Like many others across the globe, Filipinos continue to suffer from the COVID-19 pandemic. To shed light on how the Philippines initially managed the disease, our paper analyzed the early phase of the government's pandemic response. Using machine learning, we compiled the official press releases issued by the Department of Health from early January to mid-April 2020 where a total of 283,560 datasets amounting to 2.5 megabytes (Mb) were analyzed using the Latent Dirichlet Allocation (LDA) algorithm. Our results revealed five latent themes: the highest effort (40%) centered on "Nationwide Reporting of COVID-19 Status", while "Contact Tracing of Suspected and Infected Individuals" had the least focus at only 11.68%- indicating a lack of priority in this area. Our findings suggest that while the government was ill-prepared in the early phase of the pandemic, it exerted efforts in rearranging its fiscal and operational priorities toward the management of the disease. However, we emphasize that this article should be read and understood with caution. More than a year has already passed since the outbreak in the country and many (in)actions and challenges have adversely impacted its response. These include the Duterte administration's securitization and militarization of pandemic response and its apparent failure to find a balance between the lives and livelihoods of Filipinos, to name a few. We strongly recommend that other scholars study the various aspects of the government's response, i.e., economic, peace and security, agriculture, and business, to assess better how the country responded and continually responds to the pandemic.
Keywords: COVID-19; Government response; Machine learning; Pandemic; Philippines.
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