publication . Article . Other literature type . Preprint . 2016

Model distinguishability and inference robustness in mechanisms of cholera transmission and loss of immunity

Brad M. Ochocki;
  • Published: 22 May 2016
Abstract Mathematical models of cholera and waterborne disease vary widely in their structures, in terms of transmission pathways, loss of immunity, and a range of other features. These differences can affect model dynamics, with different models potentially yielding different predictions and parameter estimates from the same data. Given the increasing use of mathematical models to inform public health decision-making, it is important to assess model distinguishability (whether models can be distinguished based on fit to data) and inference robustness (whether inferences from the model are robust to realistic variations in model structure). In this paper, we exa...
free text keywords: Article, Quantitative Biology - Populations and Evolution, General Biochemistry, Genetics and Molecular Biology, Modelling and Simulation, Statistics and Probability, General Immunology and Microbiology, Applied Mathematics, General Agricultural and Biological Sciences, General Medicine, Inference, Estimation theory, Biology, Trajectory, Econometrics, Data set, Mathematical model, Robustness (computer science), Basic reproduction number, Statistics, Data collection
Funded by
NSF| Modeling the Effects of Heterogeneity in Water Quality on Cholera Disease Dynamics
  • Funder: National Science Foundation (NSF)
  • Project Code: 1115881
  • Funding stream: Directorate for Geosciences | Division of Ocean Sciences
NSF| NIMBioS: National Institute for Mathematical and Biological Synthesis
  • Funder: National Science Foundation (NSF)
  • Project Code: 1300426
  • Funding stream: Directorate for Biological Sciences | Division of Biological Infrastructure
NIH| Modeling the Effects of the Environment on Enteric Pathogen Dynamics
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5U01GM110712-03
NSF| Mathematical Biosciences Institute
  • Funder: National Science Foundation (NSF)
  • Project Code: 0931642
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Mathematical Sciences
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