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Background: Over the past few decades, numerous forecasting methods have been proposed in the field of epidemic forecasting. Such methods can be classified into different categories such as deterministic vs. probabilistic, comparative methods vs. generative methods, and so on. In some of the more popular comparative methods, researchers compare observed epidemiological data from the early stages of an outbreak with the output of proposed models to forecast the future trend and prevalence of the pandemic. A significant problem in this area is the lack of standard well-defined evaluation measures to select the best algorithm among different ones, as well as for selecting the best possible configuration for a particular algorithm.Results: In this paper we present an evaluation framework which allows for combining different features, error measures, and ranking schema to evaluate forecasts. We describe the various epidemic features (Epi-features) included to characterize the output of forecasting methods and provide suitable error measures that could be used to evaluate the accuracy of the methods with respect to these Epi-features. We focus on long-term predictions rather than short-term forecasting and demonstrate the utility of the framework by evaluating six forecasting methods for predicting influenza in the United States. Our results demonstrate that different error measures lead to different rankings even for a single Epi-feature. Further, our experimental analyses show that no single method dominates the rest in predicting all Epi-features when evaluated across error measures. As an alternative, we provide various Consensus Ranking schema that summarize individual rankings, thus accounting for different error measures. Since each Epi-feature presents a different aspect of the epidemic, multiple methods need to be combined to provide a comprehensive forecast. Thus we call for a more nuanced approach while evaluating epidemic forecasts and we believe that a comprehensive evaluation framework, as presented in this paper, will add value to the computational epidemiology community. [ABSTRACT FROM AUTHOR]

Background: Health care associated transmission of Mycobacterium tuberculosis (TB) is well described. A previous survey of infection control (IC) practices at clinical research sites in low and middle income countries (LMIC) funded by the National Institute of Allergy and Infectious Diseases (NIAID) conducting HIV research identified issues with respiratory IC practices. A guideline for TB IC based on international recommendations was developed and promulgated. This paper reports on adherence to the guideline at sites conducting or planning to conduct TB studies with the intention of supporting improvement.Methods: A survey was developed that assessed IC activities in three domains: facility level measures, administrative control measures and environmental measures. An external site monitor visited each site in 2013-2014, to complete the audit. A central review committee evaluated the site-level survey and results were tabulated. Fisher's exact test was performed to determine whether there were significant differences in practices at sites that had IC officers versus sites that did not have IC officers. Significance was assessed at p

Background: People who inject drugs (PWID) constitute 60% of the approximately 5 million people in the U.S. infected with hepatitis C virus (HCV). Treatment of PWID is complex due to addiction, mental illness, poverty, homelessness, lack of positive social support, poor adherence-related skills, low motivation and knowledge, and poor access to and trust in the health care system. New direct-acting antiviral medications are available for HCV with high cure rates and few side effects. The life expectancy and economic benefits of new HCV treatments will not be realized unless we determine optimal models of care for the majority of HCV-infected patients. The purpose of this study is to evaluate the effectiveness of directly observed therapy and group treatment compared with self-administered individual treatment in a large, urban opioid agonist therapy clinic setting in the Bronx, New York.Methods/design: In this randomized controlled trial 150 PWID with chronic HCV were recruited from opioid agonist treatment (OAT) clinics and randomized to one of three models of onsite HCV treatment in OAT: 1) modified directly observed therapy; 2) group treatment; or 3) control - self-administered individual treatment. Participants were age 18 or older, HCV genotype 1, English or Spanish speaking, treatment naïve (or treatment experienced after 12/3/14), willing to receive HCV treatment onsite, receiving methadone or buprenorphine at the medication window at least once per week, and able to provide informed consent. Outcomes of interest include adherence (as measured by self-report and electronic blister packs), HCV treatment completion, sustained virologic response, drug resistance, and cost-effectiveness.Discussion: This paper describes the design and rationale of a randomized controlled trial comparing three models of care for HCV therapy delivered in an opioid agonist treatment program. Our trial will be critical to rigorously identify models of care that result in high adherence and cure rates. Use of blister pack technology will help us determine the role of adherence in successful cure of HCV. Moreover, the trial methodology outlined here can serve as a template for the development of future programs and studies among HCV-infected drug users receiving opioid agonist therapy, as well as the cost-effectiveness of such programs.Trial Registration: This trial was registered with ClinicalTrials.gov ( NCT01857245 ). Trial registration was obtained prospectively on May 20th, 2013. [ABSTRACT FROM AUTHOR]

Background: Influenza is a major cause of morbidity and mortality in Africa. However, a lack of epidemiological data remains for this pathology, and the performances of the influenza-like illness (ILI) case definitions used for sentinel surveillance have never been evaluated in Senegal. This study aimed to i) assess the performance of three different ILI case definitions, adopted by the WHO, USA-CDC (CDC) and European-CDC (ECDC) and ii) identify clinical factors associated with a positive diagnosis for Influenza in order to develop an algorithm fitted for the Senegalese context.Methods: All 657 patients with a febrile pathological episode (FPE) between January 2013 and December 2016 were followed in a cohort study in two rural villages in Senegal, accounting for 1653 FPE observations with nasopharyngeal sampling and influenza virus screening by rRT-PCR. For each FPE, general characteristics and clinical signs presented by patients were collected. Sensitivity, Specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) for the three ILI case definitions were assessed using PCR result as the reference test. Associations between clinical signs and influenza infection were analyzed using logistic regression with generalized estimating equations. Sore throat, arthralgia or myalgia were missing for children under 5 years.Results: WHO, CDC and ECDC case definitions had similar sensitivity (81.0%; 95%CI: 77.0-85.0) and NPV (91.0%; 95%CI: 89.0-93.1) while the WHO and CDC ILI case definitions had the highest specificity (52.0%; 95%CI: 49.1-54.5) and PPV (32.0%; 95%CI: 30.0-35.0). These performances varied by age groups. In children < 5 years, the significant predictors of influenza virus infection were cough and nasal discharge. In patients from 5 years, cough, nasal discharge, sore throat and asthenia grade 3 best predicted influenza infection. The addition of "nasal discharge" as a symptom to the WHO case definition decreased sensitivity but increased specificity, particularly in the pediatric population.Conclusion: In summary, all three definitions studies (WHO, ECDC & CDC) have similar performance, even by age group. The revised WHO ILI definition could be chosen for surveillance purposes for its simplicity. Symptomatic predictors of influenza virus infection vary according the age group. [ABSTRACT FROM AUTHOR]

Background: Accurate influenza activity forecasting helps public health officials prepare and allocate resources for unusual influenza activity. Traditional flu surveillance systems, such as the Centers for Disease Control and Prevention's (CDC) influenza-like illnesses reports, lag behind real-time by one to 2 weeks, whereas information contained in cloud-based electronic health records (EHR) and in Internet users' search activity is typically available in near real-time. We present a method that combines the information from these two data sources with historical flu activity to produce national flu forecasts for the United States up to 4 weeks ahead of the publication of CDC's flu reports.Methods: We extend a method originally designed to track flu using Google searches, named ARGO, to combine information from EHR and Internet searches with historical flu activities. Our regularized multivariate regression model dynamically selects the most appropriate variables for flu prediction every week. The model is assessed for the flu seasons within the time period 2013-2016 using multiple metrics including root mean squared error (RMSE).Results: Our method reduces the RMSE of the publicly available alternative (Healthmap flutrends) method by 33, 20, 17 and 21%, for the four time horizons: real-time, one, two, and 3 weeks ahead, respectively. Such accuracy improvements are statistically significant at the 5% level. Our real-time estimates correctly identified the peak timing and magnitude of the studied flu seasons.Conclusions: Our method significantly reduces the prediction error when compared to historical publicly available Internet-based prediction systems, demonstrating that: (1) the method to combine data sources is as important as data quality; (2) effectively extracting information from a cloud-based EHR and Internet search activity leads to accurate forecast of flu. [ABSTRACT FROM AUTHOR]

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season.Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet).Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones.Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. [ABSTRACT FROM AUTHOR]

Background: Laboratory testing is a fundamental component of influenza surveillance for detecting novel strains with pandemic potential and informing biannual vaccine strain selection. The United States (U.S.) Centers for Disease Control and Prevention (CDC), under the auspices of its WHO Collaborating Center for Influenza, is one of the major public health agencies which provides support globally to build national capacity for influenza surveillance. Our main objective was to determine if laboratory assessments supported capacity building efforts for improved global influenza surveillance.Methods: In 2010, 35 national influenza laboratories were assessed in 34 countries, using a standardized tool. Post-assessment, each laboratory received a report with a list of recommendations for improvement. Uptake of recommendations were reviewed 3.2 mean years after the initial assessments and categorized as complete, in-progress, no action or no update. This was a retrospective study; follow-up took place through routine project management rather than at a set time-point post-assessment. WHO data on National Influenza Centre (NIC) designation, External Quality Assessment Project (EQAP) participation and FluNet reporting was used to measure laboratory capacity longitudinally and independently of the assessments. All data was further stratified by World Bank country income category.Results: At follow-up, 81% of 614 recommendations were either complete (350) or in-progress (145) for 32 laboratories (91% response rate). The number of countries reporting to FluNet and the number of specimens they reported annually increased between 2005, when they were first funded by CDC, and 2010, the assessment year (p < 0.01). Improvements were also seen in EQAP participation and NIC designation over time and more so for low and lower-middle income countries.Conclusions: Assessments using a standardized tool have been beneficial to improving laboratory-based influenza surveillance. Specific recommendations helped countries identify and prioritize areas for improvement. Data from assessments helped CDC focus its technical assistance by country and region. Low and lower-middle income countries made greater improvements in their laboratories compared with upper-middle income countries. Future research could include an analysis of annual funding and technical assistance by country. Our approach serves as an example for capacity building for other diseases. [ABSTRACT FROM AUTHOR]

Background: Many people have multiple infections at the same time, but the combined contribution of those infections to disease-related mortality is unknown. Registered causes of death offer a unique opportunity to study associations between multiple infections.Methods: We analysed over 900,000 death certificates that reported infectious causes of death. We tested whether reports of multiple infections (i.e., co-infections) differed across individuals' age or sex. We also tested whether each pair of infections were reported together more or less often than expected by chance, and whether this co-reporting was associated with the number of biological characteristics they had in common.Results: In England and Wales, and the USA, 10 and 6 % respectively of infection-related deaths involved co-infection. Co-infection was reported reported most often in young adults; 30 % of infection-related deaths among those aged 25-44 from the USA, and 20 % of infection-related deaths among those aged 30-39 from England and Wales, reported multiple infections. The proportion of infection-related deaths involving co-infection declined with age more slowly in males than females, to less than 10 % among those aged >65. Most associated pairs of infections co-occurred more often than expected from their frequency of being reported alone (488/683 [71 %] in the USA, 129/233 [55 %] in England and Wales), and tended to share biological characteristics (taxonomy, transmission mode, tropism or timescale).Conclusions: Age, sex, and biologically similar infections are associated with death from co-infection, and may help indicate patients at risk of severe co-infection. [ABSTRACT FROM AUTHOR]

Background: Despite the widespread availability of pneumococcal vaccines, rates of pneumococcal disease are disproportionately high in adults with chronic and immunocompromising conditions. This study investigated pneumococcal disease rates and associated resource utilization and costs in this group.Methods: A retrospective, observational study was conducted using the Truven Health MarketScan® Commercial Claims and Encounters database. The study population was adults aged 19-64 years with continuous health plan enrollment for at least one year before and at least one day after January 1st 2012, 2013 and/or 2014. Medical conditions were identified using ICD-9-CM diagnosis codes and grouped into at-risk (chronic) and high-risk (immunocompromising) conditions. Pneumococcal disease was stratified into all-cause pneumonia (ACP) and invasive pneumococcal disease (IPD).Results: Thirty-six million adults aged 19-64 years were included in the study. 17% had a condition that put them at increased risk for pneumococcal disease. Rates of ACP and IPD in adults with at-risk conditions were 3.6 and 4.6 times the rate in healthy adults, respectively, and 5.3 and 10.5 for adults with high-risk conditions. Risk was particularly high in adults with ≥2 medical conditions: rates of ACP and IPD were 8.1 and 10.6 times higher in adults with at-risk conditions than healthy adults and 6.3 and 13.4 times higher in adults with high-risk conditions, respectively. Resource use and costs were substantially higher per episode of ACP in at-risk and high-risk adults, with costs reaching $6,534 and $9,168, compared to $4,725 for healthy adults.Conclusions: Pneumococcal disease rates in at-risk and high-risk adults are significantly higher than healthy adults leading to substantial economic burden. [ABSTRACT FROM AUTHOR]