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Foodborne illness source attribution is foundational to a risk-based food safety system. We describe a method for attributing US foodborne illnesses caused by nontyphoidal Salmonella enterica, Escherichia coli O157, Listeria monocytogenes, and Campylobacter to 17 food categories using statistical modeling of outbreak data. This method adjusts for epidemiologic factors associated with outbreak size, down-weights older outbreaks, and estimates credibility intervals. On the basis of 952 reported outbreaks and 32,802 illnesses during 1998-2012, we attribute 77% of foodborne Salmonella illnesses to 7 food categories (seeded vegetables, eggs, chicken, other produce, pork, beef, and fruits), 82% of E. coli O157 illnesses to beef and vegetable row crops, 81% of L. monocytogenes illnesses to fruits and dairy, and 74% of Campylobacter illnesses to dairy and chicken. However, because Campylobacter outbreaks probably overrepresent dairy as a source of nonoutbreak campylobacteriosis, we caution against using these Campylobacter attribution estimates without further adjustment.

Norovirus is the leading cause of acute gastroenteritis outbreaks in the United States. We estimated the basic (R ) and effective (R ) reproduction numbers for 7,094 norovirus outbreaks reported to the National Outbreak Reporting System (NORS) during 2009-2017 and used regression models to assess whether transmission varied by outbreak setting. The median R was 2.75 (interquartile range [IQR] 2.38-3.65), and median R was 1.29 (IQR 1.12-1.74). Long-term care and assisted living facilities had an R  of 3.35 (95% CI 3.26-3.45), but R did not differ substantially for outbreaks in other settings, except for outbreaks in schools, colleges, and universities, which had an R  of 2.92 (95% CI 2.82-3.03). Seasonally, R was lowest (3.11 [95% CI 2.97-3.25]) in summer and peaked in fall and winter. Overall, we saw little variability in transmission across different outbreaks settings in the United States.

The 1918-19 influenza epidemic killed more than fifty million people worldwide. The SARS epidemic of 2002-3, by comparison, killed fewer than a thousand. The success in containing the spread of SARS was due largely to the rapid global response of public health authorities, which was aided by insights resulting from mathematical models. Models enabled authorities to better understand how the disease spread and to assess the relative effectiveness of different control strategies. In this book, Lisa Sattenspiel and Alun Lloyd provide a comprehensive introduction to mathematical models in epidemiology and show how they can be used to predict and control the geographic spread of major infectious diseases. Key concepts in infectious disease modeling are explained, readers are guided from simple mathematical models to more complex ones, and the strengths and weaknesses of these models are explored. The book highlights the breadth of techniques available to modelers today, such as population-based and individual-based models, and covers specific applications as well. Sattenspiel and Lloyd examine the powerful mathematical models that health authorities have developed to understand the spatial distribution and geographic spread of influenza, measles, foot-and-mouth disease, and SARS. Analytic methods geographers use to study human infectious diseases and the dynamics of epidemics are also discussed. A must-read for students, researchers, and practitioners, no other book provides such an accessible introduction to this exciting and fast-evolving field.

•MD epidemiology and vaccination strategies in Latin America were reviewed.•Routine infant MCC vaccination program was implemented in Brazil in 2010.•Brazil: a dramatic reduction in MD incidence rates was observed in those <2 years.•Emergence of serogroup W disease was identified in Chile and Argentina.•Chile: reactive MenACWY vaccination implemented in children aged 9 months to 5 years has impacted only in this age group. The Global Meningococcal Initiative (GMI) was established in 2009 and comprises an international team of scientists, clinicians, and public health officials with expertise in meningococcal disease (MD). Its primary goal is to promote global prevention of MD through education, research, international cooperation, and developing recommendations that include decreasing the burden of severe disease. The group held its first roundtable meeting with experts from Latin American countries in 2011, and subsequently proposed several recommendations to reduce the regional burden of MD. A second roundtable meeting was convened with Latin American representatives in June 2013 to reassess MD epidemiology, vaccination strategies, and unmet needs in the region, as well as to update the earlier recommendations. Special emphasis was placed on the emergence and spread of serogroup W disease in Argentina and Chile, and the control measures put in place in Chile were a particular focus of discussions. The impact of routine meningococcal vaccination programs, notably in Brazil, was also evaluated. There have been considerable improvements in MD surveillance systems and diagnostic techniques in some countries (e.g., Brazil and Chile), but the lack of adequate infrastructure, trained personnel, and equipment/reagents remains a major barrier to progress in resource-poor countries. The Pan American Health Organization's Revolving Fund is likely to play an important role in improving access to meningococcal vaccines in Latin America. Additional innovative approaches are needed to redress the imbalance in expertise and resources between countries, and thereby improve the control of MD. In Latin America, the GMI recommends establishment of a detailed and comprehensive national/regional surveillance system, standardization of laboratory procedures, adoption of a uniform MD case definition, maintaining laboratory-based surveillance, replacement of polysaccharide vaccines with conjugate formulations (wherever possible), monitoring and evaluating implemented vaccination strategies, conducting cost-effectiveness studies, and developing specific recommendations for vaccination of high-risk groups.

Introduction Literature on influenza focuses on influenza A, despite influenza B having a large public health impact. The Global Influenza B Study aims to collect information on global epidemiology and burden of disease of influenza B since 2000. Methods Twenty‐six countries in the Southern (n = 5) and Northern (n = 7) hemispheres and intertropical belt (n = 14) provided virological and epidemiological data. We calculated the proportion of influenza cases due to type B and Victoria and Yamagata lineages in each country and season; tested the correlation between proportion of influenza B and maximum weekly influenza‐like illness (ILI) rate during the same season; determined the frequency of vaccine mismatches; and described the age distribution of cases by virus type. Results The database included 935 673 influenza cases (2000–2013). Overall median proportion of influenza B was 22·6%, with no statistically significant differences across seasons. During seasons where influenza B was dominant or co‐circulated (>20% of total detections), Victoria and Yamagata lineages predominated during 64% and 36% of seasons, respectively, and a vaccine mismatch was observed in ≈25% of seasons. Proportion of influenza B was inversely correlated with maximum ILI rate in the same season in the Northern and (with borderline significance) Southern hemispheres. Patients infected with influenza B were usually younger (5–17 years) than patients infected with influenza A. Conclusion Influenza B is a common disease with some epidemiological differences from influenza A. This should be considered when optimizing control/prevention strategies in different regions and reducing the global burden of disease due to influenza.

Recent years have seen an explosion in new kinds of data on infectious diseases, including data on social contacts, whole genome sequences of pathogens, biomarkers for susceptibility to infection, serological panel data, and surveillance data. The Handbook of Infectious Disease Data Analysis provides an overview of many key statistical methods that have been developed in response to such new data streams and the associated ability to address key scientific and epidemiological questions. A unique feature of the Handbook is the wide range of topics covered. Key features Contributors include many leading researchers in the field Divided into four main sections: Basic concepts, Analysis of Outbreak Data, Analysis of Seroprevalence Data, Analysis of Surveillance Data Numerous case studies and examples throughout Provides both introductory material and key reference material 　 　 　 　 　 I Introduction 1. Introduction Leonhard Held, Niel Hens, Philip O’Neill, Jacco Wallinga II Basic Concepts 2. Population dynamics of pathogens Ottar Bjornstad 3. Infectious disease data from surveillance, outbreak investigation and epidemiological studies Susan Hahné, Richard Pebody 4. Key concepts in infectious disease epidemiology Nick Jewell 5. Key parameters in infectious disease epidemiology Laura White 6. Contact patterns for contagious diseases Jacco Wallinga, Jan van de Kassteele, Niel Hens 7. Basic stochastic transmission models and their inference Tom Britton 8. Analysis of vaccine studies and causal inference Betz Halloran III Analysis of Outbreak Data 9. Markov chain Monte Carlo methods for outbreak data Philip O’Neill, Theodore Kypraios 10. Approximate Bayesian Computation methods for epidemic models Peter Neal 11. Iterated filtering methods for Markov process epidemic models Theresa Stocks 12. Pairwise survival analysis of infectious disease transmission data Eben Kenah 13. Methods for outbreaks using genomic data Don Klinkenberg, Caroline Colijn, Xavier Didelot IV Analysis of Seroprevalence Data 14. Persistence of passive immunity, natural immunity (and vaccination) Amy Winter, Jess Metcalf 15. Inferring the time of infection from serological data Maciej Boni, Kåre Mølbak, Karen Angeliki Krogfelt 16. The use of seroprevalence data to estimate cumulative incidence of infection Ben Cowling, Jessica Wong 17. The analysis of serological data with transmission models Marc Baguelin 18. The analysis of multivariate serological data Steven Abrams 19. Mixture modelling Emanuele Del Fava, Ziv Shkedy V Analysis of Surveillance Data 20. Modeling infectious diseases distributions: applications of point process methods Peter J Diggle 21. Prospective detection of outbreaks Benjamin Allevius, Michael Höhle 22. Underreporting and reporting delays Angela Noufaily 23. Spatio-temporal analysis of surveillance data Jon Wakefield, Tracy Q Dong, Vladimir N Minin 24. Analysing multiple epidemic data sources Daniela De Angelis, Anne Presanis 25. Forecasting based on surveillance data Leonhard Held, Sebastian Meyer 26. Spatial mapping of infectious disease risk Ewan Cameron \"One of the editors of the book, Jacco Wallinga, is heading the group at the Dutch Institute of Public Health and the Environment that does all of the statistical analyses to feed their director with information. The latter has had a strong influence on the policy our government chose . . . The book is well produced . . . \" ~Paul Eilers, ISCB News Leonhard Held is Professor of Biostatistics at the University of Zurich. Niel Hens is Professor of Biostatistics at Hasselt University and the University of Antwerp. Philip O’Neill is Professor of Applied Probability at the University of Nottingham. Jacco Wallinga is Professor of Mathematical Modelling of Infectious Diseases at the Leiden University Medical Center.

Influenza is an important cause of morbidity and mortality for frail older people. Whilst the antiviral drug oseltamivir (a neuraminidase inhibitor) is approved for treatment and prophylaxis of influenza during outbreaks, there have been no trials comparing treatment only (T) versus treatment and prophylaxis (T&P) in Aged Care Facilities (ACFs). Our objective was to compare a policy of T versus T&P for influenza outbreaks in ACFs. We performed a cluster randomised controlled trial in 16 ACFs, that followed a policy of either \"T\"-oseltamivir treatment (75 mg twice a day for 5 days)-or \"T&P\"-treatment and prophylaxis (75 mg once a day for 10 days) for influenza outbreaks over three years, in addition to enhanced surveillance. The primary outcome measure was the attack rate of influenza. Secondary outcomes measures were deaths, hospitalisation, pneumonia and adverse events. Laboratory testing was performed to identify the viral cause of influenza-like illness (ILI) outbreaks. The study period 30 June 2006 to 23 December 2008 included three southern hemisphere winters. During that time, influenza was confirmed as the cause of nine of the 23 ILI outbreaks that occurred amongst the 16 ACFs. The policy of T&P resulted in a significant reduction in the influenza attack rate amongst residents: 93/255 (36%) in residents in T facilities versus 91/397 (23%) in T&P facilities (p=0.002). We observed a non-significant reduction in staff: 46/216 (21%) in T facilities versus 47/350 (13%) in T&P facilities (p=0.5). There was a significant reduction in mean duration of outbreaks (T=24 days, T&P=11 days, p=0.04). Deaths, hospitalisations and pneumonia were non-significantly reduced in the T&P allocated facilities. Drug adverse events were common but tolerated. Our trial lacked power but these results provide some support for a policy of \"treatment and prophylaxis\" with oseltamivir in controlling influenza outbreaks in ACFs. [corrected] Australian Clinical Trials Registry ACTRN12606000278538.

The current outbreak of Ebola in eastern DR Congo, beginning in 2018, emerged in a complex and violent political and security environment. Community-level prevention and outbreak control measures appear to be dependent on public trust in relevant authorities and information, but little scholarship has explored these issues. We aimed to investigate the role of trust and misinformation on individual preventive behaviours during an outbreak of Ebola virus disease (EVD). We surveyed 961 adults between Sept 1 and Sept 16, 2018. We used a multistage sampling design in Beni and Butembo in North Kivu, DR Congo. Of 412 avenues and cells (the lowest administrative structures; 99 in Beni and 313 in Butembo), we randomly selected 30 in each city. In each avenue or cell, 16 households were selected using the WHO Expanded Programme on Immunization's random walk approach. In each household, one adult (aged ≥18 years) was randomly selected for interview. Standardised questionnaires were administered by experienced interviewers. We used multivariate models to examine the intermediate variables of interest, including institutional trust and belief in selected misinformation, with outcomes of interest related to EVD prevention behaviours. Among 961 respondents, 349 (31·9%, 95% CI 27·4–36·9) trusted that local authorities represent their interest. Belief in misinformation was widespread, with 230 (25·5%, 21·7–29·6) respondents believing that the Ebola outbreak was not real. Low institutional trust and belief in misinformation were associated with a decreased likelihood of adopting preventive behaviours, including acceptance of Ebola vaccines (odds ratio 0·22, 95% CI 0·21–0·22, and 1·40, 1·39–1·42) and seeking formal health care (0·06, 0·05–0·06, and 1·16, 1·15–1·17). The findings underscore the practical implications of mistrust and misinformation for outbreak control. These factors are associated with low compliance with messages of social and behavioural change and refusal to seek formal medical care or accept vaccines, which in turn increases the risk of spread of EVD. The Harvard Humanitarian Initiative Innovation Fund.

Highly pathogenic avian influenza (HPAI) H5N1 activity has intensified globally since 2021, increasingly causing mass mortality in wild birds and poultry and incidental infections in mammals 1 – 3 . However, the ecological and virological properties that underscore future mitigation strategies still remain unclear. Using epidemiological, spatial and genomic approaches, we demonstrate changes in the origins of resurgent HPAI H5 and reveal significant shifts in virus ecology and evolution. Outbreak data show key resurgent events in 2016–2017 and 2020–2021, contributing to the emergence and panzootic spread of H5N1 in 2021–2022. Genomic analysis reveals that the 2016–2017 epizootics originated in Asia, where HPAI H5 reservoirs are endemic. In 2020–2021, 2.3.4.4b H5N8 viruses emerged in African poultry, featuring mutations altering HA structure and receptor binding. In 2021–2022, a new H5N1 virus evolved through reassortment in wild birds in Europe, undergoing further reassortment with low-pathogenic avian influenza in wild and domestic birds during global dissemination. These results highlight a shift in the HPAI H5 epicentre beyond Asia and indicate that increasing persistence of HPAI H5 in wild birds is facilitating geographic and host range expansion, accelerating dispersion velocity and increasing reassortment potential. As earlier outbreaks of H5N1 and H5N8 were caused by more stable genomic constellations, these recent changes reflect adaptation across the domestic-bird–wild-bird interface. Elimination strategies in domestic birds therefore remain a high priority to limit future epizootics. Recent resurgences of highly pathogenic avian influenza H5 viruses have different origins and virus ecologies as their epicentres shift and viruses evolve, with changes indicating increased adaptation among domestic birds.

In the wake of community coronavirus disease 2019 (COVID-19) transmission in the United States, there is a growing public health concern regarding the adequacy of resources to treat infected cases. Hospital beds, intensive care units (ICUs), and ventilators are vital for the treatment of patients with severe illness. To project the timing of the outbreak peak and the number of ICU beds required at peak, we simulated a COVID-19 outbreak parameterized with the US population demographics. In scenario analyses, we varied the delay from symptom onset to self-isolation, the proportion of symptomatic individuals practicing self-isolation, and the basic reproduction number R₀. Without self-isolation, when R₀ = 2.5, treatment of critically ill individuals at the outbreak peak would require 3.8 times more ICU beds than exist in the United States. Self-isolation by 20% of cases 24 h after symptom onset would delay and flatten the outbreak trajectory, reducing the number of ICU beds needed at the peak by 48.4% (interquartile range 46.4–50.3%), although still exceeding existing capacity. When R₀ = 2, twice as many ICU beds would be required at the peak of outbreak in the absence of self-isolation. In this scenario, the proportional impact of self-isolation within 24 h on reducing the peak number of ICU beds is substantially higher at 73.5% (interquartile range 71.4–75.3%). Our estimates underscore the inadequacy of critical care capacity to handle the burgeoning outbreak. Policies that encourage self-isolation, such as paid sick leave, may delay the epidemic peak, giving a window of time that could facilitate emergency mobilization to expand hospital capacity.