Analysis of Longitudinal Binomial Data with Positive Association between the Number of Successes and the Number of Failures: An Application to Stock Instability Study.

Numerous methods have been developed for longitudinal binomial data in the literature. These traditional methods are reasonable for longitudinal binomial data with a negative association between the number of successes and the number of failures over time; however, a positive association may occur between the number of successes and the number of failures over time in some behaviour, economic, disease aggregation and toxicological studies as the numbers of trials are often random. In this paper, we propose a joint Poisson mixed modelling approach to longitudinal binomial data with a positive association between longitudinal counts of successes and longitudinal counts of failures.

Tweedie Compound Poisson Models with Covariate-Dependent Random Effects for Multilevel Semicontinuous Data.

Multilevel semicontinuous data occur frequently in medical, environmental, insurance and financial studies. Such data are often measured with covariates at different levels; however, these data have traditionally been modelled with covariate-independent random effects. Ignoring dependence of cluster-specific random effects and cluster-specific covariates in these traditional approaches may lead to ecological fallacy and result in misleading results.

Forecasting waved daily COVID-19 death count series with a novel combination of segmented Poisson model and ARIMA models.

Autoregressive Integrated Moving Average (ARIMA) models have been widely used to forecast and model the development of various infectious diseases including COVID-19 outbreaks; however, such use of ARIMA models does not respect the count nature of the pandemic development data. For example, the daily COVID-19 death count series data for Canada and the United States (USA) are generally skewed with lots of low counts. In addition, there are generally waved patterns with turning points influenced by government major interventions against the spread of COVID-19 during different periods and seasons.

Linking dynamic patterns of COVID-19 spreads in Italy with regional characteristics: a two level longitudinal modelling approach.

The current statistical modeling of coronavirus (COVID-19) spread has mainly focused on spreading patterns and forecasting of COVID-19 development; these patterns have been found to vary among locations. As the survival time of coronaviruses on surfaces depends on temperature, some researchers have explored the association of daily confirmed cases with environmental factors. Furthermore, some researchers have studied the link between daily fatality rates with regional factors such as health resources, but found no significant factors.

Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries.

In this paper, we employed a segmented Poisson model to analyze the available daily new cases data of the COVID-19 outbreaks in the six Western countries of the Group of Seven, namely, Canada, France, Germany, Italy, UK and USA. We incorporated the governments' interventions (stay-at-home advises/orders, lockdowns, quarantines and social distancing) against COVID-19 into consideration. Our analysis allowed us to make a statistical prediction on the turning point (the time that the daily new cases peak), the duration (the period that the outbreak lasts) and the attack rate (the percentage of the total population that will be infected over the course of the outbreak) for these countries.

A Joint Poisson State-Space Modelling Approach to Analysis of Binomial Series with Random Cluster Sizes.

Serially correlation binomial data with random cluster sizes occur frequently in environmental and health studies. Such data series have traditionally been analyzed using binomial state-space or hidden Markov models without appropriately accounting for the randomness in the cluster sizes. To characterize correlation and extra-variation arising from the random cluster sizes properly, we introduce a joint Poisson state-space modelling approach to analysis of binomial series with random cluster sizes.

Tweedie family of generalized linear models with distribution-free random effects for skewed longitudinal data.

Generalized linear mixed models have played an important role in the analysis of longitudinal data; however, traditional approaches have limited flexibility in accommodating skewness and complex correlation structures. In addition, the existing estimation approaches generally rely heavily on the specifications of random effects distributions; therefore, the corresponding inferences are sometimes sensitive to the choice of random effect distributions under certain circumstance. In this paper, we incorporate serially dependent distribution-free random effects into Tweedie generalized linear models to accommodate a wide range of skewness and covariance structures for discrete and continuous longitudinal data.

Probing Laser-Induced Heterogeneous Microenvironment Changes in Room-Temperature Ionic Liquids.

Modulating the heterogeneous microenvironment in room-temperature ionic liquids (RTILs) by external stimuli is an important approach for understanding and designing external field-induced chemical reactions in natural and applied systems. Here, we report for the first time the redistribution of oxygen molecules related to microstructure changes in RTILs induced by an external laser field, which is probed simultaneously by the triplet-state dynamics of porphyrin. A remarkably long-lived triplet state of porphyrin is observed with changes of microstructures after irradiation, suggesting that charge-shifted O molecules are induced by the external field and/or rearranged intrinsic ions move from nonpolar domains into the polar domains of RTILs through electrostatic interactions.

Intramolecular Charge Transfer and Solvation of Photoactive Molecules with Conjugated Push-Pull Structures.

A comparative investigation on the photophysical properties and solvation-related ICT dynamics of three push-pull compounds containing different donors including carbazole, triphenylamine and phenothiazine, was performed. The steady-state spectra and theoretical calculations show the charge transfers from the central donors to the acceptors at each side. The characterization of the extent of charge transfer was determined by various means, including estimation of the dipole moment, the electron density distribution of HOMO and LUMO, CDD and change in Gibb's free energy, which show the charge transfer strength to be in the order PDHP > BDHT > PDHC.

Enhanced fluorescence of [[5'-(4-hydroxyphenyl)[2,2'-bithiophen]-5-yl]methylene]-propanedinitrile (NIAD-4): solvation induced micro-viscosity enhancement.

Excited state solvation plays a very important role in modulating the emission behavior of fluorophores upon excitation. Here, the solvation effects on the local micro-environment around a fluorophore are proposed by investigating the fantastic emission behavior of a novel amyloid fibril marker, NIAD-4, in different alcoholic and aprotic solvents. In alcoholic solvents, high solvent viscosity causes an obvious enhancement of fluorescence because of the restriction of torsion of NIAD-4, where the formation of a non-fluorescent twist intramolecular charge transfer (TICT) state is suppressed.

Pattern-mixture zero-inflated mixed models for longitudinal unbalanced count data with excessive zeros.

Analysis of longitudinal data with excessive zeros has gained increasing attention in recent years; however, current approaches to the analysis of longitudinal data with excessive zeros have primarily focused on balanced data. Dropouts are common in longitudinal studies; therefore, the analysis of the resulting unbalanced data is complicated by the missing mechanism. Our study is motivated by the analysis of longitudinal skin cancer count data presented by Greenberg, Baron, Stukel, Stevens, Mandel, Spencer, Elias, Lowe, Nierenberg, Bayrd, Vance, Freeman, Clendenning, Kwan, and the Skin Cancer Prevention Study Group[New England Journal of Medicine 323, 789-795].

Extended follow-up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality.

We conducted an extended follow-up and spatial analysis of the American Cancer Society (ACS) Cancer Prevention Study II (CPS-II) cohort in order to further examine associations between long-term exposure to particulate air pollution and mortality in large U.S. cities.

Modelling heterogeneity in clustered count data with extra zeros using compound Poisson random effect.

In medical and health studies, heterogeneities in clustered count data have been traditionally modeled by positive random effects in Poisson mixed models; however, excessive zeros often occur in clustered medical and health count data. In this paper, we consider a three-level random effects zero-inflated Poisson model for health-care utilization data where data are clustered by both subjects and families. To accommodate zero and positive components in the count response compatibly, we model the subject level random effects by a compound Poisson distribution.

Spatial analysis of air pollution and mortality in Los Angeles.

Background: The assessment of air pollution exposure using only community average concentrations may lead to measurement error that lowers estimates of the health burden attributable to poor air quality. To test this hypothesis, we modeled the association between air pollution and mortality using small-area exposure measures in Los Angeles, California.

Methods: Data on 22,905 subjects were extracted from the American Cancer Society cohort for the period 1982-2000 (5,856 deaths).

Spatial regression models for large-cohort studies linking community air pollution and health.

Cohort study designs are often used to assess the association between community-based ambient air pollution concentrations and health outcomes, such as mortality, development and prevalence of disease, and pulmonary function. Typically, a large number of subjects are enrolled in the study in each of a small number of communities. Fixed-site monitors are used to determine long-term exposure to ambient pollution.