Evidence map›Paper›PMID 36603015›Full record

ArticlePloS one2023

Multi-dimensional resilience: A quantitative exploration of disease outcomes and economic, political, and social resilience to the COVID-19 pandemic in six countries.

Lauren J Beesley, Paolo Patelli, Kimberly Kaufeld, Jon Schwenk, Kaitlyn M Martinez, Travis Pitts, Martha Barnard, Ben McMahon, Sara Y Del Valle

Abstract read
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In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Lauren J BeesleyInformation Systems & Modeling, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.ORCID 0000-0002-3788-5944
Paolo PatelliInformation Systems & Modeling, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
Kimberly KaufeldStatistical Sciences, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
Jon SchwenkEarth Systems & Observations, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.ORCID 0000-0001-5803-9686
Kaitlyn M MartinezInformation Systems & Modeling, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.ORCID 0000-0003-0975-2949
Travis PittsIntelligence & Systems Analysis, Los Alamos National Laboratory,Los Alamos, New Mexico, United States of America.
Martha BarnardInformation Systems & Modeling, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.ORCID 0000-0003-3677-3964
Ben McMahonTheoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
Sara Y Del ValleInformation Systems & Modeling, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.ORCID 0000-0002-0159-1952

Funding

Development of an Open-Source and Data-Driven Modeling Platform to Monitor and Forecast Disease ActivityR01GM130668 · NIGMS · NORTHEASTERN UNIVERSITY · PI SANTILLANA, MAURICIO · 2018 to 2022
$1.9M
NIGMS NIH HHS R01 GM130668
6 · The paper itself

Abstract

The COVID-19 pandemic has highlighted a need for better understanding of countries' vulnerability and resilience to not only pandemics but also disasters, climate change, and other systemic shocks. A comprehensive characterization of vulnerability can inform efforts to improve infrastructure and guide disaster response in the future. In this paper, we propose a data-driven framework for studying countries' vulnerability and resilience to incident disasters across multiple dimensions of society. To illustrate this methodology, we leverage the rich data landscape surrounding the COVID-19 pandemic to characterize observed resilience for several countries (USA, Brazil, India, Sweden, New Zealand, and Israel) as measured by pandemic impacts across a variety of social, economic, and political domains. We also assess how observed responses and outcomes (i.e., resilience) of the COVID-19 pandemic are associated with pre-pandemic characteristics or vulnerabilities, including (1) prior risk for adverse pandemic outcomes due to population density and age and (2) the systems in place prior to the pandemic that may impact the ability to respond to the crisis, including health infrastructure and economic capacity. Our work demonstrates the importance of viewing vulnerability and resilience in a multi-dimensional way, where a country's resources and outcomes related to vulnerability and resilience can differ dramatically across economic, political, and social domains. This work also highlights key gaps in our current understanding about vulnerability and resilience and a need for data-driven, context-specific assessments of disaster vulnerability in the future.

Indexed as

COVID-19DisastersBrazilHumansIndiaPandemics

Identifiers

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LicenceCC BY
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Registered trials

None linked

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.