A retrospective assessment of COVID-19 model performance in the USA.

Coronavirus disease 2019 (COVID-19) forecasts from over 100 models are readily available. However, little published information exists regarding the performance of their uncertainty estimates (i.e. probabilistic performance). To evaluate their probabilistic performance, we employ the classical model (CM), an established method typically used to validate expert opinion. In this analysis, we assess both the predictive and probabilistic performance of COVID-19 forecasting models during 2021. We also compare the performance of aggregated forecasts (i.e. ensembles) based on equal and CM performance-based weights to an established ensemble from the Centers for Disease Control and Prevention (CDC). Our analysis of forecasts of COVID-19 mortality from 22 individual models and three ensembles across 49 states indicates that-(i) good predictive performance does not imply good probabilistic performance, and vice versa; (ii) models often provide tight but inaccurate uncertainty estimates; (iii) most models perform worse than a naive baseline model; (iv) both the CDC and CM performance-weighted ensembles perform well; but (v) while the CDC ensemble was more informative, the CM ensemble was more statistically accurate across states. This study presents a worthwhile method for appropriately assessing the performance of probabilistic forecasts and can potentially improve both public health decision-making and COVID-19 modelling.

COVID-19 stressors on migrant workers in Kuwait: cumulative risk considerations.

As a marginalised subpopulation, migrant workers often fall short from protection by public policies, they take precarious jobs with unsafe working and living conditions and they grapple with cultural and linguistic barriers. In light of the current COVID-19 pandemic, migrant workers are now exposed to additional stressors of the virus and related responses. We applied a comprehensive qualitative cumulative risk assessment framework for migrant workers living in Kuwait. This pandemic could be one of the few examples where the stressors overlap all domains of migrant workers' lives. No single intervention can solve all the problems; there must be a set of interventions to address all domains. Local authorities and employers must act quickly to stop the spread, ensure easy access to testing and treatment, provide adequate housing and clear communication, encourage wide social support, safeguard financial protection and mental well-being and continuously re-evaluate the situation as more data are collected.