scholarly journals NBS: A Community-Based Approach to Developing an Integrated Surveillance System

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Christi Hildebrandt ◽  
Jennifer Ward ◽  
Akshar Patel
Keyword(s):  
Public Health ◽  
Data Collection ◽  
Best Practices ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Data Exchange ◽  
Inclusive Development ◽  
Base System ◽  
Open Communication ◽  
Community Based

ObjectiveThe NEDSS Base System (NBS) is designed and developed usinginput from CDC programs, public health standards organizations,as well as its expansive user community. This community-basedapproach to development of an integrated surveillance system isdescribed.IntroductionThe NEDSS Base System (NBS) is a web-based, standards-driven, integrated disease surveillance system launched in 2001 andis currently in use in twenty-two public health jurisdictions. Over thepast fifteen years, the NBS has grown into a highly functional, modernapplication that supports: case management, electronic data exchange,metadata-driven data collection, workflow decision support, and ahost of other functionalities, all of which are defined and designedthrough a community-based approach.MethodsIn order to encourage open communication and collaborationacross and among the community, there is a well-publicized, long-standing communication plan in place. Further, tools such as an onlinecollaboration and support forum, NBSCentral, are made availableto any person who requests access. Also, the NBS source code isprovided in an open source package to anyone interested, alongwith each release, and a demonstration version of the applicationcan be accessed online by anyone to review the latest release of theapplication. All of these channels are in place to ensure there are waysfor all who have in interest in collaborating to easily participate.The NBS community regularly meets to provide input into furtherdevelopment of the system, as well as discuss topics affecting publichealth. As a community, members:■ Share best practices, tools, and lessons learned across jurisdictions■ Share innovative local approaches to disease surveillance andreporting■ Access NBSCentral for support and collaboration■ Participate in the change control and planning process for eachNBS release■ Work collaboratively with CDC to define high-level vision andpriorities■ Provide input to create community-defined requirements forsystem development■ Participate in weekly subject matter expert (SME) calls to discussdevelopment and best practices■ Have the opportunity to participate in beta testing for releases■ Attend a bi-weekly NBS User Group (NUG) call to discussthe system as well as reach out to colleagues to brainstorm creativesolutions to common problems in public health surveillanceAll meetings with stakeholders are recorded and shared withthe larger community to ensure full transparency and for historicalreference.ResultsThrough this inclusive development approach, the NBS hasevolved into a highly extensible, configurable system that can meetthat needs of twenty-two very different public health jurisdictions; thesystem can be implemented without the need for custom developmentin a relatively short timeframe due to the fact that it was designed tomeet the needs of many. Further, it has encouraged interoperabilityprojects, such as: piloting electronic case reporting use cases betweenNBS implementation sites and building and sharing electronic caseinvestigation forms for data collection using the NBS Page Buildermodule. All NBS sites use the same translation routes for electroniclab report, case report, and Nationally Notifiable Disease messageprocessing – embracing the build once, use many concept. Mostrecently, having this collaboration network in place made it very easyfor the NBS community to quickly adapt to the changing needs ofZika virus surveillance.ConclusionsIt does require clear definition of processes and communicationchannels, as well as regular update and transparency into the processfor community-based development to work. However, when theproper tools and processes are in place, the benefits of collaborationwith all key stakeholders are exponential when realized. Developingan application in this way has provided NBS users not only with amuch better, integrated surveillance system, but also a forum forunderstanding how other jurisdictions have solved similar issues; itprovides a springboard for sharing and building upon novel ideas andnew approaches in public health surveillance.

scholarly journals Building Electronic Disease Surveillance Capacity in the Peruvian Navy with SAGES

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Shraddha Patel ◽  
Miles Stewart ◽  
Martina Siwek
Keyword(s):  
Public Health ◽  
Data Collection ◽  
Open Source ◽  
Open Source Software ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Public Health Surveillance ◽  
Health Surveillance ◽  
Resource Limited ◽  
Reporting Rates

ObjectiveTo introduce SMS-based data collection into the Peruvian Navy’s public health surveillance system for increased reporting rates and timeliness, particularly from remote areas, as well as improve capabilities for analysis of surveillance data by decision makers.IntroductionIn the past 15 years, public health surveillance has undergone a revolution driven by advances in information technology (IT) with vast improvements in the collection, analysis, visualization, and reporting of health data. Mobile technologies and open source software have played a key role in advancing surveillance techniques, particularly in resource-limited settings. Johns Hopkins University Applied Physics Laboratory (JHU/APL) is an internationally recognized leader in the area of electronic disease surveillance. In addition to the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) used by several state and local jurisdictions and the CDC in the U.S., JHU/APL has also developed the Suite for Automated Global Electronic bioSurveillance (SAGES). SAGES is a collection of modular, open-source software tools designed to meet the challenges of electronic disease surveillance in resource-limited settings.JHU/APL is working with the Peruvian Navy health system to improve their electronic disease surveillance capabilities. The Peruvian Navy currently uses a SAGES-based system called Alerta DISAMAR that was implemented several years ago in an effort supported by the Armed Forces Health Surveillance Branch, and in collaboration with the Naval Medical Research Unit No. 6 (NAMRU-6). The system uses both web-based and IVR-based (interactive voice response) data collection from several Navy health facilities in Peru. For the present effort, JHU/APL is implementing a new SMS-based data collection capability for the Peruvian Navy.MethodsJHU/APL is engaged with the Peruvian Navy Health System to upgrade the existing SAGES-based Alerta DISAMAR surveillance system which relies on remote data collection using IVR (interactive voice recording) technology, with a SAGES-based system that uses SMS (short message service) text messages for remote data collection. Based on Peruvian Navy requirements, JHU/APL created mobile data entry forms for Android smartphones using the SAGES mCollect application. SAGES mCollect is built using Open Data Kit open source tools along with added features such as 128-bit encryption and quality checks.The JHU/APL team engages closely with end users and other stakeholders to determine system requirements and to deploy the system, as well as to train end users and the system administrators who will need to maintain the system once it is deployed. The JHU/APL team, consisting of both information technology and public health expertise, conduct a country-level capabilities and needs assessment to address design considerations and operational end user requirements. This assessment takes into account the requirements and objectives of the Peruvian Navy, while keeping in mind infrastructure, cost, and personnel constraints. A pilot test of SMS-based data collection is currently underway with 10 health clinics within the Navy.ResultsMany challenges exist when implementing electronic disease surveillance tools in resource-limited settings, but using a tailored approach to implementation in which specific needs, constraints, and expectations are identified with stakeholders helps increase the overall adoption and sustainment of the system. JHU/APL believes SMS-based data collection will be more sustainable than IVR-based data collection for the Peruvian Navy.ConclusionsJHU/APL is deploying a SAGES-based electronic disease surveillance system for the Peruvian Navy that has great potential to increase reporting rates from its health facilities as well as improve data quality and timeliness, thus resulting in greater awareness and enhanced public health decision making. 

scholarly journals NEDSS Base System (NBS): Electronic Data Exchange and Workflow Decision Support

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Jennifer Ward ◽  
Christi Hildebrandt ◽  
Akshar Patel
Keyword(s):  
Public Health ◽  
Case Report ◽  
Data Integration ◽  
Surveillance System ◽  
Data Exchange ◽  
Case Reports ◽  
Use Cases ◽  
High Morbidity ◽  
Base System ◽  
Surveillance Systems

ObjectiveThe NEDSS Base System (NBS), an integrated disease surveillancesystem, implemented extensible functionality to support electronicdata exchange for multiple use cases and public health workflowmanagement of incoming messages and documents.IntroductionThe NBS is an integrated disease surveillance system deployedin 22 public health jurisdictions to support receipt, investigation,analysis and reporting, and data exchange for state reportableconditions. The NBS is governed by the Centers for Disease Controland Prevention (CDC) and state, local, and territorial users that makeup the NBS Community. In the early 2000’s, electronic laboratoryresults reporting (ELR) was implemented in an effort to improvetimeliness and completeness of disease reporting. As standards-basedelectronic health records (EHRs) are adopted and more surveillancedata become available, modern surveillance systems must consumeinformation in an automated way and provide more functionality toautomate key surveillance processes.MethodsMany use cases exist for exchanging data with an integrated publichealth surveillance system. These can include exchange of electroniccase and laboratory reports from healthcare, data sharing betweenpublic health entities, data migration from legacy systems, andongoing exchange with other public health systems (e.g. immunizationregistries). The NBS implemented an interface specification called thePublic Health Document Container (PHDC). PHDC is based on HL7version 3 Clinical Document Architecture (CDA). It allows import ofpatient (cases and contacts), investigation, treatment, interview, andlaboratory information into NBS. CDA was chosen as the buildingblock to facilitate data exchange with the healthcare community.Through use of data integration tools, incoming data can be mappedfrom any format to PHDC and imported into the system. Existingservices, such as patient, provider, and organization deduplicationare applied. To assist with management of incoming electronicdocuments, NBS implemented a functionality called WorkflowDecision Support (WDS). WDS uses configurable algorithms toautomatically process incoming documents (including case reports,laboratory reports, etc.) into the public health workflow. Users canchoose to mark an incoming document as reviewed or automaticallycreate an investigation and case notification message to CDC (fornationally notifiable conditions).ResultsThrough PHDC, NBS is able to receive data from healthcare usingnational standards, such as the HL7 Electronic Initial Case Report(eICR). Three NBS partners are currently collaborating to pilot eICRfunctionality. PHDC was successfully used to migrate large volumesof data from a legacy surveillance system into the NBS. Two NBSstates are using PHDC to implement ongoing data exchange betweenseparate surveillance systems within their jurisdiction. In several NBSjurisdictions, WDS is used to automatically create investigations andcase notifications for high-morbidity conditions such as gonorrheaand chlamydia. In other jurisdictions, WDS is used to assist withmanaging high volumes of Hepatitis B and C reports.ConclusionsCDA-based PHDC does require that public health have knowledgeof standards and data integration resources to transform incomingmessages to the PHDC interface; however, the flexibility providedby this approach ensures the system is able to respond to newand changing standards without system development. Additionalenhancements are needed to support data exchange with immunizationregistries. WDS functionality does reduce burden on public healthstaff, especially when dealing with high-volume diseases. Futurefunctionalities include the ability to define more criteria (such as ageor gender) to drive the actions taken on an incoming lab or case report.

scholarly journals Use of Peripheral Health Units in Low-Transmission Ebola Virus Disease Surveillance

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Alyssa J. Young ◽  
Allison Connolly ◽  
Adam Hoar ◽  
Brooke Mancuso ◽  
John Mark Esplana ◽  
...  
Keyword(s):  
Sierra Leone ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Ebola Virus ◽  
Virus Disease ◽  
Malaria Prevalence ◽  
Ebola Virus Disease ◽  
Alert System ◽  
Community Based

Surveillance strategies for Ebola Virus Disease (EVD) in Sierra Leone use a centralized "live alert" system to refer suspect cases from the community to specialized Ebola treatment centers. As EVD case burden declined in Port Loko District, Sierra Leone so did the number of reported alerts. Because EVD presents similarly to malaria, the number of alerts should remain consistent with malaria prevalence in malaria-endemic areas, irrespective of the reduction in true EVD cases. A community-based EVD surveillance system with improved symptom recording and follow-up of malaria-confirmed patients at PHUs was implemented in order to strengthen the sensitivity of EVD reporting.

scholarly journals Use of Severity Indicators in a Public Health Surveillance System

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Rhonda A. Lizewski ◽  
Howard Burkom ◽  
Joseph Lombardo ◽  
Christopher Cuellar ◽  
Yevgeniy Elbert ◽  
...  
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Public Health Surveillance ◽  
Additional Data ◽  
Outpatient Setting ◽  
Health Surveillance ◽  
Surveillance Systems ◽  
Fusion Model ◽  
Community Based ◽  
Public Health Surveillance System

While other surveillance systems may only use death and admissions as severity indicators, these serious events may overshadow the more subtle severity signals based on appointment type, disposition from an outpatient setting, and whether that patient had to return for care if they their condition has not improved.  This abstract discusses how these additional data fields were utilized in a fusion model to improve the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE).

scholarly journals Cholera Public Health Surveillance System in Cameroon

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Moise C. Ngwa ◽  
Song Liang ◽  
Leonard Mbam ◽  
Mouhaman Arabi ◽  
Andrew Teboh ◽  
...  
Keyword(s):  
Public Health ◽  
Data Analysis ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Public Health Surveillance ◽  
National Level ◽  
Health Surveillance ◽  
Rapid Response ◽  
Surveillance And Response ◽  
Public Health Surveillance System

Public health surveillance is essential for early detection and rapid response to cholera outbreaks. In 2003, Cameroon adopted the integrated disease surveillance and response (IDSR) strategy. We describe cholera surveillance within IDSR-strategy in Cameroon. Data is captured at health facility, forwarded to health district that compiles and directs data to RDPH in paper format. RDPH sends the data to the national level via internet and from there to the WHO. The surveillance system is passive with no data analysis at districts. Thus the goal of IDSR-strategy of data analysis and rapid response at the district has not been met yet.

scholarly journals Precision public health through clinic-based syndromic surveillance in communities

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Ta-Chien Chan ◽  
Yung-Chu Teng ◽  
Yen-Hua Chu ◽  
Tzu-Yu Lin
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Early Stage ◽  
Age Distribution ◽  
Local Health ◽  
Syndromic Surveillance System ◽  
Taipei City ◽  
Kaohsiung City

ObjectiveSentinel physician surveillance in the communities has played an important role in detecting early aberrations in epidemics. The traditional approach is to ask primary care physicians to actively report some diseases such as influenza-like illness (ILI), and hand, foot, and mouth disease (HFMD) to health authorities on a weekly basis. However, this is labor-intensive and time-consuming work. In this study, we try to set up an automatic sentinel surveillance system to detect 23 syndromic groups in the communites.IntroductionIn December 2009, Taiwan’s CDC stopped its sentinel physician surveillance system. Currently, infectious disease surveillance systems in Taiwan rely on not only the national notifiable disease surveillance system but also real-time outbreak and disease surveillance (RODS) from emergency rooms, and the outpatient and hospitalization surveillance system from National Health Insurance data. However, the timeliness of data exchange and the number of monitored syndromic groups are limited. The spatial resolution of monitoring units is also too coarse, at the city level. Those systems can capture the epidemic situation at the nationwide level, but have difficulty reflecting the real epidemic situation in communities in a timely manner. Based on past epidemic experience, daily and small area surveillance can detect early aberrations. In addition, emerging infectious diseases do not have typical symptoms at the early stage of an epidemic. Traditional disease-based reporting systems cannot capture this kind of signal. Therefore, we have set up a clinic-based surveillance system to monitor 23 kinds of syndromic groups. Through longitudinal surveillance and sensitive statistical models, the system can automatically remind medical practitioners of the epidemic situation of different syndromic groups, and will help them remain vigilant to susceptible patients. Local health departments can take action based on aberrations to prevent an epidemic from getting worse and to reduce the severity of the infected cases.MethodsWe collected data on 23 syndromic groups from participating clinics in Taipei City (in northern Taiwan) and Kaohsiung City (in southern Taiwan). The definitions of 21 of those syndromic groups with ICD-10 diagnoses were adopted from the International Society for Disease Surveillance (https://www.surveillancerepository.org/icd-10-cm-master-mapping-reference-table). The definitions of the other two syndromic groups, including dengue-like illness and enterovirus-like illness, were suggested by infectious disease and emergency medicine specialists.An enhanced sentinel surveillance system named “Sentinel plus” was designed for sentinel clinics and community hospitals. The system was designed with an interactive interface and statistical models for aberration detection. The data will be computed for different combinations of syndromic groups, age groups and gender groups. Every day, each participating clinic will automatically upload the data to the provider of the health information system (HIS) and then the data will be transferred to the research team.This study was approved by the committee of the Institutional Review Board (IRB) at Academia Sinica (AS-IRB02-106262, and AS-IRB02-107139). The databases we used were all stripped of identifying information and thus informed consent of participants was not required.ResultsThis system started to recruit the clinics in May 2018. As of August 2018, there are 89 clinics in Kaohsiung City and 33 clinics and seven community hospitals in Taipei City participating in Sentinel plus. The recruiting process is still ongoing. On average, the monitored volumes of outpatient visits in Kaohsiung City and Taipei City are 5,000 and 14,000 per day.Each clinic is provided one list informing them of the relative importance of syndromic groups, the age distribution of each syndromic group and a time-series chart of outpatient rates at their own clinic. In addition, they can also view the village-level risk map, with different alert colors. In this way, medical practitioners can know what’s going on, not only in their own clinics and communities but also in the surrounding communities.The Department of Health (Figure 1) can know the current increasing and decreasing trends of 23 syndromic groups by red and blue color, respectively. The spatial resolution has four levels including city, township, village and clinic. The map and bar chart represent the difference in outpatient rate between yesterday and the average for the past week. The line chart represents the daily outpatient rates for one selected syndromic group in the past seven days. The age distribution of each syndromic group and age-specific outpatient rates in different syndromic groups can be examined.ConclusionsSentinel plus is still at the early stage of development. The timeliness and the accuracy of the system will be evaluated by comparing with some syndromic groups in emergency rooms and the national notifiable disease surveillance system. The system is designed to assist with surveillance of not only infectious diseases but also some chronic diseases such as asthma. Integrating with external environmental data, Sentinel plus can alert public health workers to implement better intervention for the right population.References1. James W. Buehler AS, Marc Paladini, Paula Soper, Farzad Mostashari: Syndromic Surveillance Practice in the United States: Findings from a Survey of State, Territorial, and Selected Local Health Departments. Advances in Disease Surveillance 2008, 6(3).2. Ding Y, Fei Y, Xu B, Yang J, Yan W, Diwan VK, Sauerborn R, Dong H: Measuring costs of data collection at village clinics by village doctors for a syndromic surveillance system — a cross sectional survey from China. BMC Health Services Research 2015, 15:287.3. Kao JH, Chen CD, Tiger Li ZR, Chan TC, Tung TH, Chu YH, Cheng HY, Liu JW, Shih FY, Shu PY et al.: The Critical Role of Early Dengue Surveillance and Limitations of Clinical Reporting -- Implications for Non-Endemic Countries. PloS one 2016, 11(8):e0160230.4. Chan TC, Hu TH, Hwang JS: Daily forecast of dengue fever incidents for urban villages in a city. International Journal of Health Geographics 2015, 14:9.5. Chan TC, Teng YC, Hwang JS: Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models. BMC Public Health 2015, 15:168.6. Ma HT: Syndromic surveillance system for detecting enterovirus outbreaks evaluation and applications in public health. Taipei, Taiwan: National Taiwan University; 2007. 

Bioterrorism

2021 ◽  
pp. 287-296
Author(s):  
Peter Katona
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Global Economy ◽  
Large Scale ◽  
Severe Disease ◽  
Public Health Response ◽  
Health Response ◽  
Bioterrorism Agent ◽  
Public Health Surveillance System

History shows us that individuals have used and likely will continue to use biological agents for terrorism purposes. Bioterrorism agents can be easily disseminated, cause severe disease and high mortality rates if cases are not treated properly, and pose significant challenges for management and response. A robust public health surveillance system that includes laboratory (including routine reportable disease surveillance), syndromic, and environmental surveillance is crucial for detection of the release of a bioterrorism agent and the resulting cases. This detection can then set into motion a robust and comprehensive public health response to minimize morbidity and mortality. A large-scale bioterrorism event would be unprecedented, straining and challenging every facet of medical and public health response and would quickly become a global emergency because of both the potential risk of infection and the shock to the global economy. A robust public health and medical workforce is necessary to respond effectively and efficiently to these types of events.

scholarly journals Evaluation of community-based disease surveillance systems for priority disease detection in Nairobi County Kenya

2021 ◽  
Vol 8 (10) ◽  
pp. 4692
Author(s):  
Annastacia Katuvee Muange ◽  
John Kariuki ◽  
James Mwitari
Keyword(s):  
Community Involvement ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Cross Sectional Study ◽  
Health Facilities ◽  
Surveillance Systems ◽  
Case Definitions ◽  
Community Based ◽  
Study Program ◽  
Cross Sectional

Background: Community based disease surveillance (CBDS) may be defined as an active process of community involvement in identification, reporting, responding to and monitoring diseases and public health events of concern in the community. The scope of CBS is limited to systematic continuous collection of health data on events and diseases guided by simplified lay case definitions and reporting to health facilities for verification, investigation, collation, analysis and response as necessary.Methods: A cross sectional study design, interventions study program was adopted to determine the effectiveness of CBDS in detecting of priority diseases. Purposive and random sampling methods was employed to select the respondents.Results: The results of the study assisted the Ministry of health to understand the effectiveness of Community based surveillance in detection of priority diseases and hence strengthen the community-based surveillance initiative. From the findings, the integrated disease surveillance data for five years from 2014-2018 shows, more cases of priority diseases reported in health facilities linked to a community unit trained on CBDS. Cholera (9/5), Malaria (4757/2789), Neonatal tetanus (27/3) respectively.Conclusions: The study concluded that, use of community-based surveillance system, improves detection of the notifiable diseases in the community. The study revealed that there is a gap on training of community-based disease surveillance system and therefore there is need for continuous refresher trainings on CBDS to the CHVs and CHAs to accommodate also the newly recruited.

scholarly journals Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering (Preprint)

2020 ◽  
Author(s):  
Mehnaz Adnan ◽  
Xiaoying Gao ◽  
Xiaohan Bai ◽  
Elizabeth Newbern ◽  
Jill Sherwood ◽  
...  
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Case Reports ◽  
Surveillance Data ◽  
Data Sources ◽  
Google Trends ◽  
School Absenteeism ◽  
Disease Surveillance System ◽  
Spatiotemporal Clustering

BACKGROUND Over one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time. OBJECTIVE The first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source. METHODS We combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran’s I statistics to investigate the extent of the outbreak in both space and time within the affected area. RESULTS Models that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak. CONCLUSIONS Alternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice.

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