Socio-environmental predictors of diabetes incidence disparities in Tanzania mainland: a comparison of regression models for count data.

BACKGROUND: Diabetes is one of the top four non-communicable diseases that cause death and illness to many people around the world. This study aims to use an efficient count data model to estimate socio-environmental factors associated with diabetes incidences in Tanzania mainland, addressing lack of evidence on the efficient count data model for estimating factors associated with disease incidences disparities. METHODS: This study analyzed diabetes counts in 184 Tanzania mainland councils collected in 2020. The study applied generalized Poisson, negative binomial, and Poisson count data models and evaluated their adequacy using information criteria and Pearson chi-square values. RESULTS: The data were over-dispersed, as evidenced by the mean and variance values and the positively skewed histograms. The results revealed uneven distribution of diabetes incidence across geographical locations, with northern and urban councils having more cases. Factors like population, GDP, and hospital numbers were associated with diabetes counts. The GP model performed better than NB and Poisson models. CONCLUSION: The occurrence of diabetes can be attributed to geographical locations. To address this public health issue, environmental interventions can be implemented. Additionally, the generalized Poisson model is an effective tool for analyzing health information system count data across different population subgroups.

A beta-Poisson model for infectious disease transmission.

Outbreaks of emerging and zoonotic infections represent a substantial threat to human health and well-being. These outbreaks tend to be characterised by highly stochastic transmission dynamics with intense variation in transmission potential between cases. The negative binomial distribution is commonly used as a model for transmission in the early stages of an epidemic as it has a natural interpretation as the convolution of a Poisson contact process and a gamma-distributed infectivity. In this study we expand upon the negative binomial model by introducing a beta-Poisson mixture model in which infectious individuals make contacts at the points of a Poisson process and then transmit infection along these contacts with a beta-distributed probability. We show that the negative binomial distribution is a limit case of this model, as is the zero-inflated Poisson distribution obtained by combining a Poisson-distributed contact process with an additional failure probability. We assess the beta-Poisson model's applicability by fitting it to secondary case distributions (the distribution of the number of subsequent cases generated by a single case) estimated from outbreaks covering a range of pathogens and geographical settings. We find that while the beta-Poisson mixture can achieve a closer to fit to data than the negative binomial distribution, it is consistently outperformed by the negative binomial in terms of Akaike Information Criterion, making it a suboptimal choice on parsimonious grounds. The beta-Poisson performs similarly to the negative binomial model in its ability to capture features of the secondary case distribution such as overdispersion, prevalence of superspreaders, and the probability of a case generating zero subsequent cases. Despite this possible shortcoming, the beta-Poisson distribution may still be of interest in the context of intervention modelling since its structure allows for the simulation of measures which change contact structures while leaving individual-level infectivity unchanged, and vice-versa.

Variation vs. specialization: the dose-time-effect of technical and physiological variety in the development of elite swimmers.

OBJECTIVE: It is heavily discussed whether larger variety or specialization benefit elite performance at peak age. Therefore, this study aimed to determine technical (number of different swimming strokes) and physiological (number of different race distances) variety required to become an international-class swimmer (> 750 swimming points) based on 1'522'803 race results. RESULTS: Correlation analyses showed lower technical variety in higher ranked swimmers (P < 0.001), yet with small effects (0.11-0.30). However, Poisson distribution revealed dose-time-effects and specified number of swimming strokes required during each age group. Specifically, freestyle swimmers showed highest chances when starting to compete in three to four swimming strokes but reduced their variety to three swimming strokes at the ages of 12/13yrs with another transition to two swimming strokes at the ages of 19/21yrs (female/male swimmers, respectively). Although both sexes showed similar specialization pattern throughout their career, earlier specialization was generally evident in female compared to male swimmers. At peak performance age, freestyle was most frequently combined with butterfly. Swimmers who either kept competing in all five swimming strokes or focused on only one at the beginning of their careers showed lowest probability of becoming an international-class swimmer. Physiological variety increased during junior age but declined again to three race distances towards elite age.

Testing latent classes in gut microbiome data using generalized Poisson regression models.

Human microbiome research has gained increasing importance due to its critical roles in comprehending human health and disease. Within the realm of microbiome research, the data generated often involves operational taxonomic unit counts, which can frequently present challenges such as over-dispersion and zero-inflation. To address dispersion-related concerns, the generalized Poisson model offers a flexible solution, effectively handling data characterized by over-dispersion, equi-dispersion, and under-dispersion. Furthermore, the realm of zero-inflated generalized Poisson models provides a strategic avenue to simultaneously tackle both over-dispersion and zero-inflation. The phenomenon of zero-inflation frequently stems from the heterogeneous nature of study populations. It emerges when specific microbial taxa fail to thrive in the microbial community of certain subjects, consequently resulting in a consistent count of zeros for these individuals. This subset of subjects represents a latent class, where their zeros originate from the genuine absence of the microbial taxa. In this paper, we introduce a novel testing methodology designed to uncover such latent classes within generalized Poisson regression models. We establish a closed-form test statistic and deduce its asymptotic distribution based on estimating equations. To assess its efficacy, we conduct an extensive array of simulation studies, and further apply the test to detect latent classes in human gut microbiome data from the Bogalusa Heart Study.

Application of marginalized zero-inflated models when mediators have excess zeroes.

Mediation analysis has become increasingly popular over the last decade as researchers are interested in assessing mechanistic pathways for intervention. Although available methods have increased, there are still limited options for mediation analysis with zero-inflated count variables where the distribution of response has a "cluster" of data at the zero value (i.e. distribution of number of cigarettes smoked per day, where nonsmokers cluster at zero cigarettes). The currently available methods do not obtain unbiased population average effects of mediation effects. In this paper, we propose an extension of the counterfactual approach to mediation with direct and indirect effects to scenarios where the mediator is a count variable with excess zeroes by utilizing the Marginalized Zero-Inflated Poisson Model (MZIP) for the mediator model. We derive direct and indirect effects for continuous, binary, and count outcomes, as well as adapt to allow mediator-exposure interactions. Our proposed work allows straightforward calculation of direct and indirect effects for the overall population mean values of the mediator, for scenarios in which researchers are interested in generalizing direct and indirect effects to the population. We apply this novel methodology to an application observing how alcohol consumption may explain sex differences in cholesterol and assess model performance via a simulation study comparing the proposed MZIP mediator framework to existing methods for marginal mediator effects.

Unsupervised learning of stationary and switching dynamical system models from Poisson observations.

Objective. Investigating neural population dynamics underlying behavior requires learning accurate models of the recorded spiking activity, which can be modeled with a Poisson observation distribution. Switching dynamical system models can offer both explanatory power and interpretability by piecing together successive regimes of simpler dynamics to capture more complex ones. However, in many cases, reliable regime labels are not available, thus demanding accurate unsupervised learning methods for Poisson observations. Existing learning methods, however, rely on inference of latent states in neural activity using the Laplace approximation, which may not capture the broader properties of densities and may lead to inaccurate learning. Thus, there is a need for new inference methods that can enable accurate model learning.Approach. To achieve accurate model learning, we derive a novel inference method based on deterministic sampling for Poisson observations called the Poisson Cubature Filter (PCF) and embed it in an unsupervised learning framework. This method takes a minimum mean squared error approach to estimation. Terms that are difficult to find analytically for Poisson observations are approximated in a novel way with deterministic sampling based on numerical integration and cubature rules.Main results. PCF enabled accurate unsupervised learning in both stationary and switching dynamical systems and largely outperformed prior Laplace approximation-based learning methods in both simulations and motor cortical spiking data recorded during a reaching task. These improvements were larger for smaller data sizes, showing that PCF-based learning was more data efficient and enabled more reliable regime identification. In experimental data and unsupervised with respect to behavior, PCF-based learning uncovered interpretable behavior-relevant regimes unlike prior learning methods.Significance. The developed unsupervised learning methods for switching dynamical systems can accurately uncover latent regimes and states in population spiking activity, with important applications in both basic neuroscience and neurotechnology.

Neck circumference as an indicator of Obesity for MetabolicSyndrome in Mexican Healthcare workers

Background: Neck circumference (NC) is a novel anthropometric indicator to assess adiposity in the cervical regionthat is rarely used in Mexico. The greatest advantage of this evaluation is the saving of time, minimal use of instruments, and no pre requisites for patients. Objective: This study aimed to determine the effective-ness of NC as an indicator of obesity for Metabolic Syndrome(MetS) in comparison with BMI and Waist Circumference(WC), and to define NC cutoff levels based on parameters established by the International Diabetes Federation in a groupof healthcare workers from a Public Health Hospital of the State of Morelos, Mexico. Methods: This was a no-randomized, cross-sectional-observational study. Instruments: Anthropometric evaluation and biochemical parameters: lipid profile, fasting glucose, and blood pressure. Statistical analysis: Descriptive, correlational, Poisson multiple regression adjusted by age/sex, and ROC curves using SPSS.23 program. Results: 200 healthcare workers were recruited (146 women and 54 men), age ẋ=42.87, σ=11.25 years. The prevalence of metS was 38% (37% in women and 40.7% in men). BMI, WC, and NC were significantly correlated: BMI and WC (r=.924),BMI and NC (r=.814), and NC and WC (r=.810) (p=.01).Like wise, they were related to hyperglycemia, hypertriglyceridemia, hypertension, and decreased in HDL-cholesterol levels. The NC best cut-off points coupled with two or more components of MetS in women was ≥35.12 cm [AUC=0.765 (95%CI, 0.688-0.843)] and in men ≥41.25 cm [AUC=0.787 (95%CI, 0.688-0.906)]. Conclusion: NC proved to be a reliable indicator that can be quickly and inexpensively evaluated for the determination of obesity for the preliminary diagnosis of MetS (AU)

Comparison of different statistical models for the analysis of fracture events: findings from the Prevention of Falls Injury Trial (PreFIT).

BACKGROUND: Fractures are rare events and can occur because of a fall. Fracture counts are distinct from other count data in that these data are positively skewed, inflated by excess zero counts, and events can recur over time. Analytical methods used to assess fracture data and account for these characteristics are limited in the literature. METHODS: Commonly used models for count data include Poisson regression, negative binomial regression, hurdle regression, and zero-inflated regression models. In this paper, we compare four alternative statistical models to fit fracture counts using data from a large UK based clinical trial evaluating the clinical and cost-effectiveness of alternative falls prevention interventions in older people (Prevention of Falls Injury Trial; PreFIT). RESULTS: The values of Akaike information criterion and Bayesian information criterion, the goodness-of-fit statistics, were the lowest for negative binomial model. The likelihood ratio test of no dispersion in the data showed strong evidence of dispersion (chi-square = 225.68, p-value < 0.001). This indicates that the negative binomial model fits the data better compared to the Poisson regression model. We also compared the standard negative binomial regression and mixed effects negative binomial models. The LR test showed no gain in fitting the data using mixed effects negative binomial model (chi-square = 1.67, p-value = 0.098) compared to standard negative binomial model. CONCLUSIONS: The negative binomial regression model was the most appropriate and optimal fit model for fracture count analyses. TRIAL REGISTRATION: The PreFIT trial was registered as ISRCTN71002650.

[The Validity of the Poisson Distribution to Analyze Microbial Colony Counts on Agar Plates for Food Samples].

Microbial colony counts of food samples in microbiological examinations are one of the most important items. The probability distributions for the colony counts per agar plate at the dilution of counting had not been intensively studied so far. Recently we analyzed the colony counts of food samples with several probability distributions using the Pearson's chi-square value by the "traditional" statistics as the index of fit [Fujikawa and Tsubaki, Food Hyg.Saf.Sc., 60, 88-95 (2019)]. As a result, the selected probability distributions depended on the samples. In this study we newly selected a probability distribution, namely a statistical model, suitable for the above data with the method of maximum likelihood from the probabilistic point of view. The Akaike's Information Criterion (AIC) was used as the index of fit. Consequently, the Poisson model were better than the negative binomial model for all of four food samples. The Poisson model was also better than the binomial for three of four microbial culture samples. With Baysian Information Criterion (BIC), the Poisson model was also better than these two models for all the samples. These results suggested that the Poisson distribution would be the best model to estimate the colony counts of food samples. The present study would be the first report on the statistical model selection for the colony counts of food samples with AIC and BIC.

Multivariate control charts for monitoring a bivariate correlated count process with application to meningococcal disease.

In recent years, with the increasing number and complexity of infectious diseases, the idea of using control charts to monitor public health and disease has been proposed. In this paper, we study multivariate control charts for monitoring a bivariate integer-valued autocorrelation process with bivariate Poisson distribution and select the optimal control scheme by comparing the performance of control charts. Furthermore, the meningococcal patient event in two states in Australia serves as an example to illustrate the application of these methods. The results show that the D exponentially weighted moving average control scheme can detect the changes in the mean value faster, which is a significant advantage.

Classical and Bayesian inference for the discrete Poisson Ramos-Louzada distribution with application to COVID-19 data.

The present study is based on the derivation of a new extension of the Poisson distribution using the Ramos-Louzada distribution. Several statistical properties of the new distribution are derived including, factorial moments, moment-generating function, probability moments, skewness, kurtosis, and dispersion index. Some reliability properties are also derived. The model parameter is estimated using different classical estimation techniques. A comprehensive simulation study was used to identify the best estimation method. Bayesian estimation with a gamma prior is also utilized to estimate the parameter. Three examples were used to demonstrate the utility of the proposed model. These applications revealed that the PRL-based model outperforms certain existing competing one-parameter discrete models such as the discrete Rayleigh, Poisson, discrete inverted Topp-Leone, discrete Pareto and discrete Burr-Hatke distributions.

A Poisson distribution-based general model of cancer rates and a cancer risk-dependent theory of aging.

This article presents a formula for modeling the lifetime incidence of cancer in humans. The formula utilizes a Poisson distribution-based "np" model to predict cancer incidence, with "n" representing the effective number of cell turnover and "p" representing the probability of single-cell transformation. The model accurately predicts the observed incidence of cancer in humans when a reduction in cell turnover due to aging is taken into account. The model also suggests that cancer development is ultimately inevitable. The article proposes a theory of aging based on this concept, called the "np" theory. According to this theory, an organism maintains its order by balancing cellular entropy through continuous proliferation. However, cellular "information entropy" in the form of accumulated DNA mutations increases irreversibly over time, restricting the total number of cells an organism can generate throughout its lifetime. When cell division slows down and fails to compensate for the increased entropy in the system, aging occurs. Essentially, aging is the phenomenon of running out of predetermined cell resources. Different species have evolved separate strategies to utilize their limited cell resources throughout their life cycle.

A flexible quasi-likelihood model for microbiome abundance count data.

In this article, we present a flexible model for microbiome count data. We consider a quasi-likelihood framework, in which we do not make any assumptions on the distribution of the microbiome count except that its variance is an unknown but smooth function of the mean. By comparing our model to the negative binomial generalized linear model (GLM) and Poisson GLM in simulation studies, we show that our flexible quasi-likelihood method yields valid inferential results. Using a real microbiome study, we demonstrate the utility of our method by examining the relationship between adenomas and microbiota. We also provide an R package "fql" for the application of our method.

Dependência funcional dentro e fora de casa em pessoas idosas com 75 anos ou mais: diferenças entre os sexos / Functional disability inside and outside of home of older adults aged 75 years and older: differences between males and females / Dependencia funcional dentro y fuera del hogar en ancianos de 75 años o más: diferencias entre sexos

O sexo é um importante fator a ser considerado na compreensão da dependência de cuidados na velhice. Objetivo:Verificar fatores associados à dependência, dentro e fora de casa, em pessoas idosas com 75 anos ou mais, com ênfase na diferença entre os sexos. Metodologia:Pesquisa transversal com dados do estudo FIBRA. A capacidade funcional nas Atividades Instrumentais de Vida Diária (AIVD) foi dividida em atividades realizadas dentro de casa (uso do telefone, manejo da medicação, tarefas domésticas e preparo da refeição) e atividades realizadas fora de casa (fazer compras, utilizar transporte e manejo do dinheiro). As variáveis independentes incluíram aspectos sociodemográficas e de saúde. Foram estimadas razões de prevalência por meio de modelos de regressão múltipla de Poisson a fim de verificar as variáveis associadas com dependência dentro e fora de casa. Resultados:A amostra foi composta por 804 idosos. Dentro de casa, não houve fatores associados à dependência para o sexo masculino. Enquanto para o sexo feminino, os fatores associados foram fragilidade (RP = 1,99; 95%IC: 1,26-3,15) e 80 anos e mais (RP = 1,41; 95%IC: 1,05-1,89). Quanto à dependência fora de casa, a fragilidade destacou-se como um fator associado a ambos os sexos, masculino (RP = 2,80 95%IC: 1,17-6,64) e feminino (RP = 1,98 95%IC: 1,24-3,17). Conclusões:Para o sexo feminino, a idade avançada e a fragilidade foram os fatores de maior associação com dependência, tanto para o ambiente dentro quanto fora de casa. Para o sexo masculino, a fragilidade foi o único e grande determinante de dependência nas atividades fora de casa, apresentando prevalência maior do que a encontrada na amostra do sexo feminino (AU).

Sexis an important factor to be considered tocomprehendoldage care dependencyObjective:Verify associated factors to dependency, in and out of home, in persons with 75 years or more, with emphasis on sexdifferences. Methodology:Cross-sectional research with data from the FIBRA Study. The functional dependence in Instrumental Activities of Daily Living (IADL) was divided in activities performed inside home (using telephone, managing medicine, housework and meal preparation) and activities performed outside home (shopping, transportation and managing finances).The independent variable included health and sociodemographic aspects. Estimates on prevalence ratios were made using multiple Poisson regression models to verify the many variables associated with dependency inside and outside home. Results:The sample was composed of 804 older people. Inside home there were not any factors associated with dependency in the males. However, in the females the associated factors were frailty (PR = 1.99; 95%CI: 1.26-3.15) and 80 and older (PR = 1.41; 95%CI: 1.05-1.89). As to dependency outside home, frailty was a factor that stood out in both sexes, male (PR = 2.80 95%CI: 1.17-6.64) and female (PR = 1.98 95%CI: 1.24-3.17). Conclusions:To women, older age and frailty were the strongest factors of dependency, to both inside and outside home. To men, frailty was the strongest and single dependency factor for dependency in outside activities, showing a higher prevalence than that of the female sex (AU).

El sexo es un factor importante queconsiderar en la comprensión de la dependencia del cuidado en la vejez. Objetivo:Verificar los factores vinculados a la dependencia, dentro y fuera del hogar, en ancianos de 75 años o más, con énfasis en la diferencia entre los sexos. Metodología:Investigación transversal con datos del estudio FIBRA. La capacidad funcional en las Actividades Instrumentales de la Vida Diaria (AIVD) se dividió en actividades realizadas dentro del hogar (uso del teléfono, administración de medicamentos, tareas domésticas y preparación de comidas) y actividades realizadas fuera del hogar (hacer compras, uso del transporte y manejo del dinero). Las variables independientes incluyeron aspectos sociodemográficos y de salud. Las razones de prevalencia se estimaron utilizando modelos de regresión múltiple de Poisson con el fin de verificarlas variables vinculadas con la dependencia dentro y fuera del hogar. Resultados:El muestreofue constituidopor 804 ancianos. Dentro del hogar, no hubo factores asociados con la dependencia de los hombres. Mientras que, para las mujeres, los factores asociados fueron fragilidad (RP = 1,99; IC95%: 1,26-3,15) y 80 años y más (RP = 1,41; IC95%: 1,05-1,89). En cuanto a la dependencia fuera del hogar, la fragilidad se destacó como un factor asociado a ambos sexos, masculino (RP = 2,80 IC95%: 1,17-6,64) y femenino (RP = 1,98 IC95%: 1,24-3,17). Conclusiones: Para el sexo femenino, la edad avanzada y la fragilidad fueron los factores más vinculados a la dependencia, tanto para el ambiente dentro como fuera del hogar. Para los varones, la fragilidad fue el único determinante importante de dependencia en actividades fuera del hogar, con una prevalencia mayor que la encontrada en elmuestreofemenino (AU).

The effects of summer ambient temperature on total mortality in Serbia.

In the context of recent climate change, temperature-attributable mortality has become an important public health threat worldwide. A large number of studies in Europe have identified a relationship between temperature and mortality, while only a limited number of scholars provided evidence for Serbia. In order to provide more evidence for better management of health resources at the regional and local level, this study aims to assess the impact of summer temperature on the population in Serbia, using daily average temperature (Ta) and mortality (CDR (crude death rate) per 100,000). The analysis was done for five areas (Belgrade, Novi Sad, Nis, Loznica, and Vranje), covering the summer (June-August) period of 2001-2015. In order to quantify the Ta-related CDR, a generalized additive model (GAM) assuming a quasi-Poisson distribution with log as the link function was used. Five regression models were constructed, for each area, revealing a statistically significant positive relationship between Ta and CDR in four areas. The effect of Ta on CDR was defined as the relative risk (RR), which was obtained as the exponential regression coefficient of the models. RR indicates that a 1 °C increase in Ta at lag0 was associated with an increase in CDR of 1.7% for Belgrade, Novi Sad, and Nis and 2% for Loznica. The model for Vranje did not quantify a statistically significant increase in CDR due to Ta (RR=1.006, 95% CI 0.991-1.020). Similar results were confirmed for gender, with a slightly higher risk for women. Analysis across lag structure showed different exposure, but the highest effect of Ta mainly occurs over the short term and persists for 3 days.

State-space prior distribution for parameter of nonhomogeneous Poisson spatiotemporal models.

This article proposes a new class of nonhomogeneous Poisson spatiotemporal model. In this approach, we use a state-space model-based prior distribution to handle the scale and shape parameters of the Weibull intensity function. The proposed prior distribution enables the inclusion of changes in the behavior of the intensity function over time. In defining the spatial correlation function of the model, we include anisotropy via spatial deformation. We estimate the model parameters from a Bayesian perspective, employ the Markov chain Monte Carlo approach, and validate this estimation procedure through a simulation exercise. Finally, the extreme rainfall in the southern semiarid region in northeastern Brazil is analyzed using the R10mm index. The proposed model showed better fit and prediction ability than did other nonhomogeneous Poisson spatiotemporal models available in the literature. This improvement in performance is mainly due to the flexibility of the intensity function that is achieved by allowing the incorporation, in time, of the climatic characteristics of this region.

A comprehensive assessment of hurdle and zero-inflated models for single cell RNA-sequencing analysis.

Single cell RNA-sequencing (scRNA-seq) technology has significantly advanced the understanding of transcriptomic signatures. Although various statistical models have been used to describe the distribution of gene expression across cells, a comprehensive assessment of the different models is missing. Moreover, the growing number of features associated with scRNA-seq datasets creates new challenges for analytical accuracy and computing speed. Here, we developed a Python-based package (TensorZINB) to solve the zero-inflated negative binomial (ZINB) model using the TensorFlow deep learning framework. We used a sequential initialization method to solve the numerical stability issues associated with hurdle and zero-inflated models. A recursive feature selection protocol was used to optimize feature selections for data processing and downstream differentially expressed gene (DEG) analysis. We proposed a class of hybrid models combining nested models to further improve the model's performance. Additionally, we developed a new method to convert a continuous distribution to its equivalent discrete form, so that statistical models can be fairly compared. Finally, we showed that the proposed TensorFlow algorithm (TensorZINB) was numerically stable and that its computing speed and performance were superior to those of existing ZINB solvers. Moreover, we implemented seven hurdle and zero-inflated statistical models in Python and systematically assessed their performance using a real scRNA-seq dataset. We demonstrated that the ZINB model achieved the lowest Akaike information criterion compared with other models tested. Taken together, TensorZINB was accurate, efficient and scalable for the implementation of ZINB and for large-scale scRNA-seq data analysis with DEG identification.

A time-dependent Poisson-Gamma model for recruitment forecasting in multicenter studies.

Forecasting recruitments is a key component of the monitoring phase of multicenter studies. One of the most popular techniques in this field is the Poisson-Gamma recruitment model, a Bayesian technique built on a doubly stochastic Poisson process. This approach is based on the modeling of enrollments as a Poisson process where the recruitment rates are assumed to be constant over time and to follow a common Gamma prior distribution. However, the constant-rate assumption is a restrictive limitation that is rarely appropriate for applications in real studies. In this paper, we illustrate a flexible generalization of this methodology which allows the enrollment rates to vary over time by modeling them through B-splines. We show the suitability of this approach for a wide range of recruitment behaviors in a simulation study and by estimating the recruitment progression of the Canadian Co-infection Cohort.

A zero-inflated endemic-epidemic model with an application to measles time series in Germany.

Count data with an excess of zeros are often encountered when modeling infectious disease occurrence. The degree of zero inflation can vary over time due to nonepidemic periods as well as by age group or region. A well-established approach to analyze multivariate incidence time series is the endemic-epidemic modeling framework, also known as the HHH approach. However, it assumes Poisson or negative binomial distributions and is thus not tailored to surveillance data with excess zeros. Here, we propose a multivariate zero-inflated endemic-epidemic model with random effects that extends HHH. Parameters of both the zero-inflation probability and the HHH part of this mixture model can be estimated jointly and efficiently via (penalized) maximum likelihood inference using analytical derivatives. We found proper convergence and good coverage of confidence intervals in simulation studies. An application to measles counts in the 16 German states, 2005-2018, showed that zero inflation is more pronounced in the Eastern states characterized by a higher vaccination coverage. Probabilistic forecasts of measles cases improved when accounting for zero inflation. We anticipate zero-inflated HHH models to be a useful extension also for other applications and provide an implementation in an R package.

Variable selection for mixed panel count data under the proportional mean model.

Mixed panel count data have attracted increasing attention in medical research based on event history studies. When such data arise, one either observes the number of event occurrences or only knows whether the event has happened or not over an observation period. In this article, we discuss variable selection in event history studies given such complex data, for which there does not seem to exist an established procedure. For the problem, we propose a penalized likelihood variable selection procedure and for the implementation, an expectation-maximization algorithm is developed with the use of the coordinate descent algorithm in the M-step. Furthermore, the oracle property of the proposed method is established, and a simulation study is performed and indicates that the proposed method works well in practical scenarios. Finally, the method is applied to identify the risk factors associated with medical non-adherence arising from the Sequenced Treatment Alternatives to Relieve Depression Study.