Post-COVID-19 Epidemiological Insights and SEIR Modeling for Future Global Pandemic Preparedness: Lessons from the Western Pacific

preprint OA: closed
📄 Open PDF Full text JSON View at publisher
Full text 3,538 characters · extracted from oa-doi-fallback · 4 sections · click to expand

Abstract

Objectives This study analyzes the COVID-19 pandemic’s trajectory in selected Western Pacific countries from 2019–2023 to derive key lessons for the post-COVID era. It aims to highlight global health system vulnerabilities, particularly in dynamic and diverse regions, by utilizing an extended and context-aware SEIR (Susceptible–Exposed–Infectious–Recovered) model. The study also seeks to provide actionable insights for future outbreak preparedness, specifically addressing population heterogeneity and superspreading events.

Methods

To overcome limitations of official data (e.g., underreporting, inconsistent surveillance), we employed a robust SEIR modeling framework that integrates key contextual variables, such as public health interventions, nuances of vaccination rollout, population mobility, and socioeconomic diversity. Crucially, it incorporates mechanisms to account for transmission heterogeneity, particularly superspreading, thereby enhancing realism and predictive accuracy beyond traditional homogeneous SEIR approaches.

Results

The findings provide critical lessons and a proactive tool for pandemic preparedness. The analysis demonstrates the quantitative impact of various interventions on disease dynamics, revealing how strategies can effectively mitigate transmission in heterogeneous populations. Simulation outcomes offer long-term projections to support global epidemic planning and response.

Conclusions

This study underscores the essential role of context-aware epidemiological modeling as a vital tool for extracting lessons from past pandemics to strengthen global resilience. Insights from the Western Pacific experience have broad applicability for enhancing preparedness against future emerging infectious diseases. Competing Interest Statement The authors have declared no competing interest. Funding Statement national institute of development administration Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data used in this study are publicly available from official and open access sources. Epidemiological datasets for the Western Pacific region (2019 to 2023) were obtained from the World Health Organization (WHO) COVID-19 Dashboard (https://covid19.who.int) and Our World in Data (https://ourworldindata.org/coronavirus). The supplementary modeling data and code (Appendix S1 to S4) are available upon reasonable request to the corresponding author.

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00