Understanding COVID-19 dynamics and the effects of interventions in the Philippines: A mathematical modelling study

ABSTRACT Objective 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, 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. 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 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 15-26%. The February 2021 case detection rate was estimated at ∼9%, population recovered at ∼12%, and scenario projections indicated high sensitivity to MHS adherence. Conclusions COVID-19 dynamics in the Philippines are driven by age, contact structure, mobility, and MHS adherence, and the epidemic can be understood within a similar framework as for high-income settings. Continued compliance with low-cost MHS should allow the Philippines to maintain epidemic control until vaccines are widely distributed, but disease resurgence could occur due to low population immunity and detection rates.