Wildlife health capacity enhancement in Thailand through the World Organisation for Animal Health Twinning Program.

There is an increasing need for robust wildlife health programs that provide surveillance and management for diseases in wildlife and wild aquatic populations to manage associated risks. This paper illustrates the value of a systematic method to enhancing wildlife health programs. The U.S. Geological Survey and Mahidol University, Faculty of Veterinary Science, Thailand National Wildlife Health Center formally twinned under the auspices of the World Organisation for Animal Health to enhance wildlife health capacity in Thailand and the Southeast Asia Region. We used a system-wide approach to holistically and interdependently enhance capacity. The project commenced with a wildlife health program needs assessment, and capacity enhancement focused on strengthening the general wildlife health surveillance network and improving wildlife health information management. Activities included partner surveys, interactive and didactic workshops, and individual personnel training. Topics included development of wildlife health information management systems, analysis of the current surveillance network, development of a Theory of Change for a strengthened surveillance network, planning workshops to create a wildlife health network, training on wildlife disease outbreak investigation and field sample collection, leading networks, and individual training on bioinformatics and laboratory techniques. Engagement of stakeholders at all levels, continuous communication throughout the project, use of both strategic planning tools and pedagogical methods, and using iterative and adaptive approaches, were key factors to the success of this project.

Critical Insights from Recent Outbreaks of Mycoplasma pneumoniae: Decoding the Challenges and Effective Interventions Strategies.

Mycoplasma pneumoniae (M. pneumoniae) continues to pose a significant disease burden on global public health as a respiratory pathogen. The antimicrobial resistance among M. pneumoniae strains has complicated the outbreak control efforts, emphasizing the need for robust surveillance systems and effective antimicrobial stewardship programs. This review comprehensively investigates studies stemming from previous outbreaks to emphasize the multifaceted nature of M. pneumoniae infections, encompassing epidemiological dynamics, diagnostic innovations, antibiotic resistance, and therapeutic challenges. We explored the spectrum of clinical manifestations associated with M. pneumoniae infections, emphasizing the continuum of disease severity and the challenges in gradating it accurately. Artificial Intelligence and Machine Learning have emerged as promising tools in M. pneumoniae diagnostics, offering enhanced accuracy and efficiency in identifying infections. However, their integration into clinical practice presents hurdles that need to be addressed. Further, we elucidate the pivotal role of pharmacological interventions in controlling and treating M. pneumoniae infections as the efficacy of existing therapies is jeopardized by evolving resistance mechanisms. Lessons learned from previous outbreaks underscore the importance of adaptive treatment strategies and proactive management approaches. Addressing these complexities demands a holistic approach integrating advanced technologies, genomic surveillance, and adaptive clinical strategies to effectively combat this pathogen.

Human Orthohantavirus disease prevalence and genotype distribution in the U.S., 2008-2020: a retrospective observational study.

Background: In the United States (U.S.), hantavirus pulmonary syndrome (HPS) and non-HPS hantavirus infection are nationally notifiable diseases. Criteria for identifying human cases are based on clinical symptoms (HPS or non-HPS) and acute diagnostic results (IgM+, rising IgG+ titers, RT-PCR+, or immunohistochemistry (IHC)+). Here we provide an overview of diagnostic testing and summarize human Hantavirus disease occurrence and genotype distribution in the U.S. from 2008 to 2020. Methods: Epidemiological data from the national hantavirus registry was merged with laboratory diagnostic testing results performed at the CDC. Residual hantavirus-positive specimens were sequenced, and the available epidemiological and genetic data sets were linked to conduct a genomic epidemiological study of hantavirus disease in the U.S. Findings: From 1993 to 2020, 833 human hantavirus cases have been identified, and from 2008 to 2020, 335 human cases have occurred. Among New World (NW) hantavirus cases detected at the CDC diagnostic laboratory (representing 29.2% of total cases), most (85.0%) were detected during acute disease, however, some convalescent cases were detected in states not traditionally associated with hantavirus infections (Connecticut, Missouri, New Jersey, Pennsylvania, Tennessee, and Vermont). From 1993 to 2020, 94.9% (745/785) of U.S. hantaviruses cases were detected west of the Mississippi with 45.7% (359/785) in the Four Corners region of the U.S. From 2008 to 2020, 67.7% of NW hantavirus cases were detected between the months of March and August. Sequencing of RT-PCR-positive cases demonstrates a geographic separation of Orthohantavirus sinnombreense species [Sin Nombre virus (SNV), New York virus, and Monongahela virus]; however, there is a large gap in viral sequence data from the Northwestern and Central U.S. Finally, these data indicate that commercial IgM assays are not concordant with CDC-developed assays, and that "concordant positive" (i.e., commercial IgM+ and CDC IgM+ results) specimens exhibit clinical characteristics of hantavirus disease. Interpretation: Hantaviral disease is broadly distributed in the contiguous U.S, viral variants are localised to specific geographic regions, and hantaviral disease infrequently detected in most Southeastern states. Discordant results between two diagnostic detection methods highlight the need for an improved standardised testing plan in the U.S. Hantavirus surveillance and detection will continue to improve with clearly defined, systematic reporting methods, as well as explicit guidelines for clinical characterization and diagnostic criteria. Funding: This work was funded by core funds provided to the Viral Special Pathogens Branch at CDC.

Key Characteristics and Perception of Different Outbreak Surveillance Systems in Côte d'Ivoire: Cross-Sectional Survey Among Users.

Background: Accurate and timely infectious disease surveillance is pivotal for effective public health responses. An important component of this is the disease surveillance tools used. Understanding views and experiences of users is crucial for informing policy decisions and ensuring the seamless functioning of surveillance systems. Objective: In this study, we aimed to assess the user perceptions of 3 disease surveillance tools used in Côte d'Ivoire, namely, MAGPI, District Health Information Software 2 (DHIS2), and Surveillance Outbreak Response Management and Analysis System (SORMAS), the latter was implemented in 2021 within a pilot scheme. Methods: We conducted interviews and a web-based survey distributed to users of the 3 surveillance tools. The survey assessed users' views of the surveillance tools' usefulness, ease of use, feelings toward the tool, conditions that may influence the use, and other characteristics. The descriptive analysis compared responses from SORMAS, MAGPI, and DHIS2 users, providing a comprehensive evaluation of their experiences. Results: Among the 159 respondents who actively use one of the systems, MAGPI was the most widely used surveillance tool among respondents (n=127, 79.9%), followed by DHIS2 (n=108, 67.9%), and SORMAS (n=25, 15.7%). In terms of users' perceptions, SORMAS, despite its limited implementation, emerged as a tool that allows for data analysis and had the most comprehensive set of functionalities. DHIS2 was appreciated for its frequency of report provision, although users reported occasional IT system failures. MAGPI was recognized for its ease of use but was reported to lack certain functionalities offered by the other surveillance systems. Conclusions: This study offers valuable insights into the perceptions of disease surveillance tools users in Côte d'Ivoire. While all systems were positively regarded, each exhibited strengths and weaknesses addressing different needs and functionalities. Policy makers and health officials can use these findings to enhance existing tools or consider a unified approach for infectious disease surveillance systems. Understanding users' perspectives allows them to optimize the choice of surveillance tools, ultimately strengthening public health responses in Côte d'Ivoire and potentially serving as a model for other countries facing similar decisions in their health care systems.

Infrastructures of epidemic response: Mpox and everyday repair work in southwestern Nigeria.

Mpox (formerly known as monkeypox) was declared a Public Health Emergency of International Concern (PHEIC) by the World Health Organization on 23rd July 2022, however cases of the disease have been detected in Nigeria since the 1970s and more recently since it began spreading in more urban areas of the country from 2017 onward. Nigeria has a strong track record of epidemic preparedness and response, spearheaded by the Nigeria Centre for Disease Control. Despite being somewhat separate architectures on paper, epidemic response (in particular, integrated disease surveillance and response) relies on a foundation of primary health care, which is inadequately funded not only in Nigeria, but globally. Situating mpox response within this wider landscape, we draw on ethnographic research from September 2022-March 2023 in southwestern Nigeria on lived experiences of mpox and mpox response, focusing on the perspectives of frontline health workers and community-based suspected or confirmed mpox cases. We aimed to understand how prioritization and resource constraints shape mpox response at a local level, including effects on the everyday work of frontline health workers in public health and clinical care who are left to "make do." We analyze their experiences interfacing with two intersecting infrastructures, community-based surveillance and primary health care. Health workers' improvisation and "repair work," which we detail, enables the surveillance system to function in some capacity. However, health workers must regularly contend with competing priorities and routine care that may be sidelined during an outbreak or epidemic. We argue that this reveals the limitations of a global health security agenda as it materializes at a local level and the need for strengthening primary health care for longer-term sustainability.

Machine learning surveillance of foodborne infectious diseases using wastewater microbiome, crowdsourced, and environmental data.

Clostridium perfringens (CP) is a common cause of foodborne infection, leading to significant human health risks and a high economic burden. Thus, effective CP disease surveillance is essential for preventive and therapeutic interventions; however, conventional practices often entail complex, resource-intensive, and costly procedures. This study introduced a data-driven machine learning (ML) modeling framework for CP-related disease surveillance. It leveraged an integrated dataset of municipal wastewater microbiome (e.g., CP abundance), crowdsourced (CP-related web search keywords), and environmental data. Various optimization strategies, including data integration, data normalization, model selection, and hyperparameter tuning, were implemented to improve the ML modeling performance, leading to enhanced predictions of CP cases over time. Explainable artificial intelligence methods identified CP abundance as the most reliable predictor of CP disease cases. Multi-omics subsequently revealed the presence of CP and its genotypes/toxinotypes in wastewater, validating the utility of microbiome-data-enabled ML surveillance for foodborne diseases. This ML-based framework thus exhibits significant potential for complementing and reinforcing existing disease surveillance systems.

Vaccine Hesitancy in Taiwan: Temporal, Multilayer Network Study of Echo Chambers Shaped by Influential Users.

BACKGROUND: Vaccine hesitancy is a growing global health threat that is increasingly studied through the monitoring and analysis of social media platforms. One understudied area is the impact of echo chambers and influential users on disseminating vaccine information in social networks. Assessing the temporal development of echo chambers and the influence of key users on their growth provides valuable insights into effective communication strategies to prevent increases in vaccine hesitancy. This also aligns with the World Health Organization's (WHO) infodemiology research agenda, which aims to propose new methods for social listening. OBJECTIVE: Using data from a Taiwanese forum, this study aims to examine how engagement patterns of influential users, both within and across different COVID-19 stances, contribute to the formation of echo chambers over time. METHODS: Data for this study come from a Taiwanese forum called PTT. All vaccine-related posts on the "Gossiping" subforum were scraped from January 2021 to December 2022 using the keyword "vaccine." A multilayer network model was constructed to assess the existence of echo chambers. Each layer represents either provaccination, vaccine hesitant, or antivaccination posts based on specific criteria. Layer-level metrics, such as average diversity and Spearman rank correlations, were used to measure chambering. To understand the behavior of influential users-or key nodes-in the network, the activity of high-diversity and hardliner nodes was analyzed. RESULTS: Overall, the provaccination and antivaccination layers are strongly polarized. This trend is temporal and becomes more apparent after November 2021. Diverse nodes primarily participate in discussions related to provaccination topics, both receiving comments and contributing to them. Interactions with the antivaccination layer are comparatively minimal, likely due to its smaller size, suggesting that the forum is a "healthy community." Overall, diverse nodes exhibit cross-cutting engagement. By contrast, hardliners in the vaccine hesitant and antivaccination layers are more active in commenting within their own communities. This trend is temporal, showing an increase during the Omicron outbreak. Hardliner activity potentially reinforces their stances over time. Thus, there are opposing forces of chambering and cross-cutting. CONCLUSIONS: Efforts should be made to moderate hardliner and influential nodes in the antivaccination layer and to support provaccination users engaged in cross-cutting exchanges. There are several limitations to this study. One is the bias of the platform used, and another is the lack of a comprehensive definition of "influence." To address these issues, comparative studies across different platforms can be conducted, and various metrics of influence should be explored. Additionally, examining the impact of influential users on network structure and chambering through network simulations and regression analysis provides more robust insights. The study also lacks an explanation for the reasons behind chambering trends. Conducting content analysis can help to understand the nature of engagement and inform interventions to address echo chambers. These approaches align with and further the WHO infodemic research agenda.

[Evaluation of the Viral Hemorrhagic Fever Surveillance System in the Assaba region, Mauritania (2020-2022)]. / Évaluation du système de surveillance des fièvres hémorragiques virales dans la wilaya d'Assaba, Mauritanie (2020-2022).

Introduction: Several arboviral diseases have been known to be endemic (e.g., Crimean-Congo hemorrhagic fever, Rift Valley fever) or are emerging (dengue fever, chikungunya, O'nyong-nyong) in human populations in Mauritania, while others have become rare in recent years (e.g. yellow fever). Moreover, domestic animals, especially cattle, camels, goats, and sheep, are also known to be infected with some of these arboviruses (e.g. Crimean-Congo hemorrhagic fever, Rift Valley fever). For these reasons, viral hemorrhagic fever surveillance in Mauritania is part of the Integrated Disease Surveillance and Response (IDSR). However, limited information is available on the efficacy of the viral hemorrhagic fever surveillance system in the Assaba region of Mauritania. The aim of the present study was to assess the performance of the surveillance system, in particular its general utility, simplicity, flexibility, acceptability, and reactivity. Methods: A descriptive cross-sectional study was conducted from July to August 2022 in the Assaba region with the objective of evaluating the characteristics of the system by interviewing key actors involved in the surveillance of viral hemorrhagic fevers, with a focus on Rift Valley fever and Crimean-Congo hemorrhagic fever, using questionnaires developed following the guidelines of the Centers for Disease Control and Prevention (Atlanta, Georgia, USA). Data from 2020-2022 on viral hemorrhagic fevers from the National Institute of Public Health laboratory were analyzed. Medians, interquartile ranges, and proportions were calculated using Epi Info® 7.2.5.0 and Excel® 2021. Results: The questionnaire was answered by all twenty-six persons involved in the viral hemorrhagic fever surveillance system in Assaba region. The majority of survey respondents found the system to be useful (51%), simple (63%), acceptable (46%), responsive (64%), and flexible (46%). An analysis of the data revealed a positive predictive value of 28% for Rift Valley Fever. The weekly distribution of cases within the wilaya indicates that the moughataa of Kiffa recorded the highest number of cases in September, with a notable weekly peak during that month in 2020. According to the analysis of the National Institute of Public Health database, cases of viral hemorrhagic fevers were promptly handled. Survey responses and database analysis revealed issues related to data quality and data management mechanisms. These limitations in the surveillance system are likely to be due to insufficient resources and training of the personnel, in particular with regards to data collection and management, which in turn led to incomplete or missing data and invalid data entry. These weak points can be ascribed, at least in part, to financial constraints and inadequate attribution of priority to arboviral diseases. Despite these limitations, disease data generated by the surveillance system were generally reliable. Conclusion: The viral hemorrhagic fever surveillance system in the Assaba region adheres to the organization and functioning of the national viral hemorrhagic fever surveillance system, which is part of the IDSR. The characteristics of utility, simplicity, responsiveness, and flexibility of the viral hemorrhagic fever surveillance system are good, but acceptability and flexibility need further improvement. The earlier the first arboviral human or animal cases are detected, the more likely an active intervention can be organized in response to the emerging epidemic or epizootic and prevent the spread of the disease. An efficient viral surveillance system is the key to reducing the negative impact of arboviral diseases in Assaba region.

Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 Qatar.

BACKGROUND: Public health threats can significantly impact mass gatherings and enhancing surveillance systems would thus be crucial. Epidemic Intelligence from Open Sources (EIOS) was introduced to Qatar to complement the existing surveillance measures in preparation to the FIFA World Cup Qatar 2022 (FWC22). This study estimated the empirical probability of EIOS detecting signals of public health relevance. It also looked at the factors responsible for discerning a moderate-high risk signal during a mass gathering event. METHODS: This cross-sectional descriptive study used data collected between November 8th and December 25th, 2022, through an EIOS dashboard that filtered open-source articles using specific keywords. Triage criteria and scoring scheme were developed to capture signals and these were maintained in MS Excel. EIOS' contribution to epidemic intelligence was assessed by the empirical probability estimation of relevant public health signals. Chi-squared tests of independence were performed to check for associations between various hazard categories and other independent variables. A multivariate logistic regression evaluated the predictors of moderate-high risk signals that required prompt action. RESULTS: The probability of EIOS capturing a signal relevant to public health was estimated at 0.85 % (95 % confidence interval (CI) [0.82 %-0.88 %]) with three signals requiring a national response. The hazard category of the signal had significant association to the region of occurrence (χ2 (5, N = 2543) = 1021.6, p < .001). The hazard category also showed significant association to its detection during matchdays of the tournament (χ2 (5, N = 2543) = 11.2, p < .05). The triage criteria developed was able to discern between low and moderate-high risk signals with an acceptable discrimination (Area Under the Curve=0.79). CONCLUSION: EIOS proved useful in the early warning of public health threats.

Prediction of post-PCV13 pneumococcal evolution using invasive disease data enhanced by inverse-invasiveness weighting.

After introducing pneumococcal conjugate vaccines (PCVs), serotype replacement occurred in Streptococcus pneumoniae. Predicting which pneumococcal strains will become common in carriage after vaccination can enhance vaccine design, public health interventions, and understanding of pneumococcal evolution. Invasive pneumococcal isolates were collected during 1998-2018 by the Active Bacterial Core surveillance (ABCs). Carriage data from Massachusetts (MA) and Southwest United States were used to calculate weights. Using pre-vaccine data, serotype-specific inverse-invasiveness weights were defined as the ratio of the proportion of the serotype in carriage to the proportion in invasive data. Genomic data were processed under bioinformatic pipelines to define genetically similar sequence clusters (i.e., strains), and accessory genes (COGs) present in 5-95% of isolates. Weights were applied to adjust observed strain proportions and COG frequencies. The negative frequency-dependent selection (NFDS) model predicted strain proportions by calculating the post-vaccine strain composition in the weighted invasive disease population that would best match pre-vaccine COG frequencies. Inverse-invasiveness weighting increased the correlation of COG frequencies between invasive and carriage data in linear or logit scale for pre-vaccine, post-PCV7, and post-PCV13; and between different epochs in the invasive data. Weighting the invasive data significantly improved the NFDS model's accuracy in predicting strain proportions in the carriage population in the post-PCV13 epoch, with the adjusted R2 increasing from 0.254 before weighting to 0.545 after weighting. The weighting system adjusted invasive disease data to better represent the pneumococcal carriage population, allowing the NFDS mechanism to predict strain proportions in carriage in the post-PCV13 epoch. Our methods enrich the value of genomic sequences from invasive disease surveillance.IMPORTANCEStreptococcus pneumoniae, a common colonizer in the human nasopharynx, can cause invasive diseases including pneumonia, bacteremia, and meningitis mostly in children under 5 years or older adults. The PCV7 was introduced in 2000 in the United States within the pediatric population to prevent disease and reduce deaths, followed by PCV13 in 2010, PCV15 in 2022, and PCV20 in 2023. After the removal of vaccine serotypes, the prevalence of carriage remained stable as the vacated pediatric ecological niche was filled with certain non-vaccine serotypes. Predicting which pneumococcal clones, and which serotypes, will be most successful in colonization after vaccination can enhance vaccine design and public health interventions, while also improving our understanding of pneumococcal evolution. While carriage data, which are collected from the pneumococcal population that is competing to colonize and transmit, are most directly relevant to evolutionary studies, invasive disease data are often more plentiful. Previously, evolutionary models based on negative frequency-dependent selection (NFDS) on the accessory genome were shown to predict which non-vaccine strains and serotypes were most successful in colonization following the introduction of PCV7. Here, we show that an inverse-invasiveness weighting system applied to invasive disease surveillance data allows the NFDS model to predict strain proportions in the projected carriage population in the post-PCV13/pre-PCV15 and pre-PCV20 epoch. The significance of our research lies in using a sample of invasive disease surveillance data to extend the use of NFDS as an evolutionary mechanism to predict post-PCV13 population dynamics. This has shown that we can correct for biased sampling that arises from differences in virulence and can enrich the value of genomic data from disease surveillance and advance our understanding of how NFDS impacts carriage population dynamics after both PCV7 and PCV13 vaccination.

Challenges in the case-based surveillance of infectious diseases.

To effectively inform infectious disease control strategies, accurate knowledge of the pathogen's transmission dynamics is required. Since the timings of infections are rarely known, estimates of the infection incidence, which is crucial for understanding the transmission dynamics, often rely on measurements of other quantities amenable to surveillance. Case-based surveillance, in which infected individuals are identified by a positive test, is the predominant form of surveillance for many pathogens, and was used extensively during the COVID-19 pandemic. However, there can be many biases present in case-based surveillance indicators due to, for example test sensitivity, changing testing behaviours and the co-circulation of pathogens with similar symptom profiles. Here, we develop a mathematical description of case-based surveillance of infectious diseases. By considering realistic epidemiological parameters and situations, we demonstrate many of the potential biases in common surveillance indicators based on case-based surveillance data. Crucially, we find that many of these common surveillance indicators (e.g. case numbers, test-positive proportion) are heavily biased by circulating pathogens with similar symptom profiles. Future surveillance strategies could be designed to minimize these sources of bias and uncertainty, providing more accurate estimates of a pathogen's transmission dynamics and, ultimately, more targeted application of public health measures.

Collective Intelligence-Based Participatory COVID-19 Surveillance in Accra, Ghana: Pilot Mixed Methods Study.

BACKGROUND: Infectious disease surveillance is difficult in many low- and middle-income countries. Information market (IM)-based participatory surveillance is a crowdsourcing method that encourages individuals to actively report health symptoms and observed trends by trading web-based virtual "stocks" with payoffs tied to a future event. OBJECTIVE: This study aims to assess the feasibility and acceptability of a tailored IM surveillance system to monitor population-level COVID-19 outcomes in Accra, Ghana. METHODS: We designed and evaluated a prediction markets IM system from October to December 2021 using a mixed methods study approach. Health care workers and community volunteers aged ≥18 years living in Accra participated in the pilot trading. Participants received 10,000 virtual credits to trade on 12 questions on COVID-19-related outcomes. Payoffs were tied to the cost estimation of new and cumulative cases in the region (Greater Accra) and nationwide (Ghana) at specified future time points. Questions included the number of new COVID-19 cases, the number of people likely to get the COVID-19 vaccination, and the total number of COVID-19 cases in Ghana by the end of the year. Phone credits were awarded based on the tally of virtual credits left and the participant's percentile ranking. Data collected included age, occupation, and trading frequency. In-depth interviews explored the reasons and factors associated with participants' user journey experience, barriers to system use, and willingness to use IM systems in the future. Trading frequency was assessed using trend analysis, and ordinary least squares regression analysis was conducted to determine the factors associated with trading at least once. RESULTS: Of the 105 eligible participants invited, 21 (84%) traded at least once on the platform. Questions estimating the national-level number of COVID-19 cases received 13 to 19 trades, and obtaining COVID-19-related information mainly from television and radio was associated with less likelihood of trading (marginal effect: -0.184). Individuals aged <30 years traded 7.5 times more and earned GH ¢134.1 (US $11.7) more in rewards than those aged >30 years (marginal effect: 0.0135). Implementing the IM surveillance was feasible; all 21 participants who traded found using IM for COVID-19 surveillance acceptable. Active trading by friends with communal discussion and a strong onboarding process facilitated participation. The lack of bidirectional communication on social media and technical difficulties were key barriers. CONCLUSIONS: Using an IM system for disease surveillance is feasible and acceptable in Ghana. This approach shows promise as a cost-effective source of information on disease trends in low- and middle-income countries where surveillance is underdeveloped, but further studies are needed to optimize its use.

Implementation and performance evaluation of an integrated specimen referral system in Burkina Faso using the national courier services (2020-2022).

Introduction: In 2017, the Ministry of Health and Public Hygiene (MoH) of Burkina Faso designed and piloted a specimen transport system using the national courier services (La Poste BF) in 4 districts. Based on satisfactory performance indicators, the MoH set a vision aimed at scaling up this system to strengthen disease detection and surveillance of epidemic prone diseases across the country. This work describes the implementation process, performances, and lessons learned. Methodology: This work describes the implementation process, performances, and lessons learned. Under the leadership of the Directorate of Population Health Protection within the MoH, a stepwise approach was used to bring together multiple partners across sectors to develop the first needed documents including a guide, an implementation plan, Standard Operating Procedures, and data collection tools. Then, the execution phase included equipment purchase, trainings, and consensus on a financing mechanism. Key indicators were defined to allow performance monitoring. Result: The integrated biological specimen referral system (SITEB) was officially launched in January 2020 to transport human biological specimens of priority diseases including COVID-19 from district level to reference laboratories nationwide. As of December 31, 2022, La Poste BF transported 168,856 packages containing 206,314 specimens from all 13 regions. 99.66% of packages were delivered in <24 h as required, and 99.68% of specimens were in good condition at reception. COVID-19 specimens represented respectively 18% and 63% of samples transported in 2020 and 2021. Discussion: The political will combined with the experience gained during the pilot phase and the commitment and support from all stakeholders laid to the foundation of the effective implementation of this system. Collaboration between two government entities (MoH and Minister of Transport, Urban Mobility, and Road Safety) to benefit public health has led to reasonable pricing for sustainability. Although all documents integrate the "One Health" approach, the system ensures the transport of only human samples for now. Despite security constraints, Burkina Faso has successfully set up a system using the national postal service to ensure the routine transport of specimens for all diseases under laboratory surveillance including laboratory tests for HIV and TB from the district level to reference laboratories nationwide. This system has also proved to be useful and efficient in managing public health emergency.

Statewide Linkage Study of Salmonella Infection and Colorectal Cancer Incidence in Michigan, United States.

Objective: Nontyphoidal Salmonella infection is one of the most common foodborne illnesses, and its oncogenic potential has been documented in animal models. The primary goal of this study was to examine whether individuals who were exposed to enteric Salmonella infection are more likely to develop colorectal cancer (CRC) than the general population through the linkage of 2 statewide public health surveillance databases. Materials and Methods: We designed a 2-stage probabilistic linkage, starting with 17,587 records of enteric salmonellosis reported to Michigan Department of Health and Human Services between 1992 and 2020. These records did not include unique identifiers (such as Social Security number [SSN]). The initial linkage to LexisNexis address history was conducted to obtain information to calculate each person's time in Michigan as well as SSN for the second linkage. The linkage to the state cancer registry was performed to obtain the observed number of CRC cases, while the expected number of CRC cases was calculated according to corresponding state CRC incidence by age, sex, and calendar year. Results: Ninety-three percent of the initially identified salmonellosis records were sent to LexisNexis linkage, which returned address history, death, and SSN for 97% of the records. Further linkage to the statewide cancer registry identified 98 incident CRC cases. Overall, the observed-to-expected (O/E) ratio was not different from unity (0.833; 95% CI, 0.627-1.003). Conclusions: While the new linkage strategy was found effective and should be applicable to other health conditions, we cannot rule out bias due to incomplete or underreporting of the infection in estimating the risk of CRC.

Using EPIWATCH® open-source surveillance to describe the epidemiology of lumpy skin disease outbreaks in South and Southeast Asia (2022-2023).

Lumpy skin disease (LSD) poses a significant threat to animal welfare and leads to economic losses in affected countries. LSD outbreaks may also impact trade and regional relationships. South and Southeast Asia, with its dense livestock population and intricate trade networks, are susceptible to LSD outbreaks. Indonesia confirmed its first LSD cases in March 2022, leading to substantial livestock losses by August 2023. Australia, an important player in the global beef industry, faced trade disruptions due to LSD concerns raised by Indonesia and Malaysia, claims that were refuted by Australian authorities. The dispute highlights the need for good surveillance. EPIWATCH®, employing artificial intelligence, provides real-time outbreak signals, and spatial analysis can identify LSD hotspots, leading to timely interventions. This study uses data collected by the EPIWATCH® open-source disease surveillance system at the University of New South Wales in 2022 and 2023 and compares it for timeliness and completeness with data available on the World Animal Health Information System (WAHIS). We found more timely reports of LSD outbreaks in EPIWATCH® compared to WAHIS. In conclusion, open-source surveillance tools like EPIWATCH® can provide timely alerts of disease emergence, such as LSD outbreaks in South and Southeast Asia, which can supplement formal reporting systems.

Use of Measles and Rubella Rapid Diagnostic Tests to Improve Case Detection and Targeting of Vaccinations.

Efforts to control and eliminate measles and rubella are aided by high-quality surveillance data-supported by laboratory confirmation-to guide decision-making on routine immunization strategies and locations for conducting preventive supplementary immunization activities (SIAs) and outbreak response. Important developments in rapid diagnostic tests (RDTs) for measles and rubella present new opportunities for the global measles and rubella surveillance program to greatly improve the ability to rapidly detect and respond to outbreaks. Here, we review the status of RDTs for measles and rubella Immunoglobulin M (IgM) testing, as well as ongoing questions and challenges regarding the operational use and deployment of RDTs as part of global measles and rubella surveillance. Efforts to develop IgM RDTs that can be produced at scale are underway. Once validated RDTs are available, clear information on the benefits, challenges, and costs of their implementation will be critical for shaping deployment guidance and informing country plans for sustainably deploying such tests. The wide availability of RDTs could provide new programmatic options for measles and rubella elimination efforts, potentially enabling improvements and flexibility for testing, surveillance, and vaccination.

Effective Field Sampling of Rectoanal Mucosa-Associated Lymphoid Tissue for Antemortem Chronic Wasting Disease Testing in White-tailed Deer.

Chronic wasting disease (CWD) is a fatal prion disease of cervids that has spread across much of North America. Although gold standard CWD diagnostics involve postmortem testing of medial retropharyngeal lymph nodes or obex (brain stem), a key tissue sample for antemortem testing is rectoanal mucosa-associated lymphoid tissue (RAMALT). However, collection of an adequate sample (i.e., enough lymphoid follicles) may be affected by factors such as deer age, repeated sampling, skill of the sampler, and adverse conditions during collection. Here, we document the protocol used to train personnel for RAMALT collection in a large study of free-ranging white-tailed deer (Odocoileus virginianus) in Wisconsin, USA, and determine factors that contributed to the occurrence of inadequate RAMALT samples. Our training protocol included hands-on experience with postmortem tissues, as well as a mentored collection process in the field. Collection of RAMALT under field conditions was highly successful, with 763/806 (94.7%) samples deemed adequate for subsequent testing. Although inadequate samples were rare, they were more likely to occur with older deer and when samples were collected at dusk (i.e., limited ambient lighting). We conclude that RAMALT collection can be highly successful under adverse field conditions, including with technicians with limited prior veterinary experience, and we provide details of our training program to facilitate repeatability in other antemortem CWD testing efforts.

Circulating tumor DNA assisting lymphoma genetic feature profiling and identification.

INTRODUCTION: Lymphoma tissue biopsies cannot fully capture genetic features due to accessibility and heterogeneity. We aimed to assess the applicability of circulating tumor DNA (ctDNA) for genomic profiling and disease surveillance in classic Hodgkin lymphoma (cHL), primary mediastinal large B-cell lymphoma (PMBCL), and diffuse large B-cell lymphoma (DLBCL). METHODS: Tumor tissue and/or liquid biopsies of 49 cHLs, 32 PMBCLs, and 74 DLBCLs were subject to next-generation sequencing targeting 475 genes. The concordance of genetic aberrations in ctDNA and paired tissues was investigated, followed by elevating ctDNA-based mutational landscapes and the correlation between ctDNA dynamics and radiological response/progression. RESULTS: ctDNA exhibited high concordance with tissue samples in cHL (78%), PMBCL (84%), and DLBCL (78%). In cHL, more unique mutations were detected in ctDNA than in tissue biopsies (P < 0.01), with higher variant allele frequencies (P < 0.01). Distinct genomic features in cHL, PMBCL, and DLBCL, including STAT6, SOCS1, BTG2, and PIM1 alterations, could be captured by ctDNA alone. Prevalent PD-L1/PD-L2 amplifications were associated with more concomitant alterations in PMBCL (P < 0.01). Moreover, ctDNA fluctuation could reflect treatment responses and indicate relapse before imaging diagnosis. CONCLUSIONS: Lymphoma genomic profiling by ctDNA was concordant with that by tumor tissues. ctDNA might also be applied in lymphoma surveillance.

The Transmission of SARS-CoV-2 from COVID-19-Diagnosed People to Their Pet Dogs and Cats in a Multi-Year Surveillance Project.

Recent emerging zoonotic disease outbreaks, such as that of SARS-CoV-2, have demonstrated the need for wider companion animal disease surveillance. We tested 1000 dogs and cats belonging to employees of a US veterinary hospital network that were exposed to human COVID-19 cases in the household between 1 January 2020 and 10 March 2022 for SARS-CoV-2 and surveyed their owners about clinical signs and risk factors. The seropositivity was 33% for 747 dogs and 27% for 253 cats. Pet seropositivity correlated with the US human case rates over time, exhibiting peaks corresponding with the major COVID-19 surges. Antibodies persisted longer than previously documented (828 days in dogs; 650 days in cats). Increasing age and duration of proximity to infected people were associated with increased seropositivity in dogs but not cats. Cats were more likely to have clinical signs, but an association between seropositivity and the presence of clinical signs was not found in either species.

Strengthening disease surveillance capacity at national level across five countries: a qualitative study.

OBJECTIVES: Disease surveillance is an essential component of public health and a core function of National Public Health Institutes (NPHIs), including to better prepare and respond to infectious diseases outbreaks. Strengthening NPHIs in their efforts to establish and maintain efficient surveillance systems is an opportunity to ensure future outbreak preparedness and response; yet, guidance on how to increase and prioritise capacity building efforts is limited. This study sought to investigate approaches to capacity building and training for disease surveillance at national level and understand the potential role of NPHIs. STUDY DESIGN: Qualitative study. METHODS: This is a qualitative study, based on a literature review and interviews undertaken between June and November 2022. Fifty seven in-depth interviews were conducted in five countries: Côte d'Ivoire, Ecuador, Madagascar, Namibia, and the Kingdom of Saudi Arabia. Participants included a range of professionals from government, NPHIs, academic institutions and the private sector. Interviews were thematically analysed. RESULTS: Selected countries varied in terms of their disease surveillance capacities, as well as in the structure of their surveillance systems and decision-making. Research identified shared priority areas for action at national level, identifying common challenges and opportunities: 1) capacity building, here specifically the need for a training agenda at national level to ensure sustainability and guide donor funded training offers; 2) data tools and technology-to help decision-makers select the best software tool to address countries' identified need; 3) data sharing-the need for clear data sharing standards and norms for national to international data sharing; and 4) genomic sequencing-the need for national genomic surveillance strategies and reporting guidelines. CONCLUSION: Addressing challenges and using opportunities to strengthen disease surveillance at national level is an important step to build capacity in this area and to help prevent future epidemic and pandemics globally. The findings of this study help decision-makers to identify priority areas for capacity building and understand the potential role and significance of NPHIs.