In this article, we introduce a class of self-exciting threshold integer-valued autoregressive models driven by independent Poisson-distributed random variables. Basic probabilistic and statistical properties of this class of models are discussed. Moreover, parameter estimation is also addressed. Specifically, the methods of estimation under analysis are the least squares-type and likelihood-based ones. Their performance is compared through a simulation study.

The monitoring and prediction of water quality parameters are important tasks in the management of water resources. In this work, the performances of time series statistical models were evaluated to predict and forecast the dissolved oxygen (DO) concentration in several monitoring sites located along the main river Vouga, in Portugal, during the period from January 2002 to May 2015. The models being compared are a regression model with correlated errors and a state-space model, which can be seen as a calibration model. Both models allow the incorporation of water quality variables, such as time correlation or seasonality. Results show that, for the DO variable, the calibration model outperforms the regression model for sample modeling, that is, for a short-term forecast, while the regression model with correlated errors has a better performance for the forecasting h-steps ahead framework. So, the calibration model is more useful for water monitoring using an online or real-time procedure, while the regression model with correlated errors can be applied in order to forecast over a longer period of time.

The objective was to examine the prevalence of HIV-1, HIV-2 and 10 other sexually transmitted infections (STIs), and to explore the relationship between HIV and those STIs in women attending two sexual health clinics in Bissau, Guinea-Bissau. In all, 711 women with urogenital problems were included. Clinical examination was performed and HIV-1, HIV-2, human T-cell lymphotropic virus (HTLV)-1, HTLV-2 and syphilis were diagnosed by serology. Trichomonas vaginalis was examined using wet mount microscopy. Cervical samples (and swabs from visible ulcers, if present) were used for polymerase chain reaction (PCR) diagnosis of Chlamydia trachomatis, Mycoplasma genitalium, Haemophilus ducreyi, herpes simplex virus (HSV)-1 and HSV-2, and culture diagnosis of Neisseria gonorrhoeae. The prevalence of HIV-1, HIV-2, and HIV-1 and HIV-2 (dual infection) was 9.5%, 1.8% and 1.1%, respectively. The prevalence of HTLV-1 was 2.8%, HTLV-2 0%, HSV-1 1.4%, HSV-2 7.7%, T. vaginalis 20.4%, syphilis 1.0%, N. gonorrhoeae 1.3%, H. ducreyi 2.7%, M. genitalium 7.7% and C. trachomatis 12.6%. HIV-1 and/or HIV-2 infection was significantly associated with active HSV-2 and HIV-1 was significantly associated with M. genitalium infection. In conclusion, HIV-1 and HIV-2 prevalence was higher compared with previous studies of pregnant women in Guinea-Bissau. The prevalence of co-infection of HIV and other STIs is high. National evidence-based guidelines for the management of STIs in Guinea-Bissau are essential.

In many phenomena described by stochastic processes, the implementation of an alarm system becomes fundamental to predict the occurrence of future events. In this work we develop an alarm system to predict whether a count process will upcross a certain level and give an alarm whenever the upcrossing level is predicted. We consider count models with parameters being functions of covariates of interest and varying on time. This article presents classical and Bayesian methodology for producing optimal alarm systems. Both methodologies are illustrated and their performance compared through a simulation study. The work finishes with an empirical application to a set of data concerning the number of sunspot on the surface of the sun.