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.

Public opinion concerning governments' response to the COVID-19 pandemic.

OBJECTIVES: Governments around the world have implemented numerous policies in response to the COVID-19 pandemic. This research examines the political issues resulting in public opinion concerning their responses to the pandemic via an international perspective. The objectives of this study are to: (1) measure the association and determine whether differences in political support can be attributed to the presence of approval ratings during the pandemic, and to (2) identify exceptional cases based on statistical predictions. METHODS: We collect information from several open-sourced surveys conducted between June and September 2020 of public sentiment concerning governments' response toward COVID-19. The 11 countries in our sample account for over 50% of the world's Gross Domestic Product (GDP). The study includes country-specific random effects to take into account the data's clustered structure. We consider "political partisanship" and "pre-pandemic approval ratings in 2019" as two potential explanatory variables and employ a mix-effect regression for bounded responses via variable transformation and the wild bootstrap resampling method. RESULTS: According to the wild bootstrap method, the mixed-effect regression explains 98% of the variation in approval ratings during the pandemic in September 2020. The findings reveal partisan polarization on COVID-19 policies in the U.S., with opposing supporters most likely to express negative sentiments toward the governing party. CONCLUSIONS: The evidence suggests that approval ratings during the pandemic correlate to differences in political support and pre-pandemic approval ratings, as measured by approval ratings from the views between governing coalition supporters and opponents.

Ordinal Time Series Forecasting of the Air Quality Index.

This research models and forecasts daily AQI (air quality index) levels in 16 cities/counties of Taiwan, examines their AQI level forecast performance via a rolling window approach over a one-year validation period, including multi-level forecast classification, and measures the forecast accuracy rates. We employ statistical modeling and machine learning with three weather covariates of daily accumulated precipitation, temperature, and wind direction and also include seasonal dummy variables. The study utilizes four models to forecast air quality levels: (1) an autoregressive model with exogenous variables and GARCH (generalized autoregressive conditional heteroskedasticity) errors; (2) an autoregressive multinomial logistic regression; (3) multi-class classification by support vector machine (SVM); (4) neural network autoregression with exogenous variable (NNARX). These models relate to lag-1 AQI values and the previous day's weather covariates (precipitation and temperature), while wind direction serves as an hour-lag effect based on the idea of nowcasting. The results demonstrate that autoregressive multinomial logistic regression and the SVM method are the best choices for AQI-level predictions regarding the high average and low variation accuracy rates.

What factors drive the satisfaction of citizens with governments' responses to COVID-19?

OBJECTIVES: This research scrutinizes the important factors influencing the satisfaction of citizens concerning their governments' responses to the COVID-19 pandemic based on an open-sourced survey of 14 countries. METHODS: To collect information on public sentiment regarding governments' reactions to COVID-19, we consider five factors for analysis: number of confirmed cases per million population, number of deaths per million population, and governments' containment and health policies, stringency policies, and economic support policies. We examine the Kendall correlations of variables in the 14 countries and use the wild bootstrap method for regression models to find important regressors. RESULTS: Our results show that people pay stronger attention to the results of their governments' battle against COVID-19 (number of confirmed cases and deaths per million population) rather than to what policies they initiate. Health policy and economic support do influence the approval of any national response to COVID-19. We also find that public satisfaction in Japan and South Korea toward the two governments' responses to the pandemic varies greatly compared to that of other countries' citizens to their governments' responses. CONCLUSIONS: The results herein offer some suggestions to governments when initiating policies to balance public health, livelihoods, and economic support.

Causality test of ambient fine particles and human influenza in Taiwan: Age group-specific disparity and geographic heterogeneity.

Influenza is a major global public health problem, with serious outcomes that can result in hospitalization or even death. We investigate the causal relationship between human influenza cases and air pollution, quantified by ambient fine particles <2.5µm in aerodynamic diameter (PM2.5). A modified Granger causality test is proposed to ascertain age group-specific causal relationship between weekly influenza cases and weekly adjusted accumulative PM2.5 from 2009 to 2015 in 11 cities and counties in Taiwan. We examine the causal relationship based on posterior probabilities of the log-linear integer-valued GARCH (generalized autoregressive conditional heteroscedastic) model with covariates, which enable us to handle characteristics of influenza data such as integer-value, lagged dependence, and over-dispersion. The resulting posterior probabilities show that the adult age group (25-64) and the elderly group in New Taipei in the north and cities in southwestern part of Taiwan are strongly affected by ambient fine particles. Moreover, the elderly group is clearly affected in all study sites. Globalization and economic growth have resulted in increased ambient air pollution (including PM2.5) and subsequently substantial public health concerns in the West Pacific region. Minimizing exposure to air pollutants is particularly important for the elderly and susceptible individuals with respiratory diseases.

Candidate genes associated with susceptibility for SARS-coronavirus.

Assuming that no human had any previously acquired immunoprotection against severe acute respiratory syndrome coronavirus (SARS-CoV) during the 2003 SARS outbreak, the biological bases for possible difference in individual susceptibility are intriguing. However, this issue has never been fully elucidated. Based on the premise that SARS patients belonging to a given genotype group having a significantly higher SARS infection rate than others would imply that genotype group being more susceptible, we make use of a compartmental model describing disease transmission dynamics and clinical and gene data of 100 laboratory confirmed SARS patients from Chinese Han population in Taiwan to estimate the infection rates of distinct candidate genotype groups among these SARS-infected individuals. The results show that CXCL10(-938AA) is always protective whenever it appears, but appears rarely and only jointly with either Fgl2(+158T/*) or HO-1(-497A/*), while (Fgl2)(+158T/*) is associated with higher susceptibility unless combined with CXCL10/IP-10(-938AA), when jointly is associated with lower susceptibility. The novel modeling approach proposed, which does not require sizable case and control gene datasets, could have important future public health implications in swiftly identifying potential high-risk groups associated with being highly susceptible to a particular infectious disease.

Impact of quarantine on the 2003 SARS outbreak: a retrospective modeling study.

During the 2003 Severe Acute Respiratory Syndrome (SARS) outbreak, traditional intervention measures such as quarantine and border control were found to be useful in containing the outbreak. We used laboratory verified SARS case data and the detailed quarantine data in Taiwan, where over 150,000 people were quarantined during the 2003 outbreak, to formulate a mathematical model which incorporates Level A quarantine (of potentially exposed contacts of suspected SARS patients) and Level B quarantine (of travelers arriving at borders from SARS affected areas) implemented in Taiwan during the outbreak. We obtain the average case fatality ratio and the daily quarantine rate for the Taiwan outbreak. Model simulations is utilized to show that Level A quarantine prevented approximately 461 additional SARS cases and 62 additional deaths, while the effect of Level B quarantine was comparatively minor, yielding only around 5% reduction of cases and deaths. The combined impact of the two levels of quarantine had reduced the case number and deaths by almost a half. The results demonstrate how modeling can be useful in qualitative evaluation of the impact of traditional intervention measures for newly emerging infectious diseases outbreak when there is inadequate information on the characteristics and clinical features of the new disease-measures which could become particularly important with the looming threat of global flu pandemic possibly caused by a novel mutating flu strain, including that of avian variety.

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.

SARS outbreak, Taiwan, 2003.

We studied the severe acute respiratory syndrome (SARS) outbreak in Taiwan, using the daily case-reporting data from May 5 to June 4 to learn how it had spread so rapidly. Our results indicate that most SARS-infected persons had symptoms and were admitted before their infections were reclassified as probable cases. This finding could indicate efficient admission, slow reclassification process, or both. The high percentage of nosocomial infections in Taiwan suggests that infection from hospitalized patients with suspected, but not yet classified, cases is a major factor in the spread of disease. Delays in reclassification also contributed to the problem. Because accurate diagnostic testing for SARS is currently lacking, intervention measures aimed at more efficient diagnosis, isolation of suspected SARS patients, and reclassification procedures could greatly reduce the number of infections in future outbreaks.

Estimating the number of Cubans infected sexually by human immunodeficiency virus using contact tracing data.

BACKGROUND: To estimate the yearly number of people in Cuba who are living with human immunodeficiency virus (HIV) and were infected through sexual contact but who have not developed acquired immunodeficiency syndrome (AIDS). Estimation was made directly from the yearly HIV seroprevalence data of the Cuban Partner Notification Programme from 1991 to 2000. METHODS: The generalized removal model for open populations is utilized for the estimation. The total number of known HIV-infected Cubans at each sampling time is used in the prior to provide more reasonable approximations. RESULTS: We estimated a yearly survival rate of 93%. The median estimates for the number of all living asymptomatic HIV-positive Cubans, infected by sexual contact, tripled from 714 in 1991 to 2170 in 2000. The number of unknown HIV-positive Cubans infected sexually is estimated to range from 174 in 1991 to 401 in 2000. CONCLUSIONS: A consistent increase in the number of sexually infected HIV-positive individuals in Cuba from 1991 to 2000 is evident from the estimates. From 1996 onwards more sexually active homosexual/bisexual contacts were traced and consequently more sexually-infected HIV-positives were detected. A consequence of increased detection is the levelling off and subsequent decrease in the number of unknown HIV-positives during this time period. The estimation procedure is useful in estimating prevalent population sizes of epidemiological and public health interest.