# 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 the specification. We argue that statistical model checking methods can solve the uncertaintymore »

- Authors:

- ORNL
- University of Central Florida

- Publication Date:

- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Center for Computational Sciences

- Sponsoring Org.:
- USDOE Laboratory Directed Research and Development (LDRD) Program

- OSTI Identifier:
- 1056396

- DOE Contract Number:
- DE-AC05-00OR22725

- Resource Type:
- Conference

- Resource Relation:
- Conference: 2012 Workshop on Verification and Validation of Epidemiological Models As part of 2012 ASE/IEEE International Conference on Biomedical Computing, Washington DC, DC, USA, 20121214, 20121214

- Country of Publication:
- United States

- Language:
- English

- Subject:
- model checking; verification and validation of epidemiological models; uncertainty quantification

### Citation Formats

```
Ramanathan, Arvind, and Jha, Sumit Kumar.
```*Quantifying Uncertainty in Epidemiological Models*. United States: N. p., 2012.
Web.

```
Ramanathan, Arvind, & Jha, Sumit Kumar.
```*Quantifying Uncertainty in Epidemiological Models*. United States.

```
Ramanathan, Arvind, and Jha, Sumit Kumar. Sun .
"Quantifying Uncertainty in Epidemiological Models". United States.
```

```
@article{osti_1056396,
```

title = {Quantifying Uncertainty in Epidemiological Models},

author = {Ramanathan, Arvind and Jha, Sumit Kumar},

abstractNote = {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 the specification. We argue that statistical model checking methods can solve the uncertainty quantification problem for complex epidemiological models.},

doi = {},

journal = {},

number = ,

volume = ,

place = {United States},

year = {2012},

month = {1}

}