Transmission intervals

Overview

Transmission time intervals are important components in mathematical epidemiology and outbreak analysis. Transmission time intervals describe the natural history of infection, starting from the time of acquiring infection, to different chronological stages of infection.

This group will use simulated data to explore different time intervals and understand their role in mathematical epidemiology and outbreak analysis.

Things to consider

  • When deciding on your interest in this project, please consider the following:

  • The faculty member leading this project is based in Ontario, where it is 10 hours earlier than in Dhaka and 3 hours later than in Seattle.

  • This group is recommended for:
    • Participants interested in theoretical aspects of data analysis
    • Participants interested in learning rapid responses
    • Participants with good programming skills or prior dynamic modeling experience
    • Participants interested in developing a better understanding of what pieces are required/sufficient to answer particular questions of interest
  • This group will have the opportunity to engage in any of the following:
    • Practice simulating data and exploring theoricial properties
    • Practice fitting simple phenomenological models to time series data for the purpose of estimating epi-parameters of interest (e.g. r,R0) with time intervals
    • Learn techniques to deal with data availability

Background

Transmission time intervals describes the natural history of infection starting from the time of acquiring infection, onsets of shedding, onsets of symptoms, acquiring treatment, recovery or death. Each stage has its own unique property; some can be observed within a single host and some requires linking infectors and infectees. Understanding these key transmission time intervals and how they link to infection time series data can estimate key epidemiological parameters of an epidemic as well as public health demand questions (i.e. hospital stay and etc).

Data

  • Some simulated datasets will be available as examples; however, for the most part, the group will simulate its own data.

References

  • Park, Sang Woo, et al. “Forward-looking serial intervals correctly link epidemic growth to reproduction numbers.” Proceedings of the National Academy of Sciences 118.2 (2021).

  • Wallinga, Jacco, and Marc Lipsitch. “How generation intervals shape the relationship between growth rates and reproductive numbers.” Proceedings of the Royal Society B: Biological Sciences 274.1609 (2007): 599-604.

  • Champredon, David, and Jonathan Dushoff. “Intrinsic and realized generation intervals in infectious-disease transmission.” Proceedings of the Royal Society B: Biological Sciences 282.1821 (2015): 20152026.