Predictive Modelling for Pandemic Forecasting

About the project

This study proposes a novel approach to leveraging large-scale COVID-19 datasets to enhance the predictive modelling of disease spread in its early stages.

Guidance for Policymakers and Public Health Authorities

The findings contribute to the identification of optimal predictive strategies that balance accuracy, adaptability, and data constraints. These insights offer practical guidance for policymakers and public health authorities.

Proposed Approach

This research establishes a foundation for automating predictive analysis, enabling timely and data-driven decision-making for disease control and prevention.

The proposed approach is validated using data from New Zealand and its major trading partners, China, Australia, the United States, Japan, and Germany - demonstrating its applicability across different contexts.

Funding streams: Sapienza University of Rome

Project Aims

This study aims to leverage large-scale COVID-19 datasets to explore and evaluate different predictive modeling approaches, offering valuable insights into the optimal strategies for forecasting disease spread under varying conditions. 

The findings from this research contribute to the development of automated predictive frameworks that can empower public health authorities to respond proactively to emerging health threats, even in the early stages of an outbreak.