Evidence map›Paper›PMID 34308400›Full record

ArticleThe Lancet regional health. Western Pacific2021

Understanding COVID-19 dynamics and the effects of interventions in the Philippines: A mathematical modelling study.

Jamie M Caldwell, Elvira de Lara-Tuprio, Timothy Robin Teng, Maria Regina Justina E Estuar, Raymond Francis R Sarmiento, Milinda Abayawardana, Robert Neil F Leong, Richard T Gray, James G Wood, Linh-Vi Le and 3 more

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Article in The Lancet regional health. Western Pacific, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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.

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3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

  1. Article
  2. Comparative evaluation of behavioral epidemic models using COVID-19 data.Proceedings of the National Academy of Sciences of the United States of America · 2025
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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

13 authors.

Jamie M CaldwellDepartment of Biology, University of Hawaii at Manoa, Hawaii, USA.
Elvira de Lara-TuprioDepartment of Mathematics, Ateneo de Manila University, Quezon City, Philippines.
Timothy Robin TengDepartment of Mathematics, Ateneo de Manila University, Quezon City, Philippines.
Maria Regina Justina E EstuarDepartment of Information Systems and Computer Science, Ateneo de Manila University, Quezon City, Philippines.
Raymond Francis R SarmientoNational Telehealth Center, National Institutes of Health, University of the Philippines, Manila, Philippines.
Milinda AbayawardanaSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Robert Neil F LeongSchool of Population Health and Community Medicine, University of New South Wales, Sydney, Australia.
Richard T GrayThe Kirby Institute, University of New South Wales Sydney, Sydney, Australia.
James G WoodSchool of Population Health and Community Medicine, University of New South Wales, Sydney, Australia.
Linh-Vi LeWorld Health Organization Regional Office for the Western Pacific, Manila, Philippines.
Emma S McBrydeAustralian Institute of Tropical Health and Medicine, James Cook University, Queensland, Australia.
Romain RagonnetSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
James M TrauerSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.

Funding

World Health Organization 001
6 · The paper itself

Abstract

backgroundCOVID-19 initially caused less severe outbreaks in many low- and middle-income countries (LMIC) compared with many high-income countries, possibly because of differing demographics, socioeconomics, surveillance, and policy responses. Here, we investigate the role of multiple factors on COVID-19 dynamics in the Philippines, a LMIC that has had a relatively severe COVID-19 outbreak.

methodsWe applied an age-structured compartmental model that incorporated time-varying mobility, testing, and personal protective behaviors (through a "Minimum Health Standards" policy, MHS) to represent the first wave of the Philippines COVID-19 epidemic nationally and for three highly affected regions (Calabarzon, Central Visayas, and the National Capital Region). We estimated effects of control measures, key epidemiological parameters, and interventions.

findingsPopulation age structure, contact rates, mobility, testing, and MHS were sufficient to explain the Philippines epidemic based on the good fit between modelled and reported cases, hospitalisations, and deaths. The model indicated that MHS reduced the probability of transmission per contact by 13-27%. The February 2021 case detection rate was estimated at ~8%, population recovered at ~9%, and scenario projections indicated high sensitivity to MHS adherence.

interpretationCOVID-19 dynamics in the Philippines are driven by age, contact structure, mobility, and MHS adherence. Continued compliance with low-cost MHS should help the Philippines control the epidemic until vaccines are widely distributed, but disease resurgence may be occurring due to a combination of low population immunity and detection rates and new variants of concern.

Indexed as

CDR, Case detection rateCOVID-19COVID-19, Coronavirus disease 2019HIC, High-income countriesICU, Intensive care unitLMICLMIC, Low- and middle-income countriesMHS, Minimum Health StandardsMinimum Health Standards policyNPI, Non-pharmaceutical interventionPhilippinesSEIR

Identifiers

PMID34308400
PMCPMC8279002

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LicenceCC BY-NC-ND
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Registered trials

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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.