Rodde Solene, Hammami Pachka, Mesdour Asma, Valentin Sarah, Boudoua Bahdja, Tizzani Paolo, Awada Lina, Trevennec Carlene, Pimenta Paulo, Apolloni Andrea, Arsevska Elena
French Agricultural Research Centre for International Development (CIRAD), Montpellier, France.
Joint Research Unit Animals, Health, Territories, Risks, and Ecosystems (UMR ASTRE), French Agricultural Research Centre for International Development (CIRAD), National Research Institute for Agriculture, Food and Environment (INRAE), Montpellier, France.
PLoS One. 2025 Aug 4;20(8):e0327798. doi: 10.1371/journal.pone.0327798. eCollection 2025.
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.
卫生机构的疫情情报（EI）从业者会监测各种来源，以发现并跟进疾病爆发的新闻，包括在线媒体监测。2016年为法国动物卫生流行病监测平台（ESA平台）开发的网络疾病信息自动提取平台（PADI-web），可监测并收集在线媒体中与疫情爆发相关的新闻，帮助用户发现并预测对疾病爆发的应对措施。鉴于通过PADI-web收集到大量与疫情爆发相关的新闻，我们旨在更好地了解该工具所捕获的不同在线媒体来源推动疫情相关信息传播的因素，以便用户进行更有针对性、更高效的EI工作。我们构建了一个二分网络，该网络涉及2018年至2019年期间PADI-web在全球范围内捕获的关于禽流感（AI）和非洲猪瘟（ASF）疫情爆发的信息传播来源。我们使用指数随机图模型（ERGM）来评估推动不同在线媒体来源传播疾病爆发信息的流行病学、社会经济和文化因素。我们的AI网络包含来自212个来源的436篇新闻报道中的969条传播新闻（链接），描述了199起AI疫情。ASF网络包含来自204个来源的594篇新闻报道中的1340条传播新闻（链接）以及277起ASF疫情。为每个网络拟合了ERGM。在这两个模型中，国际组织和新闻机构网站比在线新闻网站更有可能传播疫情相关信息（AI的OR = 4.8和OR = 3.2，p < 0.001；ASF的OR = 3.1和OR = 4.7，p < 0.001）。AI研究组织（OR = 2.3，p < 0.001）和ASF兽医当局（OR = 3.6，p < 0.001）也比在线新闻网站更有可能成为信息来源。我们的工作确定了在PADI-web监测的在线媒体来源中推动动物和人畜共患传染病疫情相关信息传播的因素。这些信息可以指导EI从业者和PADI-web的用户根据其专业领域、覆盖范围以及疾病的流行病学状况来监测特定来源。我们的结果还表明，EI从业者可能需要使用其他手段在数据中代表性不足的国家和地区收集EI信息。
