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During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: APP report grants from Metabiota Inc outside the submitted work. J.S. and Columbia University declare partial ownership of SK Analytics. No other authors have competing interests to declare., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Modeling complements surveillance data to inform coronavirus disease 2019 (COVID-19) public health decision making and policy development. This includes the use of modeling to improve situational awareness, assess epidemiological characteristics, and inform the evidence base for prevention strategies. To enhance modeling utility in future public health emergencies, the Centers for Disease Control and Prevention (CDC) launched the Infectious Disease Modeling and Analytics Initiative. The initiative objectives are to: (1) strengthen leadership in infectious disease modeling, epidemic forecasting, and advanced analytic work; (2) build and cultivate a community of skilled modeling and analytics practitioners and consumers across CDC; (3) strengthen and support internal and external applied modeling and analytic work; and (4) working with partners, coordinate government-wide advanced data modeling and analytics for infectious diseases. These efforts are critical to help prepare the CDC, the country, and the world to respond effectively to present and future infectious disease threats., (Published by Oxford University Press for the Infectious Diseases Society of America 2021.)

We used individual-based computer simulation models at community, regional and national levels to evaluate the likely impact of coordinated pre-emptive school dismissal policies during an influenza pandemic. Such policies involve three key decisions: when, over what geographical scale, and how long to keep schools closed. Our evaluation includes uncertainty and sensitivity analyses, as well as model output uncertainties arising from variability in serial intervals and presumed modifications of social contacts during school dismissal periods. During the period before vaccines become widely available, school dismissals are particularly effective in delaying the epidemic peak, typically by 4-6 days for each additional week of dismissal. Assuming the surveillance is able to correctly and promptly diagnose at least 5-10% of symptomatic individuals within the jurisdiction, dismissals at the city or county level yield the greatest reduction in disease incidence for a given dismissal duration for all but the most severe pandemic scenarios considered here. Broader (multi-county) dismissals should be considered for the most severe and fast-spreading (1918-like) pandemics, in which multi-month closures may be necessary to delay the epidemic peak sufficiently to allow for vaccines to be implemented., (Published by Elsevier B.V.)

Background: Many countries have acquired antiviral stockpiles for pandemic influenza mitigation and a significant part of the stockpile may be focussed towards community-based treatment., Methods: We developed a spreadsheet-based, decision tree model to assess outcomes averted and cost-effectiveness of antiviral treatment for outpatient use from the perspective of the healthcare payer in the UK. We defined five pandemic scenarios-one based on the 2009 A(H1N1) pandemic and four hypothetical scenarios varying in measures of transmissibility and severity., Results: Community-based antiviral treatment was estimated to avert 14-23% of hospitalizations in an overall population of 62.28 million. Higher proportions of averted outcomes were seen in patients with high-risk conditions, when compared to non-high-risk patients. We found that antiviral treatment was cost-saving across pandemic scenarios for high-risk population groups, and cost-saving for the overall population in higher severity influenza pandemics. Antiviral effectiveness had the greatest influence on both the number of hospitalizations averted and on cost-effectiveness., Conclusions: This analysis shows that across pandemic scenarios, antiviral treatment can be cost-saving for population groups at high risk of influenza-related complications., (Published by Oxford University Press on behalf of Faculty of Public Health [2018]. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Assessments of influenza season severity can guide public health action. We used the moving epidemic method to develop intensity thresholds (ITs) for 3 US surveillance indicators from the 2003-2004 through 2014-2015 influenza seasons (excluding the 2009 pandemic). The indicators were: 1) outpatient visits for influenza-like illness; 2) influenza-related hospitalizations; and 3) influenza- and pneumonia-related deaths. ITs were developed for the population overall and separately for children, adults, and older adults, and they were set at the upper limit of the 50% (IT50), 90% (IT90), and 98% (IT98) 1-sided confidence intervals of the geometric mean of each season's 3 highest values. Severity was classified as low if ≥2 systems peaked below IT50, moderate if ≥2 peaked between IT50 and IT90, high if ≥2 peaked between IT90 and IT98, and very high if ≥2 peaked above IT98. We pilot-tested this method with the 2015-2016 season and the 2009 pandemic. Overall, 4 seasons were classified as low severity, 7 as moderate, 2 as high, and none as very high. Among the age groups, older adults had the most seasons (n = 3) classified as high, and children were the only group to have seasons (n = 2) classified as very high. We will apply this method to classify the severity of future seasons and inform pandemic response.

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

Background: Since the introduction of pandemic influenza A (H1N1) to the USA in 2009, the Influenza Incidence Surveillance Project has monitored the burden of influenza in the outpatient setting through population-based surveillance., Methods: From Oct 1, 2009, to July 31, 2013, outpatient clinics representing 13 health jurisdictions in the USA reported counts of influenza-like illness (fever including cough or sore throat) and all patient visits by age. During four years, staff at 104 unique clinics (range 35-64 per year) with a combined median population of 368,559 (IQR 352,595-428,286) attended 35,663 patients with influenza-like illness and collected 13,925 respiratory specimens. Clinical data and a respiratory specimen for influenza testing by RT-PCR were collected from the first ten patients presenting with influenza-like illness each week. We calculated the incidence of visits for influenza-like illness using the size of the patient population, and the incidence attributable to influenza was extrapolated from the proportion of patients with positive tests each week., Findings: The site-median peak percentage of specimens positive for influenza ranged from 58.3% to 77.8%. Children aged 2 to 17 years had the highest incidence of influenza-associated visits (range 4.2-28.0 per 1000 people by year), and adults older than 65 years had the lowest (range 0.5-3.5 per 1000 population). Influenza A H3N2, pandemic H1N1, and influenza B equally co-circulated in the first post-pandemic season, whereas H3N2 predominated for the next two seasons. Of patients for whom data was available, influenza vaccination was reported in 3289 (28.7%) of 11,459 patients with influenza-like illness, and antivirals were prescribed to 1644 (13.8%) of 11,953 patients., Interpretation: Influenza incidence varied with age groups and by season after the pandemic of 2009 influenza A H1N1. High levels of influenza virus circulation, especially in young children, emphasise the need for additional efforts to increase the uptake of influenza vaccines and antivirals., Funding: US Centers for Disease Control and Prevention., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Background: Human illness from influenza A(H7N9) was identified in March 2013, and candidate vaccine viruses were soon developed. To understand factors that may impact influenza vaccination programs, we developed a model to evaluate hospitalizations and deaths averted considering various scenarios., Methods: We utilized a model incorporating epidemic curves with clinical attack rates of 20% or 30% in a single wave of illness, case hospitalization ratios of 0.5% or 4.2%, and case fatality ratios of 0.08% or 0.53%. We considered scenarios that achieved 80% vaccination coverage, various starts of vaccination programs (16 or 8 weeks before, the same week of, or 8 or 16 weeks after start of pandemic), an administration rate of 10 or 30 million doses per week (the latter rate is an untested assumption), and 2 levels of vaccine effectiveness (2 doses of vaccine required; either 62% or 80% effective for persons aged <60 years, and either 43% or 60% effective for persons aged ≥ 60 years)., Results: The start date of vaccination campaigns most influenced impact; 141,000-2,200,000 hospitalizations and 11,000-281,000 deaths were averted when campaigns started before a pandemic, and <100-1 300,000 hospitalizations and 0-165,000 deaths were averted for programs beginning the same time as or after the introduction of the pandemic virus. The rate of vaccine administration and vaccine effectiveness did not influence campaign impact as much as timing of the start of campaign., Conclusions: Our findings suggest that efforts to improve the timeliness of vaccine production will provide the greatest impacts for future pandemic vaccination programs., (Published by Oxford University Press on behalf of the Infectious Diseases Society of America 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

The rising importance of infectious disease modeling makes this an appropriate time for a guide for public health practitioners tasked with preparing for, and responding to, an influenza pandemic. We list several questions that public health practitioners commonly ask about pandemic influenza and match these with analytical methods, giving details on when during a pandemic the methods can be used, how long it might take to implement them, and what data are required. Although software to perform these tasks is available, care needs to be taken to understand: (1) the type of data needed, (2) the implementation of the methods, and (3) the interpretation of results in terms of model uncertainty and sensitivity. Public health leaders can use this article to evaluate the modeling literature, determine which methods can provide appropriate evidence for decision-making, and to help them request modeling work from in-house teams or academic groups., (© The Author 2015. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

The effects of influenza on a population are attributable to the clinical severity of illness and the number of persons infected, which can vary greatly between seasons or pandemics. To create a systematic framework for assessing the public health effects of an emerging pandemic, we reviewed data from past influenza seasons and pandemics to characterize severity and transmissibility (based on ranges of these measures in the United States) and outlined a formal assessment of the potential effects of a novel virus. The assessment was divided into 2 periods. Because early in a pandemic, measurement of severity and transmissibility is uncertain, we used a broad dichotomous scale in the initial assessment to divide the range of historic values. In the refined assessment, as more data became available, we categorized those values more precisely. By organizing and prioritizing data collection, this approach may inform an evidence-based assessment of pandemic effects and guide decision making.

Objectives: The purpose of our study was to more accurately characterize people reporting influenza-like illness (ILI) and evaluate trends in health care seeking and influenza diagnosis and treatment during the 2009 influenza pandemic., Methods: From September 2009 to March 2010, we ascertained ILI (fever with cough or sore throat), health care seeking, and clinical diagnosis and treatment of influenza with influenza antiviral drugs among adults in 51 jurisdictions, and ILI and health care seeking among children in 41 jurisdictions., Results: Among 216,431 adults and 43,511 children, 8.1% and 28.4% reported ILI, respectively. ILI peaked during November interviews and was higher among young people and American Indian/Alaska Natives. Of those with ILI, 40% of adults and 56% of children reported seeking health care; 26% of adults who sought care reported receiving a diagnosis of influenza. Of adults reporting an influenza diagnosis, 36% were treated with influenza antiviral drugs; treatment was highest among adults aged 18 to 49 years., Conclusions: Analysis of ILI data from the Behavioral Risk Factor Surveillance System enabled a better understanding of the factors associated with self-reported ILI, health care seeking, and clinical influenza diagnosis and treatment, and will help inform year-to-year influenza trends.

From April 2009 through March 2010, during the pandemic (H1N1) 2009 outbreak, ≈8.2 million prescriptions for influenza neuraminidase-inhibiting antiviral drugs were filled in the United States. We estimated the number of hospitalizations likely averted due to use of these antiviral medications. After adjusting for prescriptions that were used for prophylaxis and personal stockpiles, as well as for patients who did not complete their drug regimen, we estimated the filled prescriptions prevented ≈8,400-12,600 hospitalizations (on the basis of median values). Approximately 60% of these prevented hospitalizations were among adults 18-64 years of age, with the remainder almost equally divided between children 0-17 years of age and adults >65 years of age. Public health officials should consider these estimates an indication of success of treating patients during the 2009 pandemic and a warning of the need for renewed planning to cope with the next pandemic.

Purpose: Concerns have been raised regarding possible racial-ethnic disparities in 2009 pandemic influenza A (H1N1) (pH1N1) illness severity and health consequences for U.S. minority populations., Methods: Using data from the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System, Emerging Infections Program Influenza-Associated Hospitalization Surveillance, and Influenza-Associated Pediatric Mortality Surveillance, we calculated race-ethnicity-specific, age-adjusted rates of self-reported influenza-like illness (ILI) and pH1N1-associated hospitalizations. We used χ(2) tests to evaluate racial-ethnic disparities in ILI-associated health care-seeking behavior and pH1N1 hospitalization. To evaluate pediatric deaths, we compared racial-ethnic proportions of deaths against U.S. population distributions., Results: Prevalence of self-reported ILI was lower among Hispanics (6.5%), higher among American Indians/Alaska Natives (16.2%), and similar among non-Hispanic blacks (7.7%) compared with non-Hispanic whites (8.5%). No racial-ethnic differences were identified in ILI-associated health care-seeking behavior. Age-adjusted pH1N1-associated Emerging Infections Program hospitalization rates were higher among all minority populations (range: 8.1-10.9/100,000 population) compared with non-Hispanic whites (3.0/100,000). The proportion of pH1N1-associated pediatric deaths was higher than expected among Hispanics (31%) and lower than expected among non-Hispanic whites (45%) given the proportions of the U.S. population they comprise (22% and 58%, respectively)., Conclusions: Racial-ethnic disparities in pH1N1-associated hospitalizations and pediatric deaths were identified. Vaccination remains the primary intervention for preventing influenza., (Published by Elsevier Inc.)

In April 2009, the Centers for Disease Control and Prevention confirmed 2 cases of 2009 pandemic influenza A (H1N1) virus infection in children from southern California, marking the beginning of what would be the first influenza pandemic of the twenty-first century. This report describes the epidemiology of the 2009 H1N1 pandemic in the United States, including characterization of cases, fluctuations of disease burden over the course of a year, the age distribution of illness and severe outcomes, and estimation of the overall burden of disease.

To calculate the burden of 2009 pandemic influenza A (pH1N1) in the United States, we extrapolated from the Centers for Disease Control and Prevention's Emerging Infections Program laboratory-confirmed hospitalizations across the entire United States, and then corrected for underreporting. From 12 April 2009 to 10 April 2010, we estimate that approximately 60.8 million cases (range: 43.3-89.3 million), 274,304 hospitalizations (195,086-402,719), and 12,469 deaths (8868-18,306) occurred in the United States due to pH1N1. Eighty-seven percent of deaths occurred in those under 65 years of age with children and working adults having risks of hospitalization and death 4 to 7 times and 8 to 12 times greater, respectively, than estimates of impact due to seasonal influenza covering the years 1976-2001. In our study, adults 65 years of age or older were found to have rates of hospitalization and death that were up to 75% and 81%, respectively, lower than seasonal influenza. These results confirm the necessity of a concerted public health response to pH1N1.

Following the emergence of 2009 pandemic influenza A(H1N1) virus (pH1N1) in the United States, the incidence of pH1N1 in the community was unclear, because not all persons with influenza come to medical attention. To better estimate the incidence of pH1N1 in the community early in the pandemic, a telephone survey was conducted in 10 states. The community incidence of influenza-like illness in April 2009 was 4.7 per 100 adults (95% confidence interval: 2.8-6.6); half of adults reported seeking medical care for their illness. Such surveys may be important tools for assessing the level of illness in the general population, including those who do not seek medical care and are thus not captured using traditional surveillance methods.

During the spring of 2009, pandemic influenza A (H1N1) virus (pH1N1) was recognized and rapidly spread worldwide. To describe the geographic distribution and patient characteristics of pH1N1-associated deaths in the United States, the Centers for Disease Control and Prevention requested information from health departments on all laboratory-confirmed pH1N1 deaths reported from 17 April through 23 July 2009. Data were collected using medical charts, medical examiner reports, and death certificates. A total of 377 pH1N1-associated deaths were identified, for a mortality rate of .12 deaths per 100,000 population. Activity was geographically localized, with the highest mortality rates in Hawaii, New York, and Utah. Seventy-six percent of deaths occurred in persons aged 18-65 years, and 9% occurred in persons aged ≥ 65 years. Underlying medical conditions were reported for 78% of deaths: chronic lung disease among adults (39%) and neurologic disease among children (54%). Overall mortality associated with pH1N1 was low; however, the majority of deaths occurred in persons aged <65 years with underlying medical conditions.

Emergence of 2009 pandemic influenza A (H1N1) (pH1N1) virus in 2009 raised concern about the potential impact of widespread or severe disease on the nation's workforce. The Centers for Disease Control and Prevention recommended that employers develop flexible pandemic response plans. We used the Behavioral Risk Factor Surveillance System's influenza module as a model for a brief workplace survey to ascertain the influenza-like illness (ILI) burden on epidemiology staff in Centers for Disease Control and Prevention Emergency Operations Center. Fifty-seven (78%) of 73 recipients completed the survey. Ten (18%) met the ILI case definition. The 10 respondent ILI cases missed 24 total work days, although none sought medical care. Eleven (14%) of 77 household contacts also had ILI, but no ILI case was hospitalized. This survey enabled us to rapidly obtain information about our workforce ILI burden and evaluate the potential need for additional resources because of employee absence.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Influenza pandemics typically occur in multiple waves of infection, often associated with initial emergence of a novel virus, followed (in temperate regions) by a resurgence accompanying the onset of the annual influenza season. Here, we examined whether data collected from an initial pandemic wave could be informative, for the need to implement non-pharmaceutical measures in any resurgent wave. Drawing from the 2009 H1N1 pandemic in 10 states in the USA, we calibrated simple mathematical models of influenza transmission dynamics to data for laboratory confirmed hospitalisations during the initial 'spring' wave. We then projected pandemic outcomes (cumulative hospitalisations) during the fall wave, and compared these projections with data. Model results showed reasonable agreement for all states that reported a substantial number of cases in the spring wave. Using this model we propose a probabilistic decision framework that can be used to determine the need for preemptive measures such as postponing school openings, in advance of a fall wave. This work illustrates how model-based evidence synthesis, in real-time during an early pandemic wave, could be used to inform timely decisions for pandemic response. Author summary: Recent events have made clear the importance of pandemic preparedness. When faced with an initial wave of pandemic influenza, we offer a methodology for decision making with respect to non-pharmaceutical interventions in order to mitigate subsequent waves. For example, delayed school openings during vaccine roll-out can substantially reduce the number of hospitalisations and deaths in subsequent waves. We use first-wave data to quantify future risk and aid the associated policy decisions. [ABSTRACT FROM AUTHOR]

During the 2009 influenza A(H1N1)pdm09 pandemic, 77 countries received donated monovalent A(H1N1)pdm09 vaccine through the WHO Pandemic Influenza A(H1N1) Vaccine Deployment Initiative. However, 47% did not receive their first shipment until after the first wave of virus circulation, and 8% did not receive their first shipment until after the WHO declared the end of the pandemic. Arguably, these shipments were too late into the pandemic to have a substantial effect on virus transmission or disease burden during the first waves of the pandemic. In order to evaluate the potential benefits of earlier vaccine availability, we estimated the number of illnesses and deaths that could be averted during a 2009-like influenza pandemic under five different vaccine-availability timing scenarios. We adapted a model originally developed to estimate annual influenza morbidity and mortality burden averted through US seasonal vaccination and ran it for five vaccine availability timing scenarios in nine low- and middle-income countries that received donated vaccine. Among nine study countries, we estimated that the number of averted cases was 61–216,197 for actual vaccine receipt, increasing to 2,914–283,916 had vaccine been available simultaneously with the United States. Earlier delivery of vaccines can reduce influenza case counts during a simulated 2009-like pandemic in some low- and middle-income countries. For others, increasing the number of cases and deaths prevented through vaccination may be dependent on factors other than timely initiation of vaccine administration, such as distribution and administration capacity. [ABSTRACT FROM AUTHOR]

Health planners from global to local levels must anticipate year‐to‐year and week‐to‐week variation in seasonal influenza activity when planning for and responding to epidemics to mitigate their impact. To help with this, countries routinely collect incidence of mild and severe respiratory illness and virologic data on circulating subtypes and use these data for situational awareness, burden of disease estimates and severity assessments. Advanced analytics and modelling are increasingly used to aid planning and response activities by describing key features of influenza activity for a given location and generating forecasts that can be translated to useful actions such as enhanced risk communications, and informing clinical supply chains. Here, we describe the formation of the Influenza Incidence Analytics Group (IIAG), a coordinated global effort to apply advanced analytics and modelling to public influenza data, both epidemiological and virologic, in real‐time and thus provide additional insights to countries who provide routine surveillance data to WHO. Our objectives are to systematically increase the value of data to health planners by applying advanced analytics and forecasting and for results to be immediately reproducible and deployable using an open repository of data and code. We expect the resources we develop and the associated community to provide an attractive option for the open analysis of key epidemiological data during seasonal epidemics and the early stages of an influenza pandemic. [ABSTRACT FROM AUTHOR]

Introduction: Epidemic forecasting and prediction tools have the potential to provide actionable information in the midst of emerging epidemics. While numerous predictive studies were published during the 2016–2017 Zika Virus (ZIKV) pandemic, it remains unknown how timely, reproducible, and actionable the information produced by these studies was. Methods: To improve the functional use of mathematical modeling in support of future infectious disease outbreaks, we conducted a systematic review of all ZIKV prediction studies published during the recent ZIKV pandemic using the PRISMA guidelines. Using MEDLINE, EMBASE, and grey literature review, we identified studies that forecasted, predicted, or simulated ecological or epidemiological phenomena related to the Zika pandemic that were published as of March 01, 2017. Eligible studies underwent evaluation of objectives, data sources, methods, timeliness, reproducibility, accessibility, and clarity by independent reviewers. Results: 2034 studies were identified, of which n = 73 met the eligibility criteria. Spatial spread, R0 (basic reproductive number), and epidemic dynamics were most commonly predicted, with few studies predicting Guillain-Barré Syndrome burden (4%), sexual transmission risk (4%), and intervention impact (4%). Most studies specifically examined populations in the Americas (52%), with few African-specific studies (4%). Case count (67%), vector (41%), and demographic data (37%) were the most common data sources. Real-time internet data and pathogen genomic information were used in 7% and 0% of studies, respectively, and social science and behavioral data were typically absent in modeling efforts. Deterministic models were favored over stochastic approaches. Forty percent of studies made model data entirely available, 29% provided all relevant model code, 43% presented uncertainty in all predictions, and 54% provided sufficient methodological detail to allow complete reproducibility. Fifty-one percent of predictions were published after the epidemic peak in the Americas. While the use of preprints improved the accessibility of ZIKV predictions by a median of 119 days sooner than journal publication dates, they were used in only 30% of studies. Conclusions: Many ZIKV predictions were published during the 2016–2017 pandemic. The accessibility, reproducibility, timeliness, and incorporation of uncertainty in these published predictions varied and indicates there is substantial room for improvement. To enhance the utility of analytical tools for outbreak response it is essential to improve the sharing of model data, code, and preprints for future outbreaks, epidemics, and pandemics. [ABSTRACT FROM AUTHOR]

Background: The potential impact of an influenza pandemic can be assessed by calculating a set of transmissibility parameters, the most important being the reproduction number (R), which is defined as the average number of secondary cases generated per typical infectious case. Methods: We conducted a systematic review to summarize published estimates of R for pandemic or seasonal influenza and for novel influenza viruses (e.g. H5N1). We retained and summarized papers that estimated R for pandemic or seasonal influenza or for human infections with novel influenza viruses. Results: The search yielded 567 papers. Ninety-one papers were retained, and an additional twenty papers were identified from the references of the retained papers. Twenty-four studies reported 51 R values for the 1918 pandemic. The median R value for 1918 was 1.80 (interquartile range [IQR]: 1.47–2.27). Six studies reported seven 1957 pandemic R values. The median R value for 1957 was 1.65 (IQR: 1.53–1.70). Four studies reported seven 1968 pandemic R values. The median R value for 1968 was 1.80 (IQR: 1.56–1.85). Fifty-seven studies reported 78 2009 pandemic R values. The median R value for 2009 was 1.46 (IQR: 1.30–1.70) and was similar across the two waves of illness: 1.46 for the first wave and 1.48 for the second wave. Twenty-four studies reported 47 seasonal epidemic R values. The median R value for seasonal influenza was 1.28 (IQR: 1.19–1.37). Four studies reported six novel influenza R values. Four out of six R values were <1. Conclusions: These R values represent the difference between epidemics that are controllable and cause moderate illness and those causing a significant number of illnesses and requiring intensive mitigation strategies to control. Continued monitoring of R during seasonal and novel influenza outbreaks is needed to document its variation before the next pandemic. [ABSTRACT FROM AUTHOR]

Background: Prior to emergence in human populations, zoonoses such as SARS cause occasional infections in human populations exposed to reservoir species. The risk of widespread epidemics in humans can be assessed by monitoring the reproduction number R (average number of persons infected by a human case). However, until now, estimating R required detailed outbreak investigations of human clusters, for which resources and expertise are not always available. Additionally, existing methods do not correct for important selection and under- ascertainment biases. Here, we present simple estimation methods that overcome many of these limitations. Methods and Findings: Our approach is based on a parsimonious mathematical model of disease transmission and only requires data collected through routine surveillance and standard case investigations. We apply it to assess the transmissibility of swine-origin influenza A H3N2v-M virus in the US, Nipah virus in Malaysia and Bangladesh, and also present a non-zoonotic example (cholera in the Dominican Republic). Estimation is based on two simple summary statistics, the proportion infected by the natural reservoir among detected cases (G) and among the subset of the first detected cases in each cluster (F). If detection of a case does not affect detection of other cases from the same cluster, we find that R can be estimated by 1 -- G; otherwise R can be estimated by 1 -- F when the case detection rate is low. In more general cases, bounds on R can still be derived. Conclusions: We have developed a simple approach with limited data requirements that enables robust assessment of the risks posed by emerging zoonoses. We illustrate this by deriving transmissibility estimates for the H3N2v-M virus, an important step in evaluating the possible pandemic threat posed by this virus. [ABSTRACT FROM AUTHOR]