Early Detection Of Outbreaks Research Articles

Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change

Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change

Using the MCRISP Network to Study Acute Gastroenteritis and Influenza-Like Illness Outbreaks in Child Care Centers Compared to Statewide Epidemics

Introduction:Biosurveillance is critical for early detection of disease outbreaks and resource mobilization. Child care center (CCC) attendance has long been recognized as a significant independent predictor for respiratory and gastrointestinal diseases, but CCC surveillance is currently not part of the statewide disease surveillance system. The Michigan Child Care Related Infections Surveillance Program (MCRISP) is an independent, online reporting network with >30 local CCCs that was created to fill this surveillance gap.Aim:To describe the capability of a novel CCC biosurveillance system (MCRISP) to report pediatric Influenza-Like Illness (ILI) and Acute Gastroenteritis (AGE) illness over three years to (i) assess both the timing and magnitude of epidemics in CCCs and (ii) compare CCC outbreak patterns with those of the state database.Methods:MCRISP collates real-time syndromic reports of illness from local county CCCs. The statewide Michigan Disease Surveillance System (MDSS) collects reports of diagnosed illness from designated laboratories, clinics, and hospitals statewide. We assessed epidemic curves based on MCRISP incidence rates and MDSS case counts for ILI and AGE over three seasons (2014-7).Results:A total of 4,627 MCRISP cases (2,425 ILI and 2,202 AGE reports) were reported during the three years of study surveillance. Epidemic patterns (seasonal peaks, troughs, and breadth) for both ILI and AGE in CCCs mirrored those reported at county and state levels, respectively. Two distinguishing features of CCC ILI outbreaks were noted in all three seasons: MCRISP ILI rates remained elevated after MDSS influenza counts abated, and MCRISP rates consistently peaked prior to MDSS influenza peaks. Neither of these phenomena were observed in comparing AGE outbreaks between surveillance systems.Discussion:ILI and AGE incidence rates from the MCRISP network appeared to broadly mirror epidemics from the established state surveillance system. MCRISP may act as a sentinel system for larger community outbreaks of respiratory disease.

Classification of potential fire outbreaks: A fuzzy modeling approach based on thermal images

Fire outbreaks are a serious risk in campsites due to the surroundings and dynamic environment of these areas. Due to climate change, conditions of high ignition propensity are becoming more frequent, leading to an increased need for the development of alternative fire prevention systems that can mitigate the consequences of fire incidents. In this context, this work explores thermal imaging data for early detection of fire outbreaks, aiming for application in a real context. To that end, experimental trials were conducted in laboratory and at the venue of a summer festival under real operation conditions. The datasets acquired are characterized in detail, and a feature engineering process is devised for the analysis of the response of thermal imaging sensors to a fire ignition. To deal with high-dimensional data, the feature construction method follows a statistical color-based approach, that characterizes the dynamic changes in the data using three features. Subsequently, this paper proposes a fuzzy modeling approach based on these variables, which is transparent to interpretation and enables the assessment of patterns modeled. The performance of the classification algorithm for detection of fire outbreaks is evaluated and framed with works in the state-of-the-art for this application.

Zoonotic Semiotics: Plague Narratives and Vanishing Signs in Madagascar.

Zoonosis calls for a multispecies approach to medical semiotics, a method involving the decipherment of outward symptoms and the construction of narrative. In Madagascar, early detection of bubonic plague outbreaks relies on sightings of sick and dead rats. However, people most vulnerable to plague often do not perceive warning signs, and plague symptoms do not always present in rat and human bodies. In August 2015, a plague outbreak killed 10 residents of a rural hamlet in the central highlands. To reconstruct the transmission chain, scientists elicited survivors' memories of dead rats in the vicinity. Not only were these clues imperceptible to most, but residents had also constructed an alternative outbreak narrative based on different evidence. Stark health disparities, a lack of historical memory of the plague, and genetic adaptations of rats and plague bacteria have created a problem of "semiotic cluelessness" that complicates outbreak control measures and increases mortality.

An algorithm applied to national surveillance data for the early detection of major dengue outbreaks in Cambodia.

Dengue is a national priority disease in Cambodia. The Cambodian National Dengue Surveillance System is based on passive surveillance of dengue-like inpatients reported by public hospitals and on a sentinel, pediatric hospital-based active surveillance system. This system works well to assess trends but the sensitivity of the early warning and time-lag to usefully inform hospitals can be improved. During The ECOnomic development, ECOsystem MOdifications, and emerging infectious diseases Risk Evaluation (ECOMORE) project’s knowledge translation platforms, Cambodian hospital staff requested an early warning tool to prepare for major outbreaks. Our objective was therefore to find adapted tools to improve the early warning system and preparedness. Dengue data was provided by the National Dengue Control Program (NDCP) and are routinely obtained through passive surveillance. The data were analyzed at the provincial level for eight Cambodian provinces during 2008–2015. The R surveillance package was used for the analysis. We evaluated the effectiveness of Bayesian algorithms to detect outbreaks using count data series, comparing the current count to an expected distribution obtained from observations of past years. The analyses bore on 78,759 patients with dengue-like syndromes. The algorithm maximizing sensitivity and specificity for the detection of major dengue outbreaks was selected in each province. The overall sensitivity and specificity were 73% and 97%, respectively, for the detection of significant outbreaks during 2008–2015. Depending on the province, sensitivity and specificity ranged from 50% to 100% and 75% to 100%, respectively. The final algorithm meets clinicians’ and decisionmakers’ needs, is cost-free and is easy to implement at the provincial level.

Active and syndromic surveillance for mass gatherings: BOOM festival 2018, Portugal

Purpose: To describe the implementation and results of an active syndromic surveillance team in a planned and recurrent international mass gathering (MG) allowing early detection of outbreaks, and thereby, allowing intervention by local, national and international authorities. Methods & Materials: A field team of 17 health-care workers performed active and syndromic surveillance at Boom Festival 2018 between 21st and 29th of July 2018, in Portugal. Data including demographic characteristics, signs, symptoms, and environmental exposure was collected during admission at Boom Medical Service (BMS). Paper records were transcribed and analyzed with Microsoft Excel® 2016 (v 16.0). Descriptive analysis was performed and reported daily to relevant stakeholders. Results: Approximately 30,000 people from 147 countries attended the BOOM Festival, and 3,100 healthcare episodes occurred, including 451 (14.5%) complaints of gastrointestinal symptoms. During the Festival, there was a considerable increase in the incidence of gastrointestinal symptoms and an early warning alert for a possible outbreak of acute gastroenteritis was issued to the national health authorities on 24th of July. Interventions were carried out in collaboration with local public health authorities to control the outbreak and to address possible common-sources of infection. Contaminated food or water were suspected, and a person-to-person transmission was not excluded since several cases mentioned attending another MG and symptom onset before arrival at the festival. The concern about an early stage outbreak was valid since 62.5% (n = 282) of the cases with gastrointestinal symptoms were reported between 26th-29th of July. Conclusion: Active and syndromic surveillance are essential for timely data collection and analysis in MGs, delivering a comprehensive snapshot of daily incidents, enabling rapid response to possible outbreaks and providing early-warning alerts for stakeholders. Nevertheless, enhancements are needed: data sharing and improved communication channels between stakeholders and similar international MG are necessary to create an effective syndromic surveillance system. Author response to letter from Gomez et al.International Journal of Infectious DiseasesVol. 91PreviewThe abstract by Maki et al. (2019) contained limited information from a poster presented in a late-breaking session at the Seventh International Meeting on Emerging Diseases and Surveillance. The work had IRB approval (HFHS #12383), and informed consent was obtained prior to collecting samples. Full-Text PDF Open AccessBacterial colonization in point-of-use filters and deaths in Flint, MichiganInternational Journal of Infectious DiseasesVol. 91PreviewThe study by Maki et al. (2019) examined the effects of point-of-use (PoU) filters on bacterial infections in residents of Flint, Michigan homes. The authors noted that 12 pathogens (including Pseudomonas aeruginosa) were detected in PoU filters in 10 Flint homes and that residents of 70% of these homes had severe respiratory infections. Additionally alarming, the authors reported an astounding 50% mortality rate in their study sample. Full-Text PDF Open Access

Technology volunteerism - the red cross’ approach to developing a digital community based surveillance tool for early detection of diseases with epidemic potential

Purpose: The Red Cross/Red Crescent Movement is currently developing a cutting-edge community based surveillance (CBS) platform in partnership with volunteers from the Norwegian tech community. CBS enables volunteers in communities at risk of epidemic-prone disease outbreaks to communicate health risks in real-time, thereby closing the surveillance gap. Real-time risk monitoring at the community level provides early detection, enabling an early response. The Red Cross’ innovative technology, therefore, has the potential to detect and stop outbreaks, ultimately reducing morbidity and mortality. Methods & Materials: The project uses a novel two-fold volunteer model; volunteers in Norway build the platform software, while volunteers in-country operationalise the tool to report on health risks using SMS. This volunteer model allows for data collection, analysis and software programming to take place in conjointly, therefore providing an opportunity for the CBS platform to adapt to needs of unique operational contexts. Results: To date, 122 tech volunteers have contributed to the open source software development, with over 2000 additions made to the code. An initial version, launched in May 2018, is currently being piloted by the Somali Red Crescent Society, where 75 volunteers are monitoring and reporting on the diarrhoeal cases through SMS. Their reporting is registered and analysed real-time, informing early action, while user experiences feed directly into platform development. Conclusion: By closing the surveillance gap and enabling communities outside of traditional public health surveillance mechanisms to alert of potential outbreaks in real time, CBS could provide the means for early detection and early response. Simultaneous implementation and software creation provides an opportunity to build a contextually suitable platform, which is adapted to provide a simple and reliable means for early detection and warning of outbreaks. This ensures that health responses happen earlier. In 2019, the CBS platform will be made available to additional National Societies and in emergency contexts. This will be yet an opportunity to respond rapidly and accurately to outbreaks whilst simultaneously shaping technological developments.

Whole genome sequencing of Salmonella Chester reveals geographically distinct clusters, Norway, 2000 to 2016.

IntroductionDuring summer 2016, Norway observed an increase in Salmonella enterica subsp. enterica serovar Chester cases among travellers to Greece.AimOur aim was to investigate genetic relatedness of S. Chester for surveillance and outbreak detection by core genome multilocus sequence typing (cgMLST) and compare the results to genome mapping.MethodsWe included S. Chester isolates from 51 cases of salmonellosis between 2000 and 2016. Paired-end sequencing (2 × 250 bp) was performed on Illumina MiSeq. Genetic relatedness by cgMLST for Salmonella enterica subsp. enterica, including 3,002 genes and seven housekeeping genes, was compared by reference genome mapping with CSI Phylogeny version 1.4 and conventional MLST.ResultsConfirmed travel history was available for 80% of included cases, to Europe (n = 13), Asia (n = 12) and Africa (n = 16). Isolates were distributed into four phylogenetic clusters corresponding to geographical regions. Sequence type (ST) ST411 and a single-locus variant ST5260 (n = 17) were primarily acquired in southern Europe, ST1954 (n = 15) in Africa, ST343 (n = 11) and ST2063 (n = 8) primarily in Asia. Part of the European cluster was further divided into a Greek (n = 10) and a Cypriot (n = 4) cluster. All isolates in the African cluster displayed resistance to ≥ 1 class of antimicrobials, while resistance was rare in the other clusters.ConclusionWhole genome sequencing of S. Chester in Norway showed four geographically distinct clusters, with a possible outbreak occurring during summer 2016 related to Greece. We recommend public health institutes to implement cgMLST-based real-time Salmonella enterica surveillance for early and accurate detection of future outbreaks and further development of cluster cut-offs.

Identification of Areas at Increased Risk of Highly Pathogenic Avian Influenza Occurrence in Commercial Poultry in Poland.

The objective of the study was to identify the areas at increased risk of highly pathogenic avian influenza (HPAI) occurrence in commercial poultry in Poland. To identify the risk factors related to the occurrence of HPAI outbreaks, the opinions of Polish experts were combined with literature-driven knowledge. The relative impact of each risk factor was determined using a multicriteria decision analysis approach. The applied model suggests that the greatest risk of HPAI occurrence is concentrated in several counties in the eastern, western, and central parts of the country. The most influential risk actors responsible for HPAI occurrence in Poland included waterfowl density and proximity to waterbodies. The model had a high predictive value (area under the curve = 0.78). The developed spatial risk assessment of HPAI occurrence provides a valuable source of information for risk managers and can contribute to early detection of potential outbreaks of HPAI in poultry.

Rapid response to meningococcal disease cluster in Foya district, Lofa County, Liberia January to February 2018.

IntroductionEarly detection of disease outbreaks is paramount to averting associated morbidity and mortality. In January 2018, nine cases including four deaths associated with meningococcal disease were reported in three communities of Foya district, Lofa County, Liberia. Due to the porous borders between Lofa County and communities in neighboring Sierra Leone and Guinea, the possibility of epidemic spread of meningococcal disease could not be underestimated.MethodsThe county incidence management system (IMS) was activated that coordinated the response activities. Daily meetings were conducted to review response activities progress and challenges. The district rapid response team (DRRT) was the frontline responders. The case based investigation form; case line list and contacts list were used for data collection. A data base was established and analysed daily for action. Tablets Ciprofloxacin were given for chemoprophylaxis.ResultsSixty-seven percent (67%) of the cases were males and also 67% of the affected age range was 3 to 14 years and attending primary school. The attack rate was 7/1,000 population and case fatality rate was 44.4 % with majority of the deaths occurring within 24-48 hours of symptoms onset. Three of the cases tested positive for Neisseria Meningitidis sero-type W while six cases were Epi-linked. None of the cases had recent meningococcal vaccination and no health-worker infections were registered.ConclusionThis cluster of cases of meningococcal disease during the meningitis season in a country that is not traditionally part of the meningitis belt emphasized the need for strengthening surveillance, preparedness and response capacity to meningitis.

Scan statistics and multiple testing in networks with applications to veterinary epidemiology

Event Abstract Back to Event Scan statistics and multiple testing in networks with applications to veterinary epidemiology James Sharpnack1*, Dmitry Shemetov1, Katharina Proksch2 and Beatriz Martínez-López1 1 University of California, Davis, United States 2 University of Göttingen, Germany Scan statistics can be used for early detection of outbreaks in animal populations by testing large regions for faint but detectable diagnostic measurements. Traditional scan statistic implementations, such as the SaTScan software [1], fuse neighboring measurements to obtain more powerful global null tests. We present computationally efficient methods and software that can utilize both spatial proximity and network information, and precisely localize the outbreak. We outline modern tools for performing scan statistics on networks, such as hierarchical multiple testing [2] and combinatorial scan statistics [3]. First, we introduce a novel false discovery rate controlling hierarchical testing procedure that is combined with hierarchical decompositions of networks such as spectral and spanning tree based decompositions. Second, we introduce a computationally efficient network scanning procedure that fuses graph neighborhood data. Finally, we demonstrate applications of these methods to syndromic surveillance using transportation networks between swine farms. Acknowledgements This work was also supported by NSF DMS 1712996 and NSF BIGDATA-IA 1838207.

Field observations and experiences gained from the implementation of control measures against lumpy skin disease in South-East Europe between 2015 and 2017

The first epidemics of lumpy skin disease (LSD) reported in Europe in 2015 severely affected the cattle farming sector in several Balkan countries. After the first incursion into Greece in 2015, the disease quickly spread across the Balkan region with over 7000 outbreaks reported by the end of 2016. Thanks to a coordinated regional control and eradication policy, the spread of the disease was halted by the end of 2017. Regional large-scale vaccination campaign with effective homologous vaccines and high vaccination coverage revealed to be essential for the successful control the disease, supported by other measures such as early detection of outbreaks, total or partial stamping out and restrictions on cattle movements. The aim of this paper is to discuss the field observations, challenges and lessons learnt while dealing with the first LSD epidemics in Europe. The cross-border collaboration by the veterinary authorities of all affected countries, coordinated by the European Commission and the technical support provided by many other international organizations played a fundamental role in stopping the spread of a disease that otherwise could have expanded further to the European territory causing a large damage to the whole European cattle farming industry. The experience obtained during the control of LSD epidemics indicates that in the future LSD spread can be effectively halted, provided that appropriate surveillance plans and vigilance remains in place in the areas at risk of re-incursion, especially those bordering endemic countries.

Unrecognised Outbreak: Human parainfluenza virus infections in a pediatric oncology unit. A new diagnostic PCR and virus monitoring system may allow early detection of future outbreaks

Background: Human parainfluenza viruses (HPIVs) are significant causes of both upper and lower respiratory tract infections with type 3 (HPIV3) causing the most severe disease in the immunocompromised cohorts. The objective of this study was to analyse the epidemiological nature of a cluster of cases of HPIV3 in a pediatric oncology unit of a major teaching hospital. Methods: In order to determine whether the activity observed represented a deviation from the norm, seasonal trends of HPIV3 in the surrounding geographical area as well as on the ward in question were analysed. The genetic link between cases was established by the phylogenetic analysis of the non-coding hypervariable region between the M (Matrix) and F (fusion) genes of HPIV3. The 15 cases involved and 15 unrelated cases were sequenced. Transmission routes were subsequently inferred and visualized using Konstanz Information Miner (KNIME) 3.3.2. Results: Of the 15 cases identified, 14 were attributed to a point source outbreak. Two out of 14 outbreak cases were found to differ by a single mutation A182C. The outbreak strain was also seen in 1 out of 15 unrelated cases, indicating that it was introduced from the community. Transmission modeling was not able to link all the cases and establish a conclusive chain of transmission. No staff were tested during the outbreak period. No deaths occurred as a result of the outbreak. Conclusion: A point source outbreak of HPIV3 was recognized post factum on an oncology pediatric unit in a major teaching hospital. This raised concern about the possibility of a future more serious outbreak. Weaknesses in existing systems were identified and a new dedicated respiratory virus monitoring system introduced. Pediatric oncology units require sophisticated systems for early identification of potentially life-threatening viral outbreaks.

Whole genome sequencing for investigations of meningococcal outbreaks in the United States: a retrospective analysis

Although rare in the U.S., outbreaks due to Neisseria meningitidis do occur. Rapid, early outbreak detection is important for timely public health response. In this study, we characterized U.S. meningococcal isolates (N = 201) from 15 epidemiologically defined outbreaks (2009–2015) along with temporally and geographically matched sporadic isolates using multilocus sequence typing, pulsed-field gel electrophoresis (PFGE), and six whole genome sequencing (WGS) based methods. Recombination-corrected maximum likelihood (ML) and Bayesian phylogenies were reconstructed to identify genetically related outbreak isolates. All WGS analysis methods showed high degree of agreement and distinguished isolates with similar or indistinguishable PFGE patterns, or the same strain genotype. Ten outbreaks were caused by a single strain; 5 were due to multiple strains. Five sporadic isolates were phylogenetically related to 2 outbreaks. Analysis of 9 outbreaks using timed phylogenies identified the possible origin and estimated the approximate time that the most recent common ancestor emerged for outbreaks analyzed. U.S. meningococcal outbreaks were caused by single- or multiple-strain introduction, with organizational outbreaks mainly caused by a clonal strain and community outbreaks by divergent strains. WGS can infer linkage of meningococcal cases when epidemiological links are uncertain. Accurate identification of outbreak-associated cases requires both WGS typing and epidemiological data.

SMS-based smartphone application for disease surveillance has doubled completeness and timeliness in a limited-resource setting \u2013 evaluation of a 15-week pilot program in Central African Republic (CAR)

BackgroundIt is a challenge in low-resource settings to ensure the availability of complete, timely disease surveillance information. Smartphone applications (apps) have the potential to enhance surveillance data transmission.MethodsThe Central African Republic (CAR) Ministry of Health and Médecins Sans Frontières (MSF) conducted a 15-week pilot project to test a disease surveillance app, Argus, for 20 conditions in 21 health centers in Mambéré Kadéi district (MK 2016). Results were compared to the usual paper-based surveillance in MK the year prior (MK 2015) and simultaneously in an adjacent health district, Nana-Mambére (NM 2016). Wilcoxon rank sum and Kaplan-Meier analyses compared report completeness and timeliness; the cost of the app, and users’ perceptions of its usability were assessed.ResultsTwo hundred seventy-one weekly reports sent by app identified 3403 cases and 63 deaths; 15 alerts identified 28 cases and 4 deaths. Median completeness (IQR) for MK 2016, 81% (81–86%), was significantly higher than in MK 2015 (31% (24–36%)), and NM 2016 (52% (48–57)) (p < 0.01). Median timeliness (IQR) for MK 2016, 50% (39–57%) was also higher than in MK 2015, 19% (19–24%), and NM 2016 29% (24–36%) (p < 0.01). Kaplan-Meier Survival Analysis showed a significant progressive reduction in the time taken to transmit reports over the 15-week period (p < 0.01). Users ranked the app’s usability as greater than 4/5 on all dimensions. The total cost of the 15-week pilot project was US$40,575. It is estimated that to maintain the app in the 21 health facilities of MK will cost approximately US$18,800 in communication fees per year.ConclusionsThe app-based data transmission system more than doubled the completeness and timeliness of disease surveillance reports. This simple, low-cost intervention may permit the early detection of disease outbreaks in similar low-resource settings elsewhere.

Unrecognised Outbreak: Human parainfluenza virus infections in a pediatric oncology unit. A new diagnostic PCR and virus monitoring system may allow early detection of future outbreaks.

Background: Human parainfluenza viruses (HPIVs) are significant causes of both upper and lower respiratory tract infections with type 3 (HPIV3) causing the most severe disease in the immunocompromised cohorts. The objective of this study was to analyse the epidemiological nature of a cluster of cases of HPIV3 in a pediatric oncology unit of a major teaching hospital. Methods: In order to determine whether the activity observed represented a deviation from the norm, seasonal trends of HPIV3 in the surrounding geographical area as well as on the ward in question were analysed. The genetic link between cases was established by the phylogenetic analysis of the non-coding hypervariable region between the M (Matrix) and F (fusion) genes of HPIV3. The 15 cases involved and 15 unrelated cases were sequenced. Transmission routes were subsequently inferred and visualized using Konstanz Information Miner (KNIME) 3.3.2. Results: Of the 15 cases identified, 14 were attributed to a point source outbreak. Two out of 14 outbreak cases were found to differ by a single mutation A182C. The outbreak strain was also seen in 1 out of 15 unrelated cases, indicating that it was introduced from the community. Transmission modeling was not able to link all the cases and establish a conclusive chain of transmission. No staff were tested during the outbreak period. No deaths occurred as a result of the outbreak. Conclusion: A point source outbreak of HPIV3 was recognized post factum on an oncology pediatric unit in a major teaching hospital. This raised concern about the possibility of a future more serious outbreak. Weaknesses in existing systems were identified and a new dedicated respiratory virus monitoring system introduced. Pediatric oncology units require sophisticated systems for early identification of potentially life-threatening viral outbreaks.

EDNA detection of corallivorous seastar (Acanthaster cf. solaris) outbreaks on the Great Barrier Reef using digital droplet PCR

Coral loss through consumption by corallivorous crown-of-thorns seastars (CoTS, Acanthaster spp.) is a major contributor to the coral reef crisis in the Indo-Pacific region. The fourth wave of Acanthaster cf. solaris outbreaks since the 1960s started around 2010 on Australia’s Great Barrier Reef. Ecological monitoring failed to detect early outbreak stages, thus preventing timely intervention. Here, we develop a digital droplet PCR (ddPCR)-based method to detect environmental DNA (eDNA) of CoTS in 2-l water samples that can be compared with abundances of the species recorded by divers along 200-m2 transects. Aquarium tests demonstrated that eDNA was readily detectable and increases proportional to the biomass of CoTS (R2 = 0.99, p < 0.0001). Adaptation from a quantitative PCR technique developed for CoTS larvae (Doyle et al. in Marine Biology 164:176, 2017) to ddPCR improved the limit of quantification (LOQ) by a factor of 45. During field verification on 11 reefs, CoTS eDNA was detectable on all reefs suffering outbreaks. In contrast, CoTS eDNA was absent from ‘post-outbreak’ reefs after populations collapsed and from ‘pre-outbreak’ reefs. In linear models, CoTS densities explained a high amount of variance of eDNA concentrations, both for water samples taken at the depth of transects (R2 = 0.60, p < 0.0001) and on the sea surface (R2 = 0.46, p = 0.0004). The proportion of samples above LOQ was also correlated with CoTS densities, with a similar amount of variance explained as for the concentration (underwater R2 = 0.68, p < 0.0001; surface R2 = 0.49, p = 0.0004). We conclude that, after consideration of sampling locations and times, this method is promising for CoTS population monitoring and early detection of outbreaks and might supplement or replace traditional monitoring. Development of automated samplers and possibly on board PCR in the future will further improve early detection.

Evaluating a 24-h mobile reporting system for malaria notifications in comparison with a paper-based system in South Africa, 2015

BackgroundAs South Africa strives to achieve malaria elimination by 2018 (zero local cases) the country needs to strengthen its disease surveillance system by reducing the timeliness from case diagnosis to notification of key stakeholders in the malaria programme. This study evaluated the feasibility of a 24-h mobile reporting system, designed for speeding up malaria notifications, from primary healthcare facilities to district, provincial, and national malaria programmes in South Africa.MethodsA prospective descriptive study utilizing primary data collected from structured interviews with healthcare workers in public healthcare facilities was used to compare two reporting systems (24-h mobile reporting system and the paper-based reporting system) in malaria endemic provinces (Limpopo, Mpumalanga and KwaZulu-Natal). Data on completeness of reporting, simplicity, user acceptability and technical limitations were analysed. A Wilcoxon signed-rank test was used to compare the time difference between the two reporting systems.ResultsThere were 1819 cases of malaria reported through the paper-based system, and 63.2% (1149) of those cases were also reported through the 24-h mobile reporting system. Out of the 272 healthcare workers who were interviewed, 40% (108) had seen malaria patients and reported a case through the 24-h mobile reporting system. The median time for cases to be reported through the 24-h mobile reporting system was significantly shorter at < 1 day (range < 1 to 31 days) compared to the paper-based system at 3 days (range 2 to > 39 days) (p < 0.001). It was found that 26% (28) were able to use the system and send reports within 2 min, 94% (256) were willing to continue to use the system. Of the 108 healthcare workers who reported a case, 18.5% (20) experienced network challenges.ConclusionsThe 24-h mobile reporting system is user friendly and trained healthcare workers are willing to use the system, despite network limitations. The 24-h mobile reporting system reduces the time required for diagnosed cases to be notified by the health care facility to district, provincial and national levels. The 24-h mobile reporting system is a feasible option for malaria notification in South Africa and will assist with early detection of malaria outbreaks.

Cloud Computing for Infectious Disease Surveillance and Control: Development and Evaluation of a Hospital Automated Laboratory Reporting System

BackgroundOutbreaks of several serious infectious diseases have occurred in recent years. In response, to mitigate public health risks, countries worldwide have dedicated efforts to establish an information system for effective disease monitoring, risk assessment, and early warning management for international disease outbreaks. A cloud computing framework can effectively provide the required hardware resources and information access and exchange to conveniently connect information related to infectious diseases and develop a cross-system surveillance and control system for infectious diseases.ObjectiveThe objective of our study was to develop a Hospital Automated Laboratory Reporting (HALR) system based on such a framework and evaluate its effectiveness.MethodsWe collected data for 6 months and analyzed the cases reported within this period by the HALR and the Web-based Notifiable Disease Reporting (WebNDR) systems. Furthermore, system evaluation indicators were gathered, including those evaluating sensitivity and specificity.ResultsThe HALR system reported 15 pathogens and 5174 cases, and the WebNDR system reported 34 cases. In a comparison of the two systems, sensitivity was 100% and specificity varied according to the reported pathogens. In particular, the specificity for Streptococcus pneumoniae, Mycobacterium tuberculosis complex, and hepatitis C virus were 99.8%, 96.6%, and 97.4%, respectively. However, the specificity for influenza virus and hepatitis B virus were only 79.9% and 47.1%, respectively. After the reported data were integrated with patients’ diagnostic results in their electronic medical records (EMRs), the specificity for influenza virus and hepatitis B virus increased to 89.2% and 99.1%, respectively.ConclusionsThe HALR system can provide early reporting of specified pathogens according to test results, allowing for early detection of outbreaks and providing trends in infectious disease data. The results of this study show that the sensitivity and specificity of early disease detection can be increased by integrating the reported data in the HALR system with the cases’ clinical information (eg, diagnostic results) in EMRs, thereby enhancing the control and prevention of infectious diseases.

Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology.

BackgroundHealth care and public health professionals rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Early detection of outbreaks is improved by systems that are comprehensive and specific with respect to the pathogen but are rapid in reporting the data. It has proven difficult to implement these requirements on a large scale while maintaining patient privacy.ObjectiveThe aim of this study was to demonstrate the automated export, aggregation, and analysis of infectious disease diagnostic test results from clinical laboratories across the United States in a manner that protects patient confidentiality. We hypothesized that such a system could aid in monitoring the seasonal occurrence of respiratory pathogens and may have advantages with regard to scope and ease of reporting compared with existing surveillance systems.MethodsWe describe a system, BioFire Syndromic Trends, for rapid disease reporting that is syndrome-based but pathogen-specific. Deidentified patient test results from the BioFire FilmArray multiplex molecular diagnostic system are sent directly to a cloud database. Summaries of these data are displayed in near real time on the Syndromic Trends public website. We studied this dataset for the prevalence, seasonality, and coinfections of the 20 respiratory pathogens detected in over 362,000 patient samples acquired as a standard-of-care testing over the last 4 years from 20 clinical laboratories in the United States.ResultsThe majority of pathogens show influenza-like seasonality, rhinovirus has fall and spring peaks, and adenovirus and the bacterial pathogens show constant detection over the year. The dataset can also be considered in an ecological framework; the viruses and bacteria detected by this test are parasites of a host (the human patient). Interestingly, the rate of pathogen codetections, on average 7.94% (28,741/362,101), matches predictions based on the relative abundance of organisms present.ConclusionsSyndromic Trends preserves patient privacy by removing or obfuscating patient identifiers while still collecting much useful information about the bacterial and viral pathogens that they harbor. Test results are uploaded to the database within a few hours of completion compared with delays of up to 10 days for other diagnostic-based reporting systems. This work shows that the barriers to establishing epidemiology systems are no longer scientific and technical but rather administrative, involving questions of patient privacy and data ownership. We have demonstrated here that these barriers can be overcome. This first look at the resulting data stream suggests that Syndromic Trends will be able to provide high-resolution analysis of circulating respiratory pathogens and may aid in the detection of new outbreaks.