skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US
Analyzing the causal impact of different policies in reducing the spread of COVID-19 is of critical importance. The main challenge here is the existence of unobserved confounders (e.g., vigilance of residents) which influence both the presence of policies and the spread of COVID-19. Besides, as the confounders may be time-varying, it is even more difficult to capture them. Fortunately, the increasing prevalence of web data from various online applications provides an important resource of time-varying observational data, and enhances the opportunity to capture the confounders from them, e.g., the vigilance of residents over time can be reflected by the popularity of Google searches about COVID-19 at different time periods. In this paper, we study the problem of assessing the causal effects of different COVID-19 related policies on the outbreak dynamics in different counties at any given time period. To this end, we integrate COVID-19 related observational data covering different U.S. counties over time, and then develop a neural network based causal effect estimation framework which learns the representations of time-varying (unobserved) confounders from the observational data. Experimental results indicate the effectiveness of our proposed framework in quantifying the causal impact of policies at different granularities, ranging from a category of policies with a certain goal to a specific policy type. Compared with baseline methods, our assessment of policies is more consistent with existing epidemiological studies of COVID-19. Besides, our assessment also provides insights for future policy-making.  more » « less
Award ID(s):
2006844
PAR ID:
10357533
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of the ACM Web Conference 2022
Page Range / eLocation ID:
2678 to 2686
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; (, Applied Network Science)
    null (Ed.)
    Abstract The objective of this study is to examine the transmission risk of COVID-19 based on cross-county population co-location data from Facebook. The rapid spread of COVID-19 in the United States has imposed a major threat to public health, the real economy, and human well-being. With the absence of effective vaccines, the preventive actions of social distancing, travel reduction and stay-at-home orders are recognized as essential non-pharmacologic approaches to control the infection and spatial spread of COVID-19. Prior studies demonstrated that human movement and mobility drove the spatiotemporal distribution of COVID-19 in China. Little is known, however, about the patterns and effects of co-location reduction on cross-county transmission risk of COVID-19. This study utilizes Facebook co-location data for all counties in the United States from March to early May 2020 for conducting spatial network analysis where nodes represent counties and edge weights are associated with the co-location probability of populations of the counties. The analysis examines the synchronicity and time lag between travel reduction and pandemic growth trajectory to evaluate the efficacy of social distancing in ceasing the population co-location probabilities, and subsequently the growth in weekly new cases across counties. The results show that the mitigation effects of co-location reduction appear in the growth of weekly new confirmed cases with one week of delay. The analysis categorizes counties based on the number of confirmed COVID-19 cases and examines co-location patterns within and across groups. Significant segregation is found among different county groups. The results suggest that within-group co-location probabilities (e.g., co-location probabilities among counties with high numbers of cases) remain stable, and social distancing policies primarily resulted in reduced cross-group co-location probabilities (due to travel reduction from counties with large number of cases to counties with low numbers of cases). These findings could have important practical implications for local governments to inform their intervention measures for monitoring and reducing the spread of COVID-19, as well as for adoption in future pandemics. Public policy, economic forecasting, and epidemic modeling need to account for population co-location patterns in evaluating transmission risk of COVID-19 across counties. 
    more » « less
  2. ; (, Advances in neural information processing systems)
    We study the problem of learning personalized decision policies from observational data while accounting for possible unobserved confounding in the data-generating process. Unlike previous approaches that assume unconfoundedness, i.e., no unobserved confounders affected both treatment assignment and outcomes, we calibrate policy learning for realistic violations of this unverifiable assumption with uncertainty sets motivated by sensitivity analysis in causal inference. Our framework for confounding-robust policy improvement optimizes the minimax regret of a candidate policy against a baseline or reference "status quo" policy, over an uncertainty set around nominal propensity weights. We prove that if the uncertainty set is well-specified, robust policy learning can do no worse than the baseline, and only improve if the data supports it. We characterize the adversarial subproblem and use efficient algorithmic solutions to optimize over parametrized spaces of decision policies such as logistic treatment assignment. We assess our methods on synthetic data and a large clinical trial, demonstrating that confounded selection can hinder policy learning and lead to unwarranted harm, while our robust approach guarantees safety and focuses on well-evidenced improvement. 
    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. ; ; (, International Journal of Environmental Research and Public Health)
    null (Ed.)
    The policy induced decline of human mobility has been recognised as effective in controlling the spread of COVID-19, especially in the initial stage of the outbreak, although the relationship among mobility, policy implementation, and virus spread remains contentious. Coupling the data of confirmed COVID-19 cases with the Google mobility data in Australia, we present a state-level empirical study to: (1) inspect the temporal variation of the COVID-19 spread and the change of human mobility adherent to social restriction policies; (2) examine the extent to which different types of mobility are associated with the COVID-19 spread in eight Australian states/territories; and (3) analyse the time lag effect of mobility restriction on the COVID-19 spread. We find that social restriction policies implemented in the early stage of the pandemic controlled the COVID-19 spread effectively; the restriction of human mobility has a time lag effect on the growth rates of COVID-19, and the strength of the mobility-spread correlation increases up to seven days after policy implementation but decreases afterwards. The association between human mobility and COVID-19 spread varies across space and time and is subject to the types of mobility. Thus, it is important for government to consider the degree to which lockdown conditions can be eased by accounting for this dynamic mobility-spread relationship. 
    more » « less
  5. ; ; ; (, ArXivorg)
    This study explored how population mobility flows form commuting networks across US counties and influence the spread of COVID-19. We utilized 3-level mixed effects negative binomial regression models to estimate the impact of network COVID-19 exposure on county confirmed cases and deaths over time. We also conducted weighting-based analyses to estimate the causal effect of network exposure. Results showed that commuting networks matter for COVID-19 deaths and cases, net of spatial proximity, socioeconomic, and demographic factors. Different local racial and ethnic concentrations are also associated with unequal outcomes. These findings suggest that commuting is an important causal mechanism in the spread of COVID-19 and highlight the significance of interconnected of communities. The results suggest that local level mitigation and prevention efforts are more effective when complemented by similar efforts in the network of connected places. Implications for research on inequality in health and flexible work arrangements are discussed. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email