Aid and Assistance as a “Use of Force” Under the Jus Ad Bellum

Although the prohibition of the use of force is a cornerstone of international law, our understanding of what constitutes a “use of force” under Article 2(4) of the UN Charter nonetheless continues to evolve. While the term was traditionally understood to mean armed force, emerging interpretations are expanding our understanding of the prohibition’s breadth. The Charter’s text, travaux préparatoires, and subsequent interpretations and practice by States, reinforced by the persuasive reasoning of the International Court of Justice, all confirm that the notion of force extends to indirect force, which includes military support provided to parties to a conflict. Yet, to date, States have failed to meaningfully contend with the notion of indirect force, much less clarify its precise threshold. Accordingly, through the lens of the Russia-Ukraine conflict, this article examines whether and, if so, when a State\u27s military aid or assistance that contributes to another State\u27s use of force constitutes a separate and distinct use of force under international law. After concluding that aid or assistance can, under certain conditions, qualify as a use of force, the article proposes several non-exclusive factors that States are likely to consider when assessing whether aid or assistance from a supporting State crosses the prohibition’s threshold

Prisoner of War Status and Nationals of a Detaining Power

This article examines whether a Detaining State is obliged to recognize prisoner of war status for its own nationals under Article 4A of the 1949 Geneva Convention III. It begins with an assessment of that article from the perspective of established principles for construing treaty provisions. It then adds context to that assessment by examining relevant scholarship and State practice regarding its prescriptions before and after the Convention’s negotiation and adoption. Although it concludes that denying prisoner of war status to a national of the Detaining Power is the more persuasive interpretation of Article 4A, it concludes by highlighting the practical challenges of determining nationality that Detaining States may confront

Prisoner of war status and nationals of a detaining power

This article examines whether a Detaining State is obliged to recognize prisoner of war status for its own nationals under Article 4A of the 1949 Geneva Convention III. It begins with an assessment of that article from the perspective of established principles for construing treaty provisions. It then adds context to that assessment by examining relevant scholarship and State practice regarding its prescriptions before and after the Convention’s negotiation and adoption. Although it concludes that denying prisoner of war status to a national of the Detaining Power is the more persuasive interpretation of Article 4A, it concludes by highlighting the practical challenges of determining nationality that Detaining States may confront

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Protecting the environment in armed conflict: evaluating the US perspective

This article outlines and evaluates the US perspective on how treaty and customary international law protect the natural environment during international armed conflict. It surveys the relevant treaties to which the United States is a party and examines US views on their pertinent provisions. It then assesses claims that the environmental obligations residing in the 1977 Additional Protocol I to the 1949 Geneva Conventions have attained customary status, outlines the United States’ rejection of those claims, and evaluates the reasonableness thereof. Finally, it highlights ambiguities in certain US environmental positions, the resolution of which would bring much-needed clarity to the law

Aid and assistance as a “use of force” under the jus ad bellum

Although the prohibition of the use of force is a cornerstone of international law, our understanding of what constitutes a “use of force” under Article 2(4) of the UN Charter nonetheless continues to evolve. While the term was traditionally understood to mean armed force, emerging interpretations are expanding our understanding of the prohibition’s breadth. The Charter’s text, travaux préparatoires, and subsequent interpretations and practice by States, reinforced by the persuasive reasoning of the International Court of Justice, all confirm that the notion of force extends to indirect force, which includes military support provided to parties to a conflict. Yet, to date, States have failed to meaningfully contend with the notion of indirect force, much less clarify its precise threshold. Accordingly, through the lens of the Russia-Ukraine conflict, this article examines whether and, if so, when a State's military aid or assistance that contributes to another State's use of force constitutes a separate and distinct use of force under international law. After concluding that aid or assistance can, under certain conditions, qualify as a use of force, the article proposes several non-exclusive factors that States are likely to consider when assessing whether aid or assistance from a supporting State crosses the prohibition’s threshold

Challenges of COVID-19 Case Forecasting in the US, 2020-2021.

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

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks