Evidence map›Paper›PMID 37899455›Full record

ArticlePopulation health metrics2023

Measuring unequal distribution of pandemic severity across census years, variants of concern and interventions.

Quang Dang Nguyen, Sheryl L Chang, Christina M Jamerlan, Mikhail Prokopenko

Open access · goldAbstract read
In one paragraph

Article in Population health metrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.2field-weighted citation impact, top 13% of its field
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

6 citing papers in PubMed, 10 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Quang Dang Nguyen *Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Camperdown, NSW, Australia.
Sheryl L Chang *Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Camperdown, NSW, Australia. sheryl.chang@sydney.edu.au.
Christina M JamerlanCentre for Complex Systems, Faculty of Engineering, The University of Sydney, Camperdown, NSW, Australia.
Mikhail ProkopenkoCentre for Complex Systems, Faculty of Engineering, The University of Sydney, Camperdown, NSW, Australia.
University of Sydney · AUTaronga Conservation Society Australia · AU

Funding

Australian Research Council DP22010168University of Sydney Infectious Diseases Institute Seed Grant 220182
6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic stressed public health systems worldwide due to emergence of several highly transmissible variants of concern. Diverse and complex intervention policies deployed over the last years have shown varied effectiveness in controlling the pandemic. However, a systematic analysis and modelling of the combined effects of different viral lineages and complex intervention policies remains a challenge due to the lack of suitable measures of pandemic inequality and nonlinear effects.

methodsUsing large-scale agent-based modelling and a high-resolution computational simulation matching census-based demographics of Australia, we carried out a systematic comparative analysis of several COVID-19 pandemic scenarios. The scenarios covered two most recent Australian census years (2016 and 2021), three variants of concern (ancestral, Delta and Omicron), and five representative intervention policies. We introduced pandemic Lorenz curves measuring an unequal distribution of the pandemic severity across local areas. We also quantified pandemic biomodality, distinguishing between urban and regional waves, and measured bifurcations in the effectiveness of interventions.

resultsWe quantified nonlinear effects of population heterogeneity on the pandemic severity, highlighting that (i) the population growth amplifies pandemic peaks, (ii) the changes in population size amplify the peak incidence more than the changes in density, and (iii) the pandemic severity is distributed unequally across local areas. We also examined and delineated the effects of urbanisation on the incidence bimodality, distinguishing between urban and regional pandemic waves. Finally, we quantified and examined the impact of school closures, complemented by partial interventions, and identified the conditions when inclusion of school closures may decisively control the transmission.

conclusionsPublic health response to long-lasting pandemics must be frequently reviewed and adapted to demographic changes. To control recurrent waves, mass-vaccination rollouts need to be complemented by partial NPIs. Healthcare and vaccination resources need to be prioritised towards the localities and regions with high population growth and/or high density.

Indexed as

CensusesCOVID-19AustraliaHealth FacilitiesHumansPandemicsAgent-based modellingCOVID-19Pandemic inequalitySARS-CoV-2Urbanisation effects

Identifiers

PMID37899455
PMCPMC10613397
OpenAlexW4388011360

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.