COVID-19 dynamics in the Philippines are driven by age, contact structure, mobility, and Minimum Health Standards (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.
Caldwell JM, de Lara-Tuprio E, Teng TR, Estuar MRJE, Sarmiento RFR, Abayawardana M, Leong RNF, Gray RT, Wood JG, McBryde ES, Ragonnet R, Trauer JM.

Objective:

COVID-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.

Findings:

Population 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.

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First published: Jul 13, 2021
Department of Epidemiology and Preventive Medicine
The Monash University's [Department of Epidemiology and Preventive Medicine](https://www.monash.edu/medicine/sphpm/epidemiology) in the [School of Public Health and Preventive Medicine](https://www.monash.edu/medicine/sphpm) has initially focused on TB epidemiology and control, but recently shifted its focus to include a strong interest in the COVID-19 pandemic. The focus comprises the use of methodological approaches in the areas of deterministic and stochastic Modelling, Agent-based Simulations, Applied Mathematics, Bayesian inference, Statistics, Epidemiology, Health Economics, Computer Science and Data Visualisation.