Clinical and Epidemiological Evolution of Sheep Pox in Morocco

Sheep pox is an infectious viral disease that affects specifically sheep and it is caused by the Capripoxvirus genus.The clinical signs include fever,diarrhea,difficulty breathing,nodules,lung lesions and death.In Morocco,the 2010 epidemic of sheep pox was characterized by the emergence of a nodular form of the disease.The local strain was isolated and the analysis of affected animals was positively confirmed by virus isolation and real-time polymerase chain reaction(RT-PCR).The epidemiological analysis of 911 data records showed that the virus is endemic in the country;an average of 350 cases per year with an epizootic evolution was observed in 2010.The incidence varies depending on provinces and the disease appears confined to the central and the eastern regions of the country where a very intensive sheep breeding activity is taking place.The statistical analysis showed that there is a positive correlation between the endemicity and the significant factor of the rural market(p=0.006).The annual average morbidity and mortality rates were 2.96%(1.26%to 4.32%)and 0.71%(0.41%to 0.94%),respectively.The clinical findings associated to the epidemiological data analysis confirmed the presence of sheep pox in its nodular form and suggest that new pathogenic strains may have been introduced from Mauritania.The purpose of this work was to provide a better description of the spatiotemporal evolution of sheep pox disease based on some epidemiological indicators and to put forward plausible hypotheses regarding the emergence of the virus in order to implement an adequate control strategy.

Fusion of spatiotemporal and thematic features of textual data for animal disease surveillance

Several internet-based surveillance systems have been created to monitor the web for animal health surveillance.These systems collect a large amount of news dealing with outbreaks related to animal diseases.Automatically identifying news articles that describe the same outbreak event is a key step to quickly detect relevant epidemiological information while alleviating manual curation of news content.This paper addresses the task of retrieving news articles that are related in epidemiological terms.We tackle this issue using text mining and feature fusion methods.The main objective of this paper is to identify a textual representation in which two articles that share the same epidemiological content are close.We compared two types of representations(i.e.,features)to represent the documents:(i)morphosyntactic features(i.e.,selection and transformation of all terms from the news,based on classical textual processing steps)and(ii)lexicosemantic features(i.e.,selection,transformation and fusion of epidemiological terms including diseases,hosts,locations and dates).We compared two types of term weighing(i.e.,Boolean and TF-IDF)for both representations.To combine and transform lexicosemantic features,we compared two data fusion techniques(i.e.,early fusion and late fusion)and the effect of features generalisation,while evaluating the relative importance of each type of feature.We conducted our analysis using a corpus composed of a subset of news articles in English related to animal disease outbreaks.Our results showed that the combination of relevant lexicosemantic(epidemiological)features using fusion methods improves classical morphosyntactic representation in the context of disease-related news retrieval.The lexicosemantic representation based on TF-IDF and feature generalisation(F-measure=0.92,r-precision=0.58)outperformed the morphosyntactic representation(F-measure=0.89,r-precision=0.45),while reducing the features space.Converting the features into lower granular features(i.e.,generalisation)contributed to improving the results of the lexicosemantic representation.Our results showed no difference between the early and late fusion approaches.Temporal features performed poorly on their own.Conversely,spatial features were the most discriminative features,highlighting the need for robust methods for spatial entity extraction,disambiguation and representation in internet-based surveillance systems.

Identifying associations between epidemiological entities in news data for animal disease surveillance

Event-based surveillance systems are at the crossroads of human and animal(and plant and ecosystem)health,epidemiology,statistics,and informatics.Thus,their deployment faces many challenges specific to each domain and their intersections,such as relations among automation,artificial intelligence,and expertise.In this context,ourwork pertins to the extraction of epidemiological events in textual data(i.e.news)by unsupervised methods.We define the event extraction task as detecting pairs of epidemiological entities(e.g.a disease name and location).The quality of the ranked lists of pairs was evaluated using specific ranking evaluation metrics.We used a publicly available annotated corpus of 438 documents(i.e.news articles)related to animal disease events.The statistical approach was able to detect event-related pairs of epidemiological features with a good trade-off between precision and recall.Our results showed that using a window of words outperformed document-based and sentence-based approaches,while reducing the probability of detecting false pairs.Our results indicated that Mutual Information was less adapted than the Dice coefficient for ranking pairs of features in the event extraction framework.We believe that Mutual Information would be more relevant for rare pair detection(i.e.weak signals),but requires higher manual curation to avoid false positive extraction pairs.Moreover,generalising the country-level spatial features enabled better discrimination(i.e.ranking)of relevant disease-location pairs for event extraction.