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Nearly 1% of the global population has Epilepsy. Forecasting epileptic seizures with an acceptable confidence level, could improve the disease treatment and thus the lifestyle of the people who suffer it. To do that the electroencephalogram (EEG) signal is usually studied through spectral power band filtering, but this paper proposes an alternative novel method of preprocessing the EEG signal based on supervised filters. Such filters have been employed in a machine learning algorithm, such as the K-Nearest Neighbor (KNN), to improve the prediction of seizures. The proposed solution extends with this novel approach an algorithm that was submitted to win the third prize of an international Data Science challenge promoted by Kaggle contest platform and the American Epilepsy Society, the Epilepsy Foundation, National Institutes of Health (NIH) and Mayo Clinic. A formal description of these preprocessing methods is presented and a detailed analysis in terms of Receiver Operating Characteristics (ROC) curve and Area Under ROC curve is performed. The obtained results show statistical significant improvements when compared with the spectral power band filtering (PBF) typical baseline. A trend between performance and the dataset size is observed, suggesting that the supervised filters bring better information, compared to the conventional PBF filters, as the dataset grows in terms of monitored variables (sensors) and time length. The paper demonstrates a better accuracy in forecasting when new filters are employed and its main contribution is in the field of machine learning algorithms to develop more accurate predictive systems. [ABSTRACT FROM AUTHOR]

Background: Scientific literature on cystic echinococcosis (CE) reporting data on risk factors is limited and to the best of our knowledge, no global evaluation of human CE risk factors has to date been performed. This systematic review (SR) summarizes available data on statistically relevant potential risk factors (PRFs) associated with human CE. Methodology/Principal Findings: Database searches identified 1,367 papers, of which thirty-seven were eligible for inclusion. Of these, eight and twenty-nine were case-control and cross-sectional studies, respectively. Among the eligible papers, twenty-one were included in the meta-analyses. Pooled odds ratio (OR) were used as a measure of effect and separately analysed for the two study designs. PRFs derived from case-control studies that were significantly associated with higher odds of outcome were “dog free to roam” (OR 5.23; 95% CI 2.45–11.14), “feeding dogs with viscera” (OR 4.69; 95% CI 3.02–7.29), “slaughter at home” (OR 4.67; 95% CI 2.02–10.78) or at “slaughterhouses” (OR 2.7; 95% CI 1.15–6.3), “dog ownership” (OR 3.54; 95% CI 1.27–9.85), “living in rural areas” (OR 1.83; 95% CI 1.16–2.9) and “low income” (OR 1.68; 95% CI 1.02–2.76). Statistically significant PRFs from cross-sectional studies with higher odds of outcome were “age >16 years” (OR 6.08; 95% CI 4.05–9.13), “living in rural areas” (OR 2.26; 95% CI 1.41–3.61), “being female” (OR 1.38; 95% CI 1.06–1.8) and “dog ownership” (OR 1.37; 95% CI 1.01–1.86). Conclusions/Significance: Living in endemic rural areas, in which free roaming dogs have access to offal and being a dog-owner, seem to be among the most significant PRFs for acquiring this parasitic infection. Results of data analysed here may contribute to our understanding of the PRFs for CE and may potentially be useful in planning community interventions aimed at controlling CE in endemic areas. [ABSTRACT FROM AUTHOR]

This paper forecasts the 2016 canine Anaplasma spp. seroprevalence in the United States from eight climate, geographic and societal factors. The forecast’s construction and an assessment of its performance are described. The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 11 million Anaplasma spp. seroprevalence test results for dogs conducted in the 48 contiguous United States during 2011–2015. The forecast uses county-level data on eight predictive factors, including annual temperature, precipitation, relative humidity, county elevation, forestation coverage, surface water coverage, population density and median household income. Non-static factors are extrapolated into the forthcoming year with various statistical methods. The fitted model and factor extrapolations are used to estimate next year’s regional prevalence. The correlation between the observed and model-estimated county-by-county Anaplasma spp. seroprevalence for the five-year period 2011–2015 is 0.902, demonstrating reasonable model accuracy. The weighted correlation (accounting for different sample sizes) between 2015 observed and forecasted county-by-county Anaplasma spp. seroprevalence is 0.987, exhibiting that the proposed approach can be used to accurately forecast Anaplasma spp. seroprevalence. The forecast presented herein can a priori alert veterinarians to areas expected to see Anaplasma spp. seroprevalence beyond the accepted endemic range. The proposed methods may prove useful for forecasting other diseases. [ABSTRACT FROM AUTHOR]

Background: Human alveolar echinococcosis (AE) is a severe zoonotic disease caused by the metacestode stage of Echinococcus multilocularis. AE is commonly associated with a long incubation period that may last for more than ten years. The objective of this systematic literature review was to identify and summarize the current knowledge on statistically relevant potential risk factors (PRFs) associated with AE in humans. Methodology/Principal findings: Six bibliographic databases were searched, generating a total of 1,009 publications. Following the removal of duplicate records and the exclusion of papers that failed to meet the criteria of a previously agreed a priori protocol, 23 publications were retained; however, 6 of these did not contain data in a format that allowed their inclusion in the meta-analysis. The remaining 17 publications (6 case-control and 11 cross-sectional studies) were meta-analysed to investigate associations between AE and PRFs. Pooled odds ratio (OR) were used as a measure of effect and separately analysed for case-control and cross-sectional studies. In the case-control studies, the following PRFs for human AE showed higher odds of outcome: “dog ownership”, “cat ownership”, “have a kitchen garden”, “occupation: farmer”, “haymaking in meadows not adjacent to water”, “went to forests for vocational reasons”, “chewed grass” and “hunting / handling foxes”. In the cross-sectional studies, the following PRFs showed higher odds of outcome: “dog ownership”, “play with dogs”, “gender: female”, “age over 20 years”, “ethnic group: Tibetan”, “low income”, “source of drinking water other than well or tap”, “occupation: herding” and “low education”. Our meta-analysis confirmed that the chance of AE transmission through ingestion of food and water contaminated with E. multilocularis eggs exists, but showed also that food- and water-borne PRFs do not significantly increase the risk of infection. Conclusions/significance: This systematic review analysed international peer-reviewed articles that have over the years contributed to our current understanding of the epidemiology of human AE. The identification of potential risk factors may help researchers and decision makers improve surveillance and/or preventive measures that aim at decreasing human infection with E. multilocularis. More primary studies are needed to confirm potential risk factors and their role in the epidemiology of human AE. [ABSTRACT FROM AUTHOR]

This paper models the prevalence of antibodies to Borrelia burgdorferi in domestic dogs in the United States using climate, geographic, and societal factors. We then use this model to forecast the prevalence of antibodies to B. burgdorferi in dogs for 2016. The data available for this study consists of 11,937,925 B. burgdorferi serologic test results collected at the county level within the 48 contiguous United States from 2011-2015. Using the serologic data, a baseline B. burgdorferi antibody prevalence map was constructed through the use of spatial smoothing techniques after temporal aggregation; i.e., head-banging and Kriging. In addition, several covariates purported to be associated with B. burgdorferi prevalence were collected on the same spatio-temporal granularity, and include forestation, elevation, water coverage, temperature, relative humidity, precipitation, population density, and median household income. A Bayesian spatio-temporal conditional autoregressive (CAR) model was used to analyze these data, for the purposes of identifying significant risk factors and for constructing disease forecasts. The fidelity of the forecasting technique was assessed using historical data, and a Lyme disease forecast for dogs in 2016 was constructed. The correlation between the county level model and baseline B. burgdorferi antibody prevalence estimates from 2011 to 2015 is 0.894, illustrating that the Bayesian spatio-temporal CAR model provides a good fit to these data. The fidelity of the forecasting technique was assessed in the usual fashion; i.e., the 2011-2014 data was used to forecast the 2015 county level prevalence, with comparisons between observed and predicted being made. The weighted (to acknowledge sample size) correlation between 2015 county level observed prevalence and 2015 forecasted prevalence is 0.978. A forecast for the prevalence of B. burgdorferi antibodies in domestic dogs in 2016 is also provided. The forecast presented from this model can be used to alert veterinarians in areas likely to see above average B. burgdorferi antibody prevalence in dogs in the upcoming year. In addition, because dogs and humans can be exposed to ticks in similar habitats, these data may ultimately prove useful in predicting areas where human Lyme disease risk may emerge. [ABSTRACT FROM AUTHOR]

Background: The Philippines is one of the developing countries highly affected by rabies. Dog vaccination campaigns implemented through collaborative effort between the government and NGOs have played an important role in successfully reducing the burden of disease within the country. Nevertheless, rabies vaccination of the domestic animal population requires continuous commitment not only from governments and NGOs, but also from local communities that are directly affected by such efforts. To create such long-term sustained programs, the introduction of affordable dog vaccination and registration fees is essential and has been shown to be an important strategy in Bohol, Philippines. The aim of this study, therefore, was to estimate the average amount of money that individuals were willing to pay for dog vaccination and registration in Ilocos Norte, Philippines. This study also investigated some of the determinants of individuals’ willingness to pay (WTP). Methods: A cross-sectional questionnaire was administered to 300 households in 17 municipalities (out of a total of 21) selected through a multi-stage cluster survey technique. At the time of the survey, Ilocos Norte had a population of approximately 568,017 and was predominantly rural. The Contingent Valuation Method was used to elicit WTP for dog rabies vaccination and registration. A ‘bidding game’ elicitation strategy that aims to find the maximum amount of money individuals were willing to pay was also employed. Data were collected using paper-based questionnaires. Linear regression was used to examine factors influencing participants’ WTP for dog rabies vaccination and registration. Key Results: On average, Ilocos Norte residents were willing to pay 69.65 Philippine Pesos (PHP) (equivalent to 1.67 USD in 2012) for dog vaccination and 29.13PHP (0.70 USD) for dog registration. Eighty-six per cent of respondents were willing to pay the stated amount to vaccinate each of their dogs, annually. This study also found that WTP was influenced by demographic and knowledge factors. Among these, we found that age, income, participants’ willingness to commit to pay each year, municipality of residency, knowledge of the signs of rabies in dogs, and number of dogs owed significantly predicted WTP. [ABSTRACT FROM AUTHOR]