High-risk women with BRCA1/2 gene mutations and a familial history of breast or ovarian cancer require intensified screening, potentially incorporating ultrasound (US) and magnetic resonance imaging (MRI) alongside mammography. However, concerns arise regarding the cost and false-positive rates of MRI and the operator dependency of US. Current guidelines lack rigorous evidence-based support, fueling debate over the optimal utilization of US and MRI in conjunction with or instead of mammography in high-risk populations. In this paper, our objective is to study the multimodality breast cancer screening problem in high-risk populations and identify optimal cost-effective population screening strategies. We develop a Markov model to capture the disease dynamics in high-risk women and formulate a mixed integer linear program to identify the optimal structured strategies that are practical for implementation. We parameterize and solve this model using real data and evidence synthesized from clinical studies. Furthermore, studying the structural properties of the optimal strategies, we establish sufficient conditions under which a strategy with more frequent screens yields higher health benefits than a strategy utilizing a more sensitive modality. Our main findings are as follows: (i) for young women (women aged 25–44 years), annual screening with ultrasound alone, despite its high operator dependency, is affordable with moderate budgets, optimal over a wide range of budget levels, and cost-effective; (ii) for middle-aged women (women 45–74 years old), annual mammography screening is robustly optimal and cost-effective; and (iii) the use of MRI alone or combined with mammogram, a recommended strategy by the current guidelines, leads to outcomes that are not cost-effective. We also discuss the impact of patient adherence and operator dependency of US on these results. We find that the optimal strategy significantly shifts when adherence is less than perfect, underscoring the complex interplay between adherence and screening outcomes. This emphasizes the need to account for patient behavior to optimize health benefits particularly at the individual level. Our findings can be helpful in designing future trials, developing evidence-based guidelines and informing insurance coverage decisions. History: Accepted by J. Paul Brooks, Area Editor for Applications in Biology, Medicine, & Healthcare. Funding: This research was supported in part by the National Science Foundation [Award 1601084]. Supplemental Material: The software and data that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0373 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0373 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

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.