Objective The 2019 novel coronavirus disease (COVID-19) outbreak progressed rapidly from a public health (PH) emergency of international concern (World Health Organization [WHO], 30 January 2020) to a pandemic (WHO, 11 March 2020). The declaration of a national emergency in the United States (13 March 2020) necessitated the addition and modification of terminology related to COVID-19 and development of the disease’s case definition. During this period, the Centers for Disease Control and Prevention (CDC) and standard development organizations released guidance on data standards for reporting COVID-19 clinical encounters, laboratory results, cause-of-death certifications, and other surveillance processes for COVID-19 PH emergency operations. The CDC COVID-19 Information Management Repository was created to address the need for PH and health-care stakeholders at local and national levels to easily obtain access to comprehensive and up-to-date information management resources. Materials and Methods We introduce the clinical and health-care informatics community to the CDC COVID-19 Information Management Repository: a new, national COVID-19 information management tool. We provide a description of COVID-19 informatics resources, including data requirements for COVID-19 data reporting. Results We demonstrate the CDC COVID-19 Information Management Repository’s categorization and management of critical COVID-19 informatics documentation and standards. We also describe COVID-19 data exchange standards, forms, and specifications. Conclusions This information will be valuable to clinical and PH informaticians, epidemiologists, data analysts, standards developers and implementers, and information technology managers involved in the development of COVID-19 situational awareness and response reporting and analytics.

ObjectiveThe objective of this analysis is to leverage recent advances innatural language processing (NLP) to develop new methods andsystem capabilities for processing social media (Twitter messages)for situational awareness (SA), syndromic surveillance (SS), andevent-based surveillance (EBS). Specifically, we evaluated the useof human-in-the-loop semantic analysis to assist public health (PH)SA stakeholders in SS and EBS using massive amounts of publiclyavailable social media data.IntroductionSocial media messages are often short, informal, and ungrammatical.They frequently involve text, images, audio, or video, which makesthe identification of useful information difficult. This complexityreduces the efficacy of standard information extraction techniques1.However, recent advances in NLP, especially methods tailoredto social media2, have shown promise in improving real-time PHsurveillance and emergency response3. Surveillance data derived fromsemantic analysis combined with traditional surveillance processeshas potential to improve event detection and characterization. TheCDC Office of Public Health Preparedness and Response (OPHPR),Division of Emergency Operations (DEO) and the Georgia TechResearch Institute have collaborated on the advancement of PH SAthrough development of new approaches in using semantic analysisfor social media.MethodsTo understand how computational methods may benefit SS andEBS, we studied an iterative refinement process, in which the datauser actively cultivated text-based topics (“semantic culling”) in asemi-automated SS process. This ‘human-in-the-loop’ process wascritical for creating accurate and efficient extraction functions in large,dynamic volumes of data. The general process involved identifyinga set of expert-supplied keywords, which were used to collect aninitial set of social media messages. For purposes of this analysisresearchers applied topic modeling to categorize related messages intoclusters. Topic modeling uses statistical techniques to semanticallycluster and automatically determine salient aggregations. A user thensemantically culled messages according to their PH relevance.In June 2016, researchers collected 7,489 worldwide English-language Twitter messages (tweets) and compared three samplingmethods: a baseline random sample (C1, n=2700), a keyword-basedsample (C2, n=2689), and one gathered after semantically cullingC2 topics of irrelevant messages (C3, n=2100). Researchers utilizeda software tool, Luminoso Compass4, to sample and perform topicmodeling using its real-time modeling and Twitter integrationfeatures. For C2 and C3, researchers sampled tweets that theLuminoso service matched to both clinical and layman definitions ofRash, Gastro-Intestinal syndromes5, and Zika-like symptoms. Laymanterms were derived from clinical definitions from plain languagemedical thesauri. ANOVA statistics were calculated using SPSSsoftware, version. Post-hoc pairwise comparisons were completedusing ANOVA Turkey’s honest significant difference (HSD) test.ResultsAn ANOVA was conducted, finding the following mean relevancevalues: 3% (+/- 0.01%), 24% (+/- 6.6%) and 27% (+/- 9.4%)respectively for C1, C2, and C3. Post-hoc pairwise comparison testsshowed the percentages of discovered messages related to the eventtweets using C2 and C3 methods were significantly higher than forthe C1 method (random sampling) (p<0.05). This indicates that thehuman-in-the-loop approach provides benefits in filtering socialmedia data for SS and ESB; notably, this increase is on the basis ofa single iteration of semantic culling; subsequent iterations could beexpected to increase the benefits.ConclusionsThis work demonstrates the benefits of incorporating non-traditional data sources into SS and EBS. It was shown that an NLP-based extraction method in combination with human-in-the-loopsemantic analysis may enhance the potential value of social media(Twitter) for SS and EBS. It also supports the claim that advancedanalytical tools for processing non-traditional SA, SS, and EBSsources, including social media, have the potential to enhance diseasedetection, risk assessment, and decision support, by reducing the timeit takes to identify public health events.

Objective:The purpose of this project is to demonstrate progress in developing functional data models and semantic definitions (content standards) for data elements and value sets comprising information categories supporting PH Emergency Preparedness and Response. (EPR) The objective is to explain the concepts and methods used to define core PH Emergency Management and Preparedness and Response functions, Information Exchange Requirements (IERs), data elements, and value sets to create a PH Emergency Operations Center (EOC) Minimum Data Set Specification. The primary focus of this presentation is to describe the value of semantic data interoperability and provide operational examples of the value and return-on-investment gained through building semantically interoperable data exchange through content standardization. Introduction:Effective prevention, detection, and rapid response to PH emergencies rely on sufficient and timely delivered information. PH EOC data flows are based on critical information requirements, addressing needs of EOC staff for timely delivered analytical products that provide situational awareness, event-specific data, event investigation tools, resource management etc1. The ability of PH EOC systems to automatically and accurately interpret meaning of the exchanged data depends on a level of semantic data interoperability and utilization of a common information exchange reference model (CIERF) that conforms to established data standards. PH EOC data interoperability requires mutual development and close collaboration with partners to develop a PH EPR CIERF, common terminology and standardized vocabulary.Methods: The CDC’s Situational Awareness Branch (SAB) facilitates national activities on development PH EOC informatics through participation in the WHO EOC Network (EOC-NET) 2, and collaboration with national organizations and CDC partners on content standardization. The following sources were used for this analysis: 1) 26 content standards developed by national and international standard development organization, 2) WHO’s Framework for a Public Health Emergency Operations Centre2, and 3) PH EOC data requirements3 that were published by CDC’s SAB . These data requirements were included into the CDC Vocabulary and Access Distribution System (VADS) 4, which serves as the primary vocabulary content browser for PH EPR informatics.Results: In analyzing the PH EPR content standards, the CDC’s SAB arrived at the following results. The CDC EOC’s process of development and implementation content standards is based on the PH EOC critical information requirements. These requirement became business rules for the PH EPR CIERF.The current, version 2, of the PH EPR CIERF consists of 12 information modules including PH EOC minimum data set (MDS), patient clinical observations, emergency medical systems (EMS), data elements for emergency departments (DEEDS), WHO MDS for Health Workforce Registry, Resource Utilization Message Component (vocabulary for hospital resources), vocabulary for the national trauma standard. These PH EPR CIERF modules are interoperable and built on existing data standards. These modules were codified by VADS and ready for utilization by international and national PH EOC partners. At the stage of this analysis the PH EPR CIERF codification schema was prepared for adding it into the Logical Observation Identifiers Names and Codes (LOINC) content standard.The current PH EOC MDS version was released in September 2017. The common terminology and vocabulary that were included into this version are conformant with existing national and international content standards and specifications. Comparatively to the previous version 1, the current PH EOC MDS contains more than 60% new and updated terminology and value sets.Added to the PH EOC MDS version 2 new features are the Situational Analysis concept model, that also incorporates a nomenclature and structure for the Public Health EOC Situational Report (SITREP). Also, the Managing and Commanding conceptual model was updated by adding concepts and vocabulary for the agency internal communication, including standardized knowledge repository for managing standard operating procedures (SOP) and reports for leadership.The CDC’s SAB directly supports the CDC Surveillance Data Platform (SDP) and national organizations on development of electronic forms and form builders. These efforts will provide additional capabilities for collecting and electronically sharing standardized SA information utilizing web-enabled Services and mobile capabilities.Conclusions: CDC’s EOC and Division of Emergency Operations Staff is improving the application of emergency management and PH practice in preparing and responding to emergencies through partnerships and coordinated work with Standard Development Organizations (SDOs) to add critical EPR vocabularies to national and international standards. This work supports National Emergency Management Organizations and is a reference source for the WHO EOC-NET guiding documents supporting international efforts to strengthen Global Health Security.

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ObjectiveThe purpose of this project is to demonstrate progress in developing a scientific and practical approach for public health (PH) emergency preparedness and response informatics (EPRI) that supports the National Health Security Strategy and Global Health Security Agenda (GHSA) objectives. PH emergency operations centers (EOC) contribute to health security objectives because they operationalize response, recovery and mitigation activities during national and international PH events. The primary focus of this presentation is to describe the results of an analysis of CDC's EOC, and other EOCs, in building their EPRI capabilities. IntroductionGlobal travel and human migration patterns facilitate the spread of diseases such as influenza A/H1N1, Ebola, and Zika, increasing pressure to PH systems to protect their constituents against global health threats. Effective prevention, detection, and rapid response to threats rely heavily on adequate information sharing. This requires effective information management through PH EPRI applications such as information systems and tools, knowledge management, and a continuous cycle improvement to maintain system quality. Enhancement of PH EPRI capabilities contributes to improved decision making during emergencies 1 . It transforms public health practice and improves health outcomes through better surveillance, epidemiology, integrated delivery of services, and other emergency preparedness and response activities.EPRI activities depend on both technical systems and the people who use them. Without adequate training, these systems cannot be effective. CDC's PH EOC information processes and data flows are a notable use case, utilized by hundreds of emergency responders during large-scale PH events. By analyzing this use case, CDC's informaticians have identified multiple opportunities for advancing PH EPRI and advance the objectives of the GHSA.