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Background The infodemic accompanying the COVID-19 pandemic has led to an overwhelming amount of information, including questions, concerns and misinformation. Pandemic fatigue has been identified as a concern from early in the pandemic. With new and ongoing health emergencies in 2022, it is important to understand how pandemic fatigue is being discussed and expressed by users on digital channels. Methods This paper describes the collection of publicly available data over a 3-month period from multiple online sources using Meltwater, CrowdTangle to source data from Twitter, Facebook, Instagram, YouTube, TikTok, Pinterest, Product Reviews, Twitch, blogs & forums. A comprehensive search strategy was developed and tested. A total of 1,484,042 social media posts were identified during the time-period that included the defined search terms for pandemic fatigue. These data were initially sorted by highest levels of engagement and from this dataset, analysts reviewed the mentions to isolate and remove irrelevant content and identify dominant narratives. A thematic analysis was carried out on these narratives to identify themes related to expression of pandemic fatigue. Two researchers reviewed the data and themes. Results The thematic analysis of narratives identified six main themes relating to expression of pandemic fatigue, and one theme of counter narratives against pandemic fatigue. Data volume increased concurrent with the time of the mpox emergency announcement. Emergent themes showed the different ways users expressed pandemic fatigue and how it was interlaced with issues of trust, preventative measure acceptance and uptake, misinformation, and being overwhelmed with multiple or sustained emergencies. Conclusions This paper has identified the different ways users express pandemic fatigue on digital channels over a 3-month period. Better understanding the implications of the infodemic ecosystem on user’s perceptions, questions, and concerns regarding pandemic and more broadly emergency fatigue is vital in identifying relevant interventions and, in the longer term, strengthening the global architecture for health emergency preparedness, prevention, readiness and resilience, as evidenced in this paper. There are clear pathways for further research, including incorporating additional languages and reviewing these themes over longer time periods.

BACKGROUND Amidst the COVID-19 pandemic, there has been a need for rapid social understanding to inform infodemic management and response. While social media analysis platforms have traditionally been designed for commercial brands for marketing and sales purposes, it has been under-utilized and under-adapted for comprehensive understanding of social dynamics in areas such as public health. Traditional systems have challenges for public health usage and new tools and innovative methods are needed. The WHO Early AI-Supported Response with Social Listening (EARS) platform was developed to overcome some of these challenges. OBJECTIVE To describe the methodological approach to the development of the EARS platform, including data sourcing, development and validation of a machine learning categorization approach, as well as results from the pilot. METHODS Data for EARS is collected daily from online conversations in publicly available sources, including Twitter, Facebook public pages, online forums, news comments, and blogs in 9 languages. Public health and social media experts developed a taxonomy to categorize COVID-19 narratives to 5 relevant main categories and 41 sub-categories. We developed a semi-supervised machine learning algorithm to categorize social media posts to categories and various filters. To validate the results obtained by the machine learning-based approach we compared it to a search filters methodology applying boolean queries with the same amount of information and measured recall and precision. A Hotelling T2 was run to determine the effect of the classification method on the combined variables. RESULTS The EARS platform has been developed, validated and applied to monitor the conversation about COVID-19 since December 2020. A total of 215,469,045 social posts were collected for processing from December 2020 to February 2022. The machine learning algorithm outperformed the boolean search filters method by a large margin for precision and recall in both English and Spanish (p CONCLUSIONS The EARS platform has been specifically developed to address the changing needs of public health analysts during the COVID-19 pandemic. The application of a public health taxonomy and AI technology to a user-friendly social listening platform, accessible directly by analysts, is a significant step in better enabling understanding of global narratives. The platform was designed in a way to allow for scalability, and iterations and new countries and languages have been added. This research has shown that a machine learning approach is more accurate than using only keywords and has benefit for categorizing and understanding large amounts of digital social data during an infodemic. Further technical developments are needed and planned to provide continuous improvements and meet the challenges in generation of infodemic insights from social media faced by infodemic managers and public health professionals.

UNSTRUCTURED Easy access to evidence-based information on COVID-19 within an infodemic has been a challenging task. Chatbots have been introduced in times of emergency, when human resources are stretched thin, and individuals need a user-centred resource. The WHO Regional Office for Europe and UNICEF Europe and Central Asia came together to build a chatbot, HealthBuddy+, to assist country populations in the Region to access accurate COVID-19 information in the local languages, adapted to the country context. Working in close collaboration with thematic technical experts, colleagues and counterparts at the country level allowed the project to be tailored to a diverse range of subtopics. To ensure that HealthBuddy+ was relevant and useful in countries across the Region, the two regional offices worked closely with their counterparts in country offices, who were essential in partnering with national authorities, engaging communities, and promoting the tool, and identifying the most relevant communication channels in which to embed HealthBuddy+. Over the past two years, the project has expanded from a web-based chatbot in seven languages to a multistream, multifunction chatbot available in 16 regional languages, and HealthBuddy+ continues to expand and adjust to meet emerging health emergency needs.

Governments around the world have implemented measures to manage the transmission of coronavirus disease 2019 (COVID-19). While the majority of these measures are proving effective, they have a high social and economic cost, and response strategies are being adjusted. The World Health Organization (WHO) recommends that communities should have a voice, be informed and engaged and participate in this transition phase. We propose ten considerations to support this principle: (1) implement a phased approach to a ‘new normal’; (2) balance individual rights with the social good; (3) prioritise people at highest risk of negative consequences; (4) provide special support for healthcare workers and caring staff; (5) build, strengthen and maintain trust; (6) enlist existing social norms and foster healthy new norms; (7) increase resilience and self-efficacy; (8) use clear and positive language; (9) anticipate and manage misinformation and (10) engage with media outlets. The transition phase should also be informed by real time data according to which governmental responses should be updated.

Antimicrobial resistance (AMR) is a major public health threat. The UK Antibiotic Guardian (AG) behavioural change campaign developed to tackle AMR was expanded across Europe through translation into Russian, Dutch and French. Demographics and knowledge of AGs were analyzed between 01 November 2016 and 31 December 2016. A total of 367 pledges were received with the majority from the public and health care professionals. The pilot has significantly increased the proportion of pledges from Europe (excluding UK) (χ2 = 108.7, P < 0.001). AMR knowledge was greater in AGs (including the public) compared to the EU Eurobarometer survey. Further promotion across Europe is required to measure an impact on tackling AMR.