Understanding COVID-19 Dynamics and the Effects of Interventions in the Philippines: A Mathematical Modelling Study

Abstract

Background: COVID-19 appears to have caused less severe outbreaks in many low- and middle-income countries (LMIC) compared with high-income countries, possibly because of differing demographics, socio-economics, climate, surveillance, and policy responses. The Philippines is a LMIC that has had a relatively severe COVID-19 outbreak but has recently curtailed transmission while gradually easing interventions.Methods: We 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 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 projected the impacts of easing interventions.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. Several of the fitted epidemiological parameters were consistent with those reported in high-income settings. The model indicated that MHS reduced the probability of transmission per contact by 15-32%. The December 2020 case detection rate was estimated at ~14%, population recovered at ~12%, and scenario projections indicated high sensitivity to MHS adherence.Interpretation: COVID-19 dynamics in the Philippines are driven by age, contact structure, and mobility, and the epidemic can be understood within a similar framework as for high-income settings. Continued compliance with low-cost MHS measures should allow the Philippines to maintain epidemic control, but disease resurgence remains a threat due to low population immunity and detection rates.Funding Statement: This work was supported by the World Health Organization Regional Office for the Western Pacific to provide modelling advice to Member States. JMS is supported by a NASA Ecological Forecasting grant (NNX17AI21G). EDLT, TRT, MRJEE, and RFRS are supported by a project grant from the Philippine Council for Health Research and Development, Department of Science and Technology, Philippines. JMT is supported by an Early Career Fellowship from the National Health and Medical Research Council (APP1142638).Declaration of Interests: The authors declare no conflicts of interest.