Finer forecasting to improve public health planning

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Applying a statistical approach, KAUST researchers have developed a model that improves mortality forecasting and can be applied to population sub-groups, based on factors such as age and gender, among others.

In most countries around the world, current drops in mortality rates are associated with aging populations. Governments and other policymakers require accurate forecasting for planning and decision-making. In the field of mortality forecasting, for example, precise predictions can inform policy decisions, insurance premiums and public health strategies. Researchers have developed a method for forecasting high-dimensional functional time series (HDFTS) that significantly improves the accuracy of mortality predictions.