Intelligence supported media monitoring in veterinary medicine

Media monitoring in veterinary medicien

1. Intelligence supported media monitoring in veterinary medicine Andrzej Jarynowski Vitaly Belik 05 .0 6 .2024

2. Outline Introduction to infodemiology and infovelliance (the traditional and social-content media on the Internet) Concept of Social Listening in Veterinary medicine Example on Avian Influenza in mammals AD 2024 Example on Animal rescue during Flooding (June 2024 in Germany

3. INFODEMIOLOGY Infodemiology is concerned with the study of the demand (e.g. search engine queries) and supply (social media content creation or commenting) trajectory of information, which was strongly articulated during the COVID-19 pandemic. Monitoring actual (real-time) and declarative attitudes should, in the WHO's view, be a priority for local decision-makers.

4. INFOVEILLANCE Infoveillance deals with the analysis of web content to predict biological phenomena. Its most important advantage is the possibility of early warning (e.g. participatory reporting), or forecasting or improving estimators of incidence, prevalence or complications. Moving syndromic surveillance to the internet has great relevance (estimating the scale of health problems, early warning of events).

5. SOCIAL LISTENING What is Social Listening? The process of longitudinal surveying population and monitoring online conversations at social media platforms to gain insights into public sentiment, trends, and emerging issues. In ONE and public health, social listening involves tracking discussions related to animal health, diseases, symptoms, and outbreaks. Why is Social Listening Important in Veterinary Epidemiology? Early Detection: Identify potential outbreaks or disease clusters before they are officially reported. Real-time Surveillance: Monitor public sentiment and concerns regarding health issues. Risk Communication: Tailor public health messages based on real-time insights from social media. Outbreak Response: Evaluate the effectiveness of interventions and identify misinformation or rumors.

6. SURVEILLANCE DATA

7. Education: - disease control and animal welfare - disseminating the role of vector diseases, AMR, etc. - animal movement plans - promoting wildlife and livestock veterinary services Media Monitoring Setting up monitoring: -system readiness - early warning functionality One Health Coordination Office Data acquisition: - local traditional media - social media - google etc. search Analyze, evaluation and implementing in practice - detection and management of post event disease outbreaks Crisis communication: - supervision over the correctness of messages - reaction to mis/dis- information - supplementation and correction of information for animal displacement and live safeguarding - coordination over other services Correction and education: -recognition of people lay knowledge and needs - integrating of vets with online communities of pet owners - veterinary advices to animal breeders in local and social media PRE-CRISIS CRISIS CULMINATION LATE CRISIS

8. TOOLS Using content in with the help of monitors: Buzzsumo, EventRegistry , Medisys-JRC, PadiWeb-WOAH/FAO, Frazeo (language corpus) – traditional media Brand24 /SentiOne/Sentimenti/Sotrender ( Facebook , Instagram, Tiktok, Telegram, local media), EIOS-WHO, EARS-WHO X/Twitter API , EPITweetr-ECDC (death) Google trends Youtube stats/comments Wikipedia stats some aspects of One Health importance are discussed.

9. ‹#› INFODEMIOLOGY AS SUPPORTING TOOL FOR ONE AND PUBLIC HEALTH Measuring the social interest in/around SARS-CoV-2 and COVID-19 in the Internet media during the epidemic Quantifying dynamics of interest (demand and supply of content) and discourse patterns Internet as a digital footprint of social activities (secondary document analysis) Media Analysis of the social processes. SEO-marketing solutions as Brand24, SentiOne, SoTrender (used by Infodemic management by WHO) World Organisation for Animal Health and JRC use MedSYS, FAO uses PadiWeb mining engines Influence of foreign intelligence Serves as a complement to longitudinal surveys monitoring public perception (and other socio-economic methods) in REAL TIME

10. WHO Infodemiological intelligence Part of Hub for Pandemic and Epidemic Intelligence in Berlin https://www.who.int/news/item/01-09-2021-who-germany-open-hub- for-pandemic-and-epidemic-intelligence-in-berlin ‹#› / 17 https://www.who-ears.com/#/ ECDC Infodemiological intelligence https://joinup.ec.europa.eu/collection/open-source-observatory-osor/news/searching-infectious-diseases-open-source

11. ‹#› SENTIMENT ANALYSIS: UNDERSTANDING EMOTIONS IN TEXT What: Uses AI to determine if text expresses positive, negative, or neutral feelings (or other system of emotions). Why: Understand feedback Monitor social perception Inform decisions Track public opinion How: Train AI model on labeled examples vs LLM unsupervised approach Apply model to text

12. ‹#› TOPIC MODELING: UNCOVER HIDDEN THEMES IN TEXT What: An AI technique that automatically identifies topics in large text collections. Why: Find hidden themes Organize & summarize documents Gain insights from large text datasets How: Apply topic modeling algorithm Assign topics to documents Interpret results

13. Zoonotic potential! Unknown (under investigatigation) transmission dynamics birds <->mammals High infectivity in farms (aerosol/ airborne transmission) AVIAN INFLUENZA IN MAMMALS

14. ‹#› • January 2024: unexplained illness in dairy cattle causing drop in milk production, among other non-specific signs,in multiple states • 25 March: detection of influenza A(H5N1) in cows reported • 1 April: human case notified to WHO • 24 April: presence of HPAI using qPCR in pasteurized retail milk samples; further studies under way on milk, meat and other products • As of 3 May: detections in 36 dairy cattle herds in 9 states • Cats, raccoons, birds (wild and domestic) also affected near infected dairy cattle herds AVIAN INFLUENZA IN MAMMALS IN USA

15. “Is it possible to develop a veterinary health monitoring for livestock (including horses) and wildlife in mass casualty situations based on a human triage system?” ? The problem of disasters

16. A dded value in mass casualty incidents Infection dieases of human health Real life monitoring L ivestock (our work) vs accompany animals and wildlife (what people see) Social listening in disasters

17. Animal Health Discourse during Ecological Crises in the Media—Lessons Learnt from the Flood in Thessaly from the One Health Perspective Authors: E. Meletis, A. Jarynowski, S. Maksymowicz, P. Kostoulas, V. Belik Published in: Veterinary Sciences, 2024 Die Tragödie an der Oder. Wie Polen und Deutsche aneinander vorbeireden Authors: A. Jarynowski, S. Maksymowicz Published in: Forum Dialog, 2024 One Health Multimodal surveillance in time of change: lessons NOT learnt from case study of A/H5N1 spillover to mammals in Gdańsk metropolitan area Authors: A. Jarynowski, M Romanowska, S Maksymowicz, V Belik Accepted in: ONE health, 2024 Digital traces of protests by animal breeders and animal right defenders in Poland Authors: A. Jarynowski, D. Płatek, V. Belik Included in: Motra-K, 2024 Agroterrorism involving biological agents and related threats in Poland and Europe in the context of the COVID-19 pandemic and the war in Ukraine Author: A. Jarynowski Published in: Terroryzm – studia, analizy, prewencja, 2023 The curious case of the lion from Berlin in summer’23: how Internet media shapes risk perception from wildlife-human conflict Authors: J. Oelke, A. Jarynowski, V. Belik Published in: E-methodology, 2023 BIBLIOGRAPHY