Evidence map›Paper›PMID 38943103›Full record

ArticleBMC public health2024

Public health events and economic growth in a neoclassical framework.

Yunhao Wang, Yixuan Liu, Zhihan Peng, Zhaoyang Shang, Wei Gao

Abstract read
In one paragraph

Article in BMC public health, 2024. 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. Economic Impact Evaluation of COVID-19 Public Health Management Across Countries.The International journal of health planning and management · 2026
    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

5 authors.

Yunhao WangKey Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, 130024, China.
Yixuan LiuSchool of Information Science and Technology, Northeast Normal University, Changchun, 130117, China.
Zhihan PengDepartment of Computer Science, Duke University, Durham, 27708-0129, NC, USA.
Zhaoyang ShangSchool of Economics and Management, Northeast Normal University, Changchun, 130117, China.
Wei GaoKey Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, 130024, China. gaow@nenu.edu.cn.

Funding

National Natural Science Foundation of China 11871141
6 · The paper itself

Abstract

Public health events (PHEs) have emerged as significant threats to human life, health, and economic growth. PHEs, such as COVID-19, have prompted a reevaluation for enhanced regular prevention and control (RPC). In this study, we focus on the core concept of prevention and control intensity (PCI), and establish a neoclassical economic growth model from the long-term and macro perspective to balance life protection and economic growth. The model construct the mechanism of PCI on economic growth through population dynamics and capital accumulation under the backdrop of RPC for PHEs. We find the conditions for PCI when the economy achieves steady state, and provides an algorithm establishing the optimal strategy that maximises per capita disposable income based on the optimal PCI and consumption. Simulation result quantifies an inverted U-shaped relationship between PCI and capital per capita, output per capita and consumption per capita in the steady state. The model suggests that, given the PHEs of inducing potential unemployment shock, it is worthwhile to combine the implementation of moderate PCI with coordinated policies of income distribution.

Indexed as

COVID-19Economic DevelopmentPublic HealthHumansModels, EconomicEconomic growthEpidemicNeoclassical growth modelPrevention and control intensityPublic health event

Identifiers

PMID38943103
PMCPMC11539698

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.