| Autor: |
Timothy Robin Y. Teng, Elvira P. de Lara-Tuprio, Maria Regina Justina E. Estuar, Christian E. Pulmano, Lu Christian S. Ong, Zachary S. Pangan, Jasper John V. Segismundo, Lenard Paulo V. Tamayo, Mark Anthony C. Tolentino, Alyssa Nicole N. Ty |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Alexandria Engineering Journal, Vol 109, Iss , Pp 914-924 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1110-0168 |
| DOI: |
10.1016/j.aej.2024.08.055 |
| Popis: |
Mathematical models supported by a robust automated data pipeline proved to be useful tools for a data-driven and science-based response and policy-making during the COVID-19 pandemic in the Philippines. In the first year of the pandemic, FASSSTER (Feasibility Analysis on Syndromic Surveillance using Spatio-Temporal Epidemiological modeleR) used a compartmental model to generate scenario-based projections of COVID-19 cases. The emergence of the Delta variant, however, and the administration of vaccines over the second half of 2021 caused significant changes in the Philippine pandemic landscape. This necessitated making adjustments to the model to better capture the local disease transmission dynamics and address policy questions posed by stakeholders regarding COVID-19 over that period. The extended model was then utilized to generate case projections by applying different intervention scenarios, specifically, scenarios involving vaccination coverage, compliance to public health standards and active detection of cases. The new model demonstrated reliability in terms of capturing historical data and in producing relatively accurate short-term projections. In turn, most of the projections generated by the model had been used in support of case monitoring and policy-making in the country. This paper illustrates the significance of enhancing mathematical models in response to the dynamic nature of the COVID-19 pandemic. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
