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Bibliographic Details
Main Authors: Akter, Tahmina, Deardon, Rob
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2409.02353
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author Akter, Tahmina
Deardon, Rob
author_facet Akter, Tahmina
Deardon, Rob
contents Here, we introduce a novel framework for modelling the spatiotemporal dynamics of disease spread known as conditional logistic individual-level models (CL-ILM's). This framework alleviates much of the computational burden associated with traditional spatiotemporal individual-level models for epidemics, and facilitates the use of standard software for fitting logistic models when analysing spatiotemporal disease patterns. The models can be fitted in either a frequentist or Bayesian framework. Here, we apply the new spatial CL-ILM to both simulated and semi-real data from the UK 2001 foot-and-mouth disease epidemic.
format Preprint
id arxiv_https___arxiv_org_abs_2409_02353
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Conditional logistic individual-level models of spatial infectious disease dynamics
Akter, Tahmina
Deardon, Rob
Computation
Here, we introduce a novel framework for modelling the spatiotemporal dynamics of disease spread known as conditional logistic individual-level models (CL-ILM's). This framework alleviates much of the computational burden associated with traditional spatiotemporal individual-level models for epidemics, and facilitates the use of standard software for fitting logistic models when analysing spatiotemporal disease patterns. The models can be fitted in either a frequentist or Bayesian framework. Here, we apply the new spatial CL-ILM to both simulated and semi-real data from the UK 2001 foot-and-mouth disease epidemic.
title Conditional logistic individual-level models of spatial infectious disease dynamics
topic Computation
url https://arxiv.org/abs/2409.02353