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Dynamic survival analysis for non-Markovian epidemic models.
Francesco Di Lauro1, Wasiur R KhudaBukhsh2, István Z Kiss3
1Big Data Institute, University of Oxford, Oxford, OX3 7LF, UK.
Journal of the Royal Society, Interface
|June 1, 2022
Summary
We developed dynamic survival analysis (DSA), a new method for analyzing epidemic models with minimal assumptions. DSA accurately estimates parameters using infection and recovery times, proving versatile for real-world disease data.
Area of Science:
- Epidemiology
- Mathematical Biology
- Computational Statistics
Background:
- Stochastic epidemic models are crucial for understanding disease spread.
- Current analysis methods often require strong assumptions about disease dynamics.
- Accurate parameter estimation is vital for effective public health interventions.
Purpose of the Study:
- To introduce a novel, assumption-light method for analyzing stochastic epidemic models.
- To demonstrate the utility of dynamic survival analysis (DSA) for parameter estimation.
- To provide a practical tool for epidemic data analysis.
Main Methods:
- Developed dynamic survival analysis (DSA), linking population-level ODEs to individual-level event times.
- Constructed a non-Markovian agent-based model derived from mean-field approximations.
- Created an agent-level likelihood function for infection/recovery time data.
Main Results:
- DSA demonstrated high accuracy in analyzing both synthetic and real-world epidemic data.
- The method proved versatile for likelihood-based parameter estimation.
- Successful application to foot-and-mouth disease (UK, 2001) and COVID-19 (India, 2020) datasets.
Conclusions:
- Dynamic survival analysis (DSA) offers a powerful and flexible approach to stochastic epidemic modeling.
- The method reduces the need for strong parametric assumptions in epidemic analysis.
- A practical software package facilitates the application of DSA for researchers and public health professionals.
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