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. Real-time 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.
BackgroundEarly 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.MethodsChallenge 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).ResultsNine 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.ConclusionForecasting 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-016-1669-x) contains supplementary material, which is available to authorized users.
Significance This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.
Social distancing orders have been enacted worldwide to slow the coronavirus disease (COVID-19) pandemic, reduce strain on healthcare systems, and prevent deaths. To estimate the impact of the timing and intensity of such measures, we built a mathematical model of COVID-19 transmission that incorporates age-stratified risks and contact patterns and projects numbers of hospitalizations, patients in intensive care units, ventilator needs, and deaths within US cities. Focusing on the Austin metropolitan area of Texas, we found that immediate and extensive social distancing measures were required to ensure that COVID-19 cases did not exceed local hospital capacity by early May 2020. School closures alone hardly changed the epidemic curve. A 2-week delay in implementation was projected to accelerate the timing of peak healthcare needs by 4 weeks and cause a bed shortage in intensive care units. This analysis informed the Stay HomeWork Safe order enacted by Austin on March 24, 2020.
Background Safe and effective vaccines may help end the ongoing Ebola virus disease (EVD) epidemic in West Africa, and mitigate future outbreaks. We evaluate the statistical validity and power of randomized controlled (RCT) and stepped-wedge cluster trial (SWCT) designs in Sierra Leone, where EVD incidence is spatiotemporally heterogeneous, and rapidly declining. Methods We forecasted district-level EVD incidence over the next six months using a stochastic model fit to data from Sierra Leone. We then simulated RCT and SWCT designs in trial populations comprising geographically distinct clusters of high risk, taking into account realistic logistical constraints, as well as both individual-level and cluster-level variation in risk. We assessed false positive rates and power for parametric and nonparametric analyses of simulated trial data, across a range of vaccine efficacies and trial start dates. Findings For an SWCT, regional variation in EVD incidence trends produced inflated false positive rates (up to 0.11 at α=0.05) under standard statistical models, but not when analyzed by a permutation test, whereas all analyses of RCTs remained valid. Assuming a six-month trial starting February 18, 2015, we estimate the power to detect a 90% efficacious vaccine to be between 48% and 89% for an RCT, and between 6.4% and 26% for an SWCT, depending on incidence within the trial population. We estimate that a one-month delay in implementation will reduce the power of the RCT and SWCT by 20% and 49%, respectively. Interpretation Spatiotemporal variation in infection risk undermines the SWCT's statistical power. This variation also undercuts the SWCT's expected ethical advantages over the RCT, because the latter but not the former can prioritize high-risk clusters. Funding US National Institutes of Health, US National Science Foundation, Canadian Institutes of Health Research
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