Infodemiology and infoveillance in ONE public health - Eastern European Perspective

Infodemiology and infoveillance in ONE public health - Eastern European Perspective

1. Infodemiology and infoveillance in ONE public health - Eastern European Perspective Andrzej Jarynowski 1,2 , Eleftherios Meletis 3 , Maja Romanowska 4 , Alexander Semenov 5 , Stanisław Maksymowicz 6 , Ireneusz Skawina 1,7 , Polychronis Kostoulas 3 , Vitaly Belik 2 Polish Hygienic Society, Wrocław, Poland Systems Modelling Group, Institute of Veterinary Epidemiology and Biostatistics, Freie Universität Berlin, Germany Faculty of Public and One Health, School of Health Sciences, University of Thessaly, Karditsa , Greece Infection Prevention Institute, Warsaw, Poland Department of Industrial and Systems Engineering, University of Florida , USA School of Public Health, Collegium Medicum , University of Warmia and Mazury , Olsztyn, Poland Polish Sanitary Inspection, Świdnica, Poland

2. Avian Influenza in Mammals Oder river disaster caused by the golden algae

3. Cooperation Mathematical models (Vitaly Belik - FU Berlin) Genetics (Alisa Sergeeva - FU Berlin) Social dimension (Stanisław Maksymowicz - UMW) Infodemic (Maja Romanowska - WHO) Internet Scraping (Alexander Semenov – UF) Nature Conservation (Magdalena Lenda – PAN) Sanitary Inspection (Ireneusz Skawina - PPIS) Veterinary Inspection (Jakub Kubacki - GIW) One Health Inspection of the Polish Army (Łukasz Krzowski - WAT) The views and interpretations expressed by me in this presentation are solely my personal thoughts. They do not represent the official position or opinion of the Veterinary or Sanitary Inspection. Boris 2024

4. INFOVEILLANCE Epidemiological surveillance deals with the analysis of web content to predict bio medical 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. 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. An infodemic is an overwhelming and uncontrolled information surge where reliable and unreliable sources coexist. Accurate and credible information, misinformation, disinformation, rumors, current data, and outdated content flood communication channels, overwhelming the recipient. Monitoring actual (real- time) and declarative attitudes should, in the WHO's view, be a priority for local decision- makers.

6. 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 ONE healh 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.

7. SURVEILLANCE DATA

8. Summary interest in selected terms (disease) and medium T erm(topic)/ intensit y Weekly Google searches for RSV (01.2020- 07.2022) Number of articles per day (01.2020- 07.2022) COVID- 19 713 330390 Coronavirus 1368 255620 HPAI 2.2 29857 ASF 3.3 17893 Completely different perception on social media: almost 1,000 times more interest in human diseases than in animal diseases (number of searches per week) Slightly different perceptions of almost a few/more interest in human than animal diseases in traditional media ( number of articles per day) Perception of infectious diseases with animal and human hosts on the Polish internet. ISAH proceedings . (2022)

9. Education: - disease control and animal welfare - disseminating the role of vector diseases, AMR, etc. - animal movement plans promoting wildlife and livestock veterinary services 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 Setting up monitoring: - system readiness early warning functionality - Intergation effords of veterinary, sanitary and nature conservations Data acquisition: - local traditional media - social media - google etc. search Analyze, evaluation and implementing in practice - detection and management of post event disease outbreaks Media Monitoring PRE- CRISIS CRISIS CULMINATION LATE CRISIS ONE HEALTH COORDINATION OFFICE Animal Health Discourse during Ecological Crises in the Media. Veterinary Sciences . (2024)

10. TOOLS Using content in Slavic language s 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 (death) , Telegram API, EPITweetr- ECDC (death) Google trends YouTube stats/comments Wikipedia stats Digital traces of protests by animal breeders and animal right defenders in Poland. Motra -K   (2024)

11. Infoveillence a digital from the Gathering footprint internet INFECTIOUS DISEASES Humans, Zoonoses and vector- borne diseases, Anima and Plants Models Statistical, ML, SD or ABM Sensors Biological surveillance , Images (Satelite, drones) Standard monitoring Disease s , prescription, sales . other registers (e.g. epigenetics), surveys MULTI-DOMAIN MONITORING IN ONE HEALTH CONTEXT

12. Disaster on the Oder River in the Media Summer 2022 A fish kill of unknown origin has been spreading along the Oder and its tributaries since the last days of July. The disaster affected regions from Opole through Lower Silesia, Lubuskie , the Polish-German border to the Szczecin Lagoon, which is shared with Germany and connected to the Baltic Sea. The causative biological agent - the golden algae (and its toxins) - was confirmed around 18.08 and for more than 2 weeks many hypotheses were investigated.

13. End of June, dead fishes and water quality on the Gliwice Canal 25-27.07 agricultural organizations extremely low water levels 27.07, the social media of Oława and the anglers' associations 29-31.07 media coverage in Olawa media about fish kill 31.07 Lower Silesian regional media (including Radio Wroclaw and Gazeta Wroclawska, TVP3 Wroclaw) 31.07 a warning was issued on Silesian internet portals about a rapid increase in the state of the Oder River from Chałupki to Kędzierzyn Koźle Between 01-03.08 there was no mention of the appearance of the dead fishes outside angling forums (dead fishes appeared in the mainstream of the Oder in Wroclaw, but not in quantities indicating an ecological disaster). July, movement of contaminated sediment from the river bed (discussion on ecological media) 03.08. The Wroclaw Left-wing party members intervened in the public and political sphere. Die Tragödie an der Oder. Wie Polen und Deutsche aneinander vorbeireden. Forum Dialog . (2024)

14. On 11.08 German regional broadcaster spread a rumour about 10.08 is further propagation on nationwide media and contamination of water with Mercury). This rumor W as officially cancelled by authorities on 13.08, but information became 'alive' in media for a while. nationwide social media. Only now are nationwide environmental organizations getting involved On 10.08 fist mentions appeared on the German regional internet (as the wave of death fishers reached the German p o a f r t t h e Oder river). 06- 07.08, a discussion developed on the web portals and social media of Glogow. On 09.08 the mainstream media in Lubusz (Gazeta Lubuska, Radio Zachód etc.) publicise the issue (previously Lower Silesia media did it). On 09.08 nationwide media (Onet, Dziennik, TVPinfo, Interia, Wprost, SE) reported on the incident too 04.08 there were reports from Glogow (about smell and single dead fishes). 06- 07.08, anglers discussed the large number of dead fishers No mentions?

15. Massive deaths of cats Spring/Summer 202 3 In June, the vets noticed that many cats were coming to their clinic with an unknown fatal disease, mainly neurological. At the end of June (27.06) , A/H5N1 was confirmed as the causative agent, but how did it happen? What was the source?

16. more human cases AIV in child Australia outbreaks in poultry and wildlife in Japan fur animals in Finland AIV in poultry in South America RNA of AIV in milk first human Case in USA detected cats in Poland One Health Multimodal Surveillance in Time of Change. One Health Journal . (2024)

17. Colors represent the following phases: (i) unnoticed outbreak (gray); (ii) early warning (yellow); (iii) risk assessment (orange); and (iv) mis(dis)information (red). DATA TRIANGUALATION WITH SOCIAL LISTENING

18. Participatory data Environmental data (bird migration, rodents and waterbird abundance) Infoveillance data Poultry/eggs supply chains PRE-PROCESSING AND DATA CURATION

19. chain structure of positive cases (water and migratory bird related) Cluser of suspected cases in the end of Mai (before survaillance was activated) COMBINED ANALYTICS (I.E. CLUSTERING K-MEANS)

20. Sea Project " Integrated System for Monitoring and Risk Assessment of One Health Risks". Data feeding A collection of data on various factors influencing the development of infectious diseases, including vector- borne pathogens. It integrates dynamic event monitoring databases (PZH, NFZ, PIS, IW, PIORiN data), which analyses reports, information and news about health events that may pose a serious threat to ONE's public health. Population and disease model The platform also includes a tool for visualising epidemiological data, allowing for a better understanding of the spatial and temporal progression of epidemics. Focus on diseases unknown or contracted (including intentionally) as disease entity X, it is precisely the possibility of quickly identifying the problem that would be the greatest added value of such a system. Interactions in the system Contact networks in settings, duration, seasonality, weekdays, commuting Observed and real layer Contact tracing Testing strategies Individual interventions Beh. Model and additional NPI Differentiation of parameters by region Compliance with the mandate Psychological, emotional and mental states Behavioural grouping Limitations of the health care and inspection system Multi- track and vaccine models Indirect transmission and cumulative infection rates Multiple pathogens Multiple options Multiple vaccines Cross- resistance Seasonality Hazard maps forecasting the dynamic spatial distributions of infectious diseases. This also allows the assessment of risk and spread of diseases and understanding of the role of the environment in the emergence of new pathogens. Innovative data analysis tools, including artificial intelligence and machine learning, will be developed in this area to identify patterns and trends in health and environmental data.

21. Regional healthcare management Regional veterinary inspection Regional sanitary inspection Regional One and public health centre Farming animals People in contact with animals Companion animals Wild animals Environment Regional environmental conservation office Regional crisis management office National healthcare management Scientific institutions Ministry of health Ministry of agriculture Ministry of environment NGOs International organizations National One and public health centre Regional military epidemiology office Ministry of defence One Health Multimodal Surveillance in Time of Change. One Health Journal . (2024)

22. disasters Antimicrobial USE Contact networks from vision (tracking), RFID from vision, Bluetooth Human/Hum an/minks interactions from vision (tracking), RFID - - UKR/PL (Measles, TB) Spatiotemporal patterns (covariates) Wild birds, climate, poultry density , Weather seasonality Pigs density, human mobility, Production seasonality. trade registries Adherence to measures, excess mortality, causal models Exposure to environme nt (prediction) Weather seasonalit y Satellit e images Spatial clustering of Berlin Early detection (sensors) Mortality, eggs production, movements (vision), coughing (sound) Mortality, movements (vision) Coughin g (sound analysis) eggs production, movements, (time series) - biomonitoring Risk Maps Infoveillance Participatory epidemiology - Bug of words (demand and supply of information - discussio n and search on AM Discussion of group of interests - Infodemiology Adherence to biosecurity, social tensions (NLP) Adherence to biosecurity, social tensions (NLP) Antisanitaria n attitude - Perception of AM Comparing discussion in Poland and Germany Stigma (M- pox, HIV) PARASITES COVID-19 ASF HPAI Travel associated Diseases Ecological