Advances in Public Health Surveillance and Information Dissemination at the Centers for Disease Control and Prevention

Richards, Chesley L.; Iademarco, Michael F.; Atkinson, Delton; Pinner, Robert W.; Yoon, Paula W.; Kenzie, William R. Mac; Lee, Brian; Qualters, Judith R.; Frieden, Thomas R.

doi:10.1177/0033354917709542

Cited by 65 publications

(55 citation statements)

References 20 publications

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“…Input from hospital-, county-, and citylevel project staff was critical for the development of the project dashboard and also helped strengthen relationships across the various jurisdictions. 11,[13][14][15] Project staff could monitor the AFI surveillance project as soon as case data and laboratory results were entered into the web-based database. Because raw TAC results were forwarded to the University of Virginia for review and verification, and to avoid additional data entry work by project staff at Guangdong CDC or YIPD, China CDC uploaded the TAC results directly into the AFI database.…”

Section: Real-time Visualization Of Datamentioning

confidence: 99%

“…11,14 This decision also helped to strengthen trust between project partners, which is essential for all effective public health surveillance projects. 12,15 According to project staff, the EDDC-AFI surveillance system was easy to use, flexible (could be modified to address changes in reporting requirements, eg, adding fields for JE and YF vaccination), included a feedback mechanism, safeguarded patient confidentiality, and ensured local data ownership. These characteristics likely strengthened local ''buy-in'' and probably improved data quality and should be considered when designing and deploying new surveillance systems.…”

Section: Data Ownership and Local Engagementmentioning

confidence: 99%

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Lessons Learned in the Development of a Web-based Surveillance Reporting System and Dashboard to Monitor Acute Febrile Illnesses in Guangdong and Yunnan Provinces, China, 2017-2019

et al. 2020

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Global spread of Zika virus in 2015 and 2016 highlighted the importance of surveillance to rapidly detect, report, and respond to emerging infections. We describe the lessons learned from the development and deployment of a web-based surveillance reporting system for Zika virus and other acute febrile illnesses (AFI) in Guangdong and Yunnan provinces, China. In less than 2 months, we customized the China Epidemiologic Dynamic Data Collection (EDDC) platform to collect, manage, and visualize data in close to real time. According to provincial and sentinel hospital staff requirements, the customized platform provided specific user authorization management, online/offline data collection, data quality control, and secure data transmission and protection and visualization tools. AFI case data and laboratory test results were entered through a web-based interface by hospital and provincial-level staff and saved on a China CDC server in Beijing. The dashboard visualized aggregate data by hospital, age, onset date, and laboratory test results. From June 2017 to December 2018, data from 768 patients with AFI were entered into the web-based surveillance system and visualized by hospital and provincial-level decision makers. Input from provincial and hospital staff was essential for developing the AFI case-reporting and feedback tools appropriate for specific settings and decision-making requirements. Web-based systems (eg, EDDC, data collection, and visualization tools that can be easily adapted to meet local surveillance needs) can help shorten time for system deployment, thereby strengthening global health security to rapidly detect and respond to emerging infections and outbreaks.

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Section: Real-time Visualization Of Datamentioning

confidence: 99%

Section: Data Ownership and Local Engagementmentioning

confidence: 99%

Lessons Learned in the Development of a Web-based Surveillance Reporting System and Dashboard to Monitor Acute Febrile Illnesses in Guangdong and Yunnan Provinces, China, 2017-2019

et al. 2020

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“…Proses yang saling berhubungan inilah yang menjadikan dasar alasan pengembangan basis data surveilans OMKABA harus dilakukan. Hasil interpretasi surveilans terintegrasi dengan penyebaran data terhadap praktisi kesehatan masyarakat, dokter, dan pembuat kebijakan yang bertanggung jawab untuk mencegah dan mengendalikan penyakit atau cidera (Richards et al, 2017).…”

Section: Pembahasanunclassified

Development of Drug and Food Surveillance Databases at Port Health Office

Putri

Hargono

2019

JBE

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Background: Recently the data of drug, food, cosmetics, medical devices, and addictive materials surveillance use a simple database system. However, this sample has major weakness in entry data processing which in turn causing a lot of errors and delayed data report. Purpose: This study aims to develop drug, food, cosmetics, medical devices, and addictive materials surveillance database to be more accurate, fast, and generate more information. Method: This study is an observational descriptive study. This research was conducted in January-February 2018 at the port health office class 1 of Surabaya. The population used in this study 6 officers of the port health office. The type of data used is primary data by conducting in-depth interviews. This research was conducted in two steps, namely logic design (data normalization, relationship design with entity relationship diagrams, and dictionary creation) and physical planning (the database design using Epi info for Windows software). The database testing was carried out by two officers in the field of quarantine control and epidemiology surveillance, to assess the ease, accuracy, timeliness, and completeness of the information produced. Results: The results showed that the trial process makes the officers easier to input data. This trial also simplifies and shortens the time of the surveillance officer in conducting data analysis. This condition promotes the report qualities (accurate, timeliness, and complete information). Conclusion: The use of epi info software can reduce the risk of data entry errors, be more accurate, faster, and the results of the analysis produced are more varied.

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“…Public Health Surveillance (PHS) is defined as the ongoing systematic gathering, analysis, and interpretation of data, closely integrated with the dissemination of these data to the public health practitioners, clinicians, and policy makers responsible for preventing and controlling disease and injury [1]. In the last decade, many experiments have demonstrated that social media data can help public health officials to detect potential outbreaks, forecast disease trends, monitor emergency situations and gauge disease awareness and reactions to official health communications [2,3].…”

Section: Introductionmentioning

confidence: 99%

Social Media Multidimensional Analysis for Intelligent Health Surveillance

Aramburu

Berlanga²,

Lanza³

2020

IJERPH

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Background: Recent work in social network analysis has shown the usefulness of analysing and predicting outcomes from user-generated data in the context of Public Health Surveillance (PHS). Most of the proposals have focused on dealing with static datasets gathered from social networks, which are processed and mined off-line. However, little work has been done on providing a general framework to analyse the highly dynamic data of social networks from a multidimensional perspective. In this paper, we claim that such a framework is crucial for including social data in PHS systems. Methods: We propose a dynamic multidimensional approach to deal with social data streams. In this approach, dynamic dimensions are continuously updated by applying unsupervised text mining methods. More specifically, we analyse the semantics and temporal patterns in posts for identifying relevant events, topics and users. We also define quality metrics to detect relevant user profiles. In this way, the incoming data can be further filtered to cope with the goals of PHS systems. Results: We have evaluated our approach over a long-term stream of Twitter. We show how the proposed quality metrics allow us to filter out the users that are out-of-domain as well as those with low quality in their messages. We also explain how specific user profiles can be identified through their descriptions. Finally, we illustrate how the proposed multidimensional model can be used to identify main events and topics, as well as to analyse their audience and impact. Conclusions: The results show that the proposed dynamic multidimensional model is able to identify relevant events and topics and analyse them from different perspectives, which is especially useful for PHS systems.

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Advances in Public Health Surveillance and Information Dissemination at the Centers for Disease Control and Prevention

Cited by 65 publications

References 20 publications

Lessons Learned in the Development of a Web-based Surveillance Reporting System and Dashboard to Monitor Acute Febrile Illnesses in Guangdong and Yunnan Provinces, China, 2017-2019

Lessons Learned in the Development of a Web-based Surveillance Reporting System and Dashboard to Monitor Acute Febrile Illnesses in Guangdong and Yunnan Provinces, China, 2017-2019

Development of Drug and Food Surveillance Databases at Port Health Office

Social Media Multidimensional Analysis for Intelligent Health Surveillance

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