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.


This content will become publicly available on December 1, 2026

Title: Identifying delayed human response to external risks: an econometric analysis of mobility change during a pandemic
Background Human behavioral responses to changes in risks are often delayed. Methods for estimating these delayed responses either rely on rigid assumptions about the delay distribution (e.g., Erlang distribution), producing a poor fit, or yield period-specific estimates (e.g., estimates from the Autoregressive Distributed Lag (ARDL) model) that are difficult to integrate into simulation models. We propose a hybrid ARDL–Erlang approach that yields an interpretable summary of behavioral responses suitable for incorporation into simulation models. Method We apply the ARDL–Erlang approach to estimate the effect of COVID-19 deaths on mobility across US counties from October 2020 to July 2021. A standard panel autoregressive distributed lag (ARDL) model first estimates the effect of past deaths and past mobility on current mobility. The ARDL model is then transformed into an Infinite Distributed Lag (IDL) model consisting of only past deaths. The coefficients of the past deaths are aggregated into an overall effect and fit to an Erlang distribution, summarized by average delay length and shape parameter. Results Our results show that on the national level, a one-standard-deviation permanent increase in weekly deaths per 100,000 population (log-transformed) is associated with a 0.46-standard-deviation decrease in human mobility in the long run, where the delay distribution follows a first-order Erlang distribution, and the average delay length is about 3.2 weeks. However, there is much heterogeneity across states, with first- to third-order Erlang delays and 2 to 18 weeks of average delay providing a theoretically cogent summary of how mobility followed changes in deaths during the first year and a half of the pandemic. Conclusion This study provides a novel approach to estimating delayed human responses to health risks using a hybrid ARDL-Erlang model. Our findings highlight significant variability in the impact and timing of responses across states, underscoring the need for tailored public health policies. This study can also serve as guidelines and an example for identifying delayed human behavior in other settings.  more » « less
Award ID(s):
2229819
PAR ID:
10645982
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Springer Nature
Date Published:
Journal Name:
BMC Medical Research Methodology
Volume:
25
Issue:
1
ISSN:
1471-2288
Subject(s) / Keyword(s):
Human behavior, Causality, Delay, COVID-19
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, Science Advances)
    null (Ed.)
    Assessing the effects of early nonpharmaceutical interventions on coronavirus disease 2019 (COVID-19) spread is crucial for understanding and planning future control measures to combat the pandemic. We use observations of reported infections and deaths, human mobility data, and a metapopulation transmission model to quantify changes in disease transmission rates in U.S. counties from 15 March to 3 May 2020. We find that marked, asynchronous reductions of the basic reproductive number occurred throughout the United States in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same measures been implemented 1 to 2 weeks earlier, substantial cases and deaths could have been averted and that delayed responses to future increased incidence will facilitate a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive control in combatting the COVID-19 pandemic. 
    more » « less
  2. ; ; (, American Political Science Review)
    Time-series cross-section (TSCS) data are prevalent in political science, yet many distinct challenges presented by TSCS data remain underaddressed. We focus on how dependence in both space and time complicates estimating either spatial or temporal dependence, dynamics, and effects. Little is known about how modeling one of temporal or cross-sectional dependence well while neglecting the other affects results in TSCS analysis. We demonstrate analytically and through simulations how misspecification of either temporal or spatial dependence inflates estimates of the other dimension’s dependence and thereby induces biased estimates and tests of other covariate effects. Therefore, we recommend the spatiotemporal autoregressive distributed lag (STADL) model with distributed lags in both space and time as an effective general starting point for TSCS model specification. We illustrate with two example reanalyses and provide R code to facilitate researchers’ implementation—from automation of common spatial-weights matrices ( W ) through estimated spatiotemporal effects/response calculations—for their own TSCS analyses. 
    more » « less
  3. ; ; ; ; ; ; (, PLOS Biology)
    Read, Andrew Fraser (Ed.)
    Two of the Coronavirus Disease 2019 (COVID-19) vaccines currently approved in the United States require 2 doses, administered 3 to 4 weeks apart. Constraints in vaccine supply and distribution capacity, together with a deadly wave of COVID-19 from November 2020 to January 2021 and the emergence of highly contagious Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants, sparked a policy debate on whether to vaccinate more individuals with the first dose of available vaccines and delay the second dose or to continue with the recommended 2-dose series as tested in clinical trials. We developed an agent-based model of COVID-19 transmission to compare the impact of these 2 vaccination strategies, while varying the temporal waning of vaccine efficacy following the first dose and the level of preexisting immunity in the population. Our results show that for Moderna vaccines, a delay of at least 9 weeks could maximize vaccination program effectiveness and avert at least an additional 17.3 (95% credible interval [CrI]: 7.8–29.7) infections, 0.69 (95% CrI: 0.52–0.97) hospitalizations, and 0.34 (95% CrI: 0.25–0.44) deaths per 10,000 population compared to the recommended 4-week interval between the 2 doses. Pfizer-BioNTech vaccines also averted an additional 0.60 (95% CrI: 0.37–0.89) hospitalizations and 0.32 (95% CrI: 0.23–0.45) deaths per 10,000 population in a 9-week delayed second dose (DSD) strategy compared to the 3-week recommended schedule between doses. However, there was no clear advantage of delaying the second dose with Pfizer-BioNTech vaccines in reducing infections, unless the efficacy of the first dose did not wane over time. Our findings underscore the importance of quantifying the characteristics and durability of vaccine-induced protection after the first dose in order to determine the optimal time interval between the 2 doses. 
    more » « less
  4. ; (, PLOS Computational Biology)
    Struchiner, Claudio José (Ed.)
    Behavioral epidemic models incorporating endogenous societal risk-response, where changes in risk perceptions prompt adjustments in contact rates, are crucial for predicting pandemic trajectories. Accurate parameter estimation in these models is vital for validation and precise projections. However, few studies have examined the problem of identifiability in models where disease and behavior parameters must be jointly estimated. To address this gap, we conduct simulation experiments to assess the effect on parameter estimation accuracy of a) delayed risk response, b) neglecting behavioral response in model structure, and c) integrating disease and public behavior data. Our findings reveal systematic biases in estimating behavior parameters even with comprehensive and accurate disease data and a well-structured simulation model when data are limited to the first wave. This is due to the significant delay between evolving risks and societal reactions, corresponding to the duration of a pandemic wave. Moreover, we demonstrate that conventional SEIR models, which disregard behavioral changes, may fit well in the early stages of a pandemic but exhibit significant errors after the initial peak. Furthermore, early on, relatively small data samples of public behavior, such as mobility, can significantly improve estimation accuracy. However, the marginal benefits decline as the pandemic progresses. These results highlight the challenges associated with the joint estimation of disease and behavior parameters in a behavioral epidemic model. 
    more » « less
  5. (, ACM SIGKDD workshop on Epidemiology meets Data Mining and Knowledge discovery)
    null (Ed.)
    In the absence of pharmaceutical interventions to curb the spread of COVID-19, countries relied on a number of nonpharmaceutical interventions to fight the first wave of the pandemic. The most prevalent one has been stay-at-home orders, whose the goal is to limit the physical contact between people, which consequently will reduce the number of secondary infections generated. In this work, we use a detailed set of mobility data to evaluate the impact that these interventions had on alleviating the spread of the virus in the US as measured through the COVID-19-related deaths. To establish this impact, we use the notion of Granger causality between two time-series. We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks. We further analyze the mobility patterns at the census block level to identify which parts of the population might encounter difficulties in adhering and complying with social distancing measures. This information is important, since it can consequently drive interventions that aim at helping these parts of the population. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email