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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.

It is impossible to address the many complex needs of respiratory virus surveillance with a single system. Therefore, multiple surveillance systems and complementary studies must fit together as tiles in a “mosaic” to provide a complete picture of the risk, transmission, severity, and impact of respiratory viruses of epidemic and pandemic potential. Below we present a framework to assist national authorities to identify priority respiratory virus surveillance objectives and the best approaches to meet them; to develop implementation plans according to national context and resources; and to prioritize and target technical assistance and financial investments to meet most pressing needs.

BACKGROUND An infodemic is excess information, including false or misleading information, that spreads in digital and physical environments during a public health emergency. The COVID-19 pandemic has been accompanied by an unprecedented global infodemic that has led to confusion about the benefits of medical and public health interventions, with substantial impact on risk-taking and health-seeking behaviors, eroding trust in health authorities and compromising the effectiveness of public health responses and policies. Standardized measures are needed to quantify the harmful impacts of the infodemic in a systematic and methodologically robust manner, as well as harmonizing highly divergent approaches currently explored for this purpose. This can serve as a foundation for a systematic, evidence-based approach to monitoring, identifying, and mitigating future infodemic harms in emergency preparedness and prevention. OBJECTIVE In this paper, we summarize the Fifth World Health Organization (WHO) Infodemic Management Conference structure, proceedings, outcomes, and proposed actions seeking to identify the interdisciplinary approaches and frameworks needed to enable the measurement of the burden of infodemics. METHODS An iterative human-centered design (HCD) approach and concept mapping were used to facilitate focused discussions and allow for the generation of actionable outcomes and recommendations. The discussions included 86 participants representing diverse scientific disciplines and health authorities from 28 countries across all WHO regions, along with observers from civil society and global public health–implementing partners. A thematic map capturing the concepts matching the key contributing factors to the public health burden of infodemics was used throughout the conference to frame and contextualize discussions. Five key areas for immediate action were identified. RESULTS The 5 key areas for the development of metrics to assess the burden of infodemics and associated interventions included (1) developing standardized definitions and ensuring the adoption thereof; (2) improving the map of concepts influencing the burden of infodemics; (3) conducting a review of evidence, tools, and data sources; (4) setting up a technical working group; and (5) addressing immediate priorities for postpandemic recovery and resilience building. The summary report consolidated group input toward a common vocabulary with standardized terms, concepts, study designs, measures, and tools to estimate the burden of infodemics and the effectiveness of infodemic management interventions. CONCLUSIONS Standardizing measurement is the basis for documenting the burden of infodemics on health systems and population health during emergencies. Investment is needed into the development of practical, affordable, evidence-based, and systematic methods that are legally and ethically balanced for monitoring infodemics; generating diagnostics, infodemic insights, and recommendations; and developing interventions, action-oriented guidance, policies, support options, mechanisms, and tools for infodemic managers and emergency program managers.

The bacterial human pathogenHelicobacter pyloriproduces a type IV secretion system (cagT4SS) to inject the oncoprotein CagA into gastric cells. ThecagT4SS external pilus mediates attachment of the apparatus to the target cell and the delivery of CagA. While the composition of the pilus is unclear, CagI is present at the surface of the bacterium and required for pilus formation. Here, we have investigated the properties of CagI by an integrative structural biology approach. Using Alpha Fold 2 and Small Angle X-ray scattering, it was found that CagI forms elongated dimers mediated by rod-shaped N-terminal domains (CagIN) and prolonged by globular C-terminal domains (CagIC). Three Designed Ankyrin Repeat Proteins (DARPins) K2, K5 and K8 selected against CagI interacted with CagICwith subnanomolar affinities. The crystal structures of the CagI:K2 and CagI:K5 complexes were solved and identified the interfaces between the molecules, thereby providing a structural explanation for the difference in affinity between the two binders. Purified CagI and CagICwere found to interact with adenocarcinoma gastric (AGS) cells, induced cell spreading and the interaction was inhibited by K2. The same DARPin inhibited CagA translocation by up to 65% in AGS cells while inhibition levels were 40% and 30% with K8 and K5, respectively. Our study suggests that CagICplays a key role incagT4SS-mediated CagA translocation and that DARPins targeting CagI represent potent inhibitors of thecagT4SS, a crucial risk factor for gastric cancer development.Author summaryHelicobacter pyloriis a bacterial pathogen that colonises the human stomach in half of the world’s population. The most virulent strains use the cag- type IV secretion system (cagT4SS), a molecular nanomachine capable of injecting the oncoprotein CagA into gastric cells. How CagA is delivered is unknown, but thecagT4SS produces an external appendage referred to as pilus, which interacts with host cell receptors, mediating CagA translocation from the cytoplasm of the bacteria to the inner membrane of the host cell. In this study we have investigated the structural and functional properties of CagI, a protein long-thought to be associated with thecagT4SS pilus but with yet unknown function. We found that CagI displays a unique dimeric structure and that its C-terminal domain is involved in interaction with the host cell. Designed Ankyrin Repeat Proteins were selected against CagI and found to interact with its C-terminal moiety with high affinity. DARPin binding was able to prevent CagI interaction with the host cell and inhibited CagA translocation byH. pylori. Our study reveals the role of CagI incagT4SS interaction with gastric cells and provides a first example of a small protein binder inhibiting thecagT4SS activity.

The first step in SARS-CoV-2 genomic surveillance is testing to identify people who are infected. However, global testing rates are falling as we emerge from the acute health emergency and remain low in many low- and middle-income countries (mean = 27 tests per 100,000 people per day). We simulated COVID-19 epidemics in a prototypical low- and middle-income country to investigate how testing rates, sampling strategies and sequencing proportions jointly impact surveillance outcomes, and showed that low testing rates and spatiotemporal biases delay time to detection of new variants by weeks to months and can lead to unreliable estimates of variant prevalence, even when the proportion of samples sequenced is increased. Accordingly, investments in wider access to diagnostics to support testing rates of approximately 100 tests per 100,000 people per day could enable more timely detection of new variants and reliable estimates of variant prevalence. The performance of global SARS-CoV-2 genomic surveillance programs is fundamentally limited by access to diagnostic testing.

Summary Viral variants of concern may emerge with dangerous resistance to the immunity generated by the current vaccines to prevent coronavirus disease 2019 (Covid-19). Moreover, if some variants of concern have increased transmissibility or virulence, the importance of efficient public health measures and vaccination programs will increase. The global response must be both timely and science based.

BACKGROUND Vaccine hesitancy is one of the many factors impeding efforts to control the COVID-19 pandemic. Exacerbated by the COVID-19 infodemic, misinformation has undermined public trust in vaccination, led to greater polarization, and resulted in a high social cost where close social relationships have experienced conflict or disagreements about the public health response. OBJECTIVE The purpose of this paper is to describe the theory behind the development of a digital behavioral science intervention—The Good Talk!—designed to target vaccine-hesitant individuals through their close contacts (eg, family, friends, and colleagues) and to describe the methodology of a research study to evaluate its efficacy. METHODS The Good Talk! uses an educational serious game approach to boost the skills and competences of vaccine advocates to have open conversations about COVID-19 with their close contacts who are vaccine hesitant. The game teaches vaccine advocates evidence-based open conversation skills to help them speak with individuals who have opposing points of view or who may ascribe to nonscientifically supported beliefs while retaining trust, identifying common ground, and fostering acceptance and respect of divergent views. The game is currently under development and will be available on the web, free to access for participants worldwide, and accompanied by a promotional campaign to recruit participants through social media channels. This protocol describes the methodology for a randomized controlled trial that will compare participants who play The Good Talk! game with a control group that plays the widely known noneducational game Tetris. The study will evaluate a participant’s open conversation skills, self-efficacy, and behavioral intentions to have an open conversation with a vaccine-hesitant individual both before and after game play. RESULTS Recruitment will commence in early 2023 and will cease once 450 participants complete the study (225 per group). The primary outcome is improvement in open conversation skills. Secondary outcomes are self-efficacy and behavioral intentions to have an open conversation with a vaccine-hesitant individual. Exploratory analyses will examine the effect of the game on implementation intentions as well as potential covariates or subgroup differences based on sociodemographic information or previous experiences with COVID-19 vaccination conversations. CONCLUSIONS The outcome of the project is to promote more open conversations regarding COVID-19 vaccination. We hope that our approach will encourage more governments and public health experts to engage in their mission to reach their citizens directly with digital health solutions and to consider such interventions as an important tool in infodemic management. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/40753

International audience

Background An infodemic is an overflow of information of varying quality that surges across digital and physical environments during an acute public health event. It leads to confusion, risk-taking, and behaviors that can harm health and lead to erosion of trust in health authorities and public health responses. Owing to the global scale and high stakes of the health emergency, responding to the infodemic related to the pandemic is particularly urgent. Building on diverse research disciplines and expanding the discipline of infodemiology, more evidence-based interventions are needed to design infodemic management interventions and tools and implement them by health emergency responders. Objective The World Health Organization organized the first global infodemiology conference, entirely online, during June and July 2020, with a follow-up process from August to October 2020, to review current multidisciplinary evidence, interventions, and practices that can be applied to the COVID-19 infodemic response. This resulted in the creation of a public health research agenda for managing infodemics. Methods As part of the conference, a structured expert judgment synthesis method was used to formulate a public health research agenda. A total of 110 participants represented diverse scientific disciplines from over 35 countries and global public health implementing partners. The conference used a laddered discussion sprint methodology by rotating participant teams, and a managed follow-up process was used to assemble a research agenda based on the discussion and structured expert feedback. This resulted in a five-workstream frame of the research agenda for infodemic management and 166 suggested research questions. The participants then ranked the questions for feasibility and expected public health impact. The expert consensus was summarized in a public health research agenda that included a list of priority research questions. Results The public health research agenda for infodemic management has five workstreams: (1) measuring and continuously monitoring the impact of infodemics during health emergencies; (2) detecting signals and understanding the spread and risk of infodemics; (3) responding and deploying interventions that mitigate and protect against infodemics and their harmful effects; (4) evaluating infodemic interventions and strengthening the resilience of individuals and communities to infodemics; and (5) promoting the development, adaptation, and application of interventions and toolkits for infodemic management. Each workstream identifies research questions and highlights 49 high priority research questions. Conclusions Public health authorities need to develop, validate, implement, and adapt tools and interventions for managing infodemics in acute public health events in ways that are appropriate for their countries and contexts. Infodemiology provides a scientific foundation to make this possible. This research agenda proposes a structured framework for targeted investment for the scientific community, policy makers, implementing organizations, and other stakeholders to consider.

BACKGROUND The COVID-19 pandemic has been accompanied by an infodemic: excess information, including false or misleading information, in digital and physical environments during an acute public health event. This infodemic is leading to confusion and risk-taking behaviors that can be harmful to health, as well as to mistrust in health authorities and public health responses. The World Health Organization (WHO) is working to develop tools to provide an evidence-based response to the infodemic, enabling prioritization of health response activities. OBJECTIVE In this work, we aimed to develop a practical, structured approach to identify narratives in public online conversations on social media platforms where concerns or confusion exist or where narratives are gaining traction, thus providing actionable data to help the WHO prioritize its response efforts to address the COVID-19 infodemic. METHODS We developed a taxonomy to filter global public conversations in English and French related to COVID-19 on social media into 5 categories with 35 subcategories. The taxonomy and its implementation were validated for retrieval precision and recall, and they were reviewed and adapted as language about the pandemic in online conversations changed over time. The aggregated data for each subcategory were analyzed on a weekly basis by volume, velocity, and presence of questions to detect signals of information voids with potential for confusion or where mis- or disinformation may thrive. A human analyst reviewed and identified potential information voids and sources of confusion, and quantitative data were used to provide insights on emerging narratives, influencers, and public reactions to COVID-19–related topics. RESULTS A COVID-19 public health social listening taxonomy was developed, validated, and applied to filter relevant content for more focused analysis. A weekly analysis of public online conversations since March 23, 2020, enabled quantification of shifting interests in public health–related topics concerning the pandemic, and the analysis demonstrated recurring voids of verified health information. This approach therefore focuses on the detection of infodemic signals to generate actionable insights to rapidly inform decision-making for a more targeted and adaptive response, including risk communication. CONCLUSIONS This approach has been successfully applied to identify and analyze infodemic signals, particularly information voids, to inform the COVID-19 pandemic response. More broadly, the results have demonstrated the importance of ongoing monitoring and analysis of public online conversations, as information voids frequently recur and narratives shift over time. The approach is being piloted in individual countries and WHO regions to generate localized insights and actions; meanwhile, a pilot of an artificial intelligence–based social listening platform is using this taxonomy to aggregate and compare online conversations across 20 countries. Beyond the COVID-19 pandemic, the taxonomy and methodology may be adapted for fast deployment in future public health events, and they could form the basis of a routine social listening program for health preparedness and response planning.

The COVID-19 pandemic is the first to unfold in the highly digitalized society of the 21st century and is therefore the first pandemic to benefit from and be threatened by a thriving real-time digital information ecosystem. For this reason, the response to the infodemic required development of a public health social listening taxonomy, a structure that can simplify the chaotic information ecosystem to enable an adaptable monitoring infrastructure that detects signals of fertile ground for misinformation and guides trusted sources of verified information to fill in information voids in a timely manner. A weekly analysis of public online conversations since 23 March 2020 has enabled the quantification of running shifts of public interest in public health-related topics concerning the pandemic and has demonstrated the frequent resumption of information voids relevant for public health interventions and risk communication in an emergency response setting.

As the COVID-19 pandemic evolves, the accompanying infodemic is being amplified through social media and has challenged effective response. The WHO Early AI-supported Response with Social Listening (EARS) is a platform that summarizes real-time information about how people are talking about COVID-19 in public spaces online in 20 pilot countries and in four languages. The aim of the platform is to better integrate social listening with other data sources and analyses that can inform infodemic response.

Background The COVID-19 pandemic has been accompanied by an infodemic: excess information, including false or misleading information, in digital and physical environments during an acute public health event. This infodemic is leading to confusion and risk-taking behaviors that can be harmful to health, as well as to mistrust in health authorities and public health responses. The World Health Organization (WHO) is working to develop tools to provide an evidence-based response to the infodemic, enabling prioritization of health response activities. Objective In this work, we aimed to develop a practical, structured approach to identify narratives in public online conversations on social media platforms where concerns or confusion exist or where narratives are gaining traction, thus providing actionable data to help the WHO prioritize its response efforts to address the COVID-19 infodemic. Methods We developed a taxonomy to filter global public conversations in English and French related to COVID-19 on social media into 5 categories with 35 subcategories. The taxonomy and its implementation were validated for retrieval precision and recall, and they were reviewed and adapted as language about the pandemic in online conversations changed over time. The aggregated data for each subcategory were analyzed on a weekly basis by volume, velocity, and presence of questions to detect signals of information voids with potential for confusion or where mis- or disinformation may thrive. A human analyst reviewed and identified potential information voids and sources of confusion, and quantitative data were used to provide insights on emerging narratives, influencers, and public reactions to COVID-19–related topics. Results A COVID-19 public health social listening taxonomy was developed, validated, and applied to filter relevant content for more focused analysis. A weekly analysis of public online conversations since March 23, 2020, enabled quantification of shifting interests in public health–related topics concerning the pandemic, and the analysis demonstrated recurring voids of verified health information. This approach therefore focuses on the detection of infodemic signals to generate actionable insights to rapidly inform decision-making for a more targeted and adaptive response, including risk communication. Conclusions This approach has been successfully applied to identify and analyze infodemic signals, particularly information voids, to inform the COVID-19 pandemic response. More broadly, the results have demonstrated the importance of ongoing monitoring and analysis of public online conversations, as information voids frequently recur and narratives shift over time. The approach is being piloted in individual countries and WHO regions to generate localized insights and actions; meanwhile, a pilot of an artificial intelligence–based social listening platform is using this taxonomy to aggregate and compare online conversations across 20 countries. Beyond the COVID-19 pandemic, the taxonomy and methodology may be adapted for fast deployment in future public health events, and they could form the basis of a routine social listening program for health preparedness and response planning.

BACKGROUND An infodemic is an overabundance of information—some accurate and some not—that occurs during an epidemic. In a similar manner to an epidemic, it spreads between humans via digital and physical information systems. It makes it hard for people to find trustworthy sources and reliable guidance when they need it. OBJECTIVE A World Health Organization (WHO) technical consultation on responding to the infodemic related to the coronavirus disease (COVID-19) pandemic was held, entirely online, to crowdsource suggested actions for a framework for infodemic management. METHODS A group of policy makers, public health professionals, researchers, students, and other concerned stakeholders was joined by representatives of the media, social media platforms, various private sector organizations, and civil society to suggest and discuss actions for all parts of society, and multiple related professional and scientific disciplines, methods, and technologies. A total of 594 ideas for actions were crowdsourced online during the discussions and consolidated into suggestions for an infodemic management framework. RESULTS The analysis team distilled the suggestions into a set of 50 proposed actions for a framework for managing infodemics in health emergencies. The consultation revealed six policy implications to consider. First, interventions and messages must be based on science and evidence, and must reach citizens and enable them to make informed decisions on how to protect themselves and their communities in a health emergency. Second, knowledge should be translated into actionable behavior-change messages, presented in ways that are understood by and accessible to all individuals in all parts of all societies. Third, governments should reach out to key communities to ensure their concerns and information needs are understood, tailoring advice and messages to address the audiences they represent. Fourth, to strengthen the analysis and amplification of information impact, strategic partnerships should be formed across all sectors, including but not limited to the social media and technology sectors, academia, and civil society. Fifth, health authorities should ensure that these actions are informed by reliable information that helps them understand the circulating narratives and changes in the flow of information, questions, and misinformation in communities. Sixth, following experiences to date in responding to the COVID-19 infodemic and the lessons from other disease outbreaks, infodemic management approaches should be further developed to support preparedness and response, and to inform risk mitigation, and be enhanced through data science and sociobehavioral and other research. CONCLUSIONS The first version of this framework proposes five action areas in which WHO Member States and actors within society can apply, according to their mandate, an infodemic management approach adapted to national contexts and practices. Responses to the COVID-19 pandemic and the related infodemic require swift, regular, systematic, and coordinated action from multiple sectors of society and government. It remains crucial that we promote trusted information and fight misinformation, thereby helping save lives.

Lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1) is implicated in cardiovascular disease by modulating apoptosis and oxidative stress. We hypothesized that LOX-1 may be involved in pathophysiology of stroke by mediating ischaemia/reperfusion (I/R)-dependent cell death. Transient middle cerebral artery occlusion (tMCAO) was performed in wild-type (WT) mice, endothelial-specific LOX-1 transgenic mice (eLOX-1TG) and WT animals treated with LOX-1 silencing RNA (siRNA). In WT mice exposed to tMCAO, LOX-1 expression and function were increased in the MCA. Compared to WT animals, eLOX-1TG mice displayed increased stroke volumes and worsened outcome after I/R. Conversely, LOX-1-silencing decreased both stroke volume and neurological impairment. Similarly, in HBMVECs, hypoxia/reoxygenation increased LOX-1 expression, while LOX-1 overexpressing cells showed increased death following hypoxia reoxygenation. Increased caspase-3 activation was observed following LOX-1 overexpression both in vivo and in vitro, thus representing a likely mediator. Finally, monocytes from ischaemic stroke patients exhibited increased LOX-1 expression which also correlated with disease severity. Our data unequivocally demonstrate a key role for LOX-1 in determining outcome following I/R brain damage. Our findings could be corroborated in human brain endothelial cells and monocytes from patients, underscoring their translational relevance and suggesting siRNA-mediated LOX-1 knockdown as a novel therapeutic strategy for stroke patients.

The recent apparent increase in human monkeypox cases across a wide geographic area, the potential for further spread, and the lack of reliable surveillance have raised the level of concern for this emerging zoonosis. In November 2017, the World Health Organization (WHO), in collaboration with CDC, hosted an informal consultation on monkeypox with researchers, global health partners, ministries of health, and orthopoxvirus experts to review and discuss human monkeypox in African countries where cases have been recently detected and also identify components of surveillance and response that need improvement. Endemic human monkeypox has been reported from more countries in the past decade than during the previous 40 years. Since 2016, confirmed cases of monkeypox have occurred in Central African Republic, Democratic Republic of the Congo, Liberia, Nigeria, Republic of the Congo, and Sierra Leone and in captive chimpanzees in Cameroon. Many countries with endemic monkeypox lack recent experience and specific knowledge about the disease to detect cases, treat patients, and prevent further spread of the virus. Specific improvements in surveillance capacity, laboratory diagnostics, and infection control measures are needed to launch an efficient response. Further, gaps in knowledge about the epidemiology and ecology of the virus need to be addressed to design, recommend, and implement needed prevention and control measures.