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Abstract: Observational studies indicate that vaccine-induced immunity can decline over time. However, few researchers have incorporated this kind of waning effect into their virus spread models. In this study, we simulate an influenza epidemic that considers the effects of waning immunity by fitting epidemiological models to CDC secondary historical data aggregated on a weekly basis, and derive the transmission rates at which susceptible individuals become infected over the course of the influenza season. Using a system of differential equations, we define four groups of individuals in a population: susceptible, vaccinated, infected, and recovered. We show that a larger number of initially infected individuals might not only bring the influenza season to an end sooner but also reduce the epidemic size. Moreover, any influenza virus that entails a faster recovery rate does not necessarily lead to a smaller epidemic size. We illustrate how simulation helps in understanding the effects of influenza epidemiological model in the presence of waning influenza vaccine immunity., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Foodborne outbreak investigations often provide data for public health officials to determine how the environment contributed to the outbreak and on how to prevent future outbreaks. State and local health departments are responsible for investigating foodborne illness outbreaks in their jurisdictions and reporting the data to national-level surveillance systems, including information from the environmental assessment. This assessment is designed to describe how the environment contributed to the outbreak and identifies factors that contributed to the outbreak and environmental antecedents to the outbreak. Environmental antecedents, also referred to as root causes, are specific reasons that allow biological or chemical agents to contaminate, survive, or grow in food. From 2017 to 2019, 24 jurisdictions reported 1,430 antecedents from 393 outbreaks to the National Environmental Assessment Reporting System. The most reported antecedents were lack of oversight of employees/enforcement of policies (89.1%), lack of training of employees on specific processes (74.0%), and lack of a food safety culture/attitude towards food safety (57.5%). These findings highlight the critical role that employees play in restaurant food safety and are heavily influenced by restaurant management, who can exercise active managerial control to manage these antecedents. Identifying antecedents during investigations is essential for understanding the outbreak's root cause and implementing sustainable corrective actions to stop the immediate outbreak and future outbreaks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Inc.)

Animal disease outbreaks, such as the recent outbreak of African Swine Fever in 2018, are a major concern for stakeholders across the food supply chain due to their potential to disrupt global food security, cause economic losses, and threaten animal welfare. As a result of their transboundary nature, discussions have shifted to preventive measures aimed at protecting livestock while ensuring food security and safety. Emergency assistance has been a critical response option during pandemics. However, this may not be sustainable in the long run because the expectation of government bailout may encourage risk taking behaviours. Our hypothesis is that an indemnity policy that is conditioned on showing biosecurity practices would increase compliance and reduce government expenditure during disease outbreaks. We developed and launched a survey from March to July 2022 targeted at swine producers across the US. From the survey, we examined livestock farmers' attitudes and intentions regarding biosecurity investment and assessed their attitudes towards the purchase of livestock insurance and reporting suspected infected livestock on their farm. We used a partial proportion odds model analysis to examine the model. Our analysis revealed that intention to call a veterinarian, trust in government agencies and risk perception of farmers were instrumental in the willingness to self-invest in biosecurity, purchase livestock insurance, and promptly report infected livestock on their farms. This provides evidence that biosecurity compliance would increase if indemnification was tied to a demonstration of effort to adopt biosecurity practices. We also show that individuals who have been in the industry for a longer period may become complacent and less likely to report outbreaks. Farmers with a higher share of income from their production operations bear a greater risk from their operational income and are more willing to report any suspected infections on their farms. The data suggest that motivating the willingness of farmers to invest in biosecurity while overcoming cost concerns is achievable., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

We consider the problem of targeted mass screening of heterogeneous populations under limited testing capacity. Mass screening is an essential tool that arises in various settings, e.g., ensuring a safe supply of blood, reducing prevalence of sexually transmitted diseases, and mitigating the spread of infectious disease outbreaks. The goal of mass screening is to classify whole population groups as positive or negative for an infectious disease as efficiently and accurately as possible. Under limited testing capacity, it is not possible to screen the entire population and hence administrators must reserve testing and target those among the population that are most in need or most susceptible. This paper addresses this decision problem by taking advantage of accessible population-level risk information to identify the optimal set of sub-populations to target for screening. We conduct a comprehensive analysis that considers the two most commonly adopted schemes: Individual testing and Dorfman group testing. For both schemes, we formulate an optimization model that aims to minimize the number of misclassifications under a testing capacity constraint. By analyzing the formulations, we establish key structural properties which we use to construct efficient and accurate solution techniques. We conduct a case study on COVID-19 in the United States using geographic-based data. Our results reveal that the considered proactive targeted schemes outperform commonly adopted practices by substantially reducing misclassifications. Our case study provides important managerial insights with regards to optimal allocation of tests, testing designs, and protocols that dictate the optimality of schemes. Such insights can inform policy-makers with tailored and implementable data-driven recommendations. [ABSTRACT FROM AUTHOR]

This paper explores differences in national responses to foodborne disease outbreaks, addressing both the sources of policy divergence and their implications for public health and coordinated emergency response. It presents findings from a comparative study of two multi-state E. coli outbreaks, one in the United States (2006) and one in Germany (2011), demonstrating important differences in how risk managers understood and responded to each nation's first major outbreak associated with fresh produce. Drawing on a qualitative analysis of 36 semi-structured interviews with key stakeholders and hundreds of archival documents, this paper traces how social constructions of the E. coli risk interacted with organizational dynamics among state and industry actors to produce divergent policy outcomes: the U.S. outbreak was understood primarily as an agricultural problem that led to an industry-led agricultural solution, whereas the German outbreak was understood as a human disease problem that did not result in a substantial policy response once the acute health crisis passed. The paper concludes by discussing how these policy processes generate partial solutions to foodborne contamination that expose modern societies to systemic vulnerabilities. [ABSTRACT FROM AUTHOR]

The purpose of this study was to estimate the impacts of rapid and accurate tracing of cattle movements during a Foot-and-Mouth Disease (FMD) outbreak in the United States (US). To simulate introduction and spread of FMD we utilized InterSpread Plus, a spatially explicit disease transmission model, and a national livestock population file. The simulations began in one of four regions of the US via beef or dairy cattle as the index infected premises (IP). The first IP was detected 8, 14, or 21 days after introduction. The tracing levels were defined by the probability of a successful trace and the time to trace completion. We evaluated three tracing performance levels, a baseline that represents a mix of paper and electronic interstate shipment records, an estimated partial implementation of electronic identification (EID) tracing, and an estimated full implementation of EID tracing. To evaluate the potential to decrease the size of control areas and surveillance zones with full EID use, we compared the standard size for each to a reduced geographical area for each. The total number of IPs in an outbreak varied with the location of the index farms. Within index farm locations and across tracing performance levels, early detection (day 8) resulted in fewer IPs and a shorter duration of the outbreak. The impact of improving tracing was most evident within introduction region when detection was delayed (day 14 or 21). Full EID use decreased the 95th percentile but had a smaller impact on the median number of IPs. Improved tracing also decreased the number of farms impacted by control efforts in control areas (0–10 km) and surveillance zones (10–20 km) by decreasing outbreak size (total IPs). Decreasing the control area (0–7 km) and surveillance zone (7–14 km) sizes while using full EID tracing further decreased the number of farms under surveillance but increased the number of IPs slightly. Consistent with previous results, this supports the potential value of early detection and improved traceability to control FMD outbreaks. Further development of the EID system in the US is necessary to achieve the modeled results. Further research into the economic impacts of enhanced tracing and decreased zone sizes are needed to determine the full impact of these results. [ABSTRACT FROM AUTHOR]

Contact tracing forms a crucial part of the public-health toolbox in mitigating and understanding emergent pathogens and nascent disease outbreaks. Contact tracing in the United States was conducted during the pre-Omicron phase of the ongoing COVID-19 pandemic. This tracing relied on voluntary reporting and responses, often using rapid antigen tests due to lack of accessibility to PCR tests. These limitations, combined with SARS-CoV-2's propensity for asymptomatic transmission, raise the question "how reliable was contact tracing for COVID-19 in the United States"? We answered this question using a Markov model to examine the efficiency with which transmission could be detected based on the design and response rates of contact tracing studies in the United States. Our results suggest that contact tracing protocols in the U.S. are unlikely to have identified more than 1.65% (95% uncertainty interval: 1.62-1.68%) of transmission events with PCR testing and 1.00% (95% uncertainty interval 0.98-1.02%) with rapid antigen testing. When considering a more robust contact tracing scenario, based on compliance rates in East Asia with PCR testing, this increases to 62.7% (95% uncertainty interval: 62.6-62.8%). We did not assume presence of asymptomatic transmission or superspreading, making our estimates upper bounds on the actual percentages traced. These findings highlight the limitations in interpretability for studies of SARS-CoV-2 disease spread based on U.S. contact tracing and underscore the vulnerability of the population to future disease outbreaks, for SARS-CoV-2 and other pathogens. [ABSTRACT FROM AUTHOR]

Background: Since spring 2013, periodic emergence of avian influenza A(H7N9) virus in China has heightened the concern for a possible pandemic outbreak among humans, though it is believed that the virus is not yet human-to-human transmittable. Till June 2017, A(H7N9) has resulted in 1533 laboratory-confirmed cases of human infections causing 592 deaths. The aim of this paper is to present disease burden estimates (measured by infection attack rates (IAR) and number of deaths) in the event of a possible pandemic outbreak caused by human-to-human transmission capability acquired by A(H7N9) virus. Even though such a pandemic will likely spread worldwide, our focus in this paper is to estimate the impact on the United States alone.Method: The method first uses a data clustering technique to divide 50 states in the U.S. into a small number of clusters. Thereafter, for a few selected states in each cluster, the method employs an agent-based (AB) model to simulate human A(H7N9) influenza pandemic outbreaks. The model uses demographic and epidemiological data. A few selected non-pharmaceutical intervention (NPI) measures are applied to mitigate the outbreaks. Disease burden for the U.S. is estimated by combining results from the clusters applying a method used in stratified sampling.Results: Two possible pandemic scenarios with R 0 = 1.5 and 1.8 are examined. Infection attack rates with 95% C.I. (Confidence Interval) for R 0 = 1.5 and 1.8 are estimated to be 18.78% (17.3-20.27) and 25.05% (23.11-26.99), respectively. The corresponding number of deaths (95% C.I.), per 100,000, are 7252.3 (6598.45-7907.33) and 9670.99 (8953.66-10,389.95).Conclusions: The results reflect a possible worst-case scenario where the outbreak extends over all states of the U.S. and antivirals and vaccines are not administered. Our disease burden estimations are also likely to be somewhat high due to the fact that only dense urban regions covering approximately 3% of the geographic area and 81% of the population are used for simulating sample outbreaks. Outcomes from these simulations are extrapolated over the remaining 19% of the population spread sparsely over 97% of the area. Furthermore, the full extent of possible NPIs, if deployed, could also have lowered the disease burden estimates. [ABSTRACT FROM AUTHOR]

This paper investigates the spillover effects of United States monetary policy on exchange rates of 11 emerging markets and 12 advanced economies during the pre-Coronavirus Disease 2019 (COVID-19) period of 2019 versus the COVID-19 period of 2020. The investigation was achieved via a structural vector autoregression model, where year-on-year changes in weekly measures of economic activity, exchange rates and policy rates were used. The empirical results suggest evidence for the spillover effects of United States monetary policy for several countries during the pre-COVID-19 period, whereas they have been effective only for certain countries during the COVID-19 period that can be explained by the disease outbreak channel. It is implied that policies keeping the pandemic under control may help mitigate the unforeseen economic effects of the COVID-19 crisis. [ABSTRACT FROM AUTHOR]

As of December 2020, since the beginning of the year 2020, the COVID-19 pandemic has claimed worldwide more than 1 million lives and has changed human life in unprecedented ways. Despite the fact that the pandemic is far from over, several countries managed at least temporarily to make their first-wave COVID-19 epidemics to subside to relatively low levels. Combining an epidemiological compartment model and a stability analysis as used in nonlinear physics and synergetics, it is shown how the first-wave epidemics in the state of New York and nationwide in the USA developed through three stages during the first half of the year 2020. These three stages are the outbreak stage, the linear stage, and the subsiding stage. Evidence is given that the COVID-19 outbreaks in these two regions were due to instabilities of the COVID-19 free states of the corresponding infection dynamical systems. It is shown that from stage 1 to stage 3, these instabilities were removed, presumably due to intervention measures, in the sense that the COVID-19 free states were stabilized in the months of May and June in both regions. In this context, stability parameters and key directions are identified that characterize the infection dynamics in the outbreak and subsiding stages. Importantly, it is shown that the directions in combination with the sign-switching of the stability parameters can explain the observed rise and decay of the epidemics in the state of New York and the USA. The nonlinear physics perspective provides a framework to obtain insights into the nature of the COVID-19 dynamics during outbreak and subsiding stages and allows to discuss possible impacts of intervention measures. For example, the directions can be used to determine how different populations (e.g., exposed versus symptomatic individuals) vary in size relative to each other during the course of an epidemic. Moreover, the timeline of the computationally obtained stages can be compared with the history of the implementation of intervention measures to discuss the effectivity of such measures., Competing Interests: The author declares that there is no conflict of interest regarding the publication of this paper., (Copyright © 2021 Till D. Frank.)

An outbreak of coronavirus disease 2019 (COVID-19) has quickly spread worldwide from December 2019, thus characterizing a pandemic. Until August 2020, the United States of America (U.S.) accounted for almost one-fourth of the total deaths by coronavirus. In this paper, a new regression is constructed to identify the variables that affected the first-wave COVID-19 mortality rates in the U.S. states. The mortality rates in these states are computed by considering the total of deaths recorded on 30, 90, and 180 days from the 10th recorded case. The proposed regression is compared to the Kumaraswamy and unit-Weibull regressions, which are useful in modeling proportional data. It provides the best goodness-of-fit measures for the mortality rates and explains 76.57 % of its variability. The population density, Gini coefficient, hospital beds, and smoking rate explain the median of the COVID-19 mortality rates in these states. We believe that this article's results reveal important points to face pandemic threats by the State Health Departments in the U.S. [ABSTRACT FROM AUTHOR]

A crucial question in the governance of infectious disease outbreaks is how to ensure that people continue to adhere to mitigation measures for the longer duration. The present paper examines this question by means of a set of cross-sectional studies conducted in the United States during the COVID-19 pandemic, in May, June, and July of 2020. Using stratified samples that mimic the demographic characteristics of the U.S. population, it seeks to understand to what extent Americans continued to adhere to social distancing measures in the period after the first lockdown ended. Moreover, it seeks to uncover which variables sustained (or undermined) adherence across this period. For this purpose, we examined a broad range of factors, relating to people's (1) knowledge and understanding of the mitigation measures, (2) perceptions of their costs and benefits, (3) perceptions of legitimacy and procedural justice, (4) personal factors, (5) social environment, and (6) practical circumstances. Our findings reveal that adherence was chiefly shaped by three major factors: respondents adhered more when they (a) had greater practical capacity to adhere, (b) morally agreed more with the measures, and (c) perceived the virus as a more severe health threat. Adherence was shaped to a lesser extent by impulsivity, knowledge of social distancing measures, opportunities for violating, personal costs, and descriptive social norms. The results also reveal, however, that adherence declined across this period, which was partly explained by changes in people's moral alignment, threat perceptions, knowledge, and perceived social norms. These findings show that adherence originates from a broad range of factors that develop dynamically across time. Practically these insights help to improve pandemic governance, as well as contributing theoretically to the study of compliance and the way that rules come to shape behavior. [ABSTRACT FROM AUTHOR]

Objective: To assess the level of pandemic preparedness at emergency departments (EDs) around the country and to better understand current barriers to preparedness in the United States represented by health professionals in the American College of Emergency Physician (ACEP) Disaster Medicine Section (DMS). Methods, design, and setting: A cross-sectional survey of ACEP DMS members was performed. A total of 300 members were surveyed both via e-mail and with paper surveys during the 2009 ACEP Scientific Assembly DMS Meeting. An optional comments section was included for section members' perspectives on barriers to preparedness. A 15-item pandemic preparedness score was calculated for each respondent based on key preparedness indicators as defined by the authors. Results were analyzed with descriptive statistics, χ 2 analysis, Cochran-Armitage trend test, and analysis of variance. Free text comments were coded and subjected to frequency-based analysis., Results: A total of 92 DMS members completed the survey with a response rate of 31 percent. Although 85 percent of those surveyed indicated that their hospital had a plan for pandemic influenza response and other infectious disease threats, only 68 percent indicated that their ED had a plan, and 52 percent indicated that their hospital or ED had conducted disaster preparedness drills. Only 57 percent indicated that there was a plan to augment ED staff in the event of a staffing shortage, and 63 percent indicated that there were adequate supplies of personal protective equipment. While 63 percent of respondents indicated that their ED had a plan for distribution of vaccines and antivirals, only 32 percent of EDs had a plan for allocation of ventilators. A total of 42 percent of respondents felt that their ED was prepared in the event of a pandemic influenza or other disease outbreak, and only 35 percent felt that their hospital was prepared. The average pandemic preparedness score among respondents was 8.30 of a total of 15. Larger EDs were more likely to have a higher preparedness score (p = 0.03) and more likely to have a pandemic preparedness plan (p = 0.037). Some major barriers to preparedness cited by section members included lack of local administration support, challenges in funding, need for dedicated disaster preparedness personnel, staffing shortages, and a lack of communication among disaster response agencies, particularly at the federal level., Conclusions: There appear to be significant gaps in pandemic influenza and other infectious disease outbreak planning among the hospitals where ACEP DMS members work. This may reflect a broader underlying inadequacy of preparedness measures.

Background: Reducing the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global priority. Contact tracing identifies people who were recently in contact with an infected individual, in order to isolate them and reduce further transmission. Digital technology could be implemented to augment and accelerate manual contact tracing. Digital tools for contact tracing may be grouped into three areas: 1) outbreak response; 2) proximity tracing; and 3) symptom tracking. We conducted a rapid review on the effectiveness of digital solutions to contact tracing during infectious disease outbreaks., Objectives: To assess the benefits, harms, and acceptability of personal digital contact tracing solutions for identifying contacts of an identified positive case of an infectious disease., Search Methods: An information specialist searched the literature from 1 January 2000 to 5 May 2020 in CENTRAL, MEDLINE, and Embase. Additionally, we screened the Cochrane COVID-19 Study Register., Selection Criteria: We included randomised controlled trials (RCTs), cluster-RCTs, quasi-RCTs, cohort studies, cross-sectional studies and modelling studies, in general populations. We preferentially included studies of contact tracing during infectious disease outbreaks (including COVID-19, Ebola, tuberculosis, severe acute respiratory syndrome virus, and Middle East respiratory syndrome) as direct evidence, but considered comparative studies of contact tracing outside an outbreak as indirect evidence. The digital solutions varied but typically included software (or firmware) for users to install on their devices or to be uploaded to devices provided by governments or third parties. Control measures included traditional or manual contact tracing, self-reported diaries and surveys, interviews, other standard methods for determining close contacts, and other technologies compared to digital solutions (e.g. electronic medical records)., Data Collection and Analysis: Two review authors independently screened records and all potentially relevant full-text publications. One review author extracted data for 50% of the included studies, another extracted data for the remaining 50%; the second review author checked all the extracted data. One review author assessed quality of included studies and a second checked the assessments. Our outcomes were identification of secondary cases and close contacts, time to complete contact tracing, acceptability and accessibility issues, privacy and safety concerns, and any other ethical issue identified. Though modelling studies will predict estimates of the effects of different contact tracing solutions on outcomes of interest, cohort studies provide empirically measured estimates of the effects of different contact tracing solutions on outcomes of interest. We used GRADE-CERQual to describe certainty of evidence from qualitative data and GRADE for modelling and cohort studies., Main Results: We identified six cohort studies reporting quantitative data and six modelling studies reporting simulations of digital solutions for contact tracing. Two cohort studies also provided qualitative data. Three cohort studies looked at contact tracing during an outbreak, whilst three emulated an outbreak in non-outbreak settings (schools). Of the six modelling studies, four evaluated digital solutions for contact tracing in simulated COVID-19 scenarios, while two simulated close contacts in non-specific outbreak settings. Modelling studies Two modelling studies provided low-certainty evidence of a reduction in secondary cases using digital contact tracing (measured as average number of secondary cases per index case - effective reproductive number (R eff )). One study estimated an 18% reduction in R eff with digital contact tracing compared to self-isolation alone, and a 35% reduction with manual contact-tracing. Another found a reduction in R eff for digital contact tracing compared to self-isolation alone (26% reduction) and a reduction in R eff for manual contact tracing compared to self-isolation alone (53% reduction). However, the certainty of evidence was reduced by unclear specifications of their models, and assumptions about the effectiveness of manual contact tracing (assumed 95% to 100% of contacts traced), and the proportion of the population who would have the app (53%). Cohort studies Two cohort studies provided very low-certainty evidence of a benefit of digital over manual contact tracing. During an Ebola outbreak, contact tracers using an app found twice as many close contacts per case on average than those using paper forms. Similarly, after a pertussis outbreak in a US hospital, researchers found that radio-frequency identification identified 45 close contacts but searches of electronic medical records found 13. The certainty of evidence was reduced by concerns about imprecision, and serious risk of bias due to the inability of contact tracing study designs to identify the true number of close contacts. One cohort study provided very low-certainty evidence that an app could reduce the time to complete a set of close contacts. The certainty of evidence for this outcome was affected by imprecision and serious risk of bias. Contact tracing teams reported that digital data entry and management systems were faster to use than paper systems and possibly less prone to data loss. Two studies from lower- or middle-income countries, reported that contact tracing teams found digital systems simpler to use and generally preferred them over paper systems; they saved personnel time, reportedly improved accuracy with large data sets, and were easier to transport compared with paper forms. However, personnel faced increased costs and internet access problems with digital compared to paper systems. Devices in the cohort studies appeared to have privacy from contacts regarding the exposed or diagnosed users. However, there were risks of privacy breaches from snoopers if linkage attacks occurred, particularly for wearable devices., Authors' Conclusions: The effectiveness of digital solutions is largely unproven as there are very few published data in real-world outbreak settings. Modelling studies provide low-certainty evidence of a reduction in secondary cases if digital contact tracing is used together with other public health measures such as self-isolation. Cohort studies provide very low-certainty evidence that digital contact tracing may produce more reliable counts of contacts and reduce time to complete contact tracing. Digital solutions may have equity implications for at-risk populations with poor internet access and poor access to digital technology. Stronger primary research on the effectiveness of contact tracing technologies is needed, including research into use of digital solutions in conjunction with manual systems, as digital solutions are unlikely to be used alone in real-world settings. Future studies should consider access to and acceptability of digital solutions, and the resultant impact on equity. Studies should also make acceptability and uptake a primary research question, as privacy concerns can prevent uptake and effectiveness of these technologies., (Copyright © 2020 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.)

Enteric pathogens, such as non-typhoidal Salmonella, Campylobacter and Escherichia coli, can reside in the intestinal tract of many animals, including livestock, companion animals, small mammals and reptiles. Often, these animals can appear healthy; nonetheless, humans can become infected after direct or indirect contact, resulting in a substantial illness burden. An estimated 14% of the 3.2 million illnesses that occur in the United States of America (USA) each year from such enteric pathogens are attributable to animal contact. Surveillance for enteric pathogens in the USA includes the compilation and interpretation of both laboratory and epidemiologic data. However, the authors feel that a collaborative, multisectoral and transdisciplinary - or One Health - approach is needed for data collection and analysis, at every level. In addition, they suggest that the future of enteric illness surveillance lies in the development of improved technologies for pathogen detection and characterisation, such as genomic sequencing and metagenomics. In particular, using whole-genome sequencing to compare genetic sequences of enteric pathogens from humans, food, animals and the environment, can help to predict antimicrobial resistance among these pathogens, determine their genetic relatedness and identify outbreaks linked to a common source. In this paper, the authors describe three recent, multi-state human enteric illness outbreaks linked to animal contact in the USA and discuss how integrated disease surveillance was essential to outbreak detection and response. Additional datasharing between public health and animal health laboratories and epidemiologists at the local, national, regional and international level may help to improve surveillance for emerging animal and human health threats and lead to new opportunities for prevention.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first discovered in late 2019 in Wuhan City, China. The virus may cause novel coronavirus disease 2019 (COVID-19) in symptomatic individuals. Since December of 2019, there have been over 7,000,000 confirmed cases and over 400,000 confirmed deaths worldwide. In the United States (U.S.), there have been over 2,000,000 confirmed cases and over 110,000 confirmed deaths. COVID-19 case data in the United States has been updated daily at the county level since the first case was reported in January of 2020. There currently lacks a study that showcases the novelty of daily COVID-19 surveillance using space-time cluster detection techniques. In this paper, we utilize a prospective Poisson space-time scan statistic to detect daily clusters of COVID-19 at the county level in the contiguous 48 U.S. and Washington D.C. As the pandemic progresses, we generally find an increase of smaller clusters of remarkably steady relative risk. Daily tracking of significant space-time clusters can facilitate decision-making and public health resource allocation by evaluating and visualizing the size, relative risk, and locations that are identified as COVID-19 hotspots., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: It is unknown how the novel Coronavirus SARS-CoV-2, the cause of the current acute respiratory illness COVID-19 pandemic that has infected millions of people, affects people with intellectual and developmental disability (IDD). The aim of this study is to describe how individuals with IDD have been affected in the first 100 days of the COVID-19 pandemic., Methods: Shortly after the first COVID-19 case was reported in the USA, our organisation, which provides continuous support for over 11 000 individuals with IDD, assembled an outbreak committee composed of senior leaders from across the health care organisation. The committee led the development and deployment of a comprehensive COVID-19 prevention and suppression strategy, utilising current evidence-based practice, while surveilling the global and local situation daily. We implemented enhanced infection control procedures across 2400 homes, which were communicated to our employees using multi-faceted channels including an electronic resource library, mobile and web applications, paper postings in locations, live webinars and direct mail. Using custom-built software applications enabling us to track patient, client and employee cases and exposures, we leveraged current public health recommendations to identify cases and to suppress transmission, which included the use of personal protective equipment. A COVID-19 case was defined as a positive nucleic acid test for SARS-CoV-2 RNA., Results: In the 100-day period between 20 January 2020 and 30 April 2020, we provided continuous support for 11 540 individuals with IDD. Sixty-four per cent of the individuals were in residential, community settings, and 36% were in intermediate care facilities. The average age of the cohort was 46 ± 12 years, and 60% were male. One hundred twenty-two individuals with IDD were placed in quarantine for exhibiting symptoms and signs of acute infection such as fever or cough. Sixty-six individuals tested positive for SARS-CoV-2, and their average age was 50. The positive individuals were located in 30 different homes (1.3% of total) across 14 states. Fifteen homes have had single cases, and 15 have had more than one case. Fifteen COVID-19-positive individuals were hospitalised. As of 30 April, seven of the individuals hospitalised have been discharged back to home and are recovering. Five remain hospitalised, with three improving and two remaining in intensive care and on mechanical ventilation. There have been three deaths. We found that among COVID-19-positive individuals with IDD, a higher number of chronic medical conditions and male sex were characteristics associated with a greater likelihood of hospitalisation., Conclusions: In the first 100 days of the COVID-19 outbreak in the USA, we observed that people with IDD living in congregate care settings can benefit from a coordinated approach to infection control, case identification and cohorting, as evidenced by the low relative case rate reported. Male individuals with higher numbers of chronic medical conditions were more likely to be hospitalised, while most younger, less chronically ill individuals recovered spontaneously at home., (© 2020 The Authors. Journal of Intellectual Disability Research published by MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.)

The ancient technique of variolation (inoculation of the smallpox) which was introduced in the United States in 1721 was replaced by vaccination (inoculation of the cowpox) soon after the procedure was published by Edward Jenner in 1798. Benjamin Waterhouse is recognized as the introducer of smallpox vaccination in the United States having conducted the first vaccination in Boston on 8 July 1800, although other American physicians also played an important role in extending vaccination in the East Coast of the United States. A different route of introduction brought the smallpox vaccine from Mexico to New Mexico (March 1805) and Texas (April 1806) which at that time where part of the Viceroyalty of New Spain. The vaccine was brought to California in 1817 by Russian merchants who obtained it in Peru, where the vaccine had arrived in 1806 with the Spanish Philanthropic Expedition of the Vaccine. It took almost 150 years of vaccination efforts before the last natural outbreak of smallpox occurred in the United States in 1949., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

This paper examines the determinants of parental decisions about infant immunization. Using the exact timing of vaccination relative to birth, we estimate the effects of local pertussis outbreaks occurring in utero and during the first two months of life on the likelihood of on-time initial immunization for pertussis and other diseases. We find that parents respond to changes in perceived disease risk: pertussis outbreaks within a state increase the rate of on-time receipt of the pertussis vaccine at two months of age, particularly among low-socioeconomic status (SES) subgroups. In addition, we find that pertussis outbreaks increase the likelihood of immunization against other vaccine-preventable diseases. Spillover effects in low-SES subgroups are as large as direct effects and are present only for vaccines given during the same visit as the pertussis vaccine, which suggests that provider contact may be a key factor in infant vaccination decisions in poor families., (Copyright © 2019 Elsevier B.V. All rights reserved.)

The significance of flu prediction is that the appropriate preventive and control measures can be taken by relevant departments after assessing predicted data; thus, morbidity and mortality can be reduced. In this paper, three flu prediction models, based on twitter and US Centers for Disease Control's (CDC's) Influenza-Like Illness (ILI) data, are proposed (models 1-3) to verify the factors that affect the spread of the flu. In this work, an Improved Particle Swarm Optimization algorithm to optimize the parameters of Support Vector Regression (IPSO-SVR) was proposed. The IPSO-SVR was trained by the independent and dependent variables of the three models (models 1-3) as input and output. The trained IPSO-SVR method was used to predict the regional unweighted percentage ILI (%ILI) events in the US. The prediction results of each model are analyzed and compared. The results show that the IPSO-SVR method (model 3) demonstrates excellent performance in real-time prediction of ILIs, and further highlights the benefits of using real-time twitter data, thus providing an effective means for the prevention and control of flu., Competing Interests: The authors have declared that no competing interests exist.

Limiting the spread and impact of Zika was a major global priority in 2016, which required a variety of vector control measures. The success of vector control campaigns is varied and often dependent on public or political will. This paper examines the change over time in the United States population's support for vector control and the factors that predicted support for three vector control strategies (i.e., indoor spraying, outdoor spraying, and use of larvacide tablets) during the 2016 Zika outbreak in the United States. Data is from a nationally representative random digit dial sample conducted at three time points in 2016. Bivariate and multivariate regression analyses were used, treating data as a pooled cross-sectional sample. Results show public support for vector control strategies depends on both perceived risk for disease and knowledge of disease characteristics, as well is confidence in government to prevent the threat. Support varied based on vector control method: indoor spraying, aerial spraying, and use of larvacide tables. Results can aide public health officials in implementing effective vector control campaigns depending on the vector control strategy of choice. Results have implications for ways to design effective prevention campaigns in future emerging infectious disease threats., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Ebola is a high consequence infectious disease-a disease with the potential to cause outbreaks, epidemics, or pandemics with deadly possibilities, highly infectious, pathogenic, and virulent. Ebola's first reported cases in the United States in September 2014 led to the development of preparedness capabilities for the mitigation of possible rapid outbreaks, with the Centers for Disease Control and Prevention (CDC) providing guidelines to assist public health officials in infectious disease response planning. These guidelines include broad goals for state and local agencies and detailed information concerning the types of resources needed at health care facilities. However, the spatial configuration of populations and existing health care facilities is neglected. An incomplete understanding of the demand landscape may result in an inefficient and inequitable allocation of resources to populations. Hence, this paper examines challenges in implementing CDC's guidance for Ebola preparedness and mitigation in the context of geospatial allocation of health resources and discusses possible strategies for addressing such challenges. (Disaster Med Public Health Preparedness. 2018;12:563-566).

Introduction: Travellers contributed substantially to the rapid spread of Zika virus (ZIKV). They act as sentinel and may unmask ongoing ZIKV transmission in countries where outbreaks have not yet been reported. Our objectives were to (i) describe the burden of ZIKV infections in international travellers over time; (ii) estimate the proportion of birth defects as a result of maternal ZIKV infection in travellers; (iii) track the extent of sexual transmission; (iv) summarize ZIKV infections in returning travellers as reported by the GeoSentinel network; and (v) identify countries without reports on local ZIKV transmission where travellers served as sentinel., Methods: We performed a systematic review from 1947 to April 2017 on travel-associated ZIKV infections. We also compared published reports on autochthonous ZIKV transmission in Asia with published reports on exportations from travellers in Asia., Results: Of 314 papers that fit the inclusion criteria, 61 were eligible for final analysis. There was an exponential increase in the number of reported ZIKV infected travellers from the years 2013 to 2016, which declined in 2017. Amongst pregnant women with ZIKV infection, (5%) resulted in a fetus or infant with ZIKV-associated birth defects. An estimated 1% of the total number of ZIKV cases reported in the USA and Europe were acquired through sexual transmission. Through the GeoSentinel network, five countries (Indonesia, Philippines, Thailand, Vietnam, Cameroon) were identified as sentinel markers where ZIKV was exported despite the absence of reported local transmission., Conclusions: Mobility patterns and travel volumes can help to identify the most likely origin of importation, and also in predicting further propagation. Studies on pregnant returning travellers have contributed to a better understanding of the risk estimates of congenital Zika syndrome/microcephaly as a result of maternal ZIKV infection, and the relative contribution of sexual transmisison.

Addressing critical global health issues, such as antimicrobial resistance, infectious disease outbreaks, and natural disasters, requires strong coordination and management across sectors. The One Health approach is the integrative effort of multiple sectors working to attain optimal health for people, animals, and the environment, and is increasingly recognized by experts as a means to address complex challenges. However, practical application of the One Health approach has been challenging. The One Health Systems Mapping and Analysis Resource Toolkit (OH-SMART) introduced in this paper was designed using a multistage prototyping process to support systematic improvement in multi-sectoral coordination and collaboration to better address complex health concerns through an operational, stepwise, and practical One Health approach. To date, OH-SMART has been used to strengthen One Health systems in 17 countries and has been deployed to revise emergency response frameworks, improve antimicrobial resistance national action plans and create multi agency infectious disease collaboration protocols. OH-SMART has proven to be user friendly, robust, and capable of fostering multi-sectoral collaboration and complex system-wide problem solving. [ABSTRACT FROM AUTHOR]

Public health security—achieved by effectively preventing, detecting, and responding to events that affect public health, such as bioterrorism, disasters, and naturally occurring disease outbreaks—is a key aspect of national security. However, effective public health preparedness depends on answering largely unanswerable questions such as determining the chance of a bioterror attack in the United States over a given time horizon, or chance of an anthrax attack, or the location and magnitude of such an attack. In "Public Health Preparedness: Answering (Largely Unanswerable) Questions with Operations Research—The 2016–2017 Philip McCord Morse Lecture," Margaret Brandeau describes the important role that OR-based analyses can play in providing insight into complex public health preparedness planning problems, thereby supporting good decisions. The author presents three examples from her work: logistics of response to an anthrax attack, prepositioning of medical countermeasures for anthrax, and stockpiling decisions for the United States' Strategic National Stockpile. Public health security—achieved by effectively preventing, detecting, and responding to events that affect public health such as bioterrorism, disasters, and naturally occurring disease outbreaks—is a key aspect of national security. However, effective public health preparedness depends on answering largely unanswerable questions. For example: What is the chance of a bioterror attack in the United States in the next five years? What is the chance of an anthrax attack? What might be the location and magnitude of such an attack? This paper describes how OR-based analyses can provide insight into complex public health preparedness planning problems—and thus support good decisions. Three examples from the author's research are presented: logistics of response to an anthrax attack, prepositioning of medical countermeasures for anthrax, and stockpiling decisions for the United States' Strategic National Stockpile. [ABSTRACT FROM AUTHOR]

The outbreak of Enterovirus D-68 (EV-D68) in the United States in 2014 raised great interest due to it affecting large numbers of people and because patients presented with severe respiratory and/or central nervous system involvement. Many studies have tried to evaluate the biologic and genetic characteristics of this virus, its association with disease development and the possibility of infection prevention and therapy. The main aim of this paper is to discuss what is presently known and what might be expected in the future regarding EV-D68. We highlight that further studies are needed to precisely define the epidemiology and total burden of EV-D68, the real age prevalence, and the factors that may lead to negative outcomes in some patients and not in others. Moreover, if recently reported clinical data are confirmed, specific efficacious prophylactic and therapeutic measures should be urgently developed.

This paper examines how people in West Africa are reacting to the Ebola virus disease, an epidemic presently prevalent in the region. Certain lifestyle changes are suggested. Additionally, the heart of the paper focuses on the request by governments to be allowed access to experimental drugs, such as Zmapp and TKM-Ebola, for their infected populations. The author argues that granting such a request would circumvent research ethics procedures, which could potentially constitute significant risk to users of the drugs. The Pfizer Kano meningitis trial of 1996 is cited as an example to buttress how unapproved drugs could prove fatal.

This paper outlines a commentary response to an article published by Young and colleagues in Preventive Medicine that evaluated the feasibility of using Twitter as a surveillance and monitoring took for HIV. We draw upon the broader literature on disease surveillance and public health prevention using social media and broader considerations of epidemiological and surveillance methods to provide readers with necessary considerations for using social media in epidemiology and surveillance., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Fresh salsa and guacamole often contain diced raw produce, are often made in large batches, and are often poorly refrigerated, which may make them prone to contamination that can cause foodborne illness. The safety of salsa and guacamole is increasingly important as these foods gain popularity. Since 1973, local, state, and territorial health departments have voluntarily reported foodborne disease outbreaks to the Centers for Disease Control and Prevention's Foodborne Disease Outbreak Surveillance System (FDOSS) using a standard reporting form. FDOSS used paper-based reporting for 1973-1997 and switched to electronic reporting for 1998-2008. We reviewed all reports of outbreaks during 1973-2008 in which salsa or guacamole was reported as a vehicle. We found 136 outbreaks in which salsa or guacamole was reported as a possible vehicle, which resulted in 5,658 illnesses. Of these 136 salsa- or guacamole-associated (SGA) outbreaks additional possible food vehicles were reported for 33 (24%) outbreaks. There were no SGA outbreaks reported before 1984. Among reported outbreaks, most were caused by norovirus (24%), nontyphoidal Salmonella (19%), and Shigella (7%). Eighty-four percent of outbreaks were caused by foods prepared in restaurants or delis; of these, 19% reported ill foodworkers, and 29% reported improper storage as possible contributing factors. Among all foodborne disease outbreaks with a reported food vehicle during 1984-1997, 26 (0.9%) of 2,966 outbreaks were SGA, and during 1998-2008, 110 (1.4%) of 7,738 outbreaks were SGA. The number of reported foodborne disease outbreaks attributable to salsa or guacamole increased in the United States from 1984 to 2008, especially in later years, and especially in restaurants. Fresh salsa and guacamole require careful preparation and storage. Focused prevention strategies should reduce the risk of illness and ensure that these foods are enjoyed safely.

Sizeable quantities of 2009 pandemic influenza A/H1N1 (H1N1pdm) vaccine in the USA became available at the end of 2009 when the autumn wave of the epidemic was declining. At that point, risk factors for H1N1-related mortality for some of the high-risk groups, particularly adults with underlying health conditions, could be estimated. Although those high-risk groups are natural candidates for being in the top priority tier for vaccine allocation, another candidate group is school-aged children through their role as vectors for transmission affecting the whole community. In this paper, we investigate the question of prioritization for vaccine allocation in a declining epidemic between two groups-a group with a high risk of mortality versus a 'core' group with a relatively low risk of mortality but fuelling transmission in the community. We show that epidemic data can be used, under certain assumptions on future decline, seasonality and vaccine efficacy in different population groups, to give a criterion when initial prioritization of a population group with a sufficiently high risk of epidemic-associated mortality is advisable over the policy of prioritizing the core group.

Reliable surveillance models are an important tool in public health because they aid in mitigating disease outbreaks, identify where and when disease outbreaks occur, and predict future occurrences. Although many statistical models have been devised for surveillance purposes, none are able to simultaneously achieve the important practical goals of good sensitivity and specificity, proper use of covariate information, inclusion of spatio-temporal dynamics, and transparent support to decision-makers. In an effort to achieve these goals, this paper proposes a spatio-temporal conditional autoregressive hidden Markov model with an absorbing state. The model performs well in both a large simulation study and in an application to influenza/pneumonia fatality data., (Copyright © 2012 John Wiley & Sons, Ltd.)

At the interface of molecular biology and epidemiology, the emerging discipline of molecular epidemiology offers unique opportunities to advance the study of diseases through the investigation of infectious agents at the molecular level. Molecular tools can increase our understanding of the factors that shape the spatial and temporal distribution of pathogens and disease. Both spatial and molecular aspects have always been important to the field of infectious disease epidemiology, but recently news tools have been developed which increase our ability to consider both elements within a common framework. This enables the epidemiologist to make inferences about disease patterns in space and time. This paper introduces some basic concepts of molecular epidemiology in a veterinary context and illustrates the application of molecular tools at a range of spatio-temporal scales. Case studies - a multi-state outbreak of Serratia mastitis, a national control program for campylobacteriosis, and evolution of foot-and-mouth-disease viruses - are used to demonstrate the importance of considering molecular aspects in modern epidemiological studies. The discipline of molecular epidemiology is in its infancy and our contribution aims to promote awareness, understanding and uptake of molecular epidemiology in veterinary science., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

In this paper we examine some of the methodologies implemented by the Centers for Disease Control and Prevention's (CDC) BioSense program. The program uses data from hospitals and public health departments to detect outbreaks using the Early Aberration Reporting System (EARS). The EARS method W2 allows one to monitor syndrome counts (W2count) from each source and the proportion of counts of a particular syndrome relative to the total number of visits (W2rate). We investigate the performance of these methods, which are designed using an empiric recurrence interval (RI), with simulated parametric data. Counts from the Poisson and negative binomial distributions are generated, and used to examine W2 properties. An adaptive threshold monitoring method is introduced based on fitting sample data to the above distributions, then converting the current value to a Z-score through a p-value. We compare the thresholds required to obtain given values of the RI for different sets of parameter values. We then simulate 1-week outbreaks in our data and calculate the proportion of times these methods correctly signal an outbreak using Shewhart and exponentially weighted moving average (EWMA) charts. Our results indicate that the adaptive threshold method gives more consistent statistical performance across different parameter sets and amounts of baseline historical data used for computing the statistics. For the sensitivity analysis, the EWMA chart is superior to its Shewhart counterpart in nearly all cases and the adaptive threshold methods tend to outperform the W2 methods. Copyright © 2011 John Wiley & Sons, Ltd., (Copyright © 2011 John Wiley & Sons, Ltd.)

There have been repeated reports of a particularly severe course of disease on H1N1 infection in pregnant women. Complemented by a review of the current literature, this paper gives an overview of recommendations with respect to vaccination, therapy and prophylaxis in this risk group.

This paper examines how companies' pandemic response planning has evolved from the 2002 SARS epidemic through to the H1N1 pandemic that emerged in 2009. While many companies had undertaken general pandemic planning prior to the emergence of H1N1, most were compelled to take their planning to a much more detailed level as the likelihood of widespread absenteeism became apparent. The paper outlines the steps necessary for completing a mission-critical inventory, identifying interdependencies between business functions and developing an effective cross-training programme that minimises the likelihood of business disruption due to the absence of key personnel.

Focusing on pandemic influenza, this chapter approaches the planning for and response to such a major worldwide health event as a complex engineering systems problem. Action-oriented analysis of pandemics requires a broad inclusion of academic disciplines since no one domain can cover a significant fraction of the problem. Numerous research papers and action plans have treated pandemics as purely medical happenings, focusing on hospitals, health care professionals, creation and distribution of vaccines and anti-virals, etc. But human behavior with regard to hygiene and social distancing constitutes a first-order partial brake or control of the spread and intensity of infection. Such behavioral options are "non-pharmaceutical interventions." (NPIs) The chapter employs simple mathematical models to study alternative controls of infection, addressing a well-known parameter in epidemiology, R0, the "reproductive number," defined as the mean number of new infections generated by an index case. Values of R0 greater than 1.0 usually indicate that the infection begins with exponential growth, the generation-to-generation growth rate being R0. R0 is broken down into constituent parts related to the frequency and intensity of human contacts, both partially under our control. It is suggested that any numerical value for R0 has little meaning outside the social context to which it pertains. Difference equation models are then employed to study the effects of heterogeneity of population social contact rates, the analysis showing that the disease tends to be driven by high frequency individuals. Related analyses show the futility of trying geographically to isolate the disease. Finally, the models are operated under a variety of assumptions related to social distancing and changes in hygienic behavior. The results are promising in terms of potentially reducing the total impact of the pandemic.

The general population is concerned about the probable devastating effects of pandemic H1N1 2009. Based upon the 1918 Spanish flu pandemic, scientific publications and theories, the idea of swine flu parties to achieve passive immunity against pandemic H1N1 2009 has been proposed. Public health officials have asked the general public not to resort to this method. However, no concrete evidence of the reasoning behind the recommendation has been given. In this paper, we have dynamically modeled the effect of swine flu parties on the immunity achieved and associated mortality for a period of two years. The simulations show that the public should not organize or participate in swine flu parties as they will likely increase swine flu-associated mortality.

Background: The early identification of influenza outbreaks has became a priority in public health practice. A large variety of statistical algorithms for the automated monitoring of influenza surveillance have been proposed, but most of them require not only a lot of computational effort but also operation of sometimes not-so-friendly software., Results: In this paper, we introduce FluDetWeb, an implementation of a prospective influenza surveillance methodology based on a client-server architecture with a thin (web-based) client application design. Users can introduce and edit their own data consisting of a series of weekly influenza incidence rates. The system returns the probability of being in an epidemic phase (via e-mail if desired). When the probability is greater than 0.5, it also returns the probability of an increase in the incidence rate during the following week. The system also provides two complementary graphs. This system has been implemented using statistical free-software (R and WinBUGS), a web server environment for Java code (Tomcat) and a software module created by us (Rdp) responsible for managing internal tasks; the software package MySQL has been used to construct the database management system. The implementation is available on-line from: http://www.geeitema.org/meviepi/fludetweb/., Conclusion: The ease of use of FluDetWeb and its on-line availability can make it a valuable tool for public health practitioners who want to obtain information about the probability that their system is in an epidemic phase. Moreover, the architecture described can also be useful for developers of systems based on computationally intensive methods.

Limited production capacity and delays inherent in vaccine development are major hurdles to the widespread use of vaccines to mitigate the effects of a new influenza pandemic. Antigen-sparing vaccines have the most potential to increase population coverage but may be less efficacious. The authors explored this trade-off by applying simple models of influenza transmission and dose response to recent clinical trial data. In this paper, these data are used to illustrate an approach to comparing vaccines on the basis of antigen supply and inferred efficacy. The effects of delays in matched vaccine availability and seroconversion on epidemic size during pandemic phase 6 were also studied. The authors infer from trial data that population benefits stem from the use of low-antigen vaccines. Delayed availability of a matched vaccine could be partially alleviated by using a 1-dose vaccination program with increased coverage and reduced time to full protection. Although less immunogenic, an overall attack rate of up to 6% lower than a 2-dose program could be achieved. However, if prevalence at vaccination is above 1%, effectiveness is much reduced, emphasizing the need for other control measures.

In an epidemic situation or large-scale disaster, medical and human resources may be stretched to the point of exhaustion. Appropriate planning must incorporate plans of action that minimize public health morbidity and mortality while maximizing the appropriate use of medical and human healthcare resources. While the current novel H1N1 influenza has spread throughout the world, the severity of this strain of influenza appears to be relatively less virulent and lethal compared to the 1918 influenza pandemic. However, the presence of this new influenza strain has reignited interest in pandemic planning. Amongst other necessary resources needed to combat pandemic influenza, a major medical resource concern is the limited number of mechanical ventilators that would be available to be used to treat ill patients. Recent reported cases of avian influenza suggest that mechanical ventilation will be required for the successful recovery of many individuals ill with this strain of virus. However, should the need for ventilators exceed the number of available machines, how will care providers make the difficult ethical decisions as to who should be placed or who should remain on these machines as more influenza patients arrive in need of care? This paper presents a decision-making model for clinicians that is based upon the bioethical principles of beneficence and justice. The model begins with the basic assumptions of triage and progresses into a useful algorithm based upon utilitarian principles. The model is intended to be used as a guide for clinicians in making decisions about the allocation of scarce resources in a just manner and to serve as an impetus for institutions to create or adapt plans to address resource allocation issues should the need arise.

Using Cox regression, this paper shows a weak association between having tuberculosis and dying from influenza among Union Army veterans in late nineteenth-century America. It has been suggested elsewhere [Noymer, A. and M. Garenne (2000). The 1918 influenza epidemic's effects on sex differentials in mortality in the United States. Population and Development Review 26(3), 565-581.] that the 1918 influenza pandemic accelerated the decline of tuberculosis, by killing many people with tuberculosis. The question remains whether individuals with tuberculosis were at greater risk of influenza death, or if the 1918/post-1918 phenomenon arose from the sheer number of deaths in the influenza pandemic. The present findings, from microdata, cautiously point toward an explanation of Noymer and Garenne's selection effect in terms of age-overlap of the 1918 pandemic mortality and tuberculosis morbidity, a phenomenon I term "passive selection". Another way to think of this is selection at the cohort, as opposed to individual, level.

Purpose of Review: This paper reviews the literature generated on atypical contact lens-related infections and their relationship with particular contact lens solutions since 2005., Recent Findings: Atypical soft contact lens-related infections are on the rise. Specifically, there has been an insurgence of Acanthamoeba keratitis and Fusarium keratitis. The rise in these infections was associated with Advanced Medical Optics Complete MoisturePlus (AMO, Santa Ana, California, USA) and ReNu with MoistureLoc (Bausch and Lomb, Rochester, New York, USA) respectively. Contact lens solutions in general are more effective against bacteria than fungal species or Acanthamoeba. Multipurpose solutions and no rub formulas may have diminished antimicrobial activity and are not as effective under real world conditions., Summary: There have been epidemic increases in Acanthamoeba and fungal keratitis associated with particular contact lens solutions. Further study of these atypical species is necessary. Greater emphasis should be placed upon efficacy of contact lens solutions rather than ease of use.

Purpose: Clostridium difficile is an anaerobic gram positive, spore-forming bacterium which is responsible for 15-25% of antibiotic-associated diarrhea and for more than 95% of pseudomembranous colitis (PMC). This paper will review the main knowledge on C. difficile-associated infections and their recent evolution., Current Knowledge and Key Points: Since 2003, outbreaks of severe C. difficile-associated diarrhea (CDAD) have been increasingly reported in Canada and the United States. This trend is assumed to be associated with the rapid emergence and spread of a specific clone of C. difficile belonging to PCR-ribotype 027 or North American Pulsotype 1, pulsotype (NAP1). This clone is characterized by the overproduction of toxins A and B and is positive for a third toxin named binary toxin. This clone has spread in UK, in Belgium, in the Netherlands, and, more recently, in France where it has been responsible for large outbreaks mainly in northern France., Future Prospects and Projects: A systematic reporting of C. difficile incidence by health facilities should enable a better assessment of this pathology in France.

Early detection and characterization of outdoor aerosol releases of Bacillus anthracis is an important problem. As health departments and other government agencies address this problem with newer methods of surveillance such as environmental surveillance through the BioWatch program and enhanced medical surveillance, they increasingly have newer types of surveillance data available. However, existing methods for the statistical analysis of surveillance data do not account for recent meteorological conditions, as human analysts did in the case of the Sverdlovsk anthrax outbreak of 1979 to determine whether the locations of victims were consistent with meteorological conditions in the week preceding their onset of illness. This paper describes the Bayesian aerosol release detector (BARD), an algorithm that analyzes both medical surveillance data and meteorological data for early detection and characterization of outdoor releases of B. anthracis. It estimates a posterior distribution over the location, quantity, and date and time conditioned on a release having occurred. We report a proof-of-concept evaluation of BARD, which demonstrates that the approach shows promise and warrants further development and evaluation., (Copyright (c) 2007 John Wiley & Sons, Ltd.)

In this paper, the authors develop a method of detecting correlations between epidemic patterns in different regions that are due to human movement and introduce a null model in which the travel-induced correlations are cancelled. They apply this method to the well-documented cases of seasonal influenza outbreaks in the United States and France. In the United States (using data for 1972-2002), the authors observed strong short-range correlations between several states and their immediate neighbors, as well as robust long-range spreading patterns resulting from large domestic air-traffic flows. The stability of these results over time allowed the authors to draw conclusions about the possible impact of travel restrictions on epidemic spread. The authors also applied this method to the case of France (1984-2004) and found that on the regional scale, there was no transportation mode that clearly dominated disease spread. The simplicity and robustness of this method suggest that it could be a useful tool for detecting transmission channels in the spread of epidemics.

Background: Abstinence-only interventions promote sexual abstinence as the only means of preventing sexual acquisition of HIV; they do not promote safer-sex strategies (e.g., condom use). Although abstinence-only programs are widespread, there has been no internationally focused review of their effectiveness for HIV prevention in high-income countries., Objectives: To assess the effects of abstinence-only programs for HIV prevention in high-income countries., Search Strategy: We searched 30 electronic databases (e.g., CENTRAL, PubMed, EMBASE, AIDSLINE, PsycINFO) ending February 2007. Cross-referencing, handsearching, and contacting experts yielded additional citations through April 2007., Selection Criteria: We included randomized and quasi-randomized controlled trials evaluating abstinence-only interventions in high-income countries (defined by the World Bank). Interventions were any efforts to encourage sexual abstinence for HIV prevention; programs that also promoted safer-sex strategies were excluded. Results were biological and behavioral outcomes., Data Collection and Analysis: Three reviewers independently appraised 20,070 records and 326 full-text papers for inclusion and methodological quality; 13 evaluations were included. Due to heterogeneity and data unavailability, we presented the results of individual studies instead of conducting a meta-analysis., Main Results: Studies involved 15,940 United States youth; participants were ethnically diverse. Seven programs were school-based, two were community-based, and one was delivered in family homes. Median final follow-up occurred 17 months after baseline. Results showed no indications that abstinence-only programs can reduce HIV risk as indicated by self-reported biological and behavioral outcomes. Compared to various controls, the evaluated programs consistently did not affect incidence of unprotected vaginal sex, frequency of vaginal sex, number of partners, sexual initiation, or condom use. One study found a significantly protective effect for incidence of recent vaginal sex (n=839), but this was limited to short-term follow-up, countered by measurement error, and offset by six studies with non-significant results (n=2615). One study found significantly harmful effects for STI incidence (n=2711), pregnancy incidence (n=1548), and frequency of vaginal sex (n=338); these effects were also offset by studies with non-significant findings. Methodological strengths included large samples, efforts to improve self-report, and analyses controlling for baseline values. Weaknesses included underutilization of relevant outcomes, underreporting of key data, self-report bias, and analyses neglecting attrition and clustered randomization., Authors' Conclusions: Evidence does not indicate that abstinence-only interventions effectively decrease or exacerbate HIV risk among participants in high-income countries; trials suggest that the programs are ineffective, but generalizability may be limited to US youth. Should funding continue, additional resources could support rigorous evaluations with behavioral or biological outcomes. More trials comparing abstinence-only and abstinence-plus interventions are needed.

An increasing number of avian flu cases in humans, arising primarily from direct contact with poultry, in several regions of the world have prompted the urgency to develop pandemic preparedness plans worldwide. Leading recommendations in these plans include basic public health control measures for minimizing transmission in hospitals and communities, the use of antiviral drugs and vaccination. This paper presents a mathematical model for the evaluation of the pandemic flu preparedness plans of the United States (US), the United Kingdom (UK) and the Netherlands. The model is used to assess single and combined interventions. Using data from the US, we show that hospital and community transmission control measures alone can be highly effective in reducing the impact of a potential flu pandemic. We further show that while the use of antivirals alone could lead to very significant reductions in the burden of a pandemic, the combination of transmission control measures, antivirals and vaccine gives the most 'optimal' result. However, implementing such an optimal strategy at the onset of a pandemic may not be realistic. Thus, it is important to consider other plausible alternatives. An optimal preparedness plan is largely dependent on the availability of resources; hence, it is country-specific. We show that countries with limited antiviral stockpiles should emphasize their use therapeutically (rather than prophylactically). However, countries with large antiviral stockpiles can achieve greater reductions in disease burden by implementing them both prophylactically and therapeutically. This study promotes alternative strategies that may be feasible and attainable for the US, UK and the Netherlands. It emphasizes the role of hospital and community transmission control measures in addition to the timely administration of antiviral treatment in reducing the burden of a flu pandemic. The latter is consistent with the preparedness plans of the UK and the Netherlands. Our results indicate that for low efficacy and coverage levels of antivirals and vaccine, the use of a vaccine leads to the greatest reduction in morbidity and mortality compared with the singular use of antivirals. However, as these efficacy and coverage levels are increased, the use of antivirals is more effective.

Virulence factor activity relationships (VFAR) is a predictive approach proposed by the National Research Council's Committee on Drinking Water Contaminants (Washington, D.C.) to classify and rank waterborne pathogens. It is based on the presumption that health threats of waterborne pathogens can be predicted from descriptors at different levels of cellular organization. This paper summarizes challenges that need to be addressed while developing VFAR, with a focus on genomics, such as genomic variability among related pathogens and the need to incorporate genetic descriptors for persistence and host susceptibility. Three key components of VFAR development and validation are also presented, including (1) compilation of a comprehensive VFAR database, (2) development of predictive mathematical models relating descriptors to health effects and other microbial responses, and (3) high-throughput molecular monitoring of drinking water supplies and sources. Bayesian approach and on-chip polymerase chain reaction are discussed as examples of mathematical models and molecular monitoring.