Forecasting and Uncertainty Quantification Using a Hybrid of Mechanistic and Non-mechanistic Models for an Age-Structured Population Model.

In this paper, we present a new method for the prediction and uncertainty quantification of data-driven multivariate systems. Traditionally, either mechanistic or non-mechanistic modeling methodologies have been used for prediction; however, it is uncommon for the two to be incorporated together. We compare the forecast accuracy of mechanistic modeling, using Bayesian inference, a non-mechanistic modeling approach based on state space reconstruction, and a novel hybrid methodology composed of the two for an age-structured population data set.

The first case of CDK5RAP2-related primary microcephaly in a non-consanguineous patient identified by next generation sequencing.

Primary autosomal recessive microcephaly (MCPH) is a genetically heterogeneous condition characterized by congenital microcephaly and intellectual disability. To date, 10 MCPH loci have been identified and due to the genetic heterogeneity of this condition, molecular testing for MCPH can be complicated. Our methods involved employing a next generation sequencing panel of MCPH-related genes allowing for the evaluation of multiple disease loci simultaneously.