Evidence map›Paper›PMID 41913581›Full record

ArticleRisk analysis : an official publication of the Society for Risk Analysis2026

Faster Uptake, Slower Let-Down: Asymmetric Community Responses to Changing Risks During a Pandemic.

Kian Babaei Shalmani, Catherine DiGennaro, Hazhir Rahmandad, Navid Ghaffarzadegan

Abstract read
In one paragraph

Article in Risk analysis : an official publication of the Society for Risk Analysis, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kian Babaei ShalmaniDepartment of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia, USA.
Catherine DiGennaroMIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Hazhir RahmandadMIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.ORCID 0000-0002-2784-9042
Navid GhaffarzadeganDepartment of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia, USA.

Funding

National Science Foundation 2229819
6 · The paper itself

Abstract

From how public opinion responds to economic outcomes to how risk perception shapes social interactions during a pandemic, many important social processes involve the assimilation of information to form opinions and perceptions, which in turn guide individual and societal actions. While such perception delays are well recognized, empirically identifying them is nontrivial. We study this problem in the context of human responses to disease dynamics, mediated by public risk perception. Public risk perception changes through an information diffusion process with significant delays, where perception adjustment delay may differ depending on whether the risk is increasing or decreasing. Despite these complexities, most models assume either fixed-delay structures or exponential structures with symmetric delay periods. First, using synthetic data (where ground truth is known), we show that incorrect delay structures and the assumption of symmetric delay periods can lead to biased and misleading estimates. We then explore alternative approaches to identify more appropriate delay structures that can overcome these challenges. Second, we apply these asymmetric delay structures to state-level US COVID-19 disease and mobility data to demonstrate how estimates of public sensitivity to mortality depend on the assumed delay structure. Our results provide evidence that during the pandemic, human risk perception adjusted with asymmetric delays to changing risk: people perceived rising risks more quickly than they perceived declining risks.

Indexed as

Communicable Disease ControlCOVID-19PandemicsBehaviorComputer SimulationHumansepidemicshuman responsemobilityrisk perceptionsystem dynamics

Identifiers

PMID41913581
PMCPMC13036392

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.