Heterogeneity and disease invasion
Overview
Whether a disease invades a population after an introduction is fundamentally a stochastic process. We have learned that individual-level variation can:
- reduce the probability of invasion by increasing the effects of stochasticity
- increase the probability of invasion by increasing the basic reproductive number as a disease invades
The group will make a simple model of these two countervailing effects and investigate how they work and when one is more important than the other.
It is expected that the group will rely mostly on simulations, but it is worth noting that this is a topic that could support mathematical exploration as well.
Participants can choose whether to link their project to available information about variability in risk factors (e.g., contact rates) or outcomes (e.g., TB prevalence)
Things to consider
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When deciding on your interest in this project, please consider the following:
- This group is recommended for:
- Participants interested in super-spreading and heterogeneity
- Participants interested in learning about stochastic simulation
- [Optionally] participants interested in mathematical approximations and results
- Participants interested in learning to use git and GitHub for collaborative code development
- This group will have the opportunity to engage in the following:
- Literature review about host jumps and disease establishment
- Examining data about human heterogeneity
- Coding and analyzing stochastic simulations
Data
- To be decided by participants. Data will be used for guidance; this will be mostly a simulation-based project.
Resources
ICI3D Heterogeneity lab