Modeling Overdispersed Dengue Data via Poisson Inverse Gaussian Regression Model: A Case Study in the City of Campo Grande, MS, Brazil.

Dengue fever is a tropical disease transmitted mainly by the female Aedes aegypti mosquito that affects millions of people every year. As there is still no safe and effective vaccine, currently the best way to prevent the disease is to control the proliferation of the transmitting mosquito. Since the proliferation and life cycle of the mosquito depend on environmental variables such as temperature and water availability, among others, statistical models are needed to understand the existing relationships between environmental variables and the recorded number of dengue cases and predict the number of cases for some future time interval. This prediction is of paramount importance for the establishment of control policies. In general, dengue-fever datasets contain the number of cases recorded periodically (in days, weeks, months or years). Since many dengue-fever datasets tend to be of the overdispersed, long-tail type, some common models like the Poisson regression model or negative binomial regression model are not adequate to model it. For this reason, in this paper we propose modeling a dengue-fever dataset by using a Poisson-inverse-Gaussian regression model. The main advantage of this model is that it adequately models overdispersed long-tailed data because it has a wider skewness range than the negative binomial distribution. We illustrate the application of this model in a real dataset and compare its performance to that of a negative binomial regression model.

Piecewise Modeling the Accumulated Daily Growth of COVID-19 Deaths: The Case of the State of São Paulo, Brazil.

The pandemic scenery caused by the new coronavirus, called SARS-CoV-2, increased interest in statistical models capable of projecting the evolution of the number of cases (and associated deaths) due to COVID-19 in countries, states and/or cities. This interest is mainly due to the fact that the projections may help the government agencies in making decisions in relation to procedures of prevention of the disease. Since the growth of the number of cases (and deaths) of COVID-19, in general, has presented a heterogeneous evolution over time, it is important that the modeling procedure is capable of identifying periods with different growth rates and proposing an adequate model for each period. Here, we present a modeling procedure based on the fit of a piecewise growth model for the cumulative number of deaths. We opt to focus on the modeling of the cumulative number of deaths because, other than for the number of cases, these values do not depend on the number of diagnostic tests performed. In the proposed approach, the model is updated in the course of the pandemic, and whenever a "new" period of the pandemic is identified, it creates a new sub-dataset composed of the cumulative number of deaths registered from the change point and a new growth model is chosen for that period. Three growth models were fitted for each period: exponential, logistic and Gompertz models. The best model for the cumulative number of deaths recorded is the one with the smallest mean square error and the smallest Akaike information criterion (AIC) and Bayesian information criterion (BIC) values. This approach is illustrated in a case study, in which we model the number of deaths due to COVID-19 recorded in the State of São Paulo, Brazil. The results have shown that the fit of a piecewise model is very effective for explaining the different periods of the pandemic evolution.

Bayesian Computational Methods for Sampling from the Posterior Distribution of a Bivariate Survival Model, Based on AMH Copula in the Presence of Right-Censored Data.

In this paper, we study the performance of Bayesian computational methods to estimate the parameters of a bivariate survival model based on the Ali-Mikhail-Haq copula with marginal distributions given by Weibull distributions. The estimation procedure was based on Monte Carlo Markov Chain (MCMC) algorithms. We present three version of the Metropolis-Hastings algorithm: Independent Metropolis-Hastings (IMH), Random Walk Metropolis (RWM) and Metropolis-Hastings with a natural-candidate generating density (MH). Since the creation of a good candidate generating density in IMH and RWM may be difficult, we also describe how to update a parameter of interest using the slice sampling (SS) method. A simulation study was carried out to compare the performances of the IMH, RWM and SS. A comparison was made using the sample root mean square error as an indicator of performance. Results obtained from the simulations show that the SS algorithm is an effective alternative to the IMH and RWM methods when simulating values from the posterior distribution, especially for small sample sizes. We also applied these methods to a real data set.

Brain activation during fear extinction recall in unmedicated patients with obsessive-compulsive disorder.

Specific brain activation patterns during fear conditioning and the recall of previously extinguished fear responses have been associated with obsessive-compulsive disorder (OCD). However, further replication studies are necessary. We measured skin-conductance response and blood oxygenation level-dependent responses in unmedicated adult patients with OCD (n = 27) and healthy participants (n = 22) submitted to a two-day fear-conditioning experiment comprising fear conditioning, extinction (day 1) and extinction recall (day 2). During conditioning, groups differed regarding the skin conductance reactivity to the aversive stimulus (shock) and regarding the activation of the right opercular cortex, insular cortex, putamen, and lingual gyrus in response to conditioned stimuli. During extinction recall, patients with OCD had higher responses to stimuli and smaller differences between responses to conditioned and neutral stimuli. For the entire sample, the higher the response delta between conditioned and neutral stimuli, the greater the dACC activation for the same contrast during early extinction recall. While activation of the dACC predicted the average difference between responses to stimuli for the entire sample, groups did not differ regarding the activation of the dACC during extinction recall. Larger unmedicated samples might be necessary to replicate the previous findings reported in patients with OCD.

Accuracy and Sensitivity of Clinical Parameters in Predicting Successful Extubation in Patients with Acute Brain Injury.

BACKGROUND: Brain injuries are frequent causes of intubation and mechanical ventilation. The aim of this study was to investigate the accuracy and sensitivity of clinical parameters in predicting successful extubation in patients with acute brain injury. METHODS: Six hundred and forty-four patients assisted at a high-complexity hospital were recruited. Patients were divided as for successful or failed extubation. The VISAGE score, maximum inspiratory and expiratory pressures, peak cough flow, and airway occlusion pressure at 0.1 s were used as predictors. Logistic regression analyses using ROC-curve identified values of accuracy and sensitivity. The Hosmer-Lemeshow test and the stepwise method calibrated the statistical model. RESULTS: VISAGE score (odds ratio of 1.975), maximum inspiratory pressure (odds ratio of 1.024), and peak cough flow (odds ratio of 0.981) are factors consistent in distinguishing success from failure extubation. The ROC curve presented an accuracy of 79.7% and a sensitivity of 95.8%. CONCLUSIONS: VISAGE score, maximum inspiratory pressure and peak cough flow showed good accuracy and sensitivity in predicting successful extubation in patients with acute brain injury. The greater impact of VISAGE score indicates that patients' neurological profile should be considered in association with ventilatory parameters in the decision of extubation.