tetano
Editor, Senior Moderator
PLoS One
. 2025 Aug 4;20(8):e0327798.
doi: 10.1371/journal.pone.0327798. eCollection 2025. Modelling the drivers of outbreak communication in online media news for improved event-based surveillance
Solene Rodde[SUP] 1 2 [/SUP], Pachka Hammami[SUP] 1 2 [/SUP], Asma Mesdour[SUP] 1 2 [/SUP], Sarah Valentin[SUP] 1 3 [/SUP], Bahdja Boudoua[SUP] 3 4 [/SUP], Paolo Tizzani[SUP] 5 [/SUP], Lina Awada[SUP] 5 [/SUP], Carlene Trevennec[SUP] 2 4 [/SUP], Paulo Pimenta[SUP] 6 [/SUP], Andrea Apolloni[SUP] 1 2 [/SUP], Elena Arsevska[SUP] 1 2 [/SUP]
Affiliations
Epidemic intelligence (EI) practitioners at health agencies monitor various sources to detect and follow up on disease outbreak news, including online media monitoring. The Platform for Automated Extraction of Disease Information from the Web (PADI-web), developed in 2016 for the French Platform for Epidemiosurveillance in Animal Health (Platform ESA), monitors and collects outbreak-related news from online media, allowing users to detect and anticipate response to disease outbreaks. Given the mass number of outbreak-related news collected with PADI-web, we aimed to understand better what drives communication on outbreaks by the different online media sources captured by this tool to allow for a more targeted and efficient EI process by its users. We built a bipartite network of sources communicating on outbreaks of avian influenza (AI) and African swine fever (ASF) captured by PADI-web between 2018 and 2019 worldwide. We used an Exponential Random Graph Model (ERGM) to assess epidemiological, socioeconomic, and cultural factors that drive communication on disease outbreaks from the different online media sources. Our AI network comprised 969 communicated news (links) from 436 news reports from 212 sources describing 199 AI outbreaks. The ASF network comprised 1340 communicated news (links) from 594 news reports from 204 sources and 277 ASF outbreaks. The ERGM was fitted for each network. In both models, international organisations and press agency sites were more likely to communicate about outbreaks than online news sites (OR = 4.8 and OR = 3.2, p < 0.001 for AI; OR = 3.1 and OR = 4.7, p < 0.001 for ASF). Research organisations for AI (OR = 2.3, p < 0.001) and veterinary authorities for ASF (OR = 3.6, p < 0.001) were also more likely to be a source of information than online news sites. Our work identified the factors driving communication about animal and zoonotic infectious disease outbreaks in online media sources monitored by PADI-web. This information can guide EI practitioners and users of PADI-web to monitor specific sources based on their specialisation and coverage and the disease's epidemiological status. Our results also suggest that EI practitioners may use other means to collect EI information in countries and regions that are not well-represented in the data.
. 2025 Aug 4;20(8):e0327798.
doi: 10.1371/journal.pone.0327798. eCollection 2025. Modelling the drivers of outbreak communication in online media news for improved event-based surveillance
Solene Rodde[SUP] 1 2 [/SUP], Pachka Hammami[SUP] 1 2 [/SUP], Asma Mesdour[SUP] 1 2 [/SUP], Sarah Valentin[SUP] 1 3 [/SUP], Bahdja Boudoua[SUP] 3 4 [/SUP], Paolo Tizzani[SUP] 5 [/SUP], Lina Awada[SUP] 5 [/SUP], Carlene Trevennec[SUP] 2 4 [/SUP], Paulo Pimenta[SUP] 6 [/SUP], Andrea Apolloni[SUP] 1 2 [/SUP], Elena Arsevska[SUP] 1 2 [/SUP]
Affiliations
- PMID: 40758723
- PMCID: PMC12321081
- DOI: 10.1371/journal.pone.0327798
Epidemic intelligence (EI) practitioners at health agencies monitor various sources to detect and follow up on disease outbreak news, including online media monitoring. The Platform for Automated Extraction of Disease Information from the Web (PADI-web), developed in 2016 for the French Platform for Epidemiosurveillance in Animal Health (Platform ESA), monitors and collects outbreak-related news from online media, allowing users to detect and anticipate response to disease outbreaks. Given the mass number of outbreak-related news collected with PADI-web, we aimed to understand better what drives communication on outbreaks by the different online media sources captured by this tool to allow for a more targeted and efficient EI process by its users. We built a bipartite network of sources communicating on outbreaks of avian influenza (AI) and African swine fever (ASF) captured by PADI-web between 2018 and 2019 worldwide. We used an Exponential Random Graph Model (ERGM) to assess epidemiological, socioeconomic, and cultural factors that drive communication on disease outbreaks from the different online media sources. Our AI network comprised 969 communicated news (links) from 436 news reports from 212 sources describing 199 AI outbreaks. The ASF network comprised 1340 communicated news (links) from 594 news reports from 204 sources and 277 ASF outbreaks. The ERGM was fitted for each network. In both models, international organisations and press agency sites were more likely to communicate about outbreaks than online news sites (OR = 4.8 and OR = 3.2, p < 0.001 for AI; OR = 3.1 and OR = 4.7, p < 0.001 for ASF). Research organisations for AI (OR = 2.3, p < 0.001) and veterinary authorities for ASF (OR = 3.6, p < 0.001) were also more likely to be a source of information than online news sites. Our work identified the factors driving communication about animal and zoonotic infectious disease outbreaks in online media sources monitored by PADI-web. This information can guide EI practitioners and users of PADI-web to monitor specific sources based on their specialisation and coverage and the disease's epidemiological status. Our results also suggest that EI practitioners may use other means to collect EI information in countries and regions that are not well-represented in the data.