Quantifying Uncertainty in Mechanistic Models of Infectious Disease
American Journal of Epidemiology
|January 21, 2021
Summary
This primer quantifies statistical uncertainty in infectious disease mechanistic models using R code. It categorizes uncertainty into data, stochastic, and structural types, with a case study on SARS-CoV-2.
Area of Science:
- Epidemiology
- Computational Biology
- Biostatistics
Background:
- Mechanistic models are crucial for understanding infectious disease dynamics.
- Quantifying statistical uncertainty in these models is essential for reliable predictions.
- Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) provides a relevant case study.
Purpose of the Study:
- To provide a primer on statistical uncertainty in mechanistic models for infectious diseases.
- To present R code for quantifying this uncertainty.
- To illustrate methods using a SARS-CoV-2 case study.
Main Methods:
- Overview of mechanistic models in infectious disease.
- Categorization of statistical uncertainty into data, stochastic, and structural types.
- Application of uncertainty measures and sensitivity analyses.
Main Results:
- Demonstration of quantifying data uncertainty.
- Methods for assessing stochastic uncertainty.
- Techniques for evaluating structural uncertainty.
Conclusions:
- Statistical uncertainty in mechanistic models can be systematically quantified.
- The presented methods and R code are applicable to real-world scenarios like SARS-CoV-2.
- Accurate estimation of parameters such as the basic reproductive number (R0) is improved.
Keywords:
Monte Carlo simulationSARS-CoV-2infectious disease modelingmechanistic modelssensitivity analysesstatisticsuncertaintyMore Related Videos
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