Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination

What is already known about this topic?The highly transmissible SARS-CoV-2 Delta variant has begun to cause increases in cases, hospitalizations, and deaths in parts of the United States. With slowed vaccination uptake, this novel variant is expected to increase the risk of pandemic resurgence in the US in July--December 2021. What is added by this report?Data from nine mechanistic models project substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant. These resurgences, which have now been observed in most states, were projected to occur across most of the US, coinciding with school and business reopening. Reaching higher vaccine coverage in July--December 2021 reduces the size and duration of the projected resurgence substantially. The expected impact of the outbreak is largely concentrated in a subset of states with lower vaccination coverage. What are the implications for public health practice?Renewed efforts to increase vaccination uptake are critical to limiting transmission and disease, particularly in states with lower current vaccination coverage. Reaching higher vaccination goals in the coming months can potentially avert 1.5 million cases and 21,000 deaths and improve the ability to safely resume social contacts, and educational and business activities. Continued or renewed non-pharmaceutical interventions, including masking, can also help limit transmission, particularly as schools and businesses reopen.

All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting

Timely, high-resolution forecasts of infectious disease incidence are useful for policy makers in deciding intervention measures and estimating healthcare resource burden. In this paper, we consider the task of forecasting COVID-19 confirmed cases at the county level for the United States. Although multiple methods have been explored for this task, their performance has varied across space and time due to noisy data and the inherent dynamic nature of the pandemic. We present a forecasting pipeline which incorporates probabilistic forecasts from multiple statistical, machine learning and mechanistic methods through a Bayesian ensembling scheme, and has been operational for nearly 6 months serving local, state and federal policymakers in the United States. While showing that the Bayesian ensemble is at least as good as the individual methods, we also show that each individual method contributes significantly for different spatial regions and time points. We compare our models performance with other similar models being integrated into CDC-initiated COVID-19 Forecast Hub, and show better performance at longer forecast horizons. Finally, we also describe how such forecasts are used to increase lead time for training mechanistic scenario projections. Our work demonstrates that such a real-time high resolution forecasting pipeline can be developed by integrating multiple methods within a performance-based ensemble to support pandemic response. ACM Reference FormatAniruddha Adiga, Lijing Wang, Benjamin Hurt, Akhil Peddireddy, Przemys-law Porebski,, Srinivasan Venkatramanan, Bryan Lewis, Madhav Marathe. 2021. All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting. In Proceedings of ACM Conference (Conference17). ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn

From 5Vs to 6Cs: Operationalizing Epidemic Data Management with COVID-19 Surveillance

The COVID-19 pandemic brought to the forefront an unprecedented need for experts, as well as citizens, to visualize spatio-temporal disease surveillance data. Web application dashboards were quickly developed to fill this gap, including those built by JHU, WHO, and CDC, but all of these dashboards supported a particular niche view of the pandemic (ie, current status or specific regions). In this paper1, we describe our work developing our own COVID-19 Surveillance Dashboard, available at https://nssac.bii.virginia.edu/covid-19/dashboard/, which offers a universal view of the pandemic while also allowing users to focus on the details that interest them. From the beginning, our goal was to provide a simple visual way to compare, organize, and track near-real-time surveillance data as the pandemic progresses. Our dashboard includes a number of advanced features for zooming, filtering, categorizing and visualizing multiple time series on a single canvas. In developing this dashboard, we have also identified 6 key metrics we call the 6Cs standard which we propose as a standard for the design and evaluation of real-time epidemic science dashboards. Our dashboard was one of the first released to the public, and remains one of the most visited and highly used. Our group uses it to support federal, state and local public health authorities, and it is used by people worldwide to track the pandemic evolution, build their own dashboards, and support their organizations as they plan their responses to the pandemic. We illustrate the utility of our dashboard by describing how it can be used to support data story-telling - an important emerging area in data science.