Title
Quantifying Uncertainty In Epidemiological Models
Abstract
Modern epidemiology has made use of a number of mathematical models, including ordinary differential equation (ODE) based models and agent based models (ABMs) to describe the dynamics of how a disease may spread within a population and enable the rational design of strategies for intervention that effectively contain the spread of the disease. Although such predictions are of fundamental importance in preventing the next global pandemic, there is a significant gap in trusting the outcomes/predictions solely based on such models. Hence, there is a need to develop approaches such that mathematical models can be calibrated against historical data. In addition, there is a need to develop rigorous uncertainty quantification approaches that can provide insights into when a model will fail and characterize the confidence in the (possibly multiple) model outcomes/predictions, when such retrospective analysis cannot be performed. In this paper, we outline an approach to develop uncertainty quantification approaches for epidemiological models using formal methods and model checking. By specifying the outcomes expected from a model in a suitable spatio-temporal logic, we use probabilistic model checking methods to quantify the probability with which the epidemiological model satisfies a given behavioral specification. We argue that statistical model checking methods can solve the uncertainty quantification problem for complex epidemiological models. © 2012 IEEE.
Publication Date
1-1-2012
Publication Title
Proceedings of the 2012 ASE International Conference on BioMedical Computing, BioMedCom 2012
Number of Pages
80-85
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/BioMedCom.2012.20
Copyright Status
Unknown
Socpus ID
84878723464 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84878723464
STARS Citation
Jha, Sumit K. and Ramanathan, Arvind, "Quantifying Uncertainty In Epidemiological Models" (2012). Scopus Export 2010-2014. 5719.
https://stars.library.ucf.edu/scopus2010/5719