skip to main content

Explore Research Products in the NSF-PAR

This content will become publicly available on May 2, 2024

Title: Multiple models for outbreak decision support in the face of uncertainty
Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.  more » « less
Award ID(s):
2126278 2037885 2028301 2029262
NSF-PAR ID:
10409984
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more » ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; « less
Date Published:
Journal Name:
Proceedings of the National Academy of Sciences
Volume:
120
Issue:
18
ISSN:
0027-8424
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ( , JMIR Public Health and Surveillance)
    null (Ed.)
    Background Significant uncertainty has existed about the safety of reopening college and university campuses before the COVID-19 pandemic is better controlled. Moreover, little is known about the effects that on-campus students may have on local higher-risk communities. Objective We aimed to estimate the range of potential community and campus COVID-19 exposures, infections, and mortality under various university reopening plans and uncertainties. Methods We developed campus-only, community-only, and campus × community epidemic differential equations and agent-based models, with inputs estimated via published and grey literature, expert opinion, and parameter search algorithms. Campus opening plans (spanning fully open, hybrid, and fully virtual approaches) were identified from websites and publications. Additional student and community exposures, infections, and mortality over 16-week semesters were estimated under each scenario, with 10% trimmed medians, standard deviations, and probability intervals computed to omit extreme outliers. Sensitivity analyses were conducted to inform potential effective interventions. Results Predicted 16-week campus and additional community exposures, infections, and mortality for the base case with no precautions (or negligible compliance) varied significantly from their medians (4- to 10-fold). Over 5% of on-campus students were infected after a mean of 76 (SD 17) days, with the greatest increase (first inflection point) occurring on average on day 84 (SD 10.2 days) of the semester and with total additional community exposures, infections, and mortality ranging from 1-187, 13-820, and 1-21 per 10,000 residents, respectively. Reopening precautions reduced infections by 24%-26% and mortality by 36%-50% in both populations. Beyond campus and community reproductive numbers, sensitivity analysis indicated no dominant factors that interventions could primarily target to reduce the magnitude and variability in outcomes, suggesting the importance of comprehensive public health measures and surveillance. Conclusions Community and campus COVID-19 exposures, infections, and mortality resulting from reopening campuses are highly unpredictable regardless of precautions. Public health implications include the need for effective surveillance and flexible campus operations. 
    more » « less
  2. ; ; ; ; ; ; ( , American Behavioral Scientist)
    COVID-19 resulted in health and logistical challenges for many sectors of the American economy, including the trucking industry. This study examined how the pandemic impacted the trucking industry, focused on the pandemic’s impacts on company operations, health, and stress of trucking industry employees. Data were collected from three sources: surveys, focus groups, and social media posts. Individuals at multiple organizational levels of trucking companies (i.e., supervisors, upper-level management, and drivers) completed an online survey and participated in online focus groups. Data from focus groups were coded using a thematic analysis approach. Publicly available social media posts from Twitter were analyzed using a sentiment analysis framework to assess changes in public sentiment about the trucking industry pre- and during-COVID-19. Two themes emerged from the focus groups: (1) trucking company business strategies and adaptations and (2) truck driver experiences and workplace safety. Participants reported supply chain disruptions and new consumer buying trends as having larger industry-wide impacts. Company adaptability emerged due to freight variability, leading organizations to pivot business models and create solutions to reduce operational costs. Companies responded to COVID-19 by accommodating employees’ concerns and implementing safety measures. Truck drivers noted an increase in positive public perception of truck drivers, but job quality factors worsened due to closed amenities and decreased social interaction. Social media sentiment analysis also illustrated an increase in positive public sentiment towards the trucking industry during COVID-19. The pandemic resulted in multi-level economic, health, and social impacts on the trucking industry, which included economic impacts on companies and economic, social and health impacts on employees within the industry levels. Further research can expand on this study to provide an understanding of the long-term impacts of the pandemic on the trucking industry companies within the industry and segments of the trucking industry workforce. 
    more » « less
  3. ; ; ; ; ; ; ; ; ( , Proceedings of the National Academy of Sciences)

    The COVID-19 pandemic is a global threat presenting health, economic, and social challenges that continue to escalate. Metapopulation epidemic modeling studies in the susceptible–exposed–infectious–removed (SEIR) style have played important roles in informing public health policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic, and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What’s more, variation of intracounty environments creates spatial heterogeneity of transmission in different regions. To address this issue, we develop a human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behaviors. This modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and business foot traffic, race and ethnicity, and age structure are then investigated. The results reveal that, in a college town (Dane County), the most important heterogeneity is age structure, while, in a large city area (Milwaukee County), racial and ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate to various reopening policies, which suggests that policy makers may need to take these heterogeneities into account very carefully when designing policies for mitigating the ongoing spread of COVID-19 and reopening.

     
    more » « less
  4. ; ; ; ; ; ; ( , Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities (ARIC '20))
    null (Ed.)
    Agent-based models (ABM) play a prominent role in guiding critical decision-making and supporting the development of effective policies for better urban resilience and response to the COVID-19 pandemic. However, many ABMs lack realistic representations of human mobility, a key process that leads to physical interaction and subsequent spread of disease. Therefore, we propose the application of Latent Dirichlet Allocation (LDA), a topic modeling technique, to foot-traffic data to develop a realistic model of human mobility in an ABM that simulates the spread of COVID-19. In our novel approach, LDA treats POIs as "words" and agent home census block groups (CBGs) as "documents" to extract "topics" of POIs that frequently appear together in CBG visits. These topics allow us to simulate agent mobility based on the LDA topic distribution of their home CBG. We compare the LDA based mobility model with competitor approaches including a naive mobility model that assumes visits to POIs are random. We find that the naive mobility model is unable to facilitate the spread of COVID-19 at all. Using the LDA informed mobility model, we simulate the spread of COVID-19 and test the effect of changes to the number of topics, various parameters, and public health interventions. By examining the simulated number of cases over time, we find that the number of topics does indeed impact disease spread dynamics, but only in terms of the outbreak's timing. Further analysis of simulation results is needed to better understand the impact of topics on simulated COVID-19 spread. This study contributes to strengthening human mobility representations in ABMs of disease spread. 
    more » « less
  5. ; ; ; ; ; ; ( , Advanced Theory and Simulations)
    Abstract

    The potential waning of the vaccination immunity to COVID‐19 could pose threats to public health, as it is tenable that the timing of such waning would synchronize with the near‐complete restoration of normalcy. Should also testing be relaxed, a resurgent COVID‐19 wave in winter 2021/2022 might be witnessed. In response to this risk, an additional vaccine dose, the booster shot, is being administered worldwide. A projected study with an outlook of 6 months explores the interplay between the rate at which boosters are distributed and the extent to which testing practices are implemented, using a highly granular agent‐based model tuned on a medium‐sized US town. Theoretical projections indicate that the administration of boosters at the rate at which the vaccine is currently administered could yield a severe resurgence of the pandemic. Projections suggest that the peak levels of mid‐spring 2021 in the vaccination rate may prevent such a scenario to occur, although exact agreement between observations and projections should not be expected due to the continuously evolving nature of the pandemic. This study highlights the importance of testing, especially to detect asymptomatic individuals in the near future, as the release of the booster reaches full speed.

     
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email