Zobrazeno 1 - 10
of 291
pro vyhledávání: '"DEARDON, Rob"'
Autor:
Akter, Tahmina, Deardon, Rob
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
Externí odkaz:
http://arxiv.org/abs/2409.02353
Autor:
Rahul, Chinmoy Roy, Deardon, Rob
Modelling epidemics is crucial for understanding the emergence, transmission, impact and control of diseases. Spatial individual-level models (ILMs) that account for population heterogeneity are a useful tool, accounting for factors such as location,
Externí odkaz:
http://arxiv.org/abs/2405.00835
During epidemics, people will often modify their behaviour patterns over time in response to changes in their perceived risk of spreading or contracting the disease. This can substantially impact the trajectory of the epidemic. However, most infectio
Externí odkaz:
http://arxiv.org/abs/2308.00815
For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in real-time. Behavioral change is often ignored in epidemic modeling efforts, maki
Externí odkaz:
http://arxiv.org/abs/2211.00122
Autor:
Hodzic-Santor, Emil, Deardon, Rob
Publikováno v:
In Spatial and Spatio-temporal Epidemiology August 2024 50
Autor:
Biesheuvel, Marit M. *, Ward, Caitlin, Penterman, Patty, van Engelen, Erik, van Schaik, Gerdien, Deardon, Rob, Barkema, Herman W.
Publikováno v:
In Journal of Dairy Science January 2024 107(1):516-529
This paper describes the R package EpiILMCT, which allows users to study the spread of infectious disease using continuous time individual level models (ILMs). The package provides tools for simulation from continuous time ILMs that are based on eith
Externí odkaz:
http://arxiv.org/abs/2006.00135
In this article, we introduce the R package EpiILM, which provides tools for simulation from, and inference for, discrete-time individual-level models of infectious disease transmission proposed by Deardon et al. (2010). The inference is set in a Bay
Externí odkaz:
http://arxiv.org/abs/2003.04963
We introduce Pathogen.jl for simulation and inference of transmission network individual level models (TN-ILMs) of infectious disease spread in continuous time. TN-ILMs can be used to jointly infer transmission networks, event times, and model parame
Externí odkaz:
http://arxiv.org/abs/2002.05850
Publikováno v:
In Infectious Disease Modelling December 2023 8(4):947-963