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.

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.

One Health: navigating plague in Madagascar amidst COVID-19

Background Africa sees the surge of plague cases in recent decades,with hotspots in the Democratic Republic of Congo,Madagascar,and Peru.A rodent-borne scourge,the bacterial infection known as plague is transmitted to humans via the sneaky bites of fleas,caused by Yersinia pestis.Bubonic plague has a case fatality rate of 20.8%with treatment,but in places such as Madagascar the mortality rate can increase to 40–70%without treatment.Main text Tragedy strikes in the Ambohidratrimo district as three lives are claimed by the plague outbreak and three more fight for survival in the hospitals,including one man in critical condition,from the Ambohimiadana,Antsaharasty,and Ampanotokana communes,bringing the total plague victims in the area to a grim to five.Presently,the biggest concern is the potential plague spread among humans during the ongoing COVID-19 pandemic.Effective disease control can be achieved through training and empowering local leaders and healthcare providers in rural areas,implementing strategies to reduce human–rodent interactions,promoting water,sanitation and hygiene practices(WASH)practices,and carrying out robust vector,reservoir and pest control,diversified animal surveillance along with human surveillance should be done to more extensively to fill the lacunae of knowledge regarding the animal to human transmission.The lack of diagnostic laboratories equipped represents a major hurdle in the early detection of plague in rural areas.To effectively combat plague,these tests must be made more widely available.Additionally,raising awareness among the general population through various means such as campaigns,posters and social media about the signs,symptoms,prevention,and infection control during funerals would greatly decrease the number of cases.Furthermore,healthcare professionals should be trained on the latest methods of identifying cases,controlling infections and protecting themselves from the disease.Conclusions Despite being endemic to Madagascar,the outbreak’s pace is unparalleled,and it may spread to non-endemic areas.The utilization of a One Health strategy that encompasses various disciplines is crucial for minimizing catastrophe risk,antibiotic resistance,and outbreak readiness.Collaboration across sectors and proper planning ensures efficient and consistent communication,risk management,and credibility during disease outbreaks.