during the 2009 influenza pandemic: national and local experiences

BACKGROUND

School closure is a non-pharmaceutical intervention that was considered in many national pandemic plans developed prior to the start of the influenza A(H1N1)pdm09 pandemic, and received considerable attention during the event. Here, we retrospectively review and compare national and local experiences with school closures in several countries during the A(H1N1)pdm09 pandemic. Our intention is not to make a systematic review of country experiences; rather, it is to present the diversity of school closure experiences and provide examples from national and local perspectives.

METHODS

Data were gathered during and following a meeting, organized by the European Centres for Disease Control, on school closures held in October 2010 in Stockholm, Sweden. A standard data collection form was developed and sent to all participants. The twelve participating countries and administrative regions (Bulgaria, China, France, Hong Kong Special Administrative Region (SAR), Italy, Japan, New Zealand, Serbia, South Africa, Thailand, United Kingdom, and United States) provided data.

RESULTS

Our review highlights the very diverse national and local experiences on school closures during the A(H1N1)pdm09 pandemic. The processes including who was in charge of making recommendations and who was in charge of making the decision to close, the school-based control strategies, the extent of school closures, the public health tradition of responses and expectations on school closure varied greatly between countries. Our review also discusses the many challenges associated with the implementation of this intervention and makes recommendations for further practical work in this area.

CONCLUSIONS

The single most important factor to explain differences observed between countries may have been the different public health practises and public expectations concerning school closures and influenza in the selected countries.

Determining the dynamics of influenza transmission by age

Background

It is widely accepted that influenza transmission dynamics vary by age; however methods to quantify the reproductive number by age group are limited. We introduce a simple method to estimate the reproductive number by modifying the method originally proposed by Wallinga and Teunis and using existing information on contact patterns between age groups. We additionally perform a sensitivity analysis to determine the potential impact of differential healthcare seeking patterns by age. We illustrate this method using data from the 2009 H1N1 Influenza pandemic in Gauteng Province, South Africa.

Results

Our results are consistent with others in showing decreased transmission with age. We show that results can change markedly when we make the account for differential healthcare seeking behaviors by age.

Conclusions

We show substantial heterogeneity in transmission by age group during the Influenza A H1N1 pandemic in South Africa. This information can greatly assist in targeting interventions and implementing social distancing measures.

Cost-effective length and timing of school closure during an influenza pandemic depend on the severity

BACKGROUND

There has been a variation in published opinions toward the effectiveness of school closure which is implemented reactively when substantial influenza transmissions are seen at schools. Parameterizing an age-structured epidemic model using published estimates of the pandemic H1N1-2009 and accounting for the cost effectiveness, we examined if the timing and length of school closure could be optimized.

METHODS

Age-structured renewal equation was employed to describe the epidemic dynamics of an influenza pandemic. School closure was assumed to take place only once during the course of the pandemic, abruptly reducing child-to-child transmission for a fixed length of time and also influencing the transmission between children and adults. Public health effectiveness was measured by reduction in the cumulative incidence, and cost effectiveness was also examined by calculating the incremental cost effectiveness ratio and adopting a threshold of 1.0 × 10⁷ Japanese Yen/life-year.

RESULTS

School closure at the epidemic peak appeared to yield the largest reduction in the final size, while the time of epidemic peak was shown to depend on the transmissibility. As the length of school closure was extended, we observed larger reduction in the cumulative incidence. Nevertheless, the cost effectiveness analysis showed that the cost of our school closure scenario with the parameters derived from H1N1-2009 was not justifiable. If the risk of death is three times or greater than that of H1N1-2009, the school closure could be regarded as cost effective.

CONCLUSIONS

There is no fixed timing and duration of school closure that can be recommended as universal guideline for different types of influenza viruses. The effectiveness of school closure depends on the transmission dynamics of a particular influenza virus strain, especially the virulence (i.e. the infection fatality risk).

The impact of illness on social networks: implications for transmission and control of influenza

We expect social networks to change as a result of illness, but social contact data are generally collected from healthy persons. Here we quantified the impact of influenza-like illness on social mixing patterns. We analyzed the contact patterns of persons from England measured when they were symptomatic with influenza-like illness during the 2009 A/H1N1pdm influenza epidemic (2009-2010) and again 2 weeks later when they had recovered. Illness was associated with a reduction in the number of social contacts, particularly in settings outside the home, reducing the reproduction number to about one-quarter of the value it would otherwise have taken. We also observed a change in the age distribution of contacts. By comparing the expected age distribution of cases resulting from transmission by (a)symptomatic persons with incidence data, we estimated the contribution of both groups to transmission. Using this, we calculated the fraction of transmission resulting from (a)symptomatic persons, assuming equal duration of infectiousness. We estimated that 66% of transmission was attributable to persons with symptomatic disease (95% confidence interval: 0.23, 1.00). This has important implications for control: Treating symptomatic persons with antiviral agents or encouraging home isolation would be expected to have a major impact on transmission, particularly since the reproduction number for this strain was low.

Selecting nonpharmaceutical interventions for influenza

Models of influenza transmission have focused on the ability of vaccination, antiviral therapy, and social distancing strategies to mitigate epidemics. Influenza transmission, however, may also be interrupted by hygiene interventions such as frequent hand washing and wearing masks or respirators. We apply a model of influenza disease transmission that incorporates hygiene and social distancing interventions. The model describes population mixing as a Poisson process, and the probability of infection upon contact between an infectious and susceptible person is parameterized by p. While social distancing interventions modify contact rates in the population, hygiene interventions modify p. Public health decision making involves tradeoffs, and we introduce an objective function that considers the direct costs of interventions and new infections to determine the optimum intervention type (social distancing versus hygiene intervention) and population compliance for epidemic mitigation. Significant simplifications have been made in these models. However, we demonstrate that the method is feasible, provides plausible results, and is sensitive to the selection of model parameters. Specifically, we show that the optimum combination of nonpharmaceutical interventions depends upon the probability of infection, intervention compliance, and duration of infectiousness. Means by which realism can be increased in the method are discussed.

Vaccination and clinical severity: is the effectiveness of contact tracing and case isolation hampered by past vaccination?

While contact tracing and case isolation are considered as the first choice of interventions against a smallpox bioterrorist event, their effectiveness under vaccination is questioned, because not only susceptibility of host and infectiousness of case but also the risk of severe clinical manifestations among cases is known to be reduced by vaccine-induced immunity, thereby potentially delaying the diagnosis and increasing mobility among vaccinated cases. We employed a multi-type stochastic epidemic model, aiming to assess the feasibility of contact tracing and case isolation in a partially vaccinated population and identify data gaps. We computed four epidemiological outcome measures, i.e., (i) the threshold of a major epidemic under the interventions; (ii) the expected total number of cases; (iii) the probability of extinction, and (iv) the expected duration of an outbreak, demonstrating that all of these outcomes critically depend on the clinical impact of past vaccination on the diagnosis and movement of vaccinated cases. We discuss that, even in the absence of smallpox in the present day, one should consider the way to empirically quantify the delay in case detection and an increase in the frequency of contacts among previously vaccinated cases compared to unvaccinated during the early stage of an epidemic so that the feasibility of contact tracing and case isolation in a vaccinated population can be explicitly assessed.

School closures and influenza: systematic review of epidemiological studies

OBJECTIVE

To review the effects of school closures on pandemic and seasonal influenza outbreaks.

DESIGN

Systematic review.

DATA SOURCES

MEDLINE and EMBASE, reference lists of identified articles, hand searches of key journals and additional papers from the authors' collections.

STUDY SELECTION

Studies were included if they reported on a seasonal or pandemic influenza outbreak coinciding with a planned or unplanned school closure.

RESULTS

Of 2579 papers identified through MEDLINE and EMBASE, 65 were eligible for inclusion in the review along with 14 identified from other sources. Influenza incidence frequently declined after school closure. The effect was sometimes reversed when schools reopened, supporting a causal role for school closure in reducing incidence. Any benefits associated with school closure appeared to be greatest among school-aged children. However, as schools often closed late in the outbreak or other interventions were used concurrently, it was sometimes unclear how much school closure contributed to the reductions in incidence.

CONCLUSIONS

School closures appear to have the potential to reduce influenza transmission, but the heterogeneity in the data available means that the optimum strategy (eg, the ideal length and timing of closure) remains unclear.

Under-explored assumptions in influenza vaccination models: implications for the universal vaccination of children

The aim of this study was to explore several important (but uncertain) assumptions in influenza models which affect the estimated benefits of vaccination programs. We combined consideration of these factors with the seasonal variability of influenza transmissibility to gain a better understanding of how they may influence influenza control efforts. As our case study, we considered the potential impact of universal seasonal childhood vaccination in Australia using a simplified age-stratified Susceptible Exposed Infectious Recovered (SEIR) model to simulate influenza epidemics and the impact of vaccination. We found that the choice of vaccine efficacy model was influential in determining the impact of vaccination. This choice interacted with other model assumption such as those around the infectiousness of asymptomatic cases and the match of the vaccine to the circulating strains. The methodological approach used to estimate influenza hospitalisations was also highly influential. Our study highlights the role that key modelling assumptions play when estimating the impact of vaccination against influenza.

Close encounters of the infectious kind: methods to measure social mixing behaviour

A central tenet of close-contact or respiratory infection epidemiology is that infection patterns within human populations are related to underlying patterns of social interaction. Until recently, few researchers had attempted to quantify potentially infectious encounters made between people. Now, however, several studies have quantified social mixing behaviour, using a variety of methods. Here, we review the methodologies employed, suggest other appropriate methods and technologies, and outline future research challenges for this rapidly advancing field of research.

Measured dynamic social contact patterns explain the spread of H1N1v influenza

Patterns of social mixing are key determinants of epidemic spread. Here we present the results of an internet-based social contact survey completed by a cohort of participants over 9,000 times between July 2009 and March 2010, during the 2009 H1N1v influenza epidemic. We quantify the changes in social contact patterns over time, finding that school children make 40% fewer contacts during holiday periods than during term time. We use these dynamically varying contact patterns to parameterise an age-structured model of influenza spread, capturing well the observed patterns of incidence; the changing contact patterns resulted in a fall of approximately 35% in the reproduction number of influenza during the holidays. This work illustrates the importance of including changing mixing patterns in epidemic models. We conclude that changes in contact patterns explain changes in disease incidence, and that the timing of school terms drove the 2009 H1N1v epidemic in the UK. Changes in social mixing patterns can be usefully measured through simple internet-based surveys.

Changes in patterns of social mixing can result in changes in epidemic behaviour; this was observed during the 2009 influenza pandemic, in which the epidemic declined during school holidays and grew during term time. Until now, little information has been available to quantify how people's mixing patterns change over time. Here, we present the results of an internet-based survey of social mixing patterns that was carried out in the UK throughout the 2009 pandemic. We show that school holidays resulted in a substantial drop in the number of social contacts made each day, particularly between children. To test whether these measured patterns of social mixing could explain the observed epidemic, we used our mixing data in a simple mathematical model of influenza spread. We found that changing social contact behaviour could explain levels of infection in the community, and conclude that the timing of school terms was responsible for the shape of the influenza epidemic.

Controlling the spread of disease in schools

Pandemic and seasonal infectious diseases such as influenza may have serious negative health and economic consequences. Certain non-pharmaceutical intervention strategies--including school closures--can be implemented rapidly as a first line of defense against spread. Such interventions attempt to reduce the effective number of contacts between individuals within a community; yet the efficacy of closing schools to reduce disease transmission is unclear, and closures certainly result in significant economic impacts for caregivers who must stay at home to care for their children. Using individual-based computer simulation models to trace contacts among schoolchildren within a stereotypical school setting, we show how alternative school-based disease interventions have great potential to be as effective as traditional school closures without the corresponding loss of workforce and economic impacts.

Recommendations for and compliance with social restrictions during implementation of school closures in the early phase of the influenza A (H1N1) 2009 outbreak in Melbourne, Australia

Background

Localized reactive school and classroom closures were implemented as part of a suite of pandemic containment measures during the initial response to influenza A (H1N1) 2009 in Melbourne, Australia. Infected individuals, and those who had been in close contact with a case, were asked to stay in voluntary home quarantine and refrain from contact with visitors for seven days from the date of symptom onset or exposure to an infected person. Oseltamivir (Tamiflu^®) was available for treatment or prophylaxis.

Methods

We surveyed affected families through schools involved in the closures. Analyses of responses were descriptive. We characterized recommendations made to case and contact households and quantified adherence to guidelines and antiviral therapy.

Results

Of the 314 respondent households, 51 contained a confirmed case. The prescribed quarantine period ranged from 1-14 days, reflecting logistic difficulties in reactive implementation relative to the stated guidelines. Household-level compliance with the requirement to stay at home was high (84.5%, 95% CI 79.3,88.5) and contact with children outside the immediate family infrequent.

Conclusions

Levels of compliance with recommendations in our sample were high compared with other studies, likely due to heightened public awareness of a newly introduced virus of uncertain severity. The variability of reported recommendations highlighted the difficulties inherent in implementing a targeted reactive strategy, such as that employed in Melbourne, on a large scale during a public health emergency. This study emphasizes the need to understand how public health measures are implemented when seeking to evaluate their effectiveness.

The impact of school holidays on the social mixing patterns of school children

School holidays are recognised to be of great epidemiological importance for a wide range of infectious diseases; this is postulated to be because the social mixing patterns of school children - a key population group - change significantly during the holiday period. However, there is little direct quantitative evidence to confirm this belief. Here, we present the results of a prospective survey designed to provide a detailed comparison of social mixing patterns of school children during school terms and during the school holidays. Paired data were collected, with participants recording their social contacts once during term time and once during the holiday period. We found that the daily number of recorded encounters approximately halved during the holidays, and that the number of close contact encounters fell by approximately one third. The holiday period also saw a change in the age structure of children's social contacts, with far fewer contacts of their own age, but an increase in the number of encounters with adults, particularly older adults. A greater amount of mixing between children at different schools was recorded during the holiday. We suggest, therefore, that whilst infections may spread rapidly within schools during term time, in the holiday period there are increased opportunities for transmission to other schools and other age groups.