Evidence map›Paper›PMID 39553664›Full record

ArticleHeliyon2024

Analyzing the dynamics of COVID-19 transmission in select regions of the Philippines: A modeling approach to assess the impact of various tiers of community quarantines.

May Anne E Mata, Rey Audie S Escosio, El Veena Grace A Rosero, Jhunas Paul T Viernes, Loreniel E Anonuevo, Bryan S Hernandez, Joel M Addawe, Rizavel C Addawe, Carlene P C Pilar-Arceo, Victoria May P Mendoza and 1 more

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In one paragraph

Article in Heliyon, 2024. 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

11 authors.

May Anne E MataMindanao Center for Disease Watch and Analytics (DiWA), University of the Philippines Mindanao, Tugbok District, Davao City, 8000, Philippines.
Rey Audie S EscosioUniversity of the Philippines Resilience Institute, University of the Philippines Diliman, Quezon City, 1101, Philippines.
El Veena Grace A RoseroInterdisciplinary Applied Modeling (IAM) Laboratory, University of the Philippines Mindanao, Tugbok District, Davao City, 8000, Philippines.
Jhunas Paul T ViernesDepartment of Mathematics and Computer Science, University of the Philippines Baguio, Baguio City, 2600, Philippines.
Loreniel E AnonuevoMindanao Center for Disease Watch and Analytics (DiWA), University of the Philippines Mindanao, Tugbok District, Davao City, 8000, Philippines.
Bryan S HernandezInstitute of Mathematics, University of the Philippines Diliman, Quezon City, 1101, Philippines.
Joel M AddaweUniversity of the Philippines Resilience Institute, University of the Philippines Diliman, Quezon City, 1101, Philippines.
Rizavel C AddaweUniversity of the Philippines Resilience Institute, University of the Philippines Diliman, Quezon City, 1101, Philippines.
Carlene P C Pilar-ArceoUniversity of the Philippines Resilience Institute, University of the Philippines Diliman, Quezon City, 1101, Philippines.
Victoria May P MendozaUniversity of the Philippines Resilience Institute, University of the Philippines Diliman, Quezon City, 1101, Philippines.
Aurelio A de Los ReyesMindanao Center for Disease Watch and Analytics (DiWA), University of the Philippines Mindanao, Tugbok District, Davao City, 8000, Philippines.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has significantly impacted communities worldwide, and effective management strategies are critical to reduce transmission rates and minimize the impact of the disease. In this study, we modeled and analyzed the COVID-19 transmission dynamics and derived relevant epidemiological values for three regions of the Philippines, namely, the National Capital Region (NCR), Davao City, and Baguio City, under different community quarantine implementations. The unique features and differences of these regions-of-interest were accounted for in simulating the disease spread and in estimating key epidemiological parameters fitted to the reported COVID-19 cases. Results support the robustness of the model formulated and provides insights into the effect of the government's implemented intervention protocols. With a forecasting feature, this modeling framework is beneficial for science-based decision support, policy making, and assessment for recent and future pandemics wherever regions-of-interest.

Indexed as

Community quarantineCompartmental modelCoronavirusPopulation factorSEIRTime-varying reproduction number

Identifiers

PMID39553664
PMCPMC11564951

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.