Dynamic survival analysis for non-Markovian epidemic models
| Autor: | Francesco Di Lauro, Wasiur R. KhudaBukhsh, István Z. Kiss, Eben Kenah, Max Jensen, Grzegorz A. Rempała |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Physics - Physics and Society Likelihood Functions Probability (math.PR) Biomedical Engineering Biophysics Populations and Evolution (q-bio.PE) COVID-19 FOS: Physical sciences Bioengineering Physics and Society (physics.soc-ph) 60G55 (Primary) 62F (Secondary) Statistics - Applications Survival Analysis Biochemistry Biomaterials FOS: Biological sciences FOS: Mathematics Animals Applications (stat.AP) Prospective Studies Quantitative Biology - Populations and Evolution Epidemics Mathematics - Probability Biotechnology |
| ISSN: | 1742-5662 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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