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Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is determined by maximizing overall ensemble accuracy over past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.

Accurate and reliable forecasts of seasonal epidemics of infectious disease can assist in the design of countermeasures and increase public awareness and preparedness. This article describes two main contributions we made recently toward this goal: a novel approach to probabilistic modeling of surveillance time series based on \"delta densities\", and an optimization scheme for combining output from multiple forecasting methods into an adaptively weighted ensemble. Delta densities describe the probability distribution of the change between one observation and the next, conditioned on available data; chaining together nonparametric estimates of these distributions yields a model for an entire trajectory. Corresponding distributional forecasts cover more observed events than alternatives that treat the whole season as a unit, and improve upon multiple evaluation metrics when extracting key targets of interest to public health officials. Adaptively weighted ensembles integrate the results of multiple forecasting methods, such as delta density, using weights that can change from situation to situation. We treat selection of optimal weightings across forecasting methods as a separate estimation task, and describe an estimation procedure based on optimizing cross-validation performance. We consider some details of the data generation process, including data revisions and holiday effects, both in the construction of these forecasting methods and when performing retrospective evaluation. The delta density method and an adaptively weighted ensemble of other forecasting methods each improve significantly on the next best ensemble component when applied separately, and achieve even better cross-validated performance when used in conjunction. We submitted real-time forecasts based on these contributions as part of CDC's 2015/2016 FluSight Collaborative Comparison. Among the fourteen submissions that season, this system was ranked by CDC as the most accurate.

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.

Infectious diseases impose considerable burden on society, despite significant advances in technology and medicine over the past century. Advanced warning can be helpful in mitigating and preparing for an impending or ongoing epidemic. Historically, such a capability has lagged for many reasons, including in particular the uncertainty in the current state of the system and in the understanding of the processes that drive epidemic trajectories. Presently we have access to data, models, and computational resources that enable the development of epidemiological forecasting systems. Indeed, several recent challenges hosted by the U.S. government have fostered an open and collaborative environment for the development of these technologies. The primary focus of these challenges has been to develop statistical and computational methods for epidemiological forecasting, but here we consider a serious alternative based on collective human judgment. We created the web-based \"Epicast\" forecasting system which collects and aggregates epidemic predictions made in real-time by human participants, and with these forecasts we ask two questions: how accurate is human judgment, and how do these forecasts compare to their more computational, data-driven alternatives? To address the former, we assess by a variety of metrics how accurately humans are able to predict influenza and chikungunya trajectories. As for the latter, we show that real-time, combined human predictions of the 2014-2015 and 2015-2016 U.S. flu seasons are often more accurate than the same predictions made by several statistical systems, especially for short-term targets. We conclude that there is valuable predictive power in collective human judgment, and we discuss the benefits and drawbacks of this approach.

Seasonal influenza epidemics cause consistent, considerable, widespread loss annually in terms of economic burden, morbidity, and mortality. With access to accurate and reliable forecasts of a current or upcoming influenza epidemic's behavior, policy makers can design and implement more effective countermeasures. This past year, the Centers for Disease Control and Prevention hosted the \"Predict the Influenza Season Challenge\", with the task of predicting key epidemiological measures for the 2013-2014 U.S. influenza season with the help of digital surveillance data. We developed a framework for in-season forecasts of epidemics using a semiparametric Empirical Bayes framework, and applied it to predict the weekly percentage of outpatient doctors visits for influenza-like illness, and the season onset, duration, peak time, and peak height, with and without using Google Flu Trends data. Previous work on epidemic modeling has focused on developing mechanistic models of disease behavior and applying time series tools to explain historical data. However, tailoring these models to certain types of surveillance data can be challenging, and overly complex models with many parameters can compromise forecasting ability. Our approach instead produces possibilities for the epidemic curve of the season of interest using modified versions of data from previous seasons, allowing for reasonable variations in the timing, pace, and intensity of the seasonal epidemics, as well as noise in observations. Since the framework does not make strict domain-specific assumptions, it can easily be applied to some other diseases with seasonal epidemics. This method produces a complete posterior distribution over epidemic curves, rather than, for example, solely point predictions of forecasting targets. We report prospective influenza-like-illness forecasts made for the 2013-2014 U.S. influenza season, and compare the framework's cross-validated prediction error on historical data to that of a variety of simpler baseline predictors.

Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015–2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged submitted forecasts into a mean ensemble model and compared them against predictions based on historical trends. Forecast skill was highest for seasonal peak intensity and short-term forecasts, while forecast skill for timing of season onset and peak week was generally low. Higher forecast skill was associated with team participation in previous influenza forecasting challenges and utilization of ensemble forecasting techniques. The mean ensemble consistently performed well and outperformed historical trend predictions. CDC and contributing teams will continue to advance influenza forecasting and work to improve the accuracy and reliability of forecasts to facilitate increased incorporation into public health response efforts.

Covariate identification in population pharmacokinetic/pharmacodynamic (popPK/PD) modeling is a key component in model development that is often prone to bias, time‐consuming, and even intractable when too many covariates or complicated models are being considered. Early work leveraging machine learning (ML) for covariate screening has shown promising results over traditional methods. In this work, we expand this effort by integrating explainable machine learning facilitated by Shapley Additive Explanations (SHAP) analysis and covariate uncertainty quantification as well as a formal framework for establishing statistical significance of covariate relationships. Finally, we have packaged the proposed methodology into a flexible set of functions (shap‐cov) to support popPK/PD modeling covariate identification.

Submacular hemorrhage (SMH) has been reported to be toxic to the retina based on animal studies. However, observational studies of patients with neovascular-related SMH and those treated with intravitreal anti-vascular growth factor (anti-VEGF) therapy have shown many favorable visual acuity outcomes. We report two cases of neovascular-related SMH with ten or more years of follow-up. The first case was an 83-year old female with a history of nonexudative age-related macular degeneration in both eyes presenting with sudden decrease in vision (20/400) in her right eye due to a large SMH, treated with anti-VEGF therapy. Over the next following months, there was resolution of the hemorrhage and return of good visual acuity. At 10-year follow-up, visual acuity was 20/30 in the right eye. The second case was a 49-year old female with a history of presumed ocular histoplasmosis syndrome (POHS), presenting with sudden vision loss (20/400) in her right eye due to large, thick SMH. With observation and intermittent anti-VEGF therapy, there was resolution of the hemorrhage. At 30-year follow-up, visual acuity was 20/20 in the right eye.

Noncompartmental analysis (NCA) is a model‐independent approach for assessing pharmacokinetics (PKs). Although the existing NCA algorithms are very well‐established and widely utilized, they suffer from low accuracies in the setting of sparse PK samples. In response, we developed Deep‐NCA, a deep learning (DL) model to improve the prediction of key noncompartmental PK parameters. Our methodology utilizes synthetic PK data for model training and uses an innovative patient‐specific normalization method for data preprocessing. Deep‐NCA demonstrated adequate performance across six previously unseen simulated drugs under multiple dosing, showcasing effective generalization. Compared to traditional NCA, Deep‐NCA exhibited superior performance for sparse PK data. This study advances the application of DL to PK studies and introduces an effective method for handling sparse PK data. With further validation and refinement, Deep‐NCA could significantly enhance the efficiency of drug development by providing more accurate NCA estimates while requiring fewer PK samples.

Astegolimab, a fully human immunoglobulin G2 monoclonal antibody, binds with high affinity to ST2, the interleukin‐33 receptor, thereby blocking ST2/interleukin‐33 binding and subsequent inflammatory cascades involved in inflammatory diseases. Here, we present three randomized, double‐blind, placebo‐controlled, Phase I studies evaluating the safety, tolerability, pharmacokinetics, and immunogenicity of single‐ascending doses of astegolimab in healthy participants and patients with mild atopic asthma (NCT01928368), multiple‐ascending doses in healthy participants (NCT02170337), and single‐ascending doses in healthy Japanese and White adults. Overall, 152 participants were enrolled, randomized, and treated with single‐ or multiple‐ascending doses of astegolimab (n = 112) or placebo (n = 40) subcutaneously (2.1–560 mg) or intravenously (210 or 700 mg). No deaths, serious adverse events, or discontinuations due to adverse events occurred during the studies. No clinically meaningful differences in incidence of TEAEs were observed between treatment arms. Pharmacokinetic exposure increased more than dose proportionally over 2.1–420 mg for single‐ascending doses but were approximately dose proportional for single‐ and multiple‐ascending doses ≥ 70 mg following subcutaneous administration. No pharmacokinetic differences were observed based on ethnicity between Japanese and White participants following body weight adjustments. Incidence of antidrug antibodies to astegolimab in healthy participants in the single‐ and multiple‐ascending dose studies was 14%–23% and 33%–50% for subcutaneous and intravenous administration, respectively. Astegolimab was well tolerated in these Phase I studies with no safety concerns identified. Thus, further assessment of astegolimab in targeted patient populations was justified; the Phase IIb ALIENTO and Phase III ARNASA trials in patients with chronic obstructive pulmonary disease are ongoing.