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Spatial information in text enables to understand the geographical context and relationships within text for better decision-making across various domains such as disease surveillance, disaster management and other locationbased services. Therefore, it is crucial to understand the precise geographical context for location-sensitive applications. In response to this necessity, we introduce the GeospaCy software tool, designed for the extraction and georeferencing of spatial information present in textual data. GeospaCy fulfils two primary objectives: 1) Geoparsing, which involves extracting spatial expressions, encompassing place names and associated spatial relations within the text data, and 2) Geocoding, which facilitates the assignment of geographical coordinates to the spatial expressions extracted during the Geoparsing task. Geoparsing is evaluated with a disease news article dataset consisting of event information, whereas a qualitative evaluation of geographical coordinates (polygons/geometries) of spatial expressions is performed by end-users for Geocoding task.

Background: Ticks are an important driver of veterinary health care, causing irritation and sometimes infection to their hosts. We explored epidemiological and geo-referenced data from > 7 million electronic health records (EHRs) from cats and dogs collected by the Small Animal Veterinary Surveillance Network (SAVSNET) in Great Britain (GB) between 2014 and 2021 to assess the factors affecting tick attachment in an individual and at a spatiotemporal level. Methods: EHRs in which ticks were mentioned were identified by text mining; domain experts confirmed those with ticks on the animal. Tick presence/absence records were overlaid with a spatiotemporal series of climate, environment, anthropogenic and host distribution factors to produce a spatiotemporal regression matrix. An ensemble machine learning spatiotemporal model was used to fine-tune hyperparameters for Random Forest, Gradient-boosted Trees and Generalized Linear Model regression algorithms, which were then used to produce a final ensemble meta-learner to predict the probability of tick attachment across GB at a monthly interval and averaged long-term through 2014–2021 at a spatial resolution of 1 km. Individual host factors associated with tick attachment were also assessed by conditional logistic regression on a matched case–control dataset. Results: In total, 11,741 consultations were identified in which a tick was recorded. The frequency of tick records was low (0.16% EHRs), suggesting an underestimation of risk. That said, increased odds for tick attachment in cats and dogs were associated with younger adult ages, longer coat length, crossbreeds and unclassified breeds. In cats, males and entire animals had significantly increased odds of recorded tick attachment. The key variables controlling the spatiotemporal risk for tick attachment were climatic (precipitation and temperature) and vegetation type (Enhanced Vegetation Index). Suitable areas for tick attachment were predicted across GB, especially i

Our understanding of the drivers of the temporal dynamics of livestock mobility networks is currently limited, despite their significant implications for the surveillance and control of infectious diseases. We analyzed the effect of time-varying environmental and economic variables—biomass production, rainfall, livestock market prices, and religious calendar on long-distance movements of cattle and small ruminant herds in Senegal in the years 2014 and 2019. We used principal component analysis to explore the variation of the hypothesized explanatory variables in space and time and a generalized additive modelling approach to assess the effect of those variables on the likelihood of herd movement between pairs of administrative units. Contrary to environmental variables, the patterns of variation of market prices show significant differences across locations. The explanatory variables at origin had the highest contribution to the model deviance reduction. Biomass production and rainfall were found to affect the likelihood of herd movement for both species on at least 1 year. Market price at origin had a strong and consistent effect on the departure of small ruminant herds. Our study shows the potential benefits of regular monitoring of market prices for future efforts at forecasting livestock movements and associated sanitary risks.

Event-Based Surveillance (EBS) tools, such as HealthMap and PADI-web, monitor online news reports and other unofficial sources, with the primary aim to provide timely information to users from health agencies on disease outbreaks occurring worldwide. In this work, we describe how outbreak-related information disseminates from a primary source, via a secondary source, to a definitive aggregator, an EBS tool, during the 2018/19 avian influenza season. We analysed 337 news items from the PADI-web and 115 news articles from HealthMap EBS tools reporting avian influenza outbreaks in birds worldwide between July 2018 and June 2019. We used the sources cited in the news to trace the path of each outbreak. We built a directed network with nodes representing the sources (characterised by type, specialisation, and geographical focus) and edges representing the flow of information. We calculated the degree as a centrality measure to determine the importance of the nodes in information dissemination. We analysed the role of the sources in early detection (detection of an event before its official notification) to the World Organisation for Animal Health (WOAH) and late detection. A total of 23% and 43% of the avian influenza outbreaks detected by the PADI-web and HealthMap, respectively, were shared on time before their notification. For both tools, national and local veterinary authorities were the primary sources of early detection. The early detection component mainly relied on the dissemination of nationally acknowledged events by online news and press agencies, bypassing international reporting to the WAOH. WOAH was the major secondary source for late detection, occupying a central position between national authorities and disseminator sources, such as online news. PADI-web and HealthMap were highly complementary in terms of detected sources, explaining why 90% of the events were detected by only one of the tools. We show that current EBS tools can provide timely outbreak

Purpose: In the first quarter of 2020, World Health Organization (WHO) declared COVID-19 as a public health emergency around the globe. Therefore, different users from all over the world shared their thoughts about COVID-19 on social media platforms i.e., Twitter, Facebook etc. So, it is important to analyze public opinions about COVID-19 from different regions over different period of time. To fulfill the spatial analysis issue, a previous work called H-TF-IDF (Hierarchy-based measure for tweet analysis) for term extraction from tweet data has been proposed. In this work, we focus on the sentiment analysis performed on terms selected by H-TF-IDF for spatial tweets groups to know local situations during the ongoing epidemic COVID-19 over different time frames. Methods & Materials: The primary step is to extract terms from tweets using H-TF-IDF approach. Moreover, these terms are utilized in two ways i.e., 1) select tweets containing terms, 2) terms used as features for sentiment analysis. Thereafter, data preprocessing is performed to clean the text. Afterwards, Vectorization models i.e., bag-of-words (BOW) and term frequency-inverse document frequency (TF-IDF) are used to extract features with the help of n-gram techniques. These features are extracted to train the prediction models for sentiment analysis. Lastly, different statistical and machine learning models i.e., Logistic regression, support vector machine (SVM), etc. are applied to classify the spatial tweets groups. For preliminary results, experiments are conducted on H-TF-IDF tweets corpus having geocoded spatial information for the period of January, 2020. These tweets are extracted from the dataset collected by E.Chen (https://github.com/echen102/COVID-19-TweetIDs) that focuses on the early beginning of the outbreak. A uniform experiment setup of train-test (80% and 20%) split scheme is used for each prediction model. Results: The results illustrate that specific terms highlighted by H-TF-IDF provide us

Spatial information has gained more attention in natural language processing tasks in different interdisciplinary domains. Moreover, the spatial information is available in two forms: Absolute Spatial Information (ASI) e.g., Paris, London, and Germany and Relative Spatial Information (RSI) e.g., south of Paris, north Madrid and 80 km from Rome. Therefore, it is challenging to extract RSI from textual data and compute its geotagging. This paper presents two strategies and the associated prototypes to address the following tasks: 1) extraction of relative spatial information from textual data and 2) geotagging of this relative spatial information. Experiments show promising results for RSI extraction and tagging.

Background: Clinical findings associated with N-terminal pro-B-type natriuretic peptide (NT-proBNP) measurement in dogs and cats in primary practice, and their relevance to published measurement indications, have not been described. Methods: Using electronic health record data collected by the Small Animal Veterinary Surveillance Network, appointments in which NT-proBNP was measured were identified using keyword-based text filtering. For these appointments, clinical findings were manually identified from each patient's clinical narrative (CN) and their frequencies described. Results: CNs of 3510 appointments (357 dogs and 257 cats) from 99practices were evaluated. The most frequently recorded clinical findings in dogs were: heart murmur (n = 147, 41.2% (95% confidence interval (CI) = 36.1%–46.3%), coughing (n = 83, 23.2% (95% CI = 18.8%–27.6%)) and panting (n = 58, 16.2% (95% CI = 12.4%–20.0%)) and in cats: heart murmur (n = 143, 55.6% (95% CI = 49.5%–61.7%)), suspected thromboembolism (n = 88, 34.2% (95% CI = 28.4%–40.0%)) and weight loss (n = 53, 20.6% (95% CI = 15.7%–25.5%)). Dyspnoea and tachypnoea were infrequently reported in dogs (n = 29, 8.1% (95% CI = 5.3%–10.9%) and n = 21, 5.9% (95% CI = 3.5%–8.3%), respectively) and cats (n = 26, 10.1% (95% CI = 6.4%–13.8%) and n = 36, 14.0% (95% CI = 9.8%–18.2%), respectively). Conclusion: Clinical findings referable to cardiac disease were recorded contemporaneously with NT-proBNP measurement and suggested both published and other indications (coughing (in dogs and cats), and serial measurements and thromboembolism (in cats)) for testing.

The main role of event-based surveillance (EBS) systems is to detect signals of disease outbreaks by mining the health information published in a broad range of online formal and informal sources. In this work, we describe for the first time the flow of outbreak information between sources used by two EBS systems. We applied network analysis to highlight the value of sources in the dissemination of information on African swine fever outbreaks in 2018 and 2019. The results showed the network was highly reactive, sharing information in less than a day. Press agencies and online news sources played an essential role in detecting and sharing outbreak information from national veterinary authorities, thus by-passing the international official organizations (OIE, FAO). The network also detected several non-official events, among which non-notifiable events relevant for risk assessment and epidemiological monitoring, such as the detection of ASF DNA in food.

In the first quarter of 2020, the World Health Organization (WHO) declared COVID-19 a public health emergency around the globe. Different users from all over the world shared their opinions about COVID-19 on social media platforms such as Twitter and Facebook. At the beginning of the pandemic, it became relevant to as- sess public opinions regarding COVID-19 using data available on social media. We used a recently proposed hierarchy-based measure for tweet analysis (H-TFIDF) for feature extraction over sentiment classifi- cation of tweets. We assessed how H- TFIDF and concatenation of H-TFIDF with bidirectional encoder representations from transformers (BH-TFIDF) perform over state-of-the-art bag-of-words (BOW) and term frequency-inverse document fre- quency (TF-IDF) features for sentiment classification of COVID-19 tweets. A uni- form experimental setup of the training- test (90% and 10%) split scheme was used to train the classifier. Moreover, evaluation was performed with the gold standard ex- pert labelled dataset to measure precision for each binary classified class.