Dynamic survival analysis for non-Markovian epidemic models.
J R Soc Interface
; 19(191): 20220124, 2022 06.
Article
in English
| MEDLINE | ID: covidwho-1874074
ABSTRACT
We present a new method for analysing stochastic epidemic models under minimal assumptions. The method, dubbed dynamic survival analysis (DSA), is based on a simple yet powerful observation, namely that population-level mean-field trajectories described by a system of partial differential equations may also approximate individual-level times of infection and recovery. This idea gives rise to a certain non-Markovian agent-based model and provides an agent-level likelihood function for a random sample of infection and/or recovery times. Extensive numerical analyses on both synthetic and real epidemic data from foot-and-mouth disease in the UK (2001) and COVID-19 in India (2020) show good accuracy and confirm the method's versatility in likelihood-based parameter estimation. The accompanying software package gives prospective users a practical tool for modelling, analysing and interpreting epidemic data with the help of the DSA approach.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Epidemics
/
COVID-19
Type of study:
Cohort study
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Limits:
Animals
Language:
English
Journal:
J R Soc Interface
Year:
2022
Document Type:
Article
Affiliation country:
Rsif.2022.0124
Similar
MEDLINE
...
LILACS
LIS