Bayesian Forecasting of Bounded Poisson Distributed Time Series.

This research models and forecasts bounded ordinal time series data that can appear in various contexts, such as air quality index (AQI) levels, economic situations, and credit ratings. This class of time series data is characterized by being bounded and exhibiting a concentration of large probabilities on a few categories, such as states 0 and 1. We propose using Bayesian methods for modeling and forecasting in zero-one-inflated bounded Poisson autoregressive (ZOBPAR) models, which are specifically designed to capture the dynamic changes in such ordinal time series data. We innovatively extend models to incorporate exogenous variables, marking a new direction in Bayesian inferences and forecasting. Simulation studies demonstrate that the proposed methods accurately estimate all unknown parameters, and the posterior means of parameter estimates are robustly close to the actual values as the sample size increases. In the empirical study we investigate three datasets of daily AQI levels from three stations in Taiwan and consider five competing models for the real examples. The results exhibit that the proposed method reasonably predicts the AQI levels in the testing period, especially for the Miaoli station.

Assessing government policies' impact on the COVID-19 pandemic and elderly deaths in East Asia.

This study assesses governments' long-term non-pharmaceutical interventions upon the coronavirus disease 2019 (COVID-19) pandemic in East Asia. It advances the literature towards a better understanding of when and which control measures are effective. We (1) provide time-varying case fatality ratios and focus on the elderly's mortality and case fatality ratios, (2) measure the correlations between daily new cases (daily new deaths) and each index based on multiple domestic pandemic waves and (3) examine the lead-lag relationship between daily new cases (daily new deaths) and each index via the cross-correlation functions on the pre-whitened series. Our results show that the interventions reduce COVID-19 infections for some periods before the period of the Omicron variant. Moreover, there is no COVID-19 policy lag in Taiwan between daily new confirmed cases and each index. As of March 2022, the case fatality ratios of the elderly group in Japan, Hong Kong and South Korea are 4.69%, 4.72% and 1.48%, respectively, while the case fatality ratio of the elderly group in Taiwan is 25.01%. A government's COVID-19 vaccination distribution and prioritisation policies are pivotal for the elderly group to reduce the number of deaths. Immunising this specific group as best as possible should undoubtedly be a top priority.

Quarantine for SARS, Taiwan.

During the 2003 outbreak of severe acute respiratory syndrome (SARS) in Taiwan, >150,000 persons were quarantined, 24 of whom were later found to have laboratory-confirmed SARS-coronavirus (SARS-CoV) infection. Since no evidence exists that SARS-CoV is infective before the onset of symptoms and the quarantined persons were exposed but not symptomatic, we thought the quarantine's effectiveness should be investigated. Using the Taiwan quarantine data, we found that the onset-to-diagnosis time of previously quarantined confirmed case-patients was significantly shortened compared to that for those who had not been quarantined. Thus, quarantine for SARS in Taiwan screened potentially infective persons for swift diagnosis and hospitalization after onset, thereby indirectly reducing infections. Full-scale quarantine measures implemented on April 28 led to a significant improvement in onset-to-diagnosis time of all SARS patients, regardless of previous quarantine status. We discuss the temporal effects of quarantine measures and other interventions on detection and isolation as well as the potential usefulness of quarantine in faster identification of persons with SARS and in improving isolation measures.