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 b...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 simulated data,semi-real data from the UK 2001 foot-and-mouth disease epidemic,and real data from a greenhouse experiment on the spread of tomato spotted wilt virus.展开更多
基金funded by an Alberta Innovates Graduate Student Scholarship for Data-Enabled Innovation and a Uni-versity of Calgary Eyes High Doctoral Scholarship,Doctoral Completion Scholarship,Natural Sciences and Engineering Research Council of Canada(NSERC)Discovery Grants program(RGPIN/03292-2022)and the Alberta Innovates Advance-NSERC Alliance program(222302037).
文摘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 simulated data,semi-real data from the UK 2001 foot-and-mouth disease epidemic,and real data from a greenhouse experiment on the spread of tomato spotted wilt virus.
