Mixing Patterns and Social Networks
Quantifying model uncertainty in agent-based simulations for forecasting the spread of infectious diseases and understanding human behavior using social media
- Principal Investigator
- Sara Del Valle
- (505) 665-9286
The lack of data on mixing patterns in the population presents a major problem for understanding the spread of diseases. To fill this data gap, we are using limited experimental data and computational models to generate mixing patterns and social networks for the United States.
We are interested in capturing contact patterns to better understand the spread of infectious diseases through populations. We are currently using OPPIE <hyperlink>, an agent-based simulation, to capture mixing patterns that reflect demographics and activities for the entire United States. Our goal is to provide better estimates to inform heterogeneous models for infectious disease transmission. In addition, we are interested in analyzing contact duration, which is critical for estimating the probability of transmission between two people.
Total number of contacts and average contact duration by age groups for the population of Portland, Oregon.
We are interested in analyzing social networks generated by individuals and their connections to study infectious disease spread. We study spatial-temporal dynamics of social networks and their implications on disease transmission. Our goal is to identify patterns, which may lead to optimal mitigation strategies to slow or halt disease spread.
Social contact network generated from OPPIE representing the population of southern California. The nodes are colored and sized by degree; red = high degree, blue = medium degree, and yellow = low degree.