Evidence map›Paper›PMID 35990556›Full record

ArticleJournal of population economics2023

Optimal interventions in networks during a pandemic.

Roland Pongou, Guy Tchuente, Jean-Baptiste Tondji

Abstract read
In one paragraph

Article in Journal of population economics, 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. Vaccine and inclusion.Journal of public economic theory · 2022
    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

3 authors.

Roland PongouDepartment of Economics, University of Ottawa, 120 University Private, Social Sciences Building, Ottawa, K1N 6N5 Ontario Canada.
Guy TchuenteSchool of Economics, University of Kent, Kennedy Building, Park Wood Road, Canterbury, CT2 7FS Kent UK.ORCID 0000-0001-8507-3337
Jean-Baptiste TondjiDepartment of Economics, The University of Texas Rio Grande Valley, 1201 West University Drive, Edinburg, 78539 TX USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We develop a model of optimal lockdown policy for a social planner who balances population health with short-term wealth accumulation. The unique solution depends on tolerable infection incidence and social network structure. We then use unique data on nursing home networks in the US to calibrate the model and quantify state-level preference for prioritizing health over wealth. We also empirically validate simulation results derived from comparative statics analyses. Our findings suggest that policies that tolerate more virus spread (laissez-faire) increase state GDP growth and COVID-19 deaths in nursing homes. The detrimental effects of laissez-faire policies are more potent for nursing homes that are more peripheral in networks, nursing homes in poorer counties, and nursing homes that operate on a for-profit basis. We also find that US states with Republican governors have a higher tolerable incidence level, but these policies tend to converge with a high death count. Supplementary Information: The online version contains supplementary material available at 10.1007/s00148-022-00916-y.

Indexed as

COVID-19Health-vs-wealth prioritizationLockdownNetworksNursing homes

Identifiers

PMID35990556
PMCPMC9375093

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.