Estimating Vaccine-Preventable COVID-19 Deaths Under Counterfactual Vaccination Scenarios in the United States

ImportanceWith an abundant supply of COVID-19 vaccines becoming available in spring and summer 2021, the major barrier to high vaccination rates in the United States has been a lack of vaccine demand. This has contributed to a higher rate of deaths from SARS-CoV-2 infections amongst unvaccinated individuals as compared to vaccinated individuals. It is important to understand how low vaccination rates directly impact deaths resulting from SARS-CoV-2 infections in unvaccinated populations across the United States. ObjectiveTo estimate a lower bound on the number of vaccine-preventable deaths from SARS-CoV-2 infections under various scenarios of vaccine completion, for every state of the United States. Design, Setting, and ParticipantsThis counterfactual simulation study varies the rates of complete vaccination coverage under the scenarios of 100%, 90% and 85% coverage of the adult (18+) population of the United States. For each scenario, we use U.S. state-level demographic information in conjunction with county-level vaccination statistics to compute a lower bound on the number of vaccine-preventable deaths for each state. ExposuresCOVID-19 vaccines, SARS-CoV-2 infection Main Outcomes and MeasuresDeath from SARS-CoV-2 infection ResultsBetween January 1st, 2021 and April 30th, 2022, there were 641,305 deaths due to COVID-19 in the United States. Assuming each state continued peak vaccination capacity after initially achieving its peak vaccination rate, a vaccination rate of 100% would have led to 322,324 deaths nationally, that of 90% would have led to 415,878 deaths, and that of 85% would have led to 463,305 deaths. As a comparison, using the state with the highest peak vaccination rate (per million population each week) for all the states, a vaccination rate of 100% would have led to 302,344 deaths nationally, that of 90% would have led to 398,289 deaths, and that of 85% would have led to 446,449 deaths. Conclusions and RelevanceOnce COVID-19 vaccine supplies peaked across the United States, if there had been 100% COVID-19 vaccination coverage of the over 18+ population, a conservative estimate of 318,981 deaths could have been potentially avoided through vaccination. For a 90% vaccination coverage, we estimate at least 225,427 deaths averted through vaccination, and at least 178,000 lives saved through vaccination for an 85% vaccination coverage.

Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement

In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs from COVID-19+ patients are relatively small, and researchers often pool CXR data from multiple sources, for example, using different x-ray machines in various patient populations under different clinical scenarios. Deep learning models trained on such datasets have been shown to overfit to erroneous features instead of learning pulmonary characteristics - a phenomenon known as shortcut learning. We propose adding feature disentanglement to the training process, forcing the models to identify pulmonary features from the images while penalizing them for learning features that can discriminate between the original datasets that the images come from. We find that models trained in this way indeed have better generalization performance on unseen data; in the best case we found that it improved AUC by 0.13 on held out data. We further find that this outperforms masking out non-lung parts of the CXRs and performing histogram equalization, both of which are recently proposed methods for removing biases in CXR datasets.

Effective Deep Learning Approaches for Predicting COVID-19 Outcomes from Chest Computed Tomography Volumes

The rapid evolution of the novel coronavirus SARS-CoV-2 pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish Novel Coronavirus Pneumonia (COVID-19+) from other cases of viral pneumonia and normal healthy chest CT volumes with state-of-the-art performance. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19+ patients.