Bmc Infect Dis
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Establishing epidemiological models and conducting predictions seems to be useful for the prevention and control of human brucellosis. Autoregressive integrated moving average (ARIMA) models can capture the long-term trends and the periodic variations in time series. However, these models cannot handle the nonlinear trends correctly. Recurrent neural networks can address problems that involve nonlinear time series data. In this study, we intended to build prediction models for human brucellosis in mainland China with Elman and Jordan neural networks. The fitting and forecasting accuracy of the neural networks were compared with a traditional seasonal ARIMA model. ⋯ The Elman and Jordan recurrent neural networks achieved much higher forecasting accuracy. These models are more suitable for forecasting nonlinear time series data, such as human brucellosis than the traditional ARIMA model.
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Tuberculosis (TB) and HIV makeup a deadly synergy of infectious disease, and the combined effect is apparent in resource limited countries like Ethiopia. Previous studies have demonstrated inconsistent results about the protective effect of isoniazid preventive therapy (IPT) on active TB incidence among HIV positive patients receiving ART. Therefore, the aim of this meta-analysis was, first, to determine the protective effect of IPT on active tuberculosis incidence, and second, to assess the pooled incidence of active TB among HIV positive patients taking ART with and without IPT intervention in Ethiopia. ⋯ CRD42018090804.
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Management of Ebola virus disease (EVD) has historically focused on infection prevention, case detection and supportive care. Several specific anti-Ebola therapies have been investigated, including during the 2014-2016 West African outbreak. Our objective was to conduct a systematic review of the effect of anti-Ebola virus therapies on clinical outcomes to guide their potential use and future evaluation. ⋯ Research evaluating anti-Ebola virus agents has reached very few patients with EVD, and inferences are limited by non-randomized study designs. ZMapp has the most promising treatment signal.