Analytical models approximating individual processes: a validation method.
Upscaling population models from fine to coarse resolutions, in space, time and/or level of description, allows the derivation of fast and tractable models based on a thorough knowledge of individual processes. The validity of such approximations is generally tested only on a limited range of parameter sets. A more general validation test, over a range of parameters, is proposed; this would estimate the error induced by the approximation, using the original model's stochastic variability as a reference. This method is illustrated by three examples taken from the field of epidemics transmitted by vectors that bite in a temporally cyclical pattern, that illustrate the use of the method: to estimate if an approximation over- or under-fits the original model; to invalidate an approximation; to rank possible approximations for their qualities. As a result, the application of the validation method to this field emphasizes the need to account for the vectors' biology in epidemic prediction models and to validate these against finer scale models.
Favier
C
C
Université Montpellier 2, CNRS, Institut des Sciences de l'Evolution, CC 061, Place Eugène Bataillon, 34095 Montpellier cedex 05, France. charly.favier@univ-montp2.fr
Degallier
N
N
Menkès
C E
CE
D000465:Algorithms; D000818:Animals; D001181:Arthropods / Q000254:growth & development / Q000382:microbiology / Q000469:parasitology / Q000821:virology; D001733:Bites and Stings / Q000382:microbiology / Q000469:parasitology / Q000821:virology; D003141:Communicable Diseases / Q000453:epidemiology* / Q000382:microbiology / Q000469:parasitology / Q000635:transmission* / Q000821:virology; D004199:Disease Vectors; D006801:Humans; D008954:Models, Biological; D011157:Population Dynamics; D013269:Stochastic Processes