Evidence map›Paper›PMID 37818298›Full record

ReviewFrontiers in public health2023

Impact of vaccination and non-pharmacological interventions on COVID-19: a review of simulation modeling studies in Asia.

Karan Thakkar, Julia Regazzini Spinardi, Jingyan Yang, Moe H Kyaw, Egemen Ozbilgili, Carlos Fernando Mendoza, Helen May Lin Oh

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.0field-weighted citation impact, top 14% 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

8 citing papers in PubMed, 9 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

7 authors at 2 institutions in 2 countries.

Karan ThakkarVaccine Medical Affairs, Emerging Markets, Pfizer Inc., Singapore, Singapore.
Julia Regazzini SpinardiVaccine Medical Affairs-Emerging Markets, Pfizer Inc., São Paulo, Brazil.
Jingyan YangVaccine Global Value and Access, Pfizer Inc., New York, NY, United States.
Moe H KyawVaccine Medical Affairs, Emerging Markets, Pfizer Inc., Reston, VA, United States.
Egemen OzbilgiliAsia Cluster Medical Affairs, Emerging Markets, Pfizer Inc., Singapore, Singapore.
Carlos Fernando MendozamRNA Vaccine Value & Evidence, Emerging Markets, Pfizer Inc., Mexico City, Mexico.
Helen May Lin OhDepartment of Infectious Diseases, Changi General Hospital, Singapore, Singapore.
Pfizer (United States) · USChangi General Hospital · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Epidemiological modeling is widely used to offer insights into the COVID-19 pandemic situation in Asia. We reviewed published computational (mathematical/simulation) models conducted in Asia that assessed impacts of pharmacological and non-pharmacological interventions against COVID-19 and their implications for vaccination strategy. Methods: A search of the PubMed database for peer-reviewed, published, and accessible articles in English was performed up to November 2022 to capture studies in Asian populations based on computational modeling of outcomes in the COVID-19 pandemic. Extracted data included model type (mechanistic compartmental/agent-based, statistical, both), intervention type (pharmacological, non-pharmacological), and procedures for parameterizing age. Findings are summarized with descriptive statistics and discussed in terms of the evolving COVID-19 situation. Results: The literature search identified 378 results, of which 59 met criteria for data extraction. China, Japan, and South Korea accounted for approximately half of studies, with fewer from South and South-East Asia. Mechanistic models were most common, either compartmental (61.0%), agent-based (1.7%), or combination (18.6%) models. Statistical modeling was applied less frequently (11.9%). Pharmacological interventions were examined in 59.3% of studies, and most considered vaccination, except one study of an antiviral treatment. Non-pharmacological interventions were also considered in 84.7% of studies. Infection, hospitalization, and mortality were outcomes in 91.5%, 30.5%, and 30.5% of studies, respectively. Approximately a third of studies accounted for age, including 10 that also examined mortality. Four of these studies emphasized benefits in terms of mortality from prioritizing older adults for vaccination under conditions of a limited supply; however, one study noted potential benefits to infection rates from early vaccination of younger adults. Few studies (5.1%) considered the impact of vaccination among children. Conclusion: Early in the COVID-19 pandemic, non-pharmacological interventions helped to mitigate the health burden of COVID-19; however, modeling indicates that high population coverage of effective vaccines will complement and reduce reliance on such interventions. Thus, increasing and maintaining immunity levels in populations through regular booster shots, particularly among at-risk and vulnerable groups, including older adults, might help to protect public health. Future modeling efforts should consider new vaccines and alternative therapies alongside an evolving virus in populations with varied vaccination histories.

Indexed as

COVID-19VaccinesAgedAsiaChildComputer SimulationHumansPandemicsVaccinationVaccinesAsiaCOVID-19epidemiological modelinginterventionvaccination

Identifiers

PMID37818298
PMCPMC10560858
OpenAlexW4387057599

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.