Sparse Spatial Autoregressive and Spatio-temporal Models for COVID-19 Incidence in the Philippines

Abstract

Philippine COVID-19 data have so many gaps resulting from lack of mass testing, late reporting of test results, and unreported cases – leading to too much noise and sparsity. A sparse spatial autoregressive model linking COVID-19 incidence and mortality rates to the healthcare system, demographic and economic indicators, disease prevalence, vaccination, urbanity, and environmental factors is proposed. The model allows for irregular spatial units accounting for temporal dependencies within a neighborhood was estimated using the Cochranne-Orcutt procedure embedded into the backfitting algorithm. Daily COVID-19 cases and deaths across provinces and cities in the National Capital Region (NCR) from 01 April 2020–15 September 2021 show a significant association of COVID-19 prevalence rate with the number of health workers, revenue of the local government unit (LGU), and prevalence of tuberculosis (TB). On the other hand, COVID-19 mortality rates are associated with the number of health workers, number of licensed COVID-19 testing laboratories, number of cities in an LGU, revenue of the LGU, prevalence rate of cancer, and prevalence rate of TB. The models emphasize the importance of resources available in the LGU that can boost the capabilities of the health care system. Pre-existing health conditions (co-morbidities) in the communities also determine the prevalence and mortality rates of COVID-19 in the Philippines.