Presentation Information
[MS07-04]Modelling the Effectiveness of COVID-19 Community Quarantine Protocols in the National Capital Region, Philippines
*Mark Anthony Cayanan Tolentino1, Timothy Robin Teng1, Destiny Lutero2 (1. Ateneo de Manila Univ. (Philippines), 2. Univ. of the Philippines Los Baños (Philippines))
Keywords:
COVID-19,mathematical modelling,community quarantine
While the COVID-19 pandemic has ended, COVID-19 and other infectious diseases continue to burden the Philippines and many countries in the world. Thus, it remains important to improve our understanding of the spread of these diseases and how different interventions can curb disease transmission in a population. During the COVID-19 pandemic, one of the main intervention policies adapted in the Philippines, particularly in the National Capital Region (NCR), was that of Community Quarantines (CQ). These were lockdown policies that consisted of stay-at-home orders which severely restricted mobility and that only allowed for limited operability of essential sectors. The type of CQ classification imposed was region-dependent, and was based on different factors such as regional case notifications, attack rate, and health care utilization rate. NCR, in particular, shifted CQ classifications four times during the period of April-December 2020.
In this work, we use mathematical modelling to quantify the effectiveness of CQ protocols in NCR during the first phase (April-December 2020) of the COVID-19 pandemic. More specifically, we have developed an age-stratified compartmental model that incorporates a piecewise-constant transmission rate and have adopted a Bayesian framework for parameter inference. This has allowed us to estimate the change in effective transmission rates and the number of COVID-19 cases averted attributable to the different CQ protocol shifts in NCR during the first phase (April-December 2020) of the COVID-19 pandemic. Our results have the potential to guide efforts towards improved disease surveillance and pandemic preparedness, especially when public health outcomes need to be considered vis à vis economic and social outcomes.
In this work, we use mathematical modelling to quantify the effectiveness of CQ protocols in NCR during the first phase (April-December 2020) of the COVID-19 pandemic. More specifically, we have developed an age-stratified compartmental model that incorporates a piecewise-constant transmission rate and have adopted a Bayesian framework for parameter inference. This has allowed us to estimate the change in effective transmission rates and the number of COVID-19 cases averted attributable to the different CQ protocol shifts in NCR during the first phase (April-December 2020) of the COVID-19 pandemic. Our results have the potential to guide efforts towards improved disease surveillance and pandemic preparedness, especially when public health outcomes need to be considered vis à vis economic and social outcomes.