Tools and Apps to Enhance Situational Awareness for Global Disease Surveillance

Deshpande, Alina; Margevicius, Kristen; Generous, Nicholas; Taylor-McCabe, Kirsten J.; Castro, Lauren; Longo, Joseph F.; Priedhorsky, Reid

doi:10.5210/ojphi.v6i1.5183

Cited by 3 publications

(2 citation statements)

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“…In [3], [10] the authors report on the development of webbased systems to carry out large computational experiments in support of epidemic planning. See [11], [20], [28] for other related efforts.…”

Section: Related Workmentioning

confidence: 99%

Scalable Epidemiological Workflows to Support COVID-19 Planning and Response

Machi

Bhattacharya

Hoops

et al. 2021

2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

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The COVID-19 global outbreak represents the most significant epidemic event since the 1918 influenza pandemic. Simulations have played a crucial role in supporting COVID-19 planning and response efforts. Developing scalable workflows to provide policymakers quick responses to important questions pertaining to logistics, resource allocation, epidemic forecasts and intervention analysis remains a challenging computational problem. In this work, we present scalable high performance computing-enabled workflows for COVID-19 pandemic planning and response. The scalability of our methodology allows us to run fine-grained simulations daily, and to generate county-level forecasts and other counterfactual analysis for each of the 50 states (and DC), 3140 counties across the USA. Our workflows use a hybrid cloud/cluster system utilizing a combination of local and remote cluster computing facilities, and using over 20,000 CPU cores running for 6-9 hours every day to meet this objective. Our state (Virginia), state hospital network, our university, the DOD and the CDC use our models to guide their COVID-19 planning and response efforts. We began executing these pipelines March 25, 2020, and have delivered and briefed weekly updates to these stakeholders for over 30 weeks without interruption.

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Section: Related Workmentioning

confidence: 99%

Scalable Epidemiological Workflows to Support COVID-19 Planning and Response

Machi

Bhattacharya

Hoops

et al. 2021

2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

View full text Add to dashboard Cite

show abstract

“…In [3], [14] the authors report on the development of web-based systems to carry out large computational experiments in support of epidemic planning. See [15], [26], [40] for other related efforts.…”

Section: Related Workmentioning

confidence: 99%

Scalable Epidemiological Workflows to Support COVID-19 Planning and Response

Machi

Bhattacharya

Hoops

et al. 2021

Preprint

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The COVID-19 global outbreak represents the most significant epidemic event since the 1918 influenza pandemic. Simulations have played a crucial role in supporting COVID-19 planning and response efforts. Developing scalable workflows to provide policymakers quick responses to important questions pertaining to logistics, resource allocation, epidemic forecasts and intervention analysis remains a challenging computational problem. In this work, we present scalable high performance computing-enabled workflows for COVID-19 pandemic planning and response. The scalability of our methodology allows us to run fine-grained simulations daily, and to generate county-level forecasts and other counter-factual analysis for each of the 50 states (and DC), 3140 counties across the USA. Our workflows use a hybrid cloud/cluster system utilizing a combination of local and remote cluster computing facilities, and using over 20,000 CPU cores running for 6--9 hours every day to meet this objective. Our state (Virginia), state hospital network, our university, the DOD and the CDC use our models to guide their COVID-19 planning and response efforts. We began executing these pipelines March 25, 2020, and have delivered and briefed weekly updates to these stakeholders for over 30 weeks without interruption.

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Advancing a Framework to Enable Characterization and Evaluation of Data Streams Useful for Biosurveillance

et al. 2014

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In recent years, biosurveillance has become the buzzword under which a diverse set of ideas and activities regarding detecting and mitigating biological threats are incorporated depending on context and perspective. Increasingly, biosurveillance practice has become global and interdisciplinary, requiring information and resources across public health, One Health, and biothreat domains. Even within the scope of infectious disease surveillance, multiple systems, data sources, and tools are used with varying and often unknown effectiveness. Evaluating the impact and utility of state-of-the-art biosurveillance is, in part, confounded by the complexity of the systems and the information derived from them. We present a novel approach conceptualizing biosurveillance from the perspective of the fundamental data streams that have been or could be used for biosurveillance and to systematically structure a framework that can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities. Moreover, the Biosurveillance Data Stream Framework and associated definitions are proposed as a starting point to facilitate the development of a standardized lexicon for biosurveillance and characterization of currently used and newly emerging data streams. Criteria for building the data stream framework were developed from an examination of the literature, analysis of information on operational infectious disease biosurveillance systems, and consultation with experts in the area of biosurveillance. To demonstrate utility, the framework and definitions were used as the basis for a schema of a relational database for biosurveillance resources and in the development and use of a decision support tool for data stream evaluation.

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Tools and Apps to Enhance Situational Awareness for Global Disease Surveillance

Cited by 3 publications

References 0 publications

Scalable Epidemiological Workflows to Support COVID-19 Planning and Response

Scalable Epidemiological Workflows to Support COVID-19 Planning and Response

Scalable Epidemiological Workflows to Support COVID-19 Planning and Response

Advancing a Framework to Enable Characterization and Evaluation of Data Streams Useful for Biosurveillance

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