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Abstract BackgroundThe coronavirus disease 2019 (COVID-19) pandemic may have disproportionally impacted vulnerable groups such as people who inject drugs (PWID) through reduced health care services as well as social changes from pandemic mitigation measures. Understanding how the COVID-19 pandemic and associated mitigation strategies subsequently changed the trajectory of hepatitis C virus (HCV) and human immunodeficiency virus (HIV) transmission is critical to estimating disease burdens, identifying outbreak risk, and developing informed intervention strategies. MethodsUsing behavioral data from the AIDS Linked to the IntraVenous Experience (ALIVE) study, an ongoing community-based cohort of PWID in Baltimore, United States, and an individual-based network model, we explored the impacts of service disruptions combined with changes in social networks and injecting behaviors of PWID on HCV and HIV transmission. ResultsAnalyses of ALIVE data showed that during the pandemic, there was an acceleration in injection cessation trajectories overall, but those who continued injecting increased the frequency of injection; at the same time, individual drug-use networks became smaller and the probability of injecting with others decreased. Simulation results demonstrated that HCV and HIV prevalence increased from service disruptions alone, but these effects were mitigated when including observed behavior changes in addition. ConclusionsModel results combined with rich individual behavioral data indicated that pandemic-induced behavioral changes of PWID that lasted longer than service disruptions could have offset the increasing disease burden caused by disrupted service access during the pandemic. 

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. 

Abstract 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.