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We present a new method for analysing stochastic epidemic models under minimal assumptions. The method, dubbed dynamic survival analysis (DSA), is based on a simple yet powerful observation, namely that population-level mean-field trajectories described by a system of partial differential equations may also approximate individual-level times of infection and recovery. This idea gives rise to a certain non-Markovian agent-based model and provides an agent-level likelihood function for a random sample of infection and/or recovery times. Extensive numerical analyses on both synthetic and real epidemic data from foot-and-mouth disease in the UK (2001) and COVID-19 in India (2020) show good accuracy and confirm the method’s versatility in likelihood-based parameter estimation. The accompanying software package gives prospective users a practical tool for modelling, analysing and interpreting epidemic data with the help of the DSA approach.
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Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support
This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of ( i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; ( ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis; ( iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC; ( iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences.
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- PAR ID:
- 10403974
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- The International Journal of High Performance Computing Applications
- Volume:
- 37
- Issue:
- 1
- ISSN:
- 1094-3420
- Page Range / eLocation ID:
- 4 to 27
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1177/10943420221127034
