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Beginning in December 2020, the COVID-19 Scenario Modeling Hub has provided quantitative scenario-based projections for cases, hospitalizations, and deaths, aggregated across up to nine modeling groups. Projections spanned multiple months into the future and provided timely information on potential impacts of epidemiological uncertainties and interventions. Projections results were shared with the public, public health partners, and the Centers for Disease Control COVID-19 Response Team. The projections provided insights on situational awareness and informed decision-making to mitigate COVID-19 disease burden (e.g., vaccination strategies). By aggregating projections from multiple modeling teams, the Scenario Modeling Hub provided rapidly synthesized information in times of great uncertainty and conveyed possible trajectories in the presence of emerging variants. Here we detail several use cases of these projections in public health practice and communication, including assessments of whether modeling results directly or indirectly informed public health communication or guidance. These include multiple examples where comparisons of projected COVID-19 disease outcomes under different vaccination scenarios were used to inform Advisory Committee for Immunization Practices recommendations. We also describe challenges and lessons learned during this highly beneficial collaboration., Competing Interests: Declaration of Competing Interest The authors report no financial/personal interests or beliefs that could affect their objectivity., (Published by Elsevier B.V.)

Background: Infectious disease forecasting aims to predict characteristics of both seasonal epidemics and future pandemics. Accurate and timely infectious disease forecasts could aid public health responses by informing key preparation and mitigation efforts., Main Body: For forecasts to be fully integrated into public health decision-making, federal, state, and local officials must understand how forecasts were made, how to interpret forecasts, and how well the forecasts have performed in the past. Since the 2013-14 influenza season, the Influenza Division at the Centers for Disease Control and Prevention (CDC) has hosted collaborative challenges to forecast the timing, intensity, and short-term trajectory of influenza-like illness in the United States. Additional efforts to advance forecasting science have included influenza initiatives focused on state-level and hospitalization forecasts, as well as other infectious diseases. Using CDC influenza forecasting challenges as an example, this paper provides an overview of infectious disease forecasting; applications of forecasting to public health; and current work to develop best practices for forecast methodology, applications, and communication., Conclusions: These efforts, along with other infectious disease forecasting initiatives, can foster the continued advancement of forecasting science.

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., Competing Interests: The authors have declared that no competing interests exist.

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.

Accurate forecasts could enable more informed public health decisions. Since 2013, CDC has worked with external researchers to improve influenza forecasts by coordinating seasonal challenges for the United States and the 10 Health and Human Service Regions. Forecasted targets for the 2014-15 challenge were the onset week, peak week, and peak intensity of the season and the weekly percent of outpatient visits due to influenza-like illness (ILI) 1-4 weeks in advance. We used a logarithmic scoring rule to score the weekly forecasts, averaged the scores over an evaluation period, and then exponentiated the resulting logarithmic score. Poor forecasts had a score near 0, and perfect forecasts a score of 1. Five teams submitted forecasts from seven different models. At the national level, the team scores for onset week ranged from

From April 23, 2009, through April 10, 2010, it is estimated that pandemic (H1N1) 2009 virus caused ≅61 million cases of influenza (range 43-89 million cases), ≅270,000 related hospitalizations (range [...]

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. [ABSTRACT FROM AUTHOR]

Influenza infects an estimated 9-35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Realtime forecasts of influenza transmission can inform public health response to outbreaks.We present the results of a multiinstitution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the United States for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of seven targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2 wk, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision making. [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. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A type A botulism outbreak was associated with commercially canned food; it is the first botulism outbreak in the United States involving a commercial cannery in >30 years. Because of possible widespread distribution of the implicated product, this outbreak was a major public health concern.Background. On 7 and 11 July 2007, health officials in Texas and Indiana, respectively, reported 4 possible cases of type A foodborne botulism to the US Centers for Disease Control and Prevention. Foodborne botulism is a rare and sometimes fatal illness caused by consuming foods containing botulinum neurotoxin.Methods. Investigators reviewed patients’ medical charts and food histories. Clinical specimens and food samples were tested for botulinum toxin and neurotoxin-producing Clostridium species. Investigators conducted inspections of the cannery that produced the implicated product.Results. Eight confirmed outbreak associated cases were identified from Indiana (n = 2), Texas (n = 3), and Ohio (n = 3). Botulinum toxin type A was identified in leftover chili sauce consumed by the Indiana patients and 1 of the Ohio patients. Cannery inspectors found violations of federal canned-food regulations that could have led to survival of Clostridium botulinum spores during sterilization. The company recalled 39 million cans of chili. Following the outbreak, the US Food and Drug Administration inspected other canneries with similar canning systems and issued warnings to the industry about the danger of C. botulinum and the importance of compliance with canned food manufacturing regulations.Conclusions. Commercially produced hot dog chili sauce caused these cases of type A botulism. This is the first US foodborne botulism outbreak involving a commercial cannery in >30 years. Sharing of epidemiologic and laboratory findings allowed for the rapid identification of implicated food items and swift removal of potentially deadly products from the market by US food regulatory authorities. [ABSTRACT FROM PUBLISHER]

The article focuses on a study done in order to know the human infections with influenza A (H3N2) variant virus. It mentions that cases and contacts of confirmed H3N2v virus infection were interviewed to determine exposure to swine and other animals and to assess potential person-to-person transmission. It concludes that the study found that H3N2v virus infections were at a high rate from August 2011 to April 2012 in the U.S. and maximum with swine exposure.

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]