Bacteremic pneumonia before and after withdrawal of 13-valent pneumococcal conjugate vaccine from a public vaccination program in Spain: a case-control study

The objective of this study is to compare the incidence and epidemiology of bacteremic community-acquired pneumonia (CAP) in the setting of changes in 13-valent pneumococcal conjugate vaccine (PCV13) coverage. In the region of Madrid, universal immunization with the PCV13 started in May 2010. In July 2012, public funding ceased. Vaccination coverage decreased from >95% to 82% in 2013 and to 67% in 2014. We performed a multicenter surveillance and case-control study from 2009-2014. Cases were hospitalized children with bacteremic CAP. Controls were children selected 1:1 from next-admitted with negative blood cultures and typical, presumed bacterial CAP. Annual incidence of bacteremic CAP declined from 7.9/100 000 children (95% CI 5.1-11.1) in 2009 to 2.1/100 000 children (95% CI 1.1-4.1) in 2012. In 2014, 2 years after PCV13 was withdrawn from the universal vaccination program, the incidence of bacteremic CAP increased to 5.4/100 000 children (95% CI 3.5-8.4). We enrolled 113 cases and 113 controls. Streptococcus pneumoniae caused most of bloodstream infections (78%). Empyema was associated with bacteremia (P = .003, OR 3.6; 95% CI 1.4-8.9). Simple parapneumonic effusion was not associated with bacteremia. Incomplete PCV immunization was not a risk factor for bacteremic pneumonia.

Optimal control of non-linear systems through hybrid cell-mapping/artificial-neural-networks techniques

In this paper, a real-time optimal control technique for non-linear plants is proposed. The control system makes use of the cell-mapping (CM) techniques, widely used for the global analysis of highly non-linear systems. The CM framework is employed for designing approximate optimal controllers via a control variable discretization. Furthermore, CM-based designs can be improved by the use of supervised feedforward artificial neural networks (ANNs), which have proved to be universal and efficient tools for function approximation, providing also very fast responses. The quantitative nature of the approximate CM solutions fits very well with ANNs characteristics. Here, we propose several control architectures which combine, in a different manner, supervised neural networks and CM control algorithms. On the one hand, different CM control laws computed for various target objectives can be employed for training a neural network, explicitly including the target information in the input vectors. This way, tracking problems, in addition to regulation ones, can be addressed in a fast and unified manner, obtaining smooth, averaged and global feedback control laws. On the other hand, adjoining CM and ANNs are also combined into a hybrid architecture to address problems where accuracy and real-time response are critical. Finally, some optimal control problems are solved with the proposed CM, neural and hybrid techniques, illustrating their good performance.