| Autor: |
Murphy RJ; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia., Maclaren OJ; Department of Engineering Science, University of Auckland, Auckland, New Zealand., Calabrese AR; Queensland Bladder Cancer Initiative and School of Biomedical Sciences, Faculty of Health, Queensland University of Technology at Translational Research Institute, Brisbane, Australia., Thomas PB; Queensland Bladder Cancer Initiative and School of Biomedical Sciences, Faculty of Health, Queensland University of Technology at Translational Research Institute, Brisbane, Australia., Warne DJ; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia., Williams ED; Queensland Bladder Cancer Initiative and School of Biomedical Sciences, Faculty of Health, Queensland University of Technology at Translational Research Institute, Brisbane, Australia., Simpson MJ; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia. |
| Abstrakt: |
Throughout the life sciences, biological populations undergo multiple phases of growth, often referred to as biphasic growth for the commonly encountered situation involving two phases. Biphasic population growth occurs over a massive range of spatial and temporal scales, ranging from microscopic growth of tumours over several days, to decades-long regrowth of corals in coral reefs that can extend for hundreds of kilometres. Different mathematical models and statistical methods are used to diagnose, understand and predict biphasic growth. Common approaches can lead to inaccurate predictions of future growth that may result in inappropriate management and intervention strategies being implemented. Here, we develop a very general computationally efficient framework, based on profile likelihood analysis, for diagnosing, understanding and predicting biphasic population growth. The two key components of the framework are as follows: (i) an efficient method to form approximate confidence intervals for the change point of the growth dynamics and model parameters and (ii) parameter-wise profile predictions that systematically reveal the influence of individual model parameters on predictions. To illustrate our framework we explore real-world case studies across the life sciences. |
