Identifying outbreaks in sewer networks: An adaptive sampling scheme under network's uncertainty.

Motivated by the implementation of a SARS-Cov-2 sewer surveillance system in Chile during the COVID-19 pandemic, we propose a set of mathematical and algorithmic tools that aim to identify the location of an outbreak under uncertainty in the network structure. Given an upper bound on the number of samples we can take on any given day, our framework allows us to detect an unknown infected node by adaptively sampling different network nodes on different days. Crucially, despite the uncertainty of the network, the method allows univocal detection of the infected node, albeit at an extra cost in time. This framework relies on a specific and well-chosen strategy that defines new nodes to test sequentially, with a heuristic that balances the granularity of the information obtained from the samples. We extensively tested our model in real and synthetic networks, showing that the uncertainty of the underlying graph only incurs a limited increase in the number of iterations, indicating that the methodology is applicable in practice.

Enhancing Water Safety: Exploring Recent Technological Approaches for Drowning Detection.

Drowning poses a significant threat, resulting in unexpected injuries and fatalities. To promote water sports activities, it is crucial to develop surveillance systems that enhance safety around pools and waterways. This paper presents an overview of recent advancements in drowning detection, with a specific focus on image processing and sensor-based methods. Furthermore, the potential of artificial intelligence (AI), machine learning algorithms (MLAs), and robotics technology in this field is explored. The review examines the technological challenges, benefits, and drawbacks associated with these approaches. The findings reveal that image processing and sensor-based technologies are the most effective approaches for drowning detection systems. However, the image-processing approach requires substantial resources and sophisticated MLAs, making it costly and complex to implement. Conversely, sensor-based approaches offer practical, cost-effective, and widely applicable solutions for drowning detection. These approaches involve data transmission from the swimmer's condition to the processing unit through sensing technology, utilising both wired and wireless communication channels. This paper explores the recent developments in drowning detection systems while considering costs, complexity, and practicality in selecting and implementing such systems. The assessment of various technological approaches contributes to ongoing efforts aimed at improving water safety and reducing the risks associated with drowning incidents.

Risk for Infection in Humans after Exposure to Birds Infected with Highly Pathogenic Avian Influenza A(H5N1) Virus, United States, 2022.

During February 7─September 3, 2022, a total of 39 US states experienced outbreaks of highly pathogenic avian influenza A(H5N1) virus in birds from commercial poultry farms and backyard flocks. Among persons exposed to infected birds, highly pathogenic avian influenza A(H5) viral RNA was detected in 1 respiratory specimen from 1 person.

Epidemiological data analysis of viral quasispecies in the next-generation sequencing era.

The unprecedented coverage offered by next-generation sequencing (NGS) technology has facilitated the assessment of the population complexity of intra-host RNA viral populations at an unprecedented level of detail. Consequently, analysis of NGS datasets could be used to extract and infer crucial epidemiological and biomedical information on the levels of both infected individuals and susceptible populations, thus enabling the development of more effective prevention strategies and antiviral therapeutics. Such information includes drug resistance, infection stage, transmission clusters and structures of transmission networks. However, NGS data require sophisticated analysis dealing with millions of error-prone short reads per patient. Prior to the NGS era, epidemiological and phylogenetic analyses were geared toward Sanger sequencing technology; now, they must be redesigned to handle the large-scale NGS datasets and properly model the evolution of heterogeneous rapidly mutating viral populations. Additionally, dedicated epidemiological surveillance systems require big data analytics to handle millions of reads obtained from thousands of patients for rapid outbreak investigation and management. We survey bioinformatics tools analyzing NGS data for (i) characterization of intra-host viral population complexity including single nucleotide variant and haplotype calling; (ii) downstream epidemiological analysis and inference of drug-resistant mutations, age of infection and linkage between patients; and (iii) data collection and analytics in surveillance systems for fast response and control of outbreaks.

The Future of Public Health: Ensuring An Adequate Infrastructure.

Policy Points The US public heath infrastructure is in disrepair and building a sustainable system is the central challenge for the nation. Doing so in a highly patrician environment is the mission for the next ten years.

Stillbirth and early neonatal mortality rates may be underestimated using recall information: A comparison of demographic surveillance methodologies.

OBJECTIVE: Child mortality and stillbirth rates (SBR) remain high in low-income countries but may be underestimated due to incomplete reporting of child deaths in retrospective pregnancy/birth histories. The aim of this study is to compare stillbirth and mortality estimates derived using two different methods: the method assuming full information and the prospective method. METHODS: Bandim Health Project's Health and Demographic Surveillance Systems (HDSS) follows women of reproductive age and children under five through routine home visits every 1, 2 or 6 months. Between 2012 and 2020, we estimated and compared early neonatal (ENMR, <7 days), neonatal (NMR, <28 days), and infant mortality (IMR, <1 year) per 1000 live births and SBR per 1000 births. Risk time for children born to registered women was calculated from birth (the method assuming full information) versus date of first observation in the HDSS (the prospective method), either at birth (for pregnancy registration) or registration. Rates were calculated using the Kaplan-Meier estimator and compared in generalised linear models allowing for within-child correlation obtaining relative risks (RR). RESULTS: We registered and followed 29,413 infants (1380 deaths; 1459 stillbirths) prospectively. An additional 164 infant deaths and 129 stillbirths were registered retrospectively and included in the method assuming full information. The ENMR was 24.5 (95%CI: 22.6-26.4) for the method assuming full information and 25.8 (23.7-27.8) for the prospective method, RR = 0.96 (0.93-0.99). Differences were smaller for the NMRs and IMRs. For SBRs, the estimates were 53.5 (50.9-56.0) and 58.6 (55.7-61.5); RR = 0.91 (0.90-0.93). The difference between methods became more pronounced when the analysis was limited to areas visited every 6 months: RR for ENMR: 0.91 (0.86-0.96) and RR for SBR: 0.85 (0.83-0.87). CONCLUSIONS: Assuming full information underestimates SBR and ENMR. Accounting for omissions of stillbirths and early neonatal deaths may lead to more accurate estimates and improved ability to monitor mortality.

A qualitative evaluation and conceptual framework on the use of the Birth weight and Age-at-death Boxes for Intervention and Evaluation System (BABIES) matrix for perinatal health in Uganda.

BACKGROUND: Perinatal mortality (newborn deaths in the first week of life and stillbirths) continues to be a significant global health threat, particularly in resource-constrained settings. Low-tech, innovative solutions that close the quality-of-care gap may contribute to progress toward the Sustainable Development Goals for health by 2030. From 2012 to 2018, the Saving Mothers, Giving Life Initiative (SMGL) implemented the Birth weight and Age-at-Death Boxes for Intervention and Evaluation System (BABIES) matrix in Western Uganda. The BABIES matrix provides a simple, standardized way to track perinatal health outcomes to inform evidence-based quality improvement strategies. METHODS: In November 2017, a facility-based qualitative evaluation was conducted using in-depth interviews with 29 health workers in 16 health facilities implementing BABIES in Uganda. Data were analyzed using directed content analysis across five domains: 1) perceived ease of use, 2) how the matrix was used, 3) changes in behavior or standard operating procedures after introduction, 4) perceived value of the matrix, and 5) program sustainability. RESULTS: Values in the matrix were easy to calculate, but training was required to ensure correct data placement and interpretation. Displaying the matrix on a highly visible board in the maternity ward fostered a sense of accountability for health outcomes. BABIES matrix reports were compiled, reviewed, and responded to monthly by interprofessional teams, prompting collaboration across units to fill data gaps and support perinatal death reviews. Respondents reported improved staff communication and performance appraisal, community engagement, and ability to track and link clinical outcomes with actions. Midwives felt empowered to participate in the problem-solving process. Respondents were motivated to continue using BABIES, although sustainability concerns were raised due to funding and staff shortages. CONCLUSIONS: District-level health systems can use data compiled from the BABIES matrix to inform policy and guide implementation of community-centered health practices to improve perinatal heath. Future work may consider using the Conceptual Framework on Use of the BABIES Matrix for Perinatal Health as a model to operationalize concepts and test the impact of the tool over time.

Physical fitness trends in a nationally representative sample of Portuguese youth: What has changed from 2008 to 2018?

This investigation aimed to describe the current physical fitness (PF) status of Portuguese youth, compare secular trends from 2008 and 2018, and establish updated age- and sex-specific percentile values for distinct PF tests. In 2008 and 2018, 22 048 and 8960 children and adolescents (10-18 years) were included in two national cross-sectional investigations. PF was evaluated using the FITESCOLA® battery tests and the handgrip strength test. Independent sample t-tests and chi-squared tests were used to model the results. Weight smoothed percentile values were calculated using Cole's Lambda-Mu-Sigma (LMS) method. All analyses were weighted according to age, sex, and geographic region. In 2018, boys surpassed girls in the 20-m shuttle run, curl-ups, push-ups, standing long, and vertical jump tests, while girls performed better in the sit-and-reach (p < 0.05). The percentage of boys and girls meeting the healthy zone in the 20-min shuttle run test did not differ between 2008 and 2018 (p ≥ 0.05). In boys, a higher percentage fell in the healthy zone for the curl-up and push-up tests in 2018 compared to 2008 (85.8% vs. 83.4%, and 57.8% vs. 53.8%; p < 0.05). Girls improved their flexibility component (sit-and-reach test), with a higher percentage meeting the healthy zone in 2018 (32.6% vs. 36.9%; p < 0.05); an opposite trend was seen for boys (65.5% vs. 50.1%; p < 0.05). The present investigation provides new and updated PF percentile curves for Portuguese youth, which can be used as a general overview of the current PF state among the Portuguese young population.

Optical Protective Window Design and Material Selection Issues in the Multi-Sensor Electro-Optical Surveillance Systems.

Multi-sensor imaging systems have a very important role and wide applications in surveillance and security systems. In many applications, it is necessary to use an optical protective window as an optical interface connecting the imaging sensor and object of interest's space; meanwhile an imaging sensor is mounted in a protective enclosure, providing separation from environmental conditions. Optical windows are often used in various optical and electro-optical systems, fulfilling different sometimes very unusual tasks. There are lots of examples in the literature that define optical window design for targeted applications. Through analysis of the various effects that follow optical window application in connection with imaging systems, we have suggested a simplified methodology and practical recommendation for how to define optical protective window specifications in multi-sensor imaging systems, using a system engineering approach. In addition, we have provided initial set of data and simplified calculation tools that can be used in initial analysis to provide proper window material selection and definition of the specifications of optical protective windows in multi-sensor systems. It is shown that although the optical window design seems as a simple task, it requires serious multidisciplinary approach.

Development, implementation, and evaluation of a local community-based ophthalmology sentinel surveillance system in a remote rural area in Japan.

INTRODUCTION: Solid and sensitive infectious disease surveillance systems need to be developed and implemented to prevent and control epidemics. Although statutory national infectious disease surveillance systems have been developed in many countries, some challenges remain, such as their limited timeliness, representativeness, and sensitivity, as well as the fact that they cannot capture all local outbreaks that occur in small communities. To overcome these limitations, local community-based infectious disease surveillance systems that meet local needs and can operate with constrained resources need to be developed, especially in remote and rural low-resource areas. This study aimed to develop, implement, and evaluate a voluntary and unique local community-based ophthalmology sentinel surveillance system in Isa city (OSSS-Isa), a remote rural area in Japan. METHODS: For the development of OSSS-Isa, one hospital in Isa city assumed a leading role and developed a network with all medical institutions - 20 hospitals and clinics in the local community, including two ophthalmology clinics - as sentinel reporting sites. Surveillance was conducted on a weekly basis from Monday to Sunday. The collection, aggregation, and reporting of the surveillance data were implemented promptly on the same day, Monday, using a paper-based form and fax. For the evaluation of OSSS-Isa, the study followed the updated guidelines for evaluating public health surveillance systems proposed by the Centers for Disease Control and Prevention to select the evaluation criteria and develop a questionnaire. The questionnaires were then distributed to 20 hospitals and clinics, with the responses evaluated on a five-point Likert scale. RESULTS: For the implementation of OSSS-Isa, the system issued alerts twice to the networked hospitals and clinics when signs of an increase in the prevalence of a target infectious eye disease appeared in Isa city. After the alerts, the number of cases decreased in the community. Regarding the evaluation survey, physicians from 18 hospitals and clinics responded to the questionnaire (response rate 90%). In contrast to flexibility, more than 75% of the respondents gave high ratings to simplicity, data quality, acceptability, timeliness, and stability in evaluating OSSS-Isa, with the mean score for these evaluation criteria higher than 3.67. CONCLUSION: The present results indicate that OSSS-Isa has high simplicity, data quality, acceptability, timeliness, and stability, which is highly embedded with the local healthcare providers in Isa city. OSSS-Isa contributed to the early and accurate detection of signs of infectious eye disease outbreaks emerging in a small remote rural local community. The success factors seem to include its simple well-designed implementation methods, good external factors, and active human factors suited to the characteristics of the small remote rural community. The OSSS-Isa initiative appears to be a meaningful practical example of successful health advocacy by healthcare providers by developing a system at the local social level while going beyond the boundaries of routine medical practice. If voluntary small-scale surveillance systems can complement statutory large-scale ones and work together locally, nationally, and internationally, it might be possible to detect small, unusual happenings that occur in the community, such as emerging infectious diseases, and thereby help avert global outbreaks.

Extending and Strengthening Routine DHIS2 Surveillance Systems for COVID-19 Responses in Sierra Leone, Sri Lanka, and Uganda.

The COVID-19 pandemic challenged countries to protect their populations from this emerging disease. One aspect of that challenge was to rapidly modify national surveillance systems or create new systems that would effectively detect new cases of COVID-19. Fifty-five countries leveraged past investments in District Health Information Software version 2 (DHIS2) to quickly adapt their national public health surveillance systems for COVID-19 case reporting and response activities. We provide background on DHIS2 and describe case studies from Sierra Leone, Sri Lanka, and Uganda to illustrate how the DHIS2 platform, its community of practice, long-term capacity building, and local autonomy enabled countries to establish an effective COVID-19 response. With these case studies, we provide valuable insights and recommendations for strategies that can be used for national electronic disease surveillance platforms to detect new and emerging pathogens and respond to public health emergencies.

National influenza surveillance systems in five European countries: a qualitative comparative framework based on WHO guidance.

BACKGROUND: Influenza surveillance systems vary widely between countries and there is no framework to evaluate national surveillance systems in terms of data generation and dissemination. This study aimed to develop and test a comparative framework for European influenza surveillance. METHODS: Surveillance systems were evaluated qualitatively in five European countries (France, Germany, Italy, Spain, and the United Kingdom) by a panel of influenza experts and researchers from each country. Seven surveillance sub-systems were defined: non-medically attended community surveillance, virological surveillance, community surveillance, outbreak surveillance, primary care surveillance, hospital surveillance, mortality surveillance). These covered a total of 19 comparable outcomes of increasing severity, ranging from non-medically attended cases to deaths, which were evaluated using 5 comparison criteria based on WHO guidance (granularity, timing, representativeness, sampling strategy, communication) to produce a framework to compare the five countries. RESULTS: France and the United Kingdom showed the widest range of surveillance sub-systems, particularly for hospital surveillance, followed by Germany, Spain, and Italy. In all countries, virological, primary care and hospital surveillance were well developed, but non-medically attended events, influenza cases in the community, outbreaks in closed settings and mortality estimates were not consistently reported or published. The framework also allowed the comparison of variations in data granularity, timing, representativeness, sampling strategy, and communication between countries. For data granularity, breakdown per risk condition were available in France and Spain, but not in the United Kingdom, Germany and Italy. For data communication, there were disparities in the timeliness and accessibility of surveillance data. CONCLUSIONS: This new framework can be used to compare influenza surveillance systems qualitatively between countries to allow the identification of structural differences as well as to evaluate adherence to WHO guidance. The framework may be adapted for other infectious respiratory diseases.

Co-establishing an infrastructure for routine data collection to address disparities in infant mortality: planning and implementation.

BACKGROUND: Efforts to address infant mortality disparities in Ohio have historically been adversely affected by the lack of consistent data collection and infrastructure across the community-based organizations performing front-line work with expectant mothers, and there is no established template for implementing such systems in the context of diverse technological capacities and varying data collection magnitude among participating organizations. METHODS: Taking into account both the needs and limitations of participating community-based organizations, we created a data collection infrastructure that was refined by feedback from sponsors and the organizations to serve as both a solution to their existing needs and a template for future efforts in other settings. RESULTS: By standardizing the collected data elements across participating organizations, integration on a scale large enough to detect changes in a rare outcome such as infant mortality was made possible. Datasets generated through the use of the established infrastructure were robust enough to be matched with other records, such as Medicaid and birth records, to allow more extensive analysis. CONCLUSION: While a consistent data collection infrastructure across multiple organizations does require buy-in at the organizational level, especially among participants with little to no existing data collection experience, an approach that relies on an understanding of existing barriers, iterative development, and feedback from sponsors and participants can lead to better coordination and sharing of information when addressing health concerns that individual organizations may struggle to quantify alone.

Acceptability and feasibility of HIV recent infection surveillance by healthcare workers using a rapid test for recent infection at HIV testing sites - Malawi, 2019.

BACKGROUND: The Malawi Ministry of Health implemented a new surveillance activity in April 2019 to detect recent HIV infections using a rapid test for recent infection (RTRI) to identify areas of ongoing transmission and guide response activities. SETTING: At 23 health facilities in Blantyre District, healthcare workers (HCWs) were trained to conduct recent infection testing. In September 2019, we conducted a cross-sectional survey at these sites to explore the acceptability and feasibility of integrating this activity into routine HIV testing services (HTS). METHODS: Research assistants interviewed HCWs using a semi-structured survey. Descriptive statistics were used to summarize quantitative responses and thematic analysis was used to group open-ended text. RESULTS: We interviewed 119 HCWs. Eighty-two percent of participants reported the RTRI was easy-to-use. HCWs perceived high client acceptability; 100% reported clients as 'somewhat' or 'very accepting'. Challenges included 68% of HCWs estimating they spend ≥20 min beyond routine HTS per client for this activity and 51% performing at least two additional finger pricks to complete the testing algorithm. HCWs differed in their perceptions of whether results should be returned to clients. CONCLUSION: This study assessed HCW experiences using point-of-care RTRIs for HIV recent infection surveillance. Overall, HCWs perceived RTRIs to be acceptable, easy-to-use, and valuable. Though only clients with new HIV diagnoses are tested for recent infection, additional time may be substantial at high-volume health service delivery points. Providing response plans or aggregated recent infection results to HCWs and/or clients may support motivation and sustainability of this novel surveillance activity.

Describing the safety of abortion at the population level using network-based survey approaches.

BACKGROUND: Despite the negative impact of unsafe abortions on women's health and rights, the degree of abortion safety remains strikingly undocumented for a large share of abortions globally. Data on how women induce abortions (method, setting, provider) are central to the measurement of abortion safety. However, health-facility statistics and direct questioning in population surveys do not yield representative data on abortion care seeking pathways in settings where access to abortion services is highly restricted. Recent developments in survey methodologies to study stigmatized / illegal behaviour and hidden populations rely on the fact that such information circulates within social networks; however, such efforts have yet to give convincing results for unsafe abortions. OBJECTIVE: This article presents the protocol of a study whose purpose is to apply and develop further two network-based methods to contribute to the generation of reliable population-level information on the safety of abortions in contexts where access to legal abortion services is highly restricted. METHODS: This study plans to obtain population-level data on abortion care seeking in two Health and Demographic Surveillance Systems in urban Kenya and rural Burkina Faso by applying two methods: Anonymous Third-Party Reporting (ATPR) (also known as confidantes' method) and Respondent Driven Sampling (RDS). We will conduct a mixed methods formative study to determine whether these network-based approaches are pertinent in the study contexts. The ATPR will be refined notably by incorporating elements of the Network Scale-Up Method (NSUM) to correct or account for certain of its biases (transmission, barrier, social desirability, selection). The RDS will provide reliable alternative estimates of abortion safety if large samples and equilibrium can be reached; an RDS multiplex variant (also including social referents) will be tested. DISCUSSION: This study aims at documenting abortion safety in two local sites using ATPR and RDS. If successful, it will provide data on the safety profiles of abortion seekers across sociodemographic categories in two contrasted settings in sub-Saharan Africa. It will advance the formative research needed to determine whether ATPR and RDS are applicable or not in a given context. It will improve the questionnaire and correcting factors for the ATPR, improve the capacity of RDS to produce quasi-representative data on abortion safety, and advance the validation of both methods.

Representative data on how women induce abortions and their consequences are central to measurements of abortion safety. However, due to the stigmatized nature of abortion, measuring the details of the process is challenging when the latter occur out of the realm of the law and do not result in complications registered in hospital statistics. Hence, there is sparse empirical population-level data on how women terminate their pregnancies in countries where access to abortion services is highly restricted, as well as little data on the side effects and complications associated with the methods they chose and health seeking for these complications. Recent developments in indirect survey methodologies to study stigmatized/illegal behaviour and hidden populations are likely to improve the quality of data collected on abortion safety in restrictive contexts: all are based on the sharing of information on stigmatized practices in social networks. We propose to refine and pilot two such network-based methods to validate their use for collecting (quasi) representative data on abortion safety in large population health surveys. These two approaches are: (i) a modified Anonymous Third-Party Reporting method (ATPR) integrating elements of the Network-Scale-up Method (NSUM) and (ii) Respondent-Driven Sampling (RDS). We will conduct this study in two African Health and Demographic Surveillance Systems (HDSS) sites, one urban (Nairobi, Kenya), and one comprising a town and adjacent villages (Kaya, Burkina Faso).

Cooperative-PHD Tracking Based on Distributed Sensors for Naval Surveillance Area.

Brazil has an extensive coastline and Exclusive Economic Zone (EEZ) area, which are of high economic and strategic importance. A Maritime Surveillance System becomes necessary to provide information and data to proper authorities, and target tracking is crucial. This paper focuses on a multitarget tracking application to a large-scale maritime surveillance system. The system is composed of a sensor network distributed over an area of interest. Due to the limited computational capabilities of nodes, the sensors send their tracking data to a central station, which is responsible for gathering and processing information obtained by the distributed components. The local Multitarget Tracking (MTT) algorithm employs a random finite set approach, which adopts a Gaussian mixture Probability Hypothesis Density (PHD) filter. The proposed data fusion scheme, utilized in the central station, consists of an additional step of prune & merge to the original GM PHD filter algorithm, which is the main contribution of this work. Through simulations, this study illustrates the performance of the proposed algorithm with a network composed of two stationary sensors providing the data. This solution yields a better tracking performance when compared to individual trackers, which is attested by the Optimal Subpattern Assignment (OSPA) metric and its location and cardinality components. The presented results illustrate the overall performance improvement attained by the proposed solution. Moreover, they also stress the need to resort to a distributed sensor network to tackle real problems related to extended targets.

Influenza Virus: Tracking, Predicting, and Forecasting.

Influenza is a common respiratory infection that causes considerable morbidity and mortality worldwide each year. In recent years, along with the improvement in computational resources, there have been a number of important developments in the science of influenza surveillance and forecasting. Influenza surveillance systems have been improved by synthesizing multiple sources of information. Influenza forecasting has developed into an active field, with annual challenges in the United States that have stimulated improved methodologies. Work continues on the optimal approaches to assimilating surveillance data and information on relevant driving factors to improve estimates of the current situation (nowcasting) and to forecast future dynamics.

Feasibility of implementing recommendations to improve neglected tropical diseases surveillance and response in Kenya: a modified Delphi study.

BACKGROUND: Effective health information systems (HIS) are critical towards achieving timely response to preventive chemotherapy neglected tropical diseases (PC-NTDs) and their eventual elimination. Strengthened HIS enable prompt case detection and effective response to halt disease transmission and prevent probable outbreaks. This study aimed to assess the importance and feasibility of implementing recommendations for improving surveillance core functions, support functions and surveillance attributes concerning PC-NTDs in Kenya. METHODS: A descriptive web-based Delphi process comprising of two survey rounds was used to achieve group consensus on the importance of recommended actions and feasibility of their implementation. In the first round, participants were enrolled to complete a five-point likert-type self-administered electronic questionnaire comprising of 60 statements across 12 sub-domains on the importance of recommendations. In the second round, participants reappraised their responses following completion of a questionnaire with 56 rephrased statements on feasibility of implementing the recommendations to improve PC-NTDs surveillance and response. Data from both rounds were analysed using descriptive statistics and thematic analysis performed for the open-ended responses. RESULTS: Sixty-two key stakeholders actively involved in surveillance and response activities in seven PC-NTDs endemic counties in Kenya were invited to participate. Of these, 50/62 completed the first round (81 % response rate) and 45/50 completed the second round (90 % response rate). Consensus was achieved (defined as > 70 % agreement) on the importance (93 %) of recommendation statements and feasibility (82 %) of implementing the important recommendations. Stakeholders agreed on the importance and feasibility of specific recommendations across the 12 sub-domains: case detection and registration, reporting, data analysis, feedback, epidemic preparedness and response, supervision, training, resources, simplicity, acceptability, stability and flexibility. However, there was lack of consensus on the feasibility of conducting routine data analysis, increasing supervision of surveillance activities at lower levels and retaining trained surveillance staff across all levels. CONCLUSIONS: Consensus among health stakeholders on implementation of the important and feasible recommendations will inform relevant strategies for strengthening specific surveillance system functions in view of PC-NTDs in Kenya.

The ethical implications of verbal autopsy: responding to emotional and moral distress.

BACKGROUND: Verbal autopsy is a pragmatic approach for generating cause-of-death data in contexts without well-functioning civil registration and vital statistics systems. It has primarily been conducted in health and demographic surveillance systems (HDSS) in Africa and Asia. Although significant resources have been invested to develop the technical aspects of verbal autopsy, ethical issues have received little attention. We explored the benefits and burdens of verbal autopsy in HDSS settings and identified potential strategies to respond to the ethical issues identified. METHODS: This research was based on a case study approach centred on two contrasting HDSS in Kenya and followed the Mapping-Framing-Shaping Framework for empirical bioethics research. Data were collected through individual interviews, focus group discussions, document reviews and non-participant observations. 115 participants were involved, including 86 community members (HDSS residents and community representatives), and 29 research staff (HDSS managers, researchers, census field workers and verbal autopsy interviewers). RESULTS: The use of verbal autopsy data for research and public health was described as the most common potential benefit of verbal autopsy in HDSS. Community members mentioned the potential uses of verbal autopsy data in addressing immediate public health problems for the local population while research staff emphasized the benefits of verbal autopsy to research and the wider public. The most prominent burden associated with the verbal autopsy was emotional distress for verbal autopsy interviewers and respondents. Moral events linked to the interview, such as being unsure of the right thing to do (moral uncertainty) or knowing the right thing to do and being constrained from acting (moral constraint), emerged as key causes of emotional distress for verbal autopsy interviewers. CONCLUSIONS: The collection of cause-of-death data through verbal autopsy in HDSS settings presents important ethical and emotional challenges for verbal autopsy interviewers and respondents. These challenges include emotional distress for respondents and moral distress for interviewers. This empirical ethics study provides detailed accounts of the distress caused by verbal autopsy and highlights ethical tensions between potential population benefits and risks to individuals. It includes recommendations for policy and practice to address emotional and moral distress in verbal autopsy.

Economic evaluation of whole genome sequencing for pathogen identification and surveillance - results of case studies in Europe and the Americas 2016 to 2019.

BackgroundWhole genome sequencing (WGS) is increasingly used for pathogen identification and surveillance.AimWe evaluated costs and benefits of routine WGS through case studies at eight reference laboratories in Europe and the Americas which conduct pathogen surveillance for avian influenza (two laboratories), human influenza (one laboratory) and food-borne pathogens (five laboratories).MethodsThe evaluation focused on the institutional perspective, i.e. the 'investment case' for implementing WGS compared with conventional methods, based on costs and benefits during a defined reference period, mostly covering at least part of 2017. A break-even analysis estimated the number of cases of illness (for the example of Salmonella surveillance) that would need to be avoided through WGS in order to 'break even' on costs.ResultsOn a per-sample basis, WGS was between 1.2 and 4.3 times more expensive than routine conventional methods. However, WGS brought major benefits for pathogen identification and surveillance, substantially changing laboratory workflows, analytical processes and outbreaks detection and control. Between 0.2% and 1.1% (on average 0.7%) of reported salmonellosis cases would need to be prevented to break even with respect to the additional costs of WGS.ConclusionsEven at cost levels documented here, WGS provides a level of additional information that more than balances the additional costs if used effectively. The substantial cost differences for WGS between reference laboratories were due to economies of scale, degree of automation, sequencing technology used and institutional discounts for equipment and consumables, as well as the extent to which sequencers are used at full capacity.