Data-Driven Real-Time Strategic Placement of Mobile Vaccine Distribution Sites

The deployment of vaccines across the US provides significant defense against serious illness and death from COVID-19. Over 70% of vaccine-eligible Americans are at least partially vaccinated, but there are pockets of the population that are under-vaccinated, such as in rural areas and some demographic groups (e.g. age, race, ethnicity). These pockets are extremely susceptible to the Delta variant, exacerbating the healthcare crisis and increasing the risk of new variants. In this paper, we describe a data-driven model that provides real-time support to Virginia public health officials by recommending mobile vaccination site placement in order to target under-vaccinated populations. Our strategy uses fine-grained mobility data, along with US Census and vaccination uptake data, to identify locations that are most likely to be visited by unvaccinated individuals. We further extend our model to choose locations that maximize vaccine uptake among hesitant groups. We show that the top recommended sites vary substantially across some demographics, demonstrating the value of developing customized recommendation models that integrate fine-grained, heterogeneous data sources. We also validate our recommendations by analyzing the success rates of deployed vaccine sites, and show that sites placed closer to our recommended areas administered higher numbers of doses. Our model is the first of its kind to consider evolving mobility patterns in real-time for suggesting placement strategies customized for different targeted demographic groups.

Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support

This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems;(ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis;(iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC;(iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences. © The Author(s) 2022.

Exercise, diet, and sleep habits of nurses working full-time during the COVID-19 pandemic: An observational study.

BACKGROUND: Healthy diet, exercise, and sleep practices may mitigate stress and prevent illness. However, lifestyle behaviors of acute care nurses working during stressful COVID-19 surges are unclear. PURPOSE: To quantify sleep, diet, and exercise practices of 12-hour acute care nurses working day or night shift during COVID-19-related surges. METHODS: Nurses across 10 hospitals in the United States wore wrist actigraphs and pedometers to quantify sleep and steps and completed electronic diaries documenting diet over 7-days. FINDINGS: Participant average sleep quantity did not meet national recommendations; night shift nurses (n = 23) slept significantly less before on-duty days when compared to day shift nurses (n = 34). Proportionally more night shift nurses did not meet daily step recommendations. Diet quality was low on average among participants. DISCUSSION: Nurses, especially those on night shift, may require resources to support healthy sleep hygiene, physical activity practices, and diet quality to mitigate stressful work environments.

Exercise, diet, and sleep habits of nurses working full-time during the COVID-19 pandemic: An observational study☆

Background Healthy diet, exercise, and sleep practices may mitigate stress and prevent illness. However, lifestyle behaviors of acute care nurses working during stressful COVID-19 surges are unclear. Purpose To quantify sleep, diet, and exercise practices of 12-hour acute care nurses working day or night shift during COVID-19-related surges. Methods Nurses across 10 hospitals in the United States wore wrist actigraphs and pedometers to quantify sleep and steps and completed electronic diaries documenting diet over 7-days. Findings Participant average sleep quantity did not meet national recommendations;night shift nurses (n = 23) slept significantly less before on-duty days when compared to day shift nurses (n = 34). Proportionally more night shift nurses did not meet daily step recommendations. Diet quality was low on average among participants. Discussion Nurses, especially those on night shift, may require resources to support healthy sleep hygiene, physical activity practices, and diet quality to mitigate stressful work environments.

Effective Social Network-Based Allocation of COVID-19 Vaccines

We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Using a realistic representation of a social contact network for the Commonwealth of Virginia, we study how a limited number of vaccine doses can be strategically distributed to individuals to reduce the overall burden of the pandemic. We show that allocation of vaccines based on individuals' degree (number of social contacts) and total social proximity time is significantly more effective than the usually used age-based allocation strategy in reducing the number of infections, hospitalizations and deaths. The overall strategy is robust even: (i) if the social contacts are not estimated correctly;(ii) if the vaccine efficacy is lower than expected or only a single dose is given;(iii) if there is a delay in vaccine production and deployment;and (iv) whether or not non-pharmaceutical interventions continue as vaccines are deployed. For reasons of implementability, we have used degree, which is a simple structural measure and can be easily estimated using several methods, including the digital technology available today. These results are significant, especially for resource-poor countries, where vaccines are less available, have lower efficacy, and are more slowly distributed. © 2022 Owner/Author.

Impact of COVID-19 Pandemic on Pulmonary Tuberculosis Evaluations and Diagnosis at a Large Safety Net Hospital in Los Angeles, California

Background. There is significant global concern that the COVID-19 pandemic may negatively impact tuberculosis (TB) control. This is a descriptive analysis of TB evaluations and diagnosis during 2019 (pre COVID-19 period) and 2020 (COVID-19 period) at the largest safety net hospital in Los Angeles County (LAC+USC Medical Center). Methods. The medical records of patients diagnosed with pulmonary TB from January 1, 2019 to December 31, 2020 were identified through laboratory and electronic medical records. We included all patients with ≥ 1 sputum positive result for Mycobacterium tuberculosis (MTB) culture and reviewed their Xpert MTB/RIF MTB PCR. Results. Table 1 shows summary of results. During the COVID-19 period, the number of patients evaluated for pulmonary TB decreased by 64% compared to the previous year (Figure 1). The proportion of patients with culture-confirmed TB disease however, was nearly identical (P=0.913) (Table 1). Sputum acid-fast bacilli (AFB) smear positivity increased 52% to 64% during COVID-19 (P=0.324) and disease severity as measured by chest radiograph, was significantly higher during the COVID-19 period (P = 0.031) (Figure 2). Trend of sputum AFB smear and culture samples collected from January 1, 2019 to December 31, 2020. Summary of results of patients diagnosed with pulmonary TB from January 1, 2019 to December 31, 2020 at LAC+USC Medical Center. Results of two-sample test for proportions of 2019 vs 2020 for cavitary lesions, extent of disease, and sputum positive AFB smear microscopy. Conclusion. These preliminary results suggest that when compared to the previous year, the number of pulmonary TB evaluations decreased by 64% during the COVID period. Whereas the proportion of patients diagnosed with TB disease was similar, TB patients during the COVID-19 period had more advanced disease at diagnosis, as measured by sputum smear AFB microscopy and disease severity on chest radiograph (P=0.031). These data suggest potentially consequential interruptions and delays in pulmonary TB diagnosis during the COVID-19 period.

AI-Driven Agent-Based Models to Study the Role of Vaccine Acceptance in Controlling COVID-19 Spread in the US

We study the role of vaccine acceptance in controlling the spread of COVID-19 in the US using AI-driven agent-based models. Our study uses a 288 million node social contact network spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12.59 billion daily interactions. The highly-resolved agent-based models use realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Developing a national model at this resolution that is driven by realistic data requires a complex scalable workflow, model calibration, simulation, and analytics components. Our workflow optimizes the total execution time and helps in improving overall human productivity.This work develops a pipeline that can execute US-scale models and associated workflows that typically present significant big data challenges. Our results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4K to 28.2K nationwide. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. Improving vaccine acceptance by 10% in all states increases averted infections from 4.5M to 4.7M (a 4.4% improvement) and total deaths from 28.2K to 29.9K (a 6% increase) nationwide. The analysis also reveals interesting spatio-temporal differences in COVID-19 dynamics as a result of vaccine acceptance. To our knowledge, this is the first national-scale analysis of the effect of vaccine acceptance on the spread of COVID-19, using detailed and realistic agent-based models. © 2021 IEEE.

Supporting COVID-19 Policy Response with Large-scale Mobility-based Modeling

Mobility restrictions have been a primary intervention for controlling the spread of COVID-19, but they also place a significant economic burden on individuals and businesses. To balance these competing demands, policymakers need analytical tools to assess the costs and benefits of different mobility reduction measures. In this paper, we present our work motivated by our interactions with the Virginia Department of Health on a decision-support tool that utilizes large-scale data and epidemiological modeling to quantify the impact of changes in mobility on infection rates. Our model captures the spread of COVID-19 by using a fine-grained, dynamic mobility network that encodes the hourly movements of people from neighborhoods to individual places, with over 3 billion hourly edges. By perturbing the mobility network, we can simulate a wide variety of reopening plans and forecast their impact in terms of new infections and the loss in visits per sector. To deploy this model in practice, we built a robust computational infrastructure to support running millions of model realizations, and we worked with policymakers to develop an interactive dashboard that communicates our model's predictions for thousands of potential policies. © 2021 ACM.

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 t his g ap, b ut a ll of these dashboards supported a particular niche view of the pandemic (ie, current status or specific r egions). I n t his paper, we describe our work developing our COVID-19 Surveillance Dashboard, which offers a unique 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 tool for comparing, organizing, and tracking near-real-time surveillance data as the pandemic progresses. In developing this dashboard, we also identified 6 key metrics 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 continues to be actively visited. Our own group uses it to support federal, state and local public health authorities, and it is used by individuals worldwide to track the evolution of the COVID-19 pandemic, build their own dashboards, and support their organizations as they plan their responses to the pandemic. © 2020 IEEE.