There is an urgent need for biosurveillance of unregulated African meat imports at border points of entry in destination markets. This is underscored by recent pandemics linked to exotic wildlife products. Our objective was to catalog the quantity of meat that is informally transported from Africa into and through Europe often without any veterinary or sanitary checks. We searched and included peer-reviewed studies that contained data on the intercontinental movement of unregulated meat from the African continent. This was followed by an investigation of the reported contamination of such meat. We included fifteen airport studies with limited data on this topic. The references included in this review describe the quantity of meat found at border inspection posts and the presence of pathogens. Disease-causing pathogens were found to be present, and the results are organized into bacteria, virus, and parasite categories. The species of animal meat found in this review were linked to CITES-protected species some of which are known reservoir hosts for infectious diseases. This represents a potential and unquantified human health risk to populations along the supply chain, and a loss to biodiversity in supply countries. Meat samples described in this review were primarily found opportunistically by Customs officials, indicating that any estimate of the total quantities passing undetected through border checkpoints must remain tentative, and cannot rule out the possibility that it is indeed considerably higher. We propose a template for future studies regarding African meat imports at border points of entry. The result of this review illustrates a gap in knowledge and lacunae regarding the amount of unregulated African meat imports worldwide, the pathogens it may contain, and the resulting biodiversity loss that occurs from the intercontinental movement of this meat.

With advances in science and technology, biotechnology is becoming more accessible to people of all demographics. These advances inevitably hold the promise to improve personal and population well-being and welfare substantially. It is paradoxical that while greater access to biotechnology on a population level has many advantages, it may also increase the likelihood and frequency of biodisasters due to accidental or malicious use. Similar to \"Disease X\" (describing unknown naturally emerging pathogenic diseases with a pandemic potential), we term this unknown risk from biotechnologies \"Biodisaster X.\" To date, no studies have examined the potential role of information technologies in preventing and mitigating Biodisaster X.
This study aimed to explore (1) what Biodisaster X might entail and (2) solutions that use artificial intelligence (AI) and emerging 6G technologies to help monitor and manage Biodisaster X threats.
A review of the literature on applying AI and 6G technologies for monitoring and managing biodisasters was conducted on PubMed, using articles published from database inception through to November 16, 2020.
Our findings show that Biodisaster X has the potential to upend lives and livelihoods and destroy economies, essentially posing a looming risk for civilizations worldwide. To shed light on Biodisaster X threats, we detailed effective AI and 6G-enabled strategies, ranging from natural language processing to deep learning-based image analysis to address issues ranging from early Biodisaster X detection (eg, identification of suspicious behaviors), remote design and development of pharmaceuticals (eg, treatment development), and public health interventions (eg, reactive shelter-at-home mandate enforcement), as well as disaster recovery (eg, sentiment analysis of social media posts to shed light on the public\'s feelings and readiness for recovery building).
Biodisaster X is a looming but avoidable catastrophe. Considering the potential human and economic consequences Biodisaster X could cause, actions that can effectively monitor and manage Biodisaster X threats must be taken promptly and proactively. Rather than solely depending on overstretched professional attention of health experts and government officials, it is perhaps more cost-effective and practical to deploy technology-based solutions to prevent and control Biodisaster X threats. This study discusses what Biodisaster X could entail and emphasizes the importance of monitoring and managing Biodisaster X threats by AI techniques and 6G technologies. Future studies could explore how the convergence of AI and 6G systems may further advance the preparedness for high-impact, less likely events beyond Biodisaster X.

This review presents the current initiatives and potential for development in the field of animal health surveillance (AHSyS), 5 years on from its advent to the front of the veterinary public health scene. A systematic review approach was used to document the ongoing AHSyS initiatives (active systems and those in pilot phase) and recent methodological developments. Clinical data from practitioners and laboratory data remain the main data sources for AHSyS. However, although not currently integrated into prospectively running initiatives, production data, mortality data, abattoir data, and new media sources (such as Internet searches) have been the objective of an increasing number of publications seeking to develop and validate new AHSyS indicators. Some limitations inherent to AHSyS such as reporting sustainability and the lack of classification standards continue to hinder the development of automated syndromic analysis and interpretation. In an era of ubiquitous electronic collection of animal health data, surveillance experts are increasingly interested in running multivariate systems (which concurrently monitor several data streams) as they are inferentially more accurate than univariate systems. Thus, Bayesian methodologies, which are much more apt to discover the interplay among multiple syndromic data sources, are foreseen to play a big part in the future of AHSyS. It has become clear that early detection of outbreaks may not be the principal expected benefit of AHSyS. As more systems will enter an active prospective phase, following the intensive development stage of the last 5 years, the study envisions AHSyS, in particular for livestock, to significantly contribute to future international-, national-, and local-level animal health intelligence, going beyond the detection and monitoring of disease events by contributing solid situation awareness of animal welfare and health at various stages along the food-producing chain, and an understanding of the risk management involving actors in this value chain.

Internet access and usage has changed how people seek and report health information. Meanwhile,infectious diseases continue to threaten humanity. The analysis of Big Data, or vast digital data, presents an opportunity to improve disease surveillance and epidemic intelligence. Epidemic intelligence contains two components: indicator based and event-based. A relatively new surveillance type has emerged called event-based Internet biosurveillance systems. These systems use information on events impacting health from Internet sources, such as social media or news aggregates. These systems circumvent the limitations of traditional reporting systems by being inexpensive, transparent, and flexible. Yet, innovations and the functionality of these systems can change rapidly.
To update the current state of knowledge on event-based Internet biosurveillance systems by identifying all systems, including current functionality, with hopes to aid decision makers with whether to incorporate new methods into comprehensive programmes of surveillance.
A systematic review was performed through PubMed, Scopus, and Google Scholar databases, while also including grey literature and other publication types.
50 event-based Internet systems were identified, including an extraction of 15 attributes for each system, described in 99 articles. Each system uses different innovative technology and data sources to gather data, process, and disseminate data to detect infectious disease outbreaks.
The review emphasises the importance of using both formal and informal sources for timely and accurate infectious disease outbreak surveillance, cataloguing all event-based Internet biosurveillance systems. By doing so, future researchers will be able to use this review as a library for referencing systems, with hopes of learning, building, and expanding Internet-based surveillance systems. Event-based Internet biosurveillance should act as an extension of traditional systems, to be utilised as an additional, supplemental data source to have a more comprehensive estimate of disease burden.

Reliable monitoring of influenza seasons and pandemic outbreaks is essential for response planning, but compilations of reports on detection and prediction algorithm performance in influenza control practice are largely missing. The aim of this study is to perform a metanarrative review of prospective evaluations of influenza outbreak detection and prediction algorithms restricted settings where authentic surveillance data have been used.
The study was performed as a metanarrative review. An electronic literature search was performed, papers selected and qualitative and semiquantitative content analyses were conducted. For data extraction and interpretations, researcher triangulation was used for quality assurance.
Eight prospective evaluations were found that used authentic surveillance data: three studies evaluating detection and five studies evaluating prediction. The methodological perspectives and experiences from the evaluations were found to have been reported in narrative formats representing biodefence informatics and health policy research, respectively. The biodefence informatics narrative having an emphasis on verification of technically and mathematically sound algorithms constituted a large part of the reporting. Four evaluations were reported as health policy research narratives, thus formulated in a manner that allows the results to qualify as policy evidence.
Awareness of the narrative format in which results are reported is essential when interpreting algorithm evaluations from an infectious disease control practice perspective.

BACKGROUND: Individual case review of spontaneous adverse event (AE) reports remains a cornerstone of medical product safety surveillance for industry and regulators. Previously we developed the Vaccine Adverse Event Text Miner (VaeTM) to offer automated information extraction and potentially accelerate the evaluation of large volumes of unstructured data and facilitate signal detection.
OBJECTIVE: To assess how the information extraction performed by VaeTM impacts the accuracy of a medical expert\'s review of the vaccine adverse event report.
METHODS: The \"outcome of interest\" (diagnosis, cause of death, second level diagnosis), \"onset time,\" and \"alternative explanations\" (drug, medical and family history) for the adverse event were extracted from 1000 reports from the Vaccine Adverse Event Reporting System (VAERS) using the VaeTM system. We compared the human interpretation, by medical experts, of the VaeTM extracted data with their interpretation of the traditional full text reports for these three variables. Two experienced clinicians alternately reviewed text miner output and full text. A third clinician scored the match rate using a predefined algorithm; the proportion of matches and 95% confidence intervals (CI) were calculated. Review time per report was analyzed.
RESULTS: Proportion of matches between the interpretation of the VaeTM extracted data, compared to the interpretation of the full text: 93% for outcome of interest (95% CI: 91-94%) and 78% for alternative explanation (95% CI: 75-81%). Extracted data on the time to onset was used in 14% of cases and was a match in 54% (95% CI: 46-63%) of those cases. When supported by structured time data from reports, the match for time to onset was 79% (95% CI: 76-81%). The extracted text averaged 136 (74%) fewer words, resulting in a mean reduction in review time of 50 (58%) seconds per report.
CONCLUSIONS: Despite a 74% reduction in words, the clinical conclusion from VaeTM extracted data agreed with the full text in 93% and 78% of reports for the outcome of interest and alternative explanation, respectively. The limited amount of extracted time interval data indicates the need for further development of this feature. VaeTM may improve review efficiency, but further study is needed to determine if this level of agreement is sufficient for routine use.

Electronic event-based biosurveillance systems (EEBS\'s) that use near real-time information from the internet are an increasingly important source of epidemiologic intelligence. However, there has not been a systematic assessment of EEBS evaluations, which could identify key uncertainties about current systems and guide EEBS development to most effectively exploit web-based information for biosurveillance. To conduct this assessment, we searched PubMed and Google Scholar to identify peer-reviewed evaluations of EEBS\'s. We included EEBS\'s that use publicly available internet information sources, cover events that are relevant to human health, and have global scope. To assess the publications using a common framework, we constructed a list of 17 EEBS attributes from published guidelines for evaluating health surveillance systems. We identified 11 EEBS\'s and 20 evaluations of these EEBS\'s. The number of published evaluations per EEBS ranged from 1 (Gen-Db, GODsN, MiTAP) to 8 (GPHIN, HealthMap). The median number of evaluation variables assessed per EEBS was 8 (range, 3-15). Ten published evaluations contained quantitative assessments of at least one key variable. No evaluations examined usefulness by identifying specific public health decisions, actions, or outcomes resulting from EEBS outputs. Future EEBS assessments should identify and discuss critical indicators of public health utility, especially the impact of EEBS\'s on public health response.

Syndromic surveillance appeared in the field of public health surveillance in the late 90\'s. Initially proposed for public health identification of bioterrorism events, the method failed to provide convincing evidence of its usefulness and potential benefits. The definition which is proposed today by the Centers for Disease Control and Prevention (CDC) of Atlanta is the most commonly accepted. It defines syndromic surveillance as an automatic process that goes from registration to transfer of data recorded within the framework of a professional rather than public health goal. Systems operating today have integrated a public health approach through routine surveillance procedures with a broader focus than bioterrorism, implying active participation of the official public health surveillance structures. Syndromic surveillance offers several advantages including quick access to a large volume of data in real time, no extra-work for data registration and construction of a historical dataset useful as an historical baseline. Nevertheless, the limitations of this type of surveillance should not be forgotten (sometimes limited sensitivity, specificity, important technical burden…). Today, recorded experience shows that there is no opposition between syndromic surveillance and classical surveillance. On the contrary, they should be presented as complementary procedures. Syndromic surveillance should be analyzed from a temporal perspective, examining its short-term use as an alert mechanism, mid-term use for constitution of historical time series, and long-term use for a description of human health in the 21st century.