Suchergebnisse

BACKGROUND: An enhanced meningitis surveillance network was established across the meningitis belt of sub-Saharan Africa in 2003 to rapidly collect, disseminate, and use district weekly data on meningitis incidence. Following 10 years' experience with enhanced surveillance that included the introduction of a group A meningococcal conjugate vaccine, PsA-TT (MenAfriVac), in 2010, we analyzed the data on meningitis incidence and case fatality from countries reporting to the network. METHODS: After de-duplication and reconciliation, data were extracted from the surveillance bulletins and the central database held by the World Health Organization Inter-country Support Team in Burkina Faso for countries reporting consistently from 2004 through 2013 (Benin, Burkina Faso, Chad, Democratic Republic of Congo, Ghana, Côte d'Ivoire, Mali, Niger, Nigeria, Togo). RESULTS: The 10 study countries reported 341 562 suspected and confirmed cases over the 10-year study period, with a marked peak in 2009 due to a large epidemic of group A Neisseria meningitidis (NmA) meningitis. Case fatality was lowest (5.9%) during this year. A mean of 71 and 67 districts annually crossed the alert and epidemic thresholds, respectively. The incidence rate of NmA meningitis fell >10-fold, from 0.27 per 100,000 in 2004-2010 to 0.02 per 100,000 in 2011-2013 (P < .0001). CONCLUSIONS: In addition to supporting timely outbreak response, the enhanced meningitis surveillance system provides a global overview of the epidemiology of meningitis in the region, despite limitations in data quality and completeness. This study confirms a dramatic fall in NmA incidence after the introduction of PsA-TT.

During 1977 the state of Washington maintained a surveillance system for reporting cases of bacterial meningitis. Hemophilus influenzae meningitis was the most common etiologic agent causing bacterial meningitis. A high incidence rate for H. influenzae meningitis was found among American Indians less than five years ago. A focus of ampicillin-resistant H. influenzae meningitis was found in Pierce County among military dependents or persons who had family members or relatives working or attending school with Fort Lewis Army Base personnel. Although relationships between the individual cases were not detected, the surveillance system continues to seek some association.

In this work, we explore data-driven techniques for the fast and early diagnosis concerning the etiological origin of meningitis, more specifically with regard to differentiating between viral and bacterial meningitis. We study how machine learning can be used to predict meningitis aetiology once a patient has been diagnosed with this disease. We have a dataset of 26,228 patients described by 19 attributes, mainly about the patient's observable symptoms and the early results of the cerebrospinal fluid analysis. Using this dataset, we have explored several techniques of dataset sampling, feature selection and classification models based both on ensemble methods and on simple techniques (mainly, decision trees). Experiments with 27 classification models (19 of them involving ensemble methods) have been conducted for this paper. Our main finding is that the combination of ensemble methods with decision trees leads to the best meningitis aetiology classifiers. The best performance indicator values (precision, recall and f-measure of 89% and an AUC value of 95%) have been achieved by the synergy between bagging and NBTrees. Nonetheless, our results also suggest that the combination of ensemble methods with certain decision tree clearly improves the performance of diagnosis in comparison with those obtained with only the corresponding decision tree. ; This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We would like to thank the Health Department of the Brazilian Government for providing the dataset and for authorizing its use in this study. We would also like to express our gratitude to the reviewers for their thoughtful comments and efforts towards improving our manuscript. Funding for open access charge: Universidad de Málaga / CBUA.