Last updated: April 14, 2023, 6:21 p.m.
Speaker: 𝙎𝙝𝙞𝙧𝙡𝙚𝙚 𝙍. 𝙊𝙘𝙖𝙢𝙥𝙤
Date: Jan. 12, 2023, 5 p.m.
Venue: via Zoom
Many spatiotemporal processes such as epidemic data, signal spectrum, weather and environmental occurrences are often sparse. Philippine COVID-19 data are said to be underreported due to lack of mass testing, contact tracing plus there are unreported cases, thus making the data messy and sparse. Modeling sparse data has been a challenge since the sparse features increase the space and time complexity of models. The study postulated sparse spatial autoregressive and spatiotemporal models. It also investigated models linking sparse data to different covariates like the healthcare system, demographic and economic indicators, disease prevalence, vaccination, urbanity, and environmental factors. Sparsity regularization terms are incorporated to improve the predictive abilities of the spatiotemporal models. The parameters were estimated using a hybrid of Cochranne-Orcutt and backfitting algorithm with integrated cross validation of sparsity regularized objective function. Moreover, hurdle models were also formulated, and these were estimated using forward search algorithm and maximum likelihood estimation infused in the backfitting framework. The postulated models were fitted to simulated data and their predictive abilities were compared using mean square error (MSE) and mean absolute error (MAE). The postulated models were applied to daily COVID-19 cases and deaths across provinces and National Capital Region (NCR) cities from April 1, 2020 to September 15, 2021 in the Philippines. The models emphasize the importance of resources available in the local government that can boost the capabilities of the health care system. Pre-existing health conditions (co-morbidities) of the communities also determine prevalence and mortality rates of COVID-19 in the Philippines.
sparse, spatiotemporal model, backfitting algorithm, forward search, COVID-19
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