Evidence map›Paper›PMID 37404333›Full record

ArticleJournal of public health in Africa2023

Simulation of the COVID-19 handling policy in Indonesia.

Erman Aminullah, Erwiza Erman, Tri Edhi Budhi Soesilo

Abstract read
In one paragraph

Article in Journal of public health in Africa, 2023. 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

3 authors.

Erman AminullahResearch Center for Industrial, Service and Trade Economics, National Research and Innovation Agency, Jakarta.
Erwiza ErmanResearch Center for Area Studies, National Research and Innovation Agency, Jakarta.
Tri Edhi Budhi SoesiloSchool of Environmental Studies, University of Indonesia, Jakarta, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study's background was inspired by the current COVID-19 handling policy, which focuses on the balance of public health and social economy. However, there is a knowledge gap on the dynamic complexity of balancing public health and social economy during the new normal period of COVID-19 handling policy. A system dynamics simulation of the COVID-19 handling policy could be used to understand that gap. Objective: This study aims to uncover the simulation of the COVID-19 handling policy in Indonesia. Methods: This study combined quantitative and qualitative modeling methods with a system dynamics tool. Results: This study revealed 3 elements in the dynamic balance of public health and social economy in the COVID handling policy system: i) COVID-19 and social-economic control; ii) COVID-19 escalation and de-escalation; iii) people's immunity enhancement. Such a mix of COVID-19-controlling policy instruments has maintained a dynamic equilibrium between easing economic suppression at the expense of worsening COVID-19 and tightening public health resolution at the expense of more economic suppression. Conclusions: The study conclusions are as follows: i) the COVID-19 handling policy worked as a leverage factor in balancing public health resolution and economic interest during the new normal period in Indonesia; ii) experiential creativity to respond to the newly serious public health problems triggered by COVID-19 implies adding public health knowledge; iii) the study's outcomes imply re-examining the strengths and deficiencies of the entire health system for a better health system.

Indexed as

COVID-19 handling policymixed policy instrumentssimulation

Identifiers

PMID37404333
PMCPMC10316700

What OpenQuestion holds

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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.