The Evolution of Dynamic Gaussian Process Model with Applications to Malaria Vaccine Coverage Prediction

TL;DR

This paper introduces a dynamic Gaussian process model that efficiently handles time series data and large datasets, and demonstrates its application in predicting global malaria vaccine coverage.

Contribution

The paper develops a novel dynamic GP model using SVD-guided convolution and localized modeling, advancing the state-of-the-art in time series emulation and large dataset analysis.

Findings

Effective prediction of malaria vaccine coverage across 78 countries.

Demonstrated computational efficiency for large-scale time series data.

Validated model accuracy using computer simulators and real-world data.

Abstract

Gaussian process (GP) based statistical surrogates are popular, inexpensive substitutes for emulating the outputs of expensive computer models that simulate real-world phenomena or complex systems. Here, we discuss the evolution of dynamic GP model - a computationally efficient statistical surrogate for a computer simulator with time series outputs. The main idea is to use a convolution of standard GP models, where the weights are guided by a singular value decomposition (SVD) of the response matrix over the time component. The dynamic GP model also adopts a localized modeling approach for building a statistical model for large datasets. In this chapter, we use several popular test function based computer simulators to illustrate the evolution of dynamic GP models. We also use this model for predicting the coverage of Malaria vaccine worldwide. Malaria is still affecting more than eighty countries concentrated in the tropical belt. In 2019 alone, it was the cause of more than 435,000 deaths worldwide. The malice is easy to cure if diagnosed in time, but the common symptoms make it difficult. We focus on a recently discovered reliable vaccine called Mos-Quirix (RTS,S) which is currently going under human trials. With the help of publicly available data on dosages, efficacy, disease incidence and communicability of other vaccines obtained from the World Health Organisation, we predict vaccine coverage for 78 Malaria-prone countries.