Epidemic modeling with discrete-space scheduled walkers: extensions and research opportunities.

BMC Public Health
Maciej BorkowskiRobert D McLeod

Abstract

This exploratory paper outlines an epidemic simulator built on an agent-based, data-driven model of the spread of a disease within an urban environment. An intent of the model is to provide insight into how a disease may reach a tipping point, spreading to an epidemic of uncontrollable proportions. As a complement to analytical methods, simulation is arguably an effective means of gaining a better understanding of system-level disease dynamics within a population and offers greater utility in its modeling capabilities. Our investigation is based on this conjecture, supported by data-driven models that are reasonable, realistic and practical, in an attempt to demonstrate their efficacy in studying system-wide epidemic phenomena. An agent-based model (ABM) offers considerable flexibility in extending the study of the phenomena before, during and after an outbreak or catastrophe. An agent-based model was developed based on a paradigm of a 'discrete-space scheduled walker' (DSSW), modeling a medium-sized North American City of 650,000 discrete agents, built upon a conceptual framework of statistical reasoning (law of large numbers, statistical mechanics) as well as a correct-by-construction bias. The model addresses where, who, whe...Continue Reading

References

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Citations

Jan 1, 2009·BMC Public Health·Robert J Smith, Richard Gordon
Jul 3, 2009·PloS One·Marek LaskowskiAttahiru S Alfa
Oct 27, 2010·Drug Discovery Today·Joo Chuan Tong, Lisa F P Ng
Aug 5, 2011·IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society·Marek LaskowskiRobert D McLeod
Apr 7, 2017·Journal of Biosocial Science·Joshua Amo-Adjei, Derek Anamaale Tuoyire
Dec 17, 2014·The Proceedings of the Nutrition Society·Carlos Celis-MoralesJohn C Mathers
Dec 16, 2009·BMC Public Health·Richard GordonEluemuno R Blyden

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Software Mentioned

PACER
DSSW
Google Flu Trends
OpenCV
Google Earth
CitiWide
MIDAS
Google Maps

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