Enhanced Disease Surveillance Research Articles

The Reproduction Number of Swine Viral Respiratory Diseases: A Systematic Review

Diseases in the swine industry can cause significant economic and health impacts. This review examines R0 estimates for respiratory diseases in pigs, assessing variations and comparing transmission risks within and between farms. A literature search of three databases aggregated peer-reviewed research articles on swine viral respiratory diseases’ R0 values. The study focused on seven diseases: Aujeszky’s disease (AD), Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), Circovirus, Influenza A (IA), Encephalomyocarditis Virus (EV), Classical Swine Fever (CSF), and African Swine Fever (ASF). R0 values were estimated for transmission within and between herds/farms using various methods, from complex mathematical models to simple calculations. Data primarily came from disease surveillance and laboratory experiments. The median R0 for within-herd and between-herd transmission was 10 and 3.31 for AD, 2.78 and 1.14 for PRRSV, 5.9 and 0.89 for Circovirus, 1.75 and 1.6 for CSF, and 3.94 and 3.15 for ASF. For IA and EV, only within-herd R0 values were estimated at 8.65 and 1.3, respectively. Diseases with high R0 values highlight the need for prompt detection and response to outbreaks. Continuous monitoring and evaluation of pathogen transmissibility are crucial for enhancing disease surveillance and reducing the impact of livestock diseases.

Medical Symptom Text Classification through NLP in Healthcare

Abstract: Medical symptom text classification through Natural Language Processing (NLP) is a rapidly evolving field that aims to leverage computational techniques to analyse and interpret vast amounts of textual data generated in healthcare settings. This paper provides a comprehensive survey of current methodologies, applications, challenges, and future directions in this domain. We begin by discussing the importance of symptom classification for improving patient outcomes, supporting clinical decisionmaking, and enhancing disease surveillance. We then review traditional machine learning approaches and advanced deep learning models, highlighting their respective strengths and limitations. Key pre-processing techniques crucial for handling medical jargon and ensuring data privacy are also examined. The paper further explores real-world applications, including clinical decision support systems, disease outbreak detection, and patient monitoring. Despite significant advancements, challenges such as data quality, model interpretability, and regulatory compliance remain. Finally, we identify emerging trends and potential future developments that could drive further innovation in NLP for healthcare. This survey aims to provide a valuable resource for researchers and practitioners seeking to understand and contribute to the field of medical symptom text classification.

Geometric morphometrics to differentiate species and explore seasonal variation in three Mansonia species (Diptera: Culicidae) in central Thailand and their association with meteorological factors.

Mansonia mosquito species are recognised as a significant vector of human pathogens, primarily transmitting the filarial nematode, Brugia malayi. In central Thailand, the three most prevalent Mansonia species are Mansonia annulifera, Mansonia indiana and Mansonia uniformis. This study explored the influence of seasonal changes on the phenotypic variation of these Mansonia species in central Thailand using the geometric morphometrics (GM). To ensure accurate species identification, we integrated GM techniques with DNA barcoding, examining distinctions in both phenotype and genotype among the species. The intraspecific genetic divergence ranged from 0.00% to 1.69%, whereas the interspecific genetic divergence ranged from 10.52% to 16.36%. The clear distinction between intra- and interspecific distances demonstrated the presence of a barcoding gap, confirming the successful differentiation of the three Mansonia mosquito species through DNA barcoding. Similarly, the interspecies GM assessment for classifying Mansonia species demonstrated a high degree of accuracy, with an overall performance of 98.12%. Exploring seasonal variation in the three Mansonia species revealed wing variations across different seasons, and pronounced variations appearing in the cool season. Regarding their association with meteorological factors, Ma. annulifera and Ma. uniformis showed significant positive correlations with temperature (p < 0.05), and Ma. uniformis also displayed a significant negative correlation with atmospheric pressure (p < 0.05). The insights from this study will deepen our understanding of the adaptive patterns of Mansonia mosquitoes in Thailand's central region, paving the way for enhanced disease surveillance related to these vectors.

Livestock and environmental characterization of Colombian municipalities: study of vesicular stomatitis.

Amid the surge in data volume generated across various fields of knowledge, there is an increasing necessity for advanced analytical methodologies to effectively process and utilize this information. Particularly in the field of animal health, this approach is pivotal for enhancing disease understanding, surveillance, and management. The main objective of the study was to conduct a comprehensive livestock and environmental characterization of Colombian municipalities and examine their relationship with the distribution of vesicular stomatitis (VS). Utilizing satellite imagery to delineate climatic and land use profiles, along with data from the Colombian Agricultural Institute (ICA) concerning animal populations and their movements, the research employed Principal Component Analysis (PCA) to explore the correlation between environmental and livestock-related variables. Additionally, municipalities were grouped through a Hierarchical Clustering process. The assessment of risk associated with VS was carried out using a Generalized Linear Model. This process resulted in the formation of four distinct clusters: three primarily characterized by climatic attributes and one predominantly defined by livestock characteristics. Cluster 1, identified as "Andino" due to its climatic and environmental features, exhibited the highest odds ratio for VS occurrence. The adopted methodology not only provides a deeper understanding of the local population and its context, but also offers valuable insights for enhancing disease surveillance and control programs.

Epidemiology of Reportable Bacterial Infectious Diseases in Saudi Arabia.

Bacterial infections have a significant impact on human health; they can cause severe morbidity and mortality, particularly in susceptible populations. Epidemiological surveillance is a critical tool for monitoring the population's health and facilitate the prevention and control of infectious disease outbreaks. Knowing the burden of bacterial communicable diseases is an initial core step toward public health goals. Saudi epidemiology surveillance data were utilized to depict the changing epidemiology of bacterial infectious diseases in Saudi Arabia from 2018 to 2021. The cumulative numbers of cases, demographics, and incidence rates were analyzed and visualized. Parametric tests were used to compare the difference in the mean values between categorical variables. Regression analysis was employed to estimate trends in disease rates over time. Statistical significance was set at pvalue ≤ 0.05. The results revealed that brucellosis, tuberculosis, and salmonellosis were the most frequently reported bacterial infectious diseases in Saudi Arabia. Males were more significantly affected by brucellosis and tuberculosis infections than females. Salmonellosis infections were more significant among Saudi citizens, while pulmonary tuberculosis was more significant in non-Saudis. Interestingly, there was a decline in the incidence rates of numerous bacterial infectious diseases during the Coronavirus Disease 2019 (COVID-19) pandemic and COVID-19 restrictions. Some bacterial infectious diseases were rarely reported in Saudi Arabia, including syphilis and diphtheria. The future perspective of this research is to enhance disease surveillance reporting by including different variables, such as the source of infection, travel history, hospitalization, and mortality rates. The aim is to improve the sensitivity and specificity of surveillance data and focus on the mortality associated with bacterial pathogens to identify the most significant threats and set a public health priority.

One Health: A Holistic Approach to Tackling Global Health Issues.

One Health approach is a concept which provides a comprehensive framework to address the interconnectedness of human, animal, and environmental health in tackling global health challenges. It emphasizes the urgent need for a multidisciplinary approach to effectively address emerging infectious diseases, antimicrobial resistance, and environmental degradation. The article highlights the importance of collaboration, communication, and coordination among diverse stakeholders as well as the integration of human and animal healthcare systems. It emphasizes the significance of sharing data, expertise, and resources to enhance disease surveillance and rapid response. The transformative potential of One Health in addressing global health issues and creating a resilient future is underscored. This article provides valuable insights for researchers, policymakers, and healthcare professionals, emphasizing the integration of disciplines to safeguard health and the environment.

Characterizing the dynamic linkages between environmental changes and wheat Fusarium head blight epidemics

Fusarium head blight (FHB) is a prevalent wheat disease, mainly influenced by environmental factors, such as irradiance, temperature, precipitation, and CO2 levels. Elucidating the relationship between FHB outbreaks and environmental changes is crucial for predictive models and control measures. This study employs historical data to map the dynamic associations between environmental changes and FHB epidemics within a phase space. Complex networks are constructed to analyze the dynamic relationships, followed by an examination and clustering of their topological features and a discussion of similarities and differences. The results revealed structural similarities between the impact of precipitation and CO2 on FHB, as well as between temperature and irradiance on FHB. In addition, precipitation consistently exerts a persistent and sensitive influence on FHB epidemics, while temperature exhibits an inverse relationship. Elevated carbon emissions enhance the effect of CO2 on FHB, while the effect of irradiance on FHB epidemics varies with the solar cycle. This study provides a novel approach to detect and understand the dynamic relationships between FHB epidemics and environmental changes, contributing to enhanced disease surveillance and assessment within complex agriculture systems.

Disparities in COVID-19 mortality amongst the immunosuppressed: A systematic review and meta-analysis for enhanced disease surveillance

Effective disease surveillance, including that for COVID-19, is compromised without a standardised method for categorising the immunosuppressed as a clinical risk group. We conducted a systematic review and meta-analysis to evaluate whether excess COVID-associated mortality compared to the immunocompetent could meaningfully subdivide the immunosuppressed. Our study adhered to UK Immunisation against infectious disease (Green Book) criteria for defining and categorising immunosuppression. Using OVID (EMBASE, MEDLINE, Transplant Library, and Global Health), PubMed, and Google Scholar, we examined relevant literature between the entirety of 2020 and 2022. We selected for cohort studies that provided mortality data for immunosuppressed subgroups and immunocompetent comparators. Meta-analyses, grey literature and any original works that failed to provide comparator data or reported all-cause or paediatric outcomes were excluded. Odds Ratios (OR) and 95% confidence intervals (CI) of COVID-19 mortality were meta-analysed by immunosuppressed category and subcategory. Subgroup analyses differentiated estimates by effect measure, country income, study setting, level of adjustment, use of matching and publication year. Study screening, extraction and bias assessment were performed blinded and independently by two researchers; conflicts were resolved with the oversight of a third researcher. PROSPERO registration number is CRD42022360755. We identified 99 unique studies, incorporating data from 1,542,097 and 56,248,181 unique immunosuppressed and immunocompetent patients with COVID-19 infection, respectively. Compared to immunocompetent people (pooled OR, 95%CI), solid organ transplants (2.12, 1.50-2.99) and malignancy (2.02, 1.69-2.42) patients had a very high risk of COVID-19 mortality. Patients with rheumatological conditions (1.28, 1.13-1.45) and HIV (1.20, 1.05-1.36) had just slightly higher risks than the immunocompetent baseline. Case type, setting income and mortality data matching and adjustment were significant modifiers of excess immunosuppressed mortality for some immunosuppressed subgroups. Excess COVID-associated mortality among the immunosuppressed compared to the immunocompetent was seen to vary significantly across subgroups. This novel means of subdivision has prospective benefit for targeting patient triage, shielding and vaccination policies during periods of high disease transmission.

AI-DRIVEN ENVIRONMENTAL HEALTH DISEASE MODELING: A REVIEW OF TECHNIQUES AND THEIR IMPACT ON PUBLIC HEALTH IN THE USA AND AFRICAN CONTEXTS

This scholarly paper embarks on an exploratory journey into the realm of AI-driven environmental health disease modeling, with a keen focus on its implications in the diverse healthcare landscapes of the USA and Africa. The study's background delves into the historical evolution of disease modeling techniques, emphasizing the revolutionary role of AI in modern public health strategies. It meticulously examines the comparative effectiveness of AI models in these distinct regions, addressing the challenges and opportunities inherent in AI-driven health models. Aiming to unravel the multifaceted impact of AI in disease prediction and public health policy, the paper navigates through various thematic corridors. It critically analyzes the significance of data sources and quality, ethical considerations in AI health modeling, and the integration of AI models into public health policies. The scope of the paper encompasses a comprehensive review of AI's efficacy in predicting environmental diseases, its role in enhancing disease surveillance systems, and the geographic and socioeconomic variations affecting model accuracy. The main findings reveal that AI models, while effective in disease prediction and surveillance, encounter challenges related to data integrity and ethical complexities. The study concludes that the integration of AI in healthcare necessitates a balanced approach, advocating for policies that support the development of context-specific AI models and address ethical concerns. Recommendations include fostering interdisciplinary collaboration and continuous evaluation of AI models to align them with evolving healthcare needs and ethical standards. This paper serves as a beacon for understanding AI's transformative potential in environmental health disease modeling, offering insights that are crucial for shaping future public health strategies and interventions.&#x0D; Keywords: AI in Healthcare, Disease Modeling, Public Health Policy, Data Quality, Ethical Considerations, Geographic Variations.

Effectiveness of Establishing Seasonal Medical Centers Designated to Meet the Health Needs of Pilgrims and Umrah Performers (Prince Sultan Armed Forces Hospital Initiative in Medina as a Model)

This study aims to investigate the feasibility and effectiveness of establishing a dedicated healthcare facility to address the health needs of pilgrims during the pilgrimage seasons. The research employs a mixed-methods approach, combining qualitative and quantitative data collection methods. The qualitative phase involves interviews with healthcare professionals, religious authorities, and pilgrimage organizers to gather insights into the specific healthcare requirements of pilgrims. Additionally, focus group discussions with pilgrims provide valuable perspectives on their healthcare experiences and expectations. Based on the qualitative findings, a comprehensive framework for a Seasonal Medical Center is developed, encompassing key components such as infrastructure, staffing, medical services, logistics, and collaboration with local healthcare providers and authorities. The survey data provides insights into pilgrims' perceptions (6247) of the healthcare services available during their pilgrimage, including accessibility, quality, and satisfaction levels. It also investigates the impact of the proposed Seasonal Medical Center on pilgrims' overall health and well-being, as well as the effectiveness of preventative measures implemented. The research findings highlight the significance of establishing a Seasonal Medical Center, emphasizing the potential benefits of improved healthcare access, enhanced disease surveillance, and timely emergency response. The study also underscores the importance of collaboration between healthcare providers, religious authorities, and local stakeholders to implement and manage the Seasonal Medical Center effectively. The research contributes to the existing body of knowledge by offering insights into the design and implementation of a dedicated healthcare facility for Hajj and Umrah pilgrims. The findings can inform policymakers, healthcare professionals, and pilgrimage organizers in developing strategies to address the unique healthcare challenges associated with these annual pilgrimages.

Defining and Risk-Stratifying Immunosuppression (the DESTINIES Study): Protocol for an Electronic Delphi Study.

Globally, there are marked inconsistencies in how immunosuppression is characterized and subdivided into clinical risk groups. This is detrimental to the precision and comparability of disease surveillance efforts-which has negative implications for the care of those who are immunosuppressed and their health outcomes. This was particularly apparent during the COVID-19 pandemic; despite collective motivation to protect these patients, conflicting clinical definitions created international rifts in how those who were immunosuppressed were monitored and managed during this period. We propose that international clinical consensus be built around the conditions that lead to immunosuppression and their gradations of severity concerning COVID-19. Such information can then be formalized into a digital phenotype to enhance disease surveillance and provide much-needed intelligence on risk-prioritizing these patients. We aim to demonstrate how electronic Delphi objectives, methodology, and statistical approaches will help address this lack of consensus internationally and deliver a COVID-19 risk-stratified phenotype for "adult immunosuppression." Leveraging existing evidence for heterogeneous COVID-19 outcomes in adults who are immunosuppressed, this work will recruit over 50 world-leading clinical, research, or policy experts in the area of immunology or clinical risk prioritization. After 2 rounds of clinical consensus building and 1 round of concluding debate, these panelists will confirm the medical conditions that should be classed as immunosuppressed and their differential vulnerability to COVID-19. Consensus statements on the time and dose dependencies of these risks will also be presented. This work will be conducted iteratively, with opportunities for panelists to ask clarifying questions between rounds and provide ongoing feedback to improve questionnaire items. Statistical analysis will focus on levels of agreement between responses. This protocol outlines a robust method for improving consensus on the definition and meaningful subdivision of adult immunosuppression concerning COVID-19. Panelist recruitment took place between April and May of 2024; the target set for over 50 panelists was achieved. The study launched at the end of May and data collection is projected to end in July 2024. This protocol, if fully implemented, will deliver a universally acceptable, clinically relevant, and electronic health record-compatible phenotype for adult immunosuppression. As well as having immediate value for COVID-19 resource prioritization, this exercise and its output hold prospective value for clinical decision-making across all diseases that disproportionately affect those who are immunosuppressed. PRR1-10.2196/56271.

Climate Change and its Impact on Animal Health: Adapting and Mitigating the Risks

Climate change is a critical global issue affecting animal health, with rising temperatures and erratic weather patterns posing new challenges. Animals play a vital role in maintaining ecosystem balance and biodiversity, and their health can be affected by climate change-induced disruptions. Strategies to mitigate these risks include sustainable agricultural practices, research for vaccines and treatments, and implementing proactive measures. Climate change can increase the risk of disease transmission, invasive species, and disrupt ecosystems, leading to decreased reproductive success and population dynamics. Preventative measures include surveillance, vaccination programs, and habitat management. Adaptation strategies for animal health include implementing sustainable agricultural practices, investing in research and development, and promoting biodiversity preservation. To combat diseases, adaptation strategies for animal health should focus on enhancing disease surveillance, promoting biosecurity measures, and improving veterinary healthcare services. These strategies include robust disease surveillance systems, strict quarantine protocols, proper hygiene practices, and educating farmers about biosecurity. Vaccination programs, heat stress management techniques, and wildlife corridors can also help prevent vector-borne diseases, protect livestock and domesticated animals, and ensure the survival of threatened species. Mitigation strategies should also be implemented to protect animal populations and human health.

Innovations in Global Health: Leveraging Technology for Disease Surveillance and Pandemic Preparedness

The emergence of infectious diseases and global pandemics necessitates continuous advancements in health surveillance and preparedness strategies. This article explores the innovations in global health that leverage technology to enhance disease surveillance and improve pandemic preparedness. The study investigates the role of various technological solutions, including artificial intelligence, data analytics, mobile applications, and digital communication platforms, in strengthening global health systems.&#x0D; The article reviews recent case studies and initiatives where technology has been successfully employed for disease surveillance, early detection of outbreaks, and rapid response to emerging health threats. It examines how digital tools facilitate real-time data collection, analysis, and dissemination, enabling more effective monitoring of infectious diseases globally. Additionally, the article discusses the integration of wearable devices, telemedicine, and other remote health technologies in enhancing the resilience of health systems during pandemics.&#x0D; Furthermore, the challenges and ethical considerations associated with the implementation of technological innovations in global health are critically assessed. Privacy concerns, data security, and the need for global collaboration are addressed to ensure the responsible and ethical use of technology in disease surveillance.&#x0D; The article concludes by highlighting the potential of technology to revolutionize global health practices, emphasizing the importance of collaborative efforts among governments, international organizations, and the private sector. As the world faces ongoing and emerging health threats, the adoption of innovative technological solutions becomes paramount for building robust disease surveillance systems and improving overall pandemic preparedness on a global scale.

873. Detection of Monkeypox Virus Using Microbial Cell-free DNA: Demonstrating the Potential of Agnostic Sequencing for Real Time Identification of an Emerging Pathogen

Abstract Background The 2022 global monkeypox (mpox) outbreak has resulted in &amp;gt;86,000 cases worldwide. Mpox virus (mpoxv) PCR has failed for some divergent strains. Additionally, currently recommended testing requires a swab of lesions, which arise well after infection and may be unrecognized, especially in atypical disease. We present mpoxv detections using plasma microbial cell-free DNA (mcfDNA) sequencing. Methods We characterized 13 case-patients in whom plasma mcfDNA sequencing, by the Karius CLIA certified/CAP accredited laboratory, detected and quantified the concentration of mpoxv in 16 samples. To confirm mpoxv detections, in-silico experiments were performed to test the inclusivity and exclusivity of the assay. Inclusivity was tested across the mpoxv genomes from the 2022 outbreak; exclusivity was tested to other Poxviridae viruses, some of which may be genetically similar to mpoxv, and other often co-occurring pathogens. In cases of sufficient sequencing coverage, mpoxv isolates were genotyped and phylodynamic information imputed using the nexstrain toolkit. Results Case-patient characteristics at the time of plasma collection for mcfDNA sequencing are shown in Table 1. Mpox was not suspected in 6, with one having documented resolution of mpox &amp;gt;6 mos previously. For those with known mpox, mcfDNA sequencing was performed to determine other co-infections. Seven had moderate to severe mpox disease, supported by high mpoxv mcfDNA concentration; 4 died. Using mcfDNA coverage, 22 mutations were identified at 10 mpoxv genomic loci in 9 case-patients. 21 of the 22 variants were G &amp;gt;A or C &amp;gt;T, consistent with the predominant variation observed in the 2022 mpoxv outbreak. Phylogenetic comparison against public data facilitated mapping case-patient mpoxv strains to specific sublineages arising at different time points and associated with different geographic locations. Conclusion We demonstrate the potential of plasma mcfDNA sequencing to detect, quantify, and subtype mpoxv using a single non-invasive test. This alternative to the current lesion-dependent PCR test may enhance disease surveillance and much needed situational awareness for public health agencies and scientists to track an emerging pathogen with potentially unexpected clinical presentations such as mpoxv. Disclosures Kevin Brick, PhD, Karius: Employee|Karius: Stocks/Bonds Nicholas Noll, Doctor of Philosophy, Karius: Employee Asim Ahmed, MD, Karius: mcfDNA detection of infectious agents|Karius: Stocks/Bonds|Tarsus: Advisor/Consultant Jason Sniffen, DO, MicroGenDX: Advisor/Consultant Gayathri Balasundaram, MSc, Karius Inc: Employee|Karius Inc: Stocks/Bonds Frederick S. Nolte, PhD, Karius: Stocks/Bonds Timothy A. Blauwkamp, PhD, Karius: Board Member|Karius: Ownership Interest Sivan Bercovici, PhD, Karius: Stocks/Bonds

Ethical Framework for Multi-Source Big Data Analytics Disease Surveillance in Public Healthcare

Big data analytics can revolutionize disease surveillance by integrating multiple data sources and analyzing those diverse data sources, allowing for early monitoring, detection, and response to disease outbreaks. Since the growth in technology, together with the advent of big data, there is growing interest in exploring the potential of multiple data sources for disease surveillance. However, there are still ethical concerns when it comes to big data usage for disease surveillance. Hence, this study uses of scoping review with the assistance of socio-technical systems theory, together with social contact theory to examine the ethical dimensions of employing big data analytics from diverse sources for disease surveillance, while emphasizing the importance of maintaining ethical standards in public healthcare practices. The study discovered that informed consent, privacy, and confidentiality are still major challenges when it comes to the integration of big data sources in disease surveillance. The study then recommends a unified ethical framework for enhanced disease surveillance in public healthcare through Multi-Data Source big data Analytics.

Barriers for successful implementation of telemedicine in developing countries: the Colombian case

Abstract Implementation of telemedicine programs in Latin America has been documented in different reports, finding that Mexico, Chile and Colombia are the countries with the highest public coverage. Nevertheless, efforts made in Colombia in this regard are considered insufficient, and previously unidentified factors have delayed the full progress of this technology in the country. An integrative review was conducted in PubMed and ScienceDirect databases, and Colombian governmental repositories between 2011 and 2021. A total of 135 articles were processed in Rayyan QCRI software to characterize the telemedicine adoption process in Colombia. Published works in this area concern the economical evaluation of telemedicine services, focusing in estimating costs for its implementation, but cost-benefit, cost-effectiveness and cost-utility studies are needed for a complete policy in this regard, which should discuss important factors as infrastructure, current legislation, financing of public actors, availability of scientific evidence for decision-making, and political will. Finally, once the project is underway, monitoring, evaluation, and adaptation of the systems must be guaranteed to ensure the planned objectives and desired benefits are met, and barriers to the effective use of the project are overcome. Successful implementation of telemedicine programs in developing countries should consider several aspects. First, complexity, adaptability, compatibility with existing work practices, and cost of the project. Second, stakeholders must be included. Third, standards for interoperability, security, and privacy must be established to improve project acceptability and implementation. Fourth, sufficient financial and legislative support must be secured to support the project. Fifth, planning must be considered to ensure that the health system is ready for the project. Sixth, necessary education must be provided to all project stakeholders. Key messages • E-medicine barriers were identified to prompt its implementation in developing countries. • Boost healthcare access, reduce costs, and enhanced disease surveillance is possible through telemedicine.

PetBERT: automated ICD-11 syndromic disease coding for outbreak detection in first opinion veterinary electronic health records

Effective public health surveillance requires consistent monitoring of disease signals such that researchers and decision-makers can react dynamically to changes in disease occurrence. However, whilst surveillance initiatives exist in production animal veterinary medicine, comparable frameworks for companion animals are lacking. First-opinion veterinary electronic health records (EHRs) have the potential to reveal disease signals and often represent the initial reporting of clinical syndromes in animals presenting for medical attention, highlighting their possible significance in early disease detection. Yet despite their availability, there are limitations surrounding their free text-based nature, inhibiting the ability for national-level mortality and morbidity statistics to occur. This paper presents PetBERT, a large language model trained on over 500 million words from 5.1 million EHRs across the UK. PetBERT-ICD is the additional training of PetBERT as a multi-label classifier for the automated coding of veterinary clinical EHRs with the International Classification of Disease 11 framework, achieving F1 scores exceeding 83% across 20 disease codings with minimal annotations. PetBERT-ICD effectively identifies disease outbreaks, outperforming current clinician-assigned point-of-care labelling strategies up to 3 weeks earlier. The potential for PetBERT-ICD to enhance disease surveillance in veterinary medicine represents a promising avenue for advancing animal health and improving public health outcomes.

Increase in invasive group A streptococcal disease among Australian children coinciding with northern hemisphere surges

Increases in invasive group A streptococcal disease (iGAS) have recently been reported in multiple countries in the northern hemisphere, occurring during, and outside of, typical spring peaks. We report the epidemiology of iGAS among children in Australia from 1 July 2018 to 31 December2022. The Paediatric Active Enhanced Disease Surveillance (PAEDS) Network prospectively collected iGAS patient notifications for children and young people aged less than 18 years admitted to five major Australian paediatric hospitals in Victoria, Queensland, Western Australia and the Northern Territory. Patients were eligible for inclusion if they had GAS isolated from a normally sterile body site, or met clinical criteria for streptococcal toxic shock syndrome or necrotising fasciitis with GAS isolated from a non-sterile site. We report patients' clinical and demographic characteristics, and estimate minimum incidence rates. We identified 280 paediatric iGAS patients, median age 4.5 years (interquartile range 1.4-6.4). We observed a pre-pandemic peak annualised incidence of 3.7 per 100,000 (95% CI 3.1-4.4) in the 3rd quarter of 2018, followed by a decline to less than 1.0 per 100,000 per quarter from 2020 to mid-2021. The annualised incidence increased sharply from mid-2022, peaking at 5.2 per 100,000 (95% CI 4.4-6.0) in the 3rd quarter and persisting into the 4th quarter (4.9 per 100,000, 95% CI 4.2-5.7). There were 3 attributable deaths and 84 (32%) patients had severe disease (overall case fatality rate 1%, 95% CI 0.2-3.3). Respiratory virus co-infection, positive in 57 of 119 patients tested, was associated with severe disease (RR 1.9, 95% CI 1.2-3.0). The most common emm-type was emm-1 (60 of 163 isolates that underwent emm-typing, 37%), followed by emm-12 (18%). Australia experienced an increase in the incidence of iGAS among children and young people in 2022 compared to pandemic years 2020-2021. This is similar to northern hemisphere observations, despite differences in seasons and circulating respiratory viruses. Outbreaks of iGAS continue to occur widely. This emphasises the unmet need for a vaccine to prevent significant morbidity associated with iGAS disease. Murdoch Children's Research Institute funded open access publishing of this manuscript.

Spatio-Temporal Analysis of Leptospirosis Hotspot Areas and Its Association With Hydroclimatic Factors in Selangor, Malaysia: Protocol for an Ecological Cross-sectional Study

Leptospirosis is considered a neglected zoonotic disease in temperate regions but an endemic disease in countries with tropical climates such as South America, Southern Asia, and Southeast Asia. There has been an increase in leptospirosis incidence in Malaysia from 1.45 to 25.94 cases per 100,000 population between 2005 and 2014. With increasing incidence in Selangor, Malaysia, and frequent climate change dynamics, a study on the disease hotspot areas and their association with the hydroclimatic factors would further enhance disease surveillance and public health interventions. This study aims to examine the association between the spatio-temporal distribution of leptospirosis hotspot areas from 2011 to 2019 with the hydroclimatic factors in Selangor using the geographical information system and remote sensing techniques to develop a leptospirosis hotspot predictive model. This will be an ecological cross-sectional study with geographical information system and remote sensing mapping and analysis concerning leptospirosis using secondary data. Leptospirosis cases in Selangor from January 2011 to December 2019 shall be obtained from the Selangor State Health Department. Laboratory-confirmed cases with data on the possible source of infection would be identified and georeferenced according to their longitude and latitudes. Topographic data consisting of subdistrict boundaries and the distribution of rivers in Selangor will be obtained from the Department of Survey and Mapping. The ArcGIS Pro software will be used to evaluate the clustering of the cases and mapped using the Getis-Ord Gi* tool. The satellite images for rainfall and land surface temperature will be acquired from the Giovanni National Aeronautics and Space Administration EarthData website and processed to obtain the average monthly values in millimeters and degrees Celsius. Meanwhile, the average monthly river hydrometric levels will be obtained from the Department of Drainage and Irrigation. Data are then inputted as thematic layers and in the ArcGIS software for further analysis. The artificial neural network analysis in artificial intelligence Phyton software will then be used to obtain the leptospirosis hotspot predictive model. This research was funded as of November 2022. Data collection, processing, and analysis commenced in December 2022, and the results of the study are expected to be published by the end of 2024. The leptospirosis distribution and clusters may be significantly associated with the hydroclimatic factors of rainfall, land surface temperature, and the river hydrometric level. This study will explore the associations of leptospirosis hotspot areas with the hydroclimatic factors in Selangor and subsequently the development of a leptospirosis predictive model. The constructed predictive model could potentially be used to design and enhance public health initiatives for disease prevention. PRR1-10.2196/43712.

Australian National Enterovirus Reference Laboratory annual report, 2022.

Australia monitors its polio-free status by conducting surveillance for cases of acute flaccid paralysis (AFP) in children less than 15 years of age, as recommended by the World Health Organization (WHO). Cases of AFP in children are notified to the Australian Paediatric Surveillance Unit or the Paediatric Active Enhanced Disease Surveillance System, and faecal specimens are referred for virological investigation to the National Enterovirus Reference Laboratory. In 2022, no cases of poliomyelitis were reported from clinical surveillance and Australia reported 1.69 non-polio AFP cases per 100,000 children, thereby meeting the WHO's performance criterion for a sensitive surveillance system. The non-polio enteroviruses coxsackievirus A2, coxsackievirus A6, coxsackievirus A10, echovirus 18, enterovirus A71 and enterovirus C96 were identified from clinical specimens collected from AFP cases. Australia also performs enterovirus and environmental surveillance to complement the clinical system focussed on children. In 2022, thirty cases of wild poliovirus were reported from three countries (Afghanistan, Mozambique and Pakistan); 24 countries also reported cases of poliomyelitis due to circulating vaccine-derived poliovirus.