Genomic surveillance of STEC/EHEC infections in Germany 2020 to 2022 permits insight into virulence gene profiles and novel O-antigen gene clusters.

Shiga toxin-producing E. coli (STEC), including the subgroup of enterohemorrhagic E. coli (EHEC), are important bacterial pathogens which cause diarrhea and the severe clinical manifestation hemolytic uremic syndrome (HUS). Genomic surveillance of STEC/EHEC is a state-of-the-art tool to identify infection clusters and to extract markers of circulating clinical strains, such as their virulence and resistance profile for risk assessment and implementation of infection prevention measures. The aim of the study was characterization of the clinical STEC population in Germany for establishment of a reference data set. To that end, from 2020 to 2022 1257 STEC isolates, including 39 of known HUS association, were analyzed and lead to a classification of 30.4 % into 129 infection clusters. Major serogroups in all clinical STEC analyzed were O26, O146, O91, O157, O103, and O145; and in HUS-associated strains were O26, O145, O157, O111, and O80. stx1 was less frequently and stx2 or a combination of stx, eaeA and ehxA were more frequently found in HUS-associated strains. Predominant stx gene subtypes in all STEC strains were stx1a (24 %) and stx2a (21 %) and in HUS-associated strains were mainly stx2a (69 %) and the combination of stx1a and stx2a (12.8 %). Furthermore, two novel O-antigen gene clusters (RKI6 and RKI7) and strains of serovars O45:H2 and O80:H2 showing multidrug resistance were detected. In conclusion, the implemented surveillance tools now allow to comprehensively define the population of clinical STEC strains including those associated with the severe disease manifestation HUS reaching a new surveillance level in Germany.

Genome-Based Characterization of Listeria monocytogenes, Costa Rica.

Genomic data on the foodborne pathogen Listeria monocytogenes from Central America are scarce. We analyzed 92 isolates collected during 2009-2019 from different regions in Costa Rica, compared those to publicly available genomes, and identified unrecognized outbreaks. Our findings suggest mandatory reporting of listeriosis in Costa Rica would improve pathogen surveillance.

Evaluation of the application of sequence data to the identification of outbreaks of disease using anomaly detection methods.

Anomaly detection methods have a great potential to assist the detection of diseases in animal production systems. We used sequence data of Porcine Reproductive and Respiratory Syndrome (PRRS) to define the emergence of new strains at the farm level. We evaluated the performance of 24 anomaly detection methods based on machine learning, regression, time series techniques and control charts to identify outbreaks in time series of new strains and compared the best methods using different time series: PCR positives, PCR requests and laboratory requests. We introduced synthetic outbreaks of different size and calculated the probability of detection of outbreaks (POD), sensitivity (Se), probability of detection of outbreaks in the first week of appearance (POD1w) and background alarm rate (BAR). The use of time series of new strains from sequence data outperformed the other types of data but POD, Se, POD1w were only high when outbreaks were large. The methods based on Long Short-Term Memory (LSTM) and Bayesian approaches presented the best performance. Using anomaly detection methods with sequence data may help to identify the emergency of cases in multiple farms, but more work is required to improve the detection with time series of high variability. Our results suggest a promising application of sequence data for early detection of diseases at a production system level. This may provide a simple way to extract additional value from routine laboratory analysis. Next steps should include validation of this approach in different settings and with different diseases.

DiD IT?: a differences-in-differences investigation tool to quantify the impact of local incidents on public health using real-time syndromic surveillance health data.

Syndromic surveillance was originally developed to provide early warning compared to laboratory surveillance, but it is increasing used for real-time situational awareness. When a potential threat to public health is identified, a rapid assessment of its impact is required for public health management. When threats are localised, analysis is more complex as local trends need to be separated from national trends and differences compared to unaffected areas may be due to confounding factors such as deprivation or age distributions. Accounting for confounding factors usually requires an in-depth study, which takes time. Therefore, a tool is required which can provide a rapid estimate of local incidents using syndromic surveillance data.Here, we present 'DiD IT?', a new investigation tool designed to measure the impact of local threats to public health. 'DiD IT?' uses a difference-in-differences statistical approach to account for temporal and spatial confounding and provide a direct estimate of impact due to incidents. Temporal confounding differences are estimated by comparing unaffected locations during and outside of exposure periods. Whilst spatial confounding differences are estimated by comparing unaffected and exposed locations outside of the exposure period. Any remaining differences can be considered to be the direct effect of the local incident.We illustrate the potential utility of the tool through four examples of localised health protection incidents in England. The examples cover a range of data sources including general practitioner (GP) consultations, emergency department (ED) attendances and a telehealth call and online health symptom checker; and different types of incidents including, infectious disease outbreak, mass-gathering, extreme weather and an industrial fire. The examples use the UK Health Security Agency's ongoing real-time syndromic surveillance systems to show how results can be obtained in near real-time.The tool identified 700 additional online difficulty breathing assessments associated with a severe thunderstorm, 53 additional GP consultations during a mumps outbreak, 2-3 telehealth line calls following an industrial fire and that there was no significant increase in ED attendances during the G7 summit in 2021.DiD IT? can provide estimates for the direct impact of localised events in real-time as part of a syndromic surveillance system. Thus, it has the potential for enhancing surveillance and can be used to evaluate the effectiveness of extending national surveillance to a more granular local surveillance.

Efficient algorithms for real-time syndromic surveillance.

OBJECTIVE: Outbreaks of influenza-like diseases often cause spikes in the demand for hospital beds. Early detection of these outbreaks can enable improved management of hospital resources. The objective of this study was to test whether surveillance algorithms designed to be responsive to a wide range of anomalous decreases in the time between emergency department (ED) presentations with influenza-like illnesses provide efficient early detection of these outbreaks. METHODS: Our study used data on ED presentations to major public hospitals in Queensland, Australia across 2017-2020. We developed surveillance algorithms for each hospital that flag potential outbreaks when the average time between successive ED presentations with influenza-like illnesses becomes anomalously small. We designed one set of algorithms to be responsive to a wide range of anomalous decreases in the time between presentations. These algorithms concurrently monitor three exponentially weighted moving averages (EWMAs) of the time between presentations and flag an outbreak when at least one EWMA falls below its control limit. We designed another set of algorithms to be highly responsive to narrower ranges of anomalous decreases in the time between presentations. These algorithms monitor one EWMA of the time between presentations and flag an outbreak when the EWMA falls below its control limit. Our algorithms use dynamic control limits to reflect that the average time between presentations depends on the time of year, time of day, and day of the week. RESULTS: We compared the performance of the algorithms in detecting the start of two epidemic events at the hospital-level: the 2019 seasonal influenza outbreak and the early-2020 COVID-19 outbreak. The algorithm that concurrently monitors three EWMAs provided significantly earlier detection of these outbreaks than the algorithms that monitor one EWMA. CONCLUSION: Surveillance algorithms designed to be responsive to a wide range of anomalous decreases in the time between ED presentations are highly efficient at detecting outbreaks of influenza-like diseases at the hospital level.

Epidemic intelligence activities among national public and animal health agencies: a European cross-sectional study.

Epidemic Intelligence (EI) encompasses all activities related to early identification, verification, analysis, assessment, and investigation of health threats. It integrates an indicator-based (IBS) component using systematically collected surveillance data, and an event-based component (EBS), using non-official, non-verified, non-structured data from multiple sources. We described current EI practices in Europe by conducting a survey of national Public Health (PH) and Animal Health (AH) agencies. We included generic questions on the structure, mandate and scope of the institute, on the existence and coordination of EI activities, followed by a section where respondents provided a description of EI activities for three diseases out of seven disease models. Out of 81 gatekeeper agencies from 41 countries contacted, 34 agencies (42%) from 26 (63%) different countries responded, out of which, 32 conducted EI activities. Less than half (15/32; 47%) had teams dedicated to EI activities and 56% (18/34) had Standard Operating Procedures (SOPs) in place. On a national level, a combination of IBS and EBS was the most common data source. Most respondents monitored the epidemiological situation in bordering countries, the rest of Europe and the world. EI systems were heterogeneous across countries and diseases. National IBS activities strongly relied on mandatory laboratory-based surveillance systems. The collection, analysis and interpretation of IBS information was performed manually for most disease models. Depending on the disease, some respondents did not have any EBS activity. Most respondents conducted signal assessment manually through expert review. Cross-sectoral collaboration was heterogeneous. More than half of the responding institutes collaborated on various levels (data sharing, communication, etc.) with neighbouring countries and/or international structures, across most disease models. Our findings emphasise a notable engagement in EI activities across PH and AH institutes of Europe, but opportunities exist for better integration, standardisation, and automatization of these efforts. A strong reliance on traditional IBS and laboratory-based surveillance systems, emphasises the key role of in-country laboratories networks. EI activities may benefit particularly from investments in cross-border collaboration, the development of methods that can automatise signal assessment in both IBS and EBS data, as well as further investments in the collection of EBS data beyond scientific literature and mainstream media.

Detecting early signals of COVID-19 outbreaks in 2020 in small areas by monitoring healthcare utilisation databases: first lessons learned from the Italian Alert_CoV project.

BackgroundDuring the COVID-19 pandemic, large-scale diagnostic testing and contact tracing have proven insufficient to promptly monitor the spread of infections.AimTo develop and retrospectively evaluate a system identifying aberrations in the use of selected healthcare services to timely detect COVID-19 outbreaks in small areas.MethodsData were retrieved from the healthcare utilisation (HCU) databases of the Lombardy Region, Italy. We identified eight services suggesting a respiratory infection (syndromic proxies). Count time series reporting the weekly occurrence of each proxy from 2015 to 2020 were generated considering small administrative areas (i.e. census units of Cremona and Mantua provinces). The ability to uncover aberrations during 2020 was tested for two algorithms: the improved Farrington algorithm and the generalised likelihood ratio-based procedure for negative binomial counts. To evaluate these algorithms' performance in detecting outbreaks earlier than the standard surveillance, confirmed outbreaks, defined according to the weekly number of confirmed COVID-19 cases, were used as reference. Performances were assessed separately for the first and second semester of the year. Proxies positively impacting performance were identified.ResultsWe estimated that 70% of outbreaks could be detected early using the proposed approach, with a corresponding false positive rate of ca 20%. Performance did not substantially differ either between algorithms or semesters. The best proxies included emergency calls for respiratory or infectious disease causes and emergency room visits.ConclusionImplementing HCU-based monitoring systems in small areas deserves further investigations as it could facilitate the containment of COVID-19 and other unknown infectious diseases in the future.

Unsuspected Clonal Spread of Methicillin-Resistant Staphylococcus aureus Causing Bloodstream Infections in Hospitalized Adults Detected Using Whole Genome Sequencing.

BACKGROUND: Though detection of transmission clusters of methicillin-resistant Staphylococcus aureus (MRSA) infections is a priority for infection control personnel in hospitals, the transmission dynamics of MRSA among hospitalized patients with bloodstream infections (BSIs) has not been thoroughly studied. Whole genome sequencing (WGS) of MRSA isolates for surveillance is valuable for detecting outbreaks in hospitals, but the bioinformatic approaches used are diverse and difficult to compare. METHODS: We combined short-read WGS with genotypic, phenotypic, and epidemiological characteristics of 106 MRSA BSI isolates collected for routine microbiological diagnosis from inpatients in 2 hospitals over 12 months. Clinical data and hospitalization history were abstracted from electronic medical records. We compared 3 genome sequence alignment strategies to assess similarity in cluster ascertainment. We conducted logistic regression to measure the probability of predicting prior hospital overlap between clustered patient isolates by the genetic distance of their isolates. RESULTS: While the 3 alignment approaches detected similar results, they showed some variation. A gene family-based alignment pipeline was most consistent across MRSA clonal complexes. We identified 9 unique clusters of closely related BSI isolates. Most BSIs were healthcare associated and community onset. Our logistic model showed that with 13 single-nucleotide polymorphisms, the likelihood that any 2 patients in a cluster had overlapped in a hospital was 50%. CONCLUSIONS: Multiple clusters of closely related MRSA isolates can be identified using WGS among strains cultured from BSI in 2 hospitals. Genomic clustering of these infections suggests that transmission resulted from a mix of community spread and healthcare exposures long before BSI diagnosis.

Genome Sequencing Identifies Previously Unrecognized Klebsiella pneumoniae Outbreaks in Neonatal Intensive Care Units in the Philippines.

BACKGROUND: Klebsiella pneumoniae is a critically important pathogen in the Philippines. Isolates are commonly resistant to at least 2 classes of antibiotics, yet mechanisms and spread of its resistance are not well studied. METHODS: A retrospective sequencing survey was performed on carbapenem-, extended spectrum beta-lactam-, and cephalosporin-resistant Klebsiella pneumoniae isolated at 20 antimicrobial resistance (AMR) surveillance sentinel sites from 2015 through 2017. We characterized 259 isolates using biochemical methods, antimicrobial susceptibility testing, and whole-genome sequencing (WGS). Known AMR mechanisms were identified. Potential outbreaks were investigated by detecting clusters from epidemiologic, phenotypic, and genome-derived data. RESULTS: Prevalent AMR mechanisms detected include blaCTX-M-15 (76.8%) and blaNDM-1 (37.5%). An epidemic IncFII(Yp) plasmid carrying blaNDM-1 was also detected in 46 isolates from 6 sentinel sites and 14 different sequence types (STs). This plasmid was also identified as the main vehicle of carbapenem resistance in 2 previously unrecognized local outbreaks of ST348 and ST283 at 2 different sentinel sites. A third local outbreak of ST397 was also identified but without the IncFII(Yp) plasmid. Isolates in each outbreak site showed identical STs and K- and O-loci, and similar resistance profiles and AMR genes. All outbreak isolates were collected from blood of children aged < 1 year. CONCLUSION: WGS provided a better understanding of the epidemiology of multidrug resistant Klebsiella in the Philippines, which was not possible with only phenotypic and epidemiologic data. The identification of 3 previously unrecognized Klebsiella outbreaks highlights the utility of WGS in outbreak detection, as well as its importance in public health and in implementing infection control programs.

Method for Economic Evaluation of Bacterial Whole Genome Sequencing Surveillance Compared to Standard of Care in Detecting Hospital Outbreaks.

BACKGROUND: Whole genome sequencing (WGS) surveillance and electronic health record data mining have the potential to greatly enhance the identification and control of hospital outbreaks. The objective was to develop methods for examining economic value of a WGS surveillance-based infection prevention (IP) program compared to standard of care (SoC). METHODS: The economic value of a WGS surveillance-based IP program was assessed from a hospital's perspective using historical outbreaks from 2011-2016. We used transmission network of outbreaks to estimate incremental cost per transmission averted. The number of transmissions averted depended on the effectiveness of intervening against transmission routes, time from transmission to positive culture results and time taken to obtain WGS results and intervene on the transmission route identified. The total cost of an IP program included cost of staffing, WGS, and treating infections. RESULTS: Approximately 41 out of 89 (46%) transmissions could have been averted under the WGS surveillance-based IP program, and it was found to be a less costly and more effective strategy than SoC. The results were most sensitive to the cost of performing WGS and the number of isolates sequenced per year under WGS surveillance. The probability of the WGS surveillance-based IP program being cost-effective was 80% if willingness to pay exceeded $2400 per transmission averted. CONCLUSIONS: The proposed economic analysis is a useful tool to examine economic value of a WGS surveillance-based IP program. These methods will be applied to a prospective evaluation of WGS surveillance compared to SoC.

Outbreak of Pseudomonas aeruginosa Infections from a Contaminated Gastroscope Detected by Whole Genome Sequencing Surveillance.

BACKGROUND: Traditional methods of outbreak investigations utilize reactive whole genome sequencing (WGS) to confirm or refute the outbreak. We have implemented WGS surveillance and a machine learning (ML) algorithm for the electronic health record (EHR) to retrospectively detect previously unidentified outbreaks and to determine the responsible transmission routes. METHODS: We performed WGS surveillance to identify and characterize clusters of genetically-related Pseudomonas aeruginosa infections during a 24-month period. ML of the EHR was used to identify potential transmission routes. A manual review of the EHR was performed by an infection preventionist to determine the most likely route and results were compared to the ML algorithm. RESULTS: We identified a cluster of 6 genetically related P. aeruginosa cases that occurred during a 7-month period. The ML algorithm identified gastroscopy as a potential transmission route for 4 of the 6 patients. Manual EHR review confirmed gastroscopy as the most likely route for 5 patients. This transmission route was confirmed by identification of a genetically-related P. aeruginosa incidentally cultured from a gastroscope used on 4of the 5 patients. Three infections, 2 of which were blood stream infections, could have been prevented if the ML algorithm had been running in real-time. CONCLUSIONS: WGS surveillance combined with a ML algorithm of the EHR identified a previously undetected outbreak of gastroscope-associated P. aeruginosa infections. These results underscore the value of WGS surveillance and ML of the EHR for enhancing outbreak detection in hospitals and preventing serious infections.

Geographically weighted generalized Farrington algorithm for rapid outbreak detection over short data accumulation periods.

The demand for rapid surveillance and early detection of local outbreaks has been growing recently. The rapid surveillance can select timely and appropriate interventions toward controlling the spread of emerging infectious diseases, such as the coronavirus disease 2019 (COVID-19). The Farrington algorithm was originally proposed by Farrington et al (1996), extended by Noufaily et al (2012), and is commonly used to estimate excess death. However, one of the major challenges in implementing this algorithm is the lack of historical information required to train it, especially for emerging diseases. Without sufficient training data the estimation/prediction accuracy of this algorithm can suffer leading to poor outbreak detection. We propose a new statistical algorithm-the geographically weighted generalized Farrington (GWGF) algorithm-by incorporating both geographically varying and geographically invariant covariates, as well as geographical information to analyze time series count data sampled from a spatially correlated process for estimating excess death. The algorithm is a type of local quasi-likelihood-based regression with geographical weights and is designed to achieve a stable detection of outbreaks even when the number of time points is small. We validate the outbreak detection performance by using extensive numerical experiments and real-data analysis in Japan during COVID-19 pandemic. We show that the GWGF algorithm succeeds in improving recall without reducing the level of precision compared with the conventional Farrington algorithm.

Monitoring sick leave data for early detection of influenza outbreaks.

BACKGROUND: Workplace absenteeism increases significantly during influenza epidemics. Sick leave records may facilitate more timely detection of influenza outbreaks, as trends in increased sick leave may precede alerts issued by sentinel surveillance systems by days or weeks. Sick leave data have not been comprehensively evaluated in comparison to traditional surveillance methods. The aim of this paper is to study the performance and the feasibility of using a detection system based on sick leave data to detect influenza outbreaks. METHODS: Sick leave records were extracted from private French health insurance data, covering on average 209,932 companies per year across a wide range of sizes and sectors. We used linear regression to estimate the weekly number of new sick leave spells between 2016 and 2017 in 12 French regions, adjusting for trend, seasonality and worker leaves on historical data from 2010 to 2015. Outbreaks were detected using a 95%-prediction interval. This method was compared to results from the French Sentinelles network, a gold-standard primary care surveillance system currently in place. RESULTS: Using sick leave data, we detected 92% of reported influenza outbreaks between 2016 and 2017, on average 5.88 weeks prior to outbreak peaks. Compared to the existing Sentinelles model, our method had high sensitivity (89%) and positive predictive value (86%), and detected outbreaks on average 2.5 weeks earlier. CONCLUSION: Sick leave surveillance could be a sensitive, specific and timely tool for detection of influenza outbreaks.

The African Network for Improved Diagnostics, Epidemiology and Management of common infectious Agents.

BACKGROUND: In sub-Saharan Africa, acute respiratory infections (ARI), acute gastrointestinal infections (GI) and acute febrile disease of unknown cause (AFDUC) have a large disease burden, especially among children, while respective aetiologies often remain unresolved. The need for robust infectious disease surveillance to detect emerging pathogens along with common human pathogens has been highlighted by the ongoing novel coronavirus disease 2019 (COVID-19) pandemic. The African Network for Improved Diagnostics, Epidemiology and Management of Common Infectious Agents (ANDEMIA) is a sentinel surveillance study on the aetiology and clinical characteristics of ARI, GI and AFDUC in sub-Saharan Africa. METHODS: ANDEMIA includes 12 urban and rural health care facilities in four African countries (Côte d'Ivoire, Burkina Faso, Democratic Republic of the Congo and Republic of South Africa). It was piloted in 2018 in Côte d'Ivoire and the initial phase will run from 2019 to 2021. Case definitions for ARI, GI and AFDUC were established, as well as syndrome-specific sampling algorithms including the collection of blood, naso- and oropharyngeal swabs and stool. Samples are tested using comprehensive diagnostic protocols, ranging from classic bacteriology and antimicrobial resistance screening to multiplex real-time polymerase chain reaction (PCR) systems and High Throughput Sequencing. In March 2020, PCR testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and analysis of full genomic information was included in the study. Standardised questionnaires collect relevant clinical, demographic, socio-economic and behavioural data for epidemiologic analyses. Controls are enrolled over a 12-month period for a nested case-control study. Data will be assessed descriptively and aetiologies will be evaluated using a latent class analysis among cases. Among cases and controls, an integrated analytic approach using logistic regression and Bayesian estimation will be employed to improve the assessment of aetiology and associated risk factors. DISCUSSION: ANDEMIA aims to expand our understanding of ARI, GI and AFDUC aetiologies in sub-Saharan Africa using a comprehensive laboratory diagnostics strategy. It will foster early detection of emerging threats and continued monitoring of important common pathogens. The network collaboration will be strengthened and site diagnostic capacities will be reinforced to improve quality management and patient care.

Integrated Whole-Genome Sequencing Infrastructure for Outbreak Detection and Source Tracing of Salmonella enterica Serotype Enteritidis.

As an important foodborne pathogen, Salmonella enterica serotype Enteritidis is recognized as one of the most common causes of human salmonellosis globally. Outbreak detection for this highly homogenous serotype, however, has remained challenging. Rapid advances in sequencing technologies have presented whole-genome sequencing (WGS) as a significant advancement for source tracing and molecular typing of foodborne pathogens. A retrospective analysis was conducted using Salmonella Enteritidis isolates (n = 65) from 11 epidemiologically confirmed outbreaks and a collection of contemporaneous sporadic isolates (n = 258) during 2007-2017 to evaluate the performance of WGS in delineating outbreak-associated isolates. Whole-genome single-nucleotide polymorphism (SNP)-based phylogenetic analysis revealed well-supported clades in concordance with epidemiological evidence and pairwise distances of ≤3 SNPs for all outbreaks. WGS-based framework of outbreak detection was thus proposed and applied prospectively to investigate isolates (n = 66) from nine outbreaks during 2018-2019. We further demonstrated the superior discriminatory power and accuracy of WGS to resolve and delineate outbreaks for pragmatic food source tracing. The proposed integrated WGS framework is the first in China for Salmonella Enteritidis and has the potential to serve as a paradigm for outbreak detection and source tracing of Salmonella throughout the stages of food production, as well as expanded to other foodborne pathogens.

Outbreak of Vancomycin-resistant Enterococcus faecium in Interventional Radiology: Detection Through Whole-genome Sequencing-based Surveillance.

BACKGROUND: Vancomycin-resistant enterococci (VRE) are a major cause of hospital-acquired infections. The risk of infection from interventional radiology (IR) procedures is not well documented. Whole-genome sequencing (WGS) surveillance of clinical bacterial isolates among hospitalized patients can identify previously unrecognized outbreaks. METHODS: We analyzed WGS surveillance data from November 2016 to November 2017 for evidence of VRE transmission. A previously unrecognized cluster of 10 genetically related VRE (Enterococcus faecium) infections was discovered. Electronic health record review identified IR procedures as a potential source. An outbreak investigation was conducted. RESULTS: Of the 10 outbreak patients, 9 had undergone an IR procedure with intravenous (IV) contrast ≤22 days before infection. In a matched case-control study, preceding IR procedure and IR procedure with contrast were associated with VRE infection (matched odds ratio [MOR], 16.72; 95% confidence interval [CI], 2.01 to 138.73; P = .009 and MOR, 39.35; 95% CI, 7.85 to infinity; P < .001, respectively). Investigation of IR practices and review of the manufacturer's training video revealed sterility breaches in contrast preparation. Our investigation also supported possible transmission from an IR technician. Infection prevention interventions were implemented, and no further IR-associated VRE transmissions have been observed. CONCLUSIONS: A prolonged outbreak of VRE infections related to IR procedures with IV contrast resulted from nonsterile preparation of injectable contrast. The fact that our VRE outbreak was discovered through WGS surveillance and the manufacturer's training video that demonstrated nonsterile technique raise the possibility that infections following invasive IR procedures may be more common than previously recognized.

Assessing 3 Outbreak Detection Algorithms in an Electronic Syndromic Surveillance System in a Resource-Limited Setting.

We evaluated the performance of X-bar chart, exponentially weighted moving average, and C3 cumulative sums aberration detection algorithms for acute diarrheal disease syndromic surveillance at naval sites in Peru during 2007-2011. The 3 algorithms' detection sensitivity was 100%, specificity was 97%-99%, and positive predictive value was 27%-46%.

Use of Surveillance Outbreak Response Management and Analysis System for Human Monkeypox Outbreak, Nigeria, 2017-2019.

In November 2017, the mobile digital Surveillance Outbreak Response Management and Analysis System was deployed in 30 districts in Nigeria in response to an outbreak of monkeypox. Adaptation and activation of the system took 14 days, and its use improved timeliness, completeness, and overall capacity of the response.

Evaluating the ALERT algorithm for local outbreak onset detection in seasonal infectious disease surveillance data.

Estimation of epidemic onset timing is an important component of controlling the spread of seasonal infectious diseases within community healthcare sites. The Above Local Elevated Respiratory Illness Threshold (ALERT) algorithm uses a threshold-based approach to suggest incidence levels that historically have indicated the transition from endemic to epidemic activity. In this paper, we present the first detailed overview of the computational approach underlying the algorithm. In the motivating example section, we evaluate the performance of ALERT in determining the onset of increased respiratory virus incidence using laboratory testing data from the Children's Hospital of Colorado. At a threshold of 10 cases per week, ALERT-selected intervention periods performed better than the observed hospital site periods (2004/2005-2012/2013) and a CUSUM method. Additional simulation studies show how data properties may effect ALERT performance on novel data. We found that the conditions under which ALERT showed ideal performance generally included high seasonality and low off-season incidence.

Social media based surveillance systems for healthcare using machine learning: A systematic review.

BACKGROUND: Real-time surveillance in the field of health informatics has emerged as a growing domain of interest among worldwide researchers. Evolution in this field has helped in the introduction of various initiatives related to public health informatics. Surveillance systems in the area of health informatics utilizing social media information have been developed for early prediction of disease outbreaks and to monitor diseases. In the past few years, the availability of social media data, particularly Twitter data, enabled real-time syndromic surveillance that provides immediate analysis and instant feedback to those who are charged with follow-ups and investigation of potential outbreaks. In this paper, we review the recent work, trends, and machine learning(ML) text classification approaches used by surveillance systems seeking social media data in the healthcare domain. We also highlight the limitations and challenges followed by possible future directions that can be taken further in this domain. METHODS: To study the landscape of research in health informatics performing surveillance of the various health-related data posted on social media or web-based platforms, we present a bibliometric analysis of the 1240 publications indexed in multiple scientific databases (IEEE, ACM Digital Library, ScienceDirect, PubMed) from the year 2010-2018. The papers were further reviewed based on the various machine learning algorithms used for analyzing health-related text posted on social media platforms. FINDINGS: Based on the corpus of 148 selected articles, the study finds the types of social media or web-based platforms used for surveillance in the healthcare domain, along with the health topic(s) studied by them. In the corpus of selected articles, we found 26 articles were using machine learning technique. These articles were studied to find commonly used ML techniques. The majority of studies (24%) focused on the surveillance of flu or influenza-like illness (ILI). Twitter (64%) is the most popular data source to perform surveillance research using social media text data, and Support Vector Machine (SVM) (33%) being the most used ML algorithm for text classification. CONCLUSIONS: The inclusion of online data in surveillance systems has improved the disease prediction ability over traditional syndromic surveillance systems. However, social media based surveillance systems have many limitations and challenges, including noise, demographic bias, privacy issues, etc. Our paper mentions future directions, which can be useful for researchers working in the area. Researchers can use this paper as a library for social media based surveillance systems in the healthcare domain and can expand such systems by incorporating the future works discussed in our paper.