Hypervirulent and carbapenem-resistant Klebsiella pneumoniae: A global public health threat.

The evolution of hypervirulent and carbapenem-resistant Klebsiella pneumoniae can be categorized into three main patterns: the evolution of KL1/KL2-hvKp strains into CR-hvKp, the evolution of carbapenem-resistant K. pneumoniae (CRKp) strains into hv-CRKp, and the acquisition of hybrid plasmids carrying carbapenem resistance and virulence genes by classical K. pneumoniae (cKp). These strains are characterized by multi-drug resistance, high virulence, and high infectivity. Currently, there are no effective methods for treating and surveillance this pathogen. In addition, the continuous horizontal transfer and clonal spread of these bacteria under the pressure of hospital antibiotics have led to the emergence of more drug-resistant strains. This review discusses the evolution and distribution characteristics of hypervirulent and carbapenem-resistant K. pneumoniae, the mechanisms of carbapenem resistance and hypervirulence, risk factors for susceptibility, infection syndromes, treatment regimens, real-time surveillance and preventive control measures. It also outlines the resistance mechanisms of antimicrobial drugs used to treat this pathogen, providing insights for developing new drugs, combination therapies, and a "One Health" approach. Narrowing the scope of surveillance but intensifying implementation efforts is a viable solution. Monitoring of strains can be focused primarily on hospitals and urban wastewater treatment plants.

LidPose: Real-Time 3D Human Pose Estimation in Sparse Lidar Point Clouds with Non-Repetitive Circular Scanning Pattern.

In this paper, we propose a novel, vision-transformer-based end-to-end pose estimation method, LidPose, for real-time human skeleton estimation in non-repetitive circular scanning (NRCS) lidar point clouds. Building on the ViTPose architecture, we introduce novel adaptations to address the unique properties of NRCS lidars, namely, the sparsity and unusual rosetta-like scanning pattern. The proposed method addresses a common issue of NRCS lidar-based perception, namely, the sparsity of the measurement, which needs balancing between the spatial and temporal resolution of the recorded data for efficient analysis of various phenomena. LidPose utilizes foreground and background segmentation techniques for the NRCS lidar sensor to select a region of interest (RoI), making LidPose a complete end-to-end approach to moving pedestrian detection and skeleton fitting from raw NRCS lidar measurement sequences captured by a static sensor for surveillance scenarios. To evaluate the method, we have created a novel, real-world, multi-modal dataset, containing camera images and lidar point clouds from a Livox Avia sensor, with annotated 2D and 3D human skeleton ground truth.

A smartphone-based crowd-sourced real-time surveillance platform (apple snail inspector) for the invasive snails: a design and development study.

BACKGROUND: The large amphibious freshwater apple snail is an important invasive species in China, but there is currently no method available for their surveillance. The development and popularization of smartphones provide a new platform for research on surveillance technologies for the early detection and effective control of invasive species. METHODS: The ASI surveillance system was developed based on the infrastructure of the WeChat platform and Amap. The user can directly enter the game interface through the WeChat port on their mobile phone, and the system automatically obtains their location. The user can then report the location of apple snails. The administrator can audit the reported information, and all information can be exported to Microsoft Excel version 2016 for analysis. The map was generated by ArcGIS 10.2 and was used to characterize the spatial and temporal distribution of apple snails in Jiangsu Province. RESULTS: The architecture of ASI consists of three parts: a mobile terminal, a server terminal and a desktop terminal. We published more than 10 tweets on the official WeChat account of the system to announce it to the public, and a total of 207 users in 2020 and 2021 correctly reported sightings of apple snails. We identified 550 apple snails breeding sites in 2020 and 2021, featuring ponds (81%), parks (17%) and farmland (2%). In addition, most of the locations contained snail eggs, and the reporting times mainly occurred between May and September. CONCLUSIONS: The ASI is an effective surveillance system that can be used to identify the breeding locations of apple snails and provides the basis of prevention and control for its dispersal. Its successful development and operation provide new potential avenues for surveillance of other public health issues.

Developing a next level integrated genomic surveillance: Advances in the molecular epidemiology of HIV in Germany.

Advances in the molecular epidemiological studies of the Human Immunodeficiency Virus (HIV) at the Robert Koch Institute (RKI) by laboratory and bioinformatic automation should allow the processing of larger numbers of samples and more comprehensive and faster data analysis in order to provide a higher resolution of the current HIV infection situation in near real-time and a better understanding of the dynamic of the German HIV epidemic. The early detection of the emergence and transmission of new HIV variants is important for the adaption of diagnostics and treatment guidelines. Likewise, the molecular epidemiological detection and characterization of spatially limited HIV outbreaks or rapidly growing sub-epidemics is of great importance in order to interrupt the transmission pathways by regionally adapting prevention strategies. These aims are becoming even more important in the context of the SARS-CoV2 pandemic and the Ukrainian refugee movement, which both have effects on the German HIV epidemic that should be monitored to identify starting points for targeted public health measures in a timely manner. To this end, a next level integrated genomic surveillance of HIV is to be established.

Medical examiner response to the drug overdose epidemic in King County Washington: "Real-time" surveillance, data science, and applied forensic epidemiology.

As the overdose epidemic overwhelmed medicolegal death investigation offices and toxicology laboratories, the King County Medical Examiner's Office responded with "real-time" fatal overdose surveillance to expedite death certification and information dissemination through assembling a team including a dedicated medicolegal death investigator, an information coordinator, and student interns. In-house testing of blood, urine, and drug evidence from scenes was performed using equipment and supplies purchased for surveillance. Collaboration with state laboratories allowed validation. Applied forensic epidemiology accelerated data dissemination. From 2010 to 2022, the epidemic claimed 5815 lives in King County; the last 4 years accounted for 47% of those deaths. After initiating the surveillance project, in-house testing was performed on blood from 2836 decedents, urine from 2807, and 4238 drug evidence items from 1775 death scenes. Time to complete death certificates decreased from weeks to months to hours to days. Overdose-specific information was distributed weekly to a network of law enforcement and public health agencies. As the surveillance project tracked the epidemic, fentanyl and methamphetamine became dominant and were associated with other indicators of social deterioration. In 2022, fentanyl was involved in 68% of 1021 overdose deaths. Homeless deaths increased sixfold; in 2022, 67% of 311 homeless deaths were due to overdose; fentanyl was involved in 49% and methamphetamine in 44%. Homicides increased 250%; in 2021, methamphetamine was positive in 35% of 149 homicides. The results are relevant to the value of rapid surveillance, its impact on standard operations, selection of cases requiring autopsy, and collaboration with other agencies in overdose prevention.

Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies.

Food Traceability 4.0 (FT 4.0) is about tracing foods in the era of the fourth industrial revolution (Industry 4.0) with techniques and technologies reflecting this new revolution. Interest in food traceability has gained momentum in response to, among others events, the outbreak of the COVID-19 pandemic, reinforcing the need for digital food traceability that prevents food fraud and provides reliable information about food. This review will briefly summarize the most common conventional methods available to determine food authenticity before highlighting examples of emerging techniques that can be used to combat food fraud and improve food traceability. A particular focus will be on the concept of FT 4.0 and the significant role of digital solutions and other relevant Industry 4.0 innovations in enhancing food traceability. Based on this review, a possible new research topic, namely FT 4.0, is encouraged to take advantage of the rapid digitalization and technological advances occurring in the era of Industry 4.0. The main FT 4.0 enablers are blockchain, the Internet of things, artificial intelligence, and big data. Digital technologies in the age of Industry 4.0 have significant potential to improve the way food is traced, decrease food waste and reduce vulnerability to fraud opening new opportunities to achieve smarter food traceability. Although most of these emerging technologies are still under development, it is anticipated that future research will overcome current limitations making large-scale applications possible.

Hypervariable-Locus Melting Typing: a Novel Approach for More Effective High-Resolution Melting-Based Typing, Suitable for Large Microbiological Surveillance Programs.

Pathogen typing is pivotal to detecting the emergence of high-risk clones in hospital settings and to limit their spread. Unfortunately, the most commonly used typing methods (i.e., pulsed-field gel electrophoresis [PFGE], multilocus sequence typing [MLST], and whole-genome sequencing [WGS]) are expensive or time-consuming, limiting their application to real-time surveillance. High-resolution melting (HRM) can be applied to perform cost-effective and fast pathogen typing, but developing highly discriminatory protocols is challenging. Here, we present hypervariable-locus melting typing (HLMT), a novel approach to HRM-based typing that enables the development of more effective and portable typing protocols. HLMT types the strains by assigning them to melting types (MTs) on the basis of a reference data set (HLMT-assignment) and/or by clustering them using melting temperatures (HLMT-clustering). We applied the HLMT protocol developed on the capsular gene wzi for Klebsiella pneumoniae on 134 strains collected during surveillance programs in four hospitals. Then, we compared the HLMT results to those obtained using wzi, MLST, WGS, and PFGE typing. HLMT distinguished most of the K. pneumoniae high-risk clones with a sensitivity comparable to that of PFGE and MLST+wzi. It also drew surveillance epidemiological curves comparable to those obtained using MLST+wzi, PFGE, and WGS typing. Furthermore, the results obtained using HLMT-assignment were consistent with those of wzi typing for 95% of the typed strains, with a Jaccard index value of 0.9. HLMT is a fast and scalable approach for pathogen typing, suitable for real-time hospital microbiological surveillance. HLMT is also inexpensive, and thus, it is applicable for infection control programs in low- and middle-income countries. IMPORTANCE In this work, we describe hypervariable-locus melting typing (HLMT), a novel fast approach to pathogen typing using the high-resolution melting (HRM) assay. The method includes a novel approach for gene target selection, primer design, and HRM data analysis. We successfully applied this method to distinguish the high-risk clones of Klebsiella pneumoniae, one of the most important nosocomial pathogens worldwide. We also compared HLMT to typing using WGS, the capsular gene wzi, MLST, and PFGE. Our results show that HLMT is a typing method suitable for real-time epidemiological investigation. The application of HLMT to hospital microbiology surveillance can help to rapidly detect outbreak emergence, improving the effectiveness of infection control strategies.

No association between enterovirus 71 (EV71) vaccination and risk of febrile seizures: a population-based near real-time surveillance study.

BACKGROUND: Since 2016, vaccines against enterovirus 71 (EV71) infection have been approved for use in China. Reports to the national passive surveillance system raised concerns about febrile seizures (FS) after EV71 vaccination. Rapid safety assessment of this novel vaccine is a public health priority. The objective was to assess risks of FS following EV71 vaccination in China. METHODS: We used data from a Regional Health Information Platform in Ningbo. The exposed population was children aged 6-71 months who received any dose of EV71 vaccine from 1 January 2016 to 31 December 2019. We implemented a multilayered approach to actively monitor FS following EV71 vaccination that included near real-time surveillance using two complementary sequential designs and further signal evaluation performing self-controlled risk interval (SCRI) analyses. RESULTS: A total of 330,668 EV71 doses were administered to the study population. During 157 weeks of sequential analyses, no statistically increased risks were detected, when compared with the self-matched control interval or the background risk. Further SCRI analyses confirmed no associations between EV71 vaccination and FS (adjusted incidence rate ratio: 1.04, 95% CI: 0.75 to 1.43). CONCLUSIONS: Our results reassured the safety of FS after EV71 vaccination using postlicensure data for the first time.

Developing a sentinel syndromic surveillance system using school-absenteeism data, example monitoring absences over the 2020 COVID-19 pandemic.

This study describes the development of a pilot sentinel school absence syndromic surveillance system. Using data from a sample of schools in England the capability of this system to monitor the impact of disease on school absences in school-aged children is shown, using the coronavirus disease 2019 (COVID-19) pandemic period as an example. Data were obtained from an online app service used by schools and parents to report their children absent, including reasons/symptoms relating to absence. For 2019 and 2020, data were aggregated into daily counts of 'total' and 'cough' absence reports. There was a large increase in the number of absence reports in March 2020 compared to March 2019, corresponding to the first wave of the COVID-19 pandemic in England. Absence numbers then fell rapidly and remained low from late March 2020 until August 2020, while lockdown was in place in England. Compared to 2019, there was a large increase in the number of absence reports in September 2020 when schools re-opened in England, although the peak number of absences was smaller than in March 2020. This information can help provide context around the absence levels in schools associated with COVID-19. Also, the system has the potential for further development to monitor the impact of other conditions on school absence, e.g. gastrointestinal infections.

MeltingPlot, a user-friendly online tool for epidemiological investigation using High Resolution Melting data.

BACKGROUND: The rapid identification of pathogen clones is pivotal for effective epidemiological control strategies in hospital settings. High Resolution Melting (HRM) is a molecular biology technique suitable for fast and inexpensive pathogen typing protocols. Unfortunately, the mathematical/informatics skills required to analyse HRM data for pathogen typing likely limit the application of this promising technique in hospital settings. RESULTS: MeltingPlot is the first tool specifically designed for epidemiological investigations using HRM data, easing the application of HRM typing to large real-time surveillance and rapid outbreak reconstructions. MeltingPlot implements a graph-based algorithm designed to discriminate pathogen clones on the basis of HRM data, producing portable typing results. The tool also merges typing information with isolates and patients metadata to create graphical and tabular outputs useful in epidemiological investigations and it runs in a few seconds even with hundreds of isolates. AVAILABILITY: https://skynet.unimi.it/index.php/tools/meltingplot/ . CONCLUSIONS: The analysis and result interpretation of HRM typing protocols can be not trivial and this likely limited its application in hospital settings. MeltingPlot is a web tool designed to help the user to reconstruct epidemiological events by combining HRM-based clustering methods and the isolate/patient metadata. The tool can be used for the implementation of HRM based real time large scale surveillance programs in hospital settings.

Real-time laboratory surveillance of Dengue antigen(NS1) - Best proposed early warning and response system for Dengue in Sri Lanka? - Results from a pilot study.

Introduction: During the recent past, dengue fever and associated complications have been the most important concern for health policy makers of Sri Lanka. The current notification system has considerable inevitable delays in preventive measures. Objectives: Implementing a laboratory-based real-time antigen (NS1) surveillance system for notification coupled with a rapid preventive response within the Colombo district as a pilot project and compares the notifications with existing national surveillance systems. Methods: An online notification platform was established with a centralized database. Seven main laboratories of the private sector linked with the notification system, where they can create new notifications at the central database, whenever the NS1 test detected as positive. Relevant Medical Officers of Health should update action implemented to complete the response process. A dashboard was designed to visualize each notification and its status with a predefined colour code. Results: Patients from 14 Medical Officer of Health (MOH) areas out of 15 were captured. The immediate preventive response was recorded from the field preventive staff for 90% of the reporting. All most all attended patients have given health advice on awareness, prevention, and source reduction through premise inspection by trained field staff with 24hrs of notification. Conclusions: Salient features of the novel system are notification of antigen-positive patients, the rapidity of notification (real-time) and response, user-friendliness, access to multiple stakeholders simultaneously without data duplication, early involvement of the field staff, the ability to trace the cases using checklists and a color-coding system from a dashboard.

Emergency department syndromic surveillance systems: a systematic review.

BACKGROUND: Syndromic surveillance provides public health intelligence to aid in early warning and monitoring of public health impacts (e.g. seasonal influenza), or reassurance when an impact has not occurred. Using information collected during routine patient care, syndromic surveillance can be based on signs/symptoms/preliminary diagnoses. This approach makes syndromic surveillance much timelier than surveillance requiring laboratory confirmed diagnoses. The provision of healthcare services and patient access to them varies globally. However, emergency departments (EDs) exist worldwide, providing unscheduled urgent care to people in acute need. This provision of care makes ED syndromic surveillance (EDSyS) a potentially valuable tool for public health surveillance internationally. The objective of this study was to identify and describe the key characteristics of EDSyS systems that have been established and used globally. METHODS: We systematically reviewed studies published in peer review journals and presented at International Society of Infectious Disease Surveillance conferences (up to and including 2017) to identify EDSyS systems which have been created and used for public health purposes. Search criteria developed to identify "emergency department" and "syndromic surveillance" were applied to NICE healthcare, Global Health and Scopus databases. RESULTS: In total, 559 studies were identified as eligible for inclusion in the review, comprising 136 journal articles and 423 conference abstracts/papers. From these studies we identified 115 EDSyS systems in 15 different countries/territories across North America, Europe, Asia and Australasia. Systems ranged from local surveillance based on a single ED, to comprehensive national systems. National EDSyS systems were identified in 8 countries/territories: 2 reported inclusion of ≥85% of ED visits nationally (France and Taiwan). CONCLUSIONS: EDSyS provides a valuable tool for the identification and monitoring of trends in severe illness. Technological advances, particularly in the emergency care patient record, have enabled the evolution of EDSyS over time. EDSyS reporting has become closer to 'real-time', with automated, secure electronic extraction and analysis possible on a daily, or more frequent basis. The dissemination of methods employed and evidence of successful application to public health practice should be encouraged to support learning from best practice, enabling future improvement, harmonisation and collaboration between systems in future. PROSPERO NUMBER: CRD42017069150 .

An Integrated mHealth App for Dengue Reporting and Mapping, Health Communication, and Behavior Modification: Development and Assessment of Mozzify.

BACKGROUND: For the last 10 years, mobile phones have provided the global health community with innovative and cost-effective strategies to address the challenges in the prevention and management of dengue fever. OBJECTIVE: The aim is to introduce and describe the design and development process of Mozzify, an integrated mobile health (mHealth) app that features real-time dengue fever case reporting and mapping system, health communication (real-time worldwide news and chat forum/timeline, within-app educational videos, links to local and international health agency websites, interactive signs and symptoms checker, and a hospital directions system), and behavior modification (reminders alert program on the preventive practices against dengue fever). We also aim to assess Mozzify in terms of engagement and information-sharing abilities, functionality, aesthetics, subjective quality, and perceived impact. METHODS: The main goals of the Mozzify app were to increase awareness, improve knowledge, and change attitudes about dengue fever, health care-seeking behavior, and intention-to-change behavior on preventive practices for dengue fever among users. It was assessed using the Mobile Application Rating Scale (MARS) among 50 purposively sampled individuals: public health experts (n=5), environment and health-related researchers (n=23), and nonclinical (end users) participants (n=22). RESULTS: High acceptability and excellent satisfaction ratings (mean scores ≥4.0 out of 5) based on the MARS subscales indicate that the app has excellent user design, functionality, usability, engagement, and information among public health experts, environment and health-related researchers, and end users. The app's subjective quality (recommending the app to other people and the app's overall star rating), and specific quality (increase awareness, improve knowledge, and change attitudes about dengue fever; health care-seeking behavior; and intention-to-change behavior on preventive practices for dengue fever) also obtained excellent satisfaction ratings from the participants. Some issues and suggestions were raised during the focus group and individual discussions regarding the availability of the app for Android devices, language options limitations, provision of predictive surveillance, and inclusion of other mosquito-borne diseases. CONCLUSIONS: Mozzify may be a promising integrated strategic health intervention system for dengue fever case reporting and mapping; increase awareness, improve knowledge, and change attitude about dengue fever; and disseminating and sharing information on dengue fever among the general population and health experts. It also can be an effective aid in the successful translation of knowledge on preventive measures against dengue fever to practice.

A simulation study of the statistical power and signaling characteristics of an early season sequential test for influenza vaccine safety.

PURPOSE: The US Food and Drug Administration monitors the risk of Guillain-Barré syndrome (GBS) following influenza vaccination using several data sources including Medicare. In the 2017 to 2018 season, we transitioned our near real-time surveillance in Medicare to more effectively detect large GBS risk increases early in the season while avoiding false positives. METHODS: We conducted a simulation study examining the ability of the updating sequential probability ratio test (USPRT) to detect substantially elevated GBS risk in the 8- to 21-day postvaccination versus 5× to 30× the historical rate. We varied the first testing week (weeks 5-8) and the null rate (1×-3×) and evaluated power. We estimated signal probability and the risk ratio (RR) after signaling when high-risk seasons were rare. RESULTS: Applying fixed alternatives, we found >80% power to detect a risk 30× the historical rate in week 5 for the 1× null and in week 6 for the 1.5× to 3× nulls. Nearly all testing schedules had >80% power for a 5× risk by week 11. To test the robustness of USPRT, we further simulated seasons where 1% were true high-risk seasons. Using a 1× null led to 10% of seasons signaling by week 11 (median RR approximately 1.4), which decreased to approximately 1% with the ≥2.5× null (median RR approximately 16.0). CONCLUSIONS: On the basis of the results from this simulation and subsequent consultations with experts and stakeholders, we specified USPRT to test continuously from weeks 7 to 11 using the null hypothesis that the observed GBS rate was 2.5× the historical rate. This helped improve the ability of USPRT to provide early detection of GBS risk following influenza vaccination as part of a multilayered system of surveillance.

Surveillance for Guillain-Barré syndrome after influenza vaccination among U.S. Medicare beneficiaries during the 2017-2018 season.

BACKGROUND: The U.S. Food and Drug Administration and the Centers for Medicare & Medicaid Services have been actively monitoring the risk of Guillain-Barré syndrome (GBS) following influenza vaccination among Fee-for-Service (FFS) Medicare beneficiaries every season since 2008. We present our evaluation of the GBS risk following influenza vaccinations during the 2017-2018 season. METHODS: We implemented a multilayered approach to active safety surveillance that included near real-time surveillance early in the season, comparing GBS rates post-vaccination during the 2017-2018 season with rates from five prior seasons using the Updating Sequential Probability Ratio Test (USPRT), and end-of-season self-controlled risk interval (SCRI) analyses. RESULTS: We identified approximately 16 million influenza vaccinations. The near real-time surveillance did not signal for a potential 2.5-fold increased GBS risk either in days 8-21 or 1-42 post-influenza vaccination. In the SCRI analyses, we did not detect statistically significant increased GBS risks among influenza-vaccinated Medicare beneficiaries ≥65 years for either the 8-21 or 1-42-day risk windows for all seasonal vaccines combined, high-dose vaccine, or standard-dose vaccines; we did detect an increased GBS risk in days 8-21 post-vaccination for individuals vaccinated with the adjuvanted vaccine (OR: 3.75; 95% CI: 1.01, 13.96), although this finding was not statistically significant after multiplicity adjustment (p = 0.146). CONCLUSIONS: Our multilayered surveillance approach-which allows for early detection of elevated GBS risk and provides reliable end-of-season SCRI estimates of effect size-did not identify an increased GBS risk following 2017-2018 influenza vaccinations. The slightly increased GBS risk with the adjuvanted vaccine, which was not statistically significant following multiplicity adjustment, is consistent with the package inserts of all U.S.-licensed influenza vaccines, which warn of a potential low increased GBS risk. The benefits of influenza vaccines in preventing morbidity and mortality heavily outweigh this potential risk.

[Evaluation of real-time surveillance of influenza incidence in Kawasaki City by comparison using the National Epidemiological Surveillance of Infectious Diseases].

Objectives　In Japan, nationwide data of the incidence of infectious diseases have been collected via the National Epidemiological Surveillance of Infectious Diseases (NESID) since 1981. In addition, since March 2014, Kawasaki City has operated its own real-time surveillance (RTS) system to collect data of the incidence of influenza from medical institutions across the city. This study aimed to describe the characteristics of the RTS system and compare the two surveillance systems to improve measures against infectious diseases in the future.Methods　NESID and RTS data from March 2014 to October 2017 were obtained from the Kawasaki City Institute for Public Health. First, the operating methodologies of the two surveillance systems were compared. Second, RTS data were used to analyze the daily epidemic curve, and then the daily number of influenza cases was converted into weekly data for comparison with NESID data. Pearson's correlation coefficients and 95% confidence intervals (CIs) were calculated. Correlations were also analyzed after data for the last and first weeks of each year were excluded because few hospitals remain open around the New Year holiday, resulting in a disproportionately large number of patients visiting the few institutions that remain open.Results　The NESID relies on data provided by a fixed number of medical institutions determined each fiscal year (mean: 56.0±4.2 institutions), while the number of institutions providing data for the RTS varies daily or monthly. In September 2017, 691 of the 1,032 eligible institutions (67.0%) were registered for the RTS. Pearson's correlation coefficient for the two surveillance systems was 0.975 (95%CI, 0.967-0.981); when data for the last and first week of each year were excluded, it was 0.989 (95%CI 0.986-0.992). In each of the three seasons that were investigated, an increase in the incidence of type A influenza preceded an increase in the incidence of type B influenza.Conclusion　The operating methodologies of the two surveillance systems differed; however, the results identified a strong correlation, confirming the reliability of the RTS. The RTS collects daily data by influenza type; therefore, it detects epidemic onsets at an earlier stage, facilitating more detailed epidemiological analysis, compared with that of the NESID. It is necessary to understand differences in the characteristics between two surveillance systems when we analyze influenza surveillance data.

Challenges and opportunities for effective data collection in global neurosurgery: traumatic brain injury surveillance experience in Malawi.

Global health research can transform clinical and surgical practice worldwide. Partnerships between US academic centers and hospitals in low- and middle-income counties can improve clinical care at the host institution hospital and give the visiting institution access to a large volume of valuable research data. Recognizing the value of these partnerships, the University of North Carolina (UNC) formed a partnership with Kamuzu Central Hospital (KCH) in Lilongwe, Malawi. The Department of Neurosurgery joined the partnership with KCH and designed a Head Trauma Surveillance Registry. The success of this registry depended on the development of methods to accurately collect head injury data at KCH. Since medical record documentation is often unreliable in this setting, data collection teams were implemented to capture data from head trauma patients on a 24-hours-a-day, 7-days-a-week basis. As data collection improved, pilot groups tested methods to collect new variables and the registry expanded. UNC provided onsite and remote oversight to strengthen the accuracy of the data. Data accuracy still remains a hurdle in global research. Data collection teams, oversight from UNC, pilot group testing, and meaningful collaboration with local physicians improved the accuracy of the head trauma registry. Overall, these methods helped create a more accurate epidemiological and outcomes-centered analysis of brain injury patients at KCH to date.

Early start of the West Nile fever transmission season 2018 in Europe.

In Europe, surveillance indicates that the 2018 West Nile fever transmission season started earlier than in previous years and with a steeper increase of locally-acquired human infections. Between 2014 and 2017, European Union/European Economic Area (EU/EEA) and EU enlargement countries notified five to 25 cases in weeks 25 to 31 compared with 168 cases in 2018. Clinicians and public health authorities should be alerted to ensure timely implementation of prevention measures including blood safety measures.

Real-time surveillance for abnormal events: the case of influenza outbreaks.

This paper introduces a method of surveillance using deviations from probabilistic forecasts. Realised observations are compared with probabilistic forecasts, and the "deviation" metric is based on low probability events. If an alert is declared, the algorithm continues to monitor until an all-clear is announced. Specifically, this article addresses the problem of syndromic surveillance for influenza (flu) with the intention of detecting outbreaks, due to new strains of viruses, over and above the normal seasonal pattern. The syndrome is hospital admissions for flu-like illness, and hence, the data are low counts. In accordance with the count properties of the observations, an integer-valued autoregressive process is used to model flu occurrences. Monte Carlo evidence suggests the method works well in stylised but somewhat realistic situations. An application to real flu data indicates that the ideas may have promise. The model estimated on a short run of training data did not declare false alarms when used with new observations deemed in control, ex post. The model easily detected the 2009 H1N1 outbreak. Copyright © 2016 John Wiley & Sons, Ltd.

Visualizing the quality of partially accruing data for use in decision making.

Secondary use of clinical health data for near real-time public health surveillance presents challenges surrounding its utility due to data quality issues. Data used for real-time surveillance must be timely, accurate and complete if it is to be useful; if incomplete data are used for surveillance, understanding the structure of the incompleteness is necessary. Such data are commonly aggregated due to privacy concerns. The Distribute project was a near real-time influenza-like-illness (ILI) surveillance system that relied on aggregated secondary clinical health data. The goal of this work is to disseminate the data quality tools developed to gain insight into the data quality problems associated with these data. These tools apply in general to any system where aggregate data are accrued over time and were created through the end-user-as-developer paradigm. Each tool was developed during the exploratory analysis to gain insight into structural aspects of data quality. Our key finding is that data quality of partially accruing data must be studied in the context of accrual lag-the difference between the time an event occurs and the time data for that event are received, i.e. the time at which data become available to the surveillance system. Our visualization methods therefore revolve around visualizing dimensions of data quality affected by accrual lag, in particular the tradeoff between timeliness and completion, and the effects of accrual lag on accuracy. Accounting for accrual lag in partially accruing data is necessary to avoid misleading or biased conclusions about trends in indicator values and data quality.