SYSTEMS AND METHODS FOR PREDICTING COVID 19 CASES AND DEATHS

Smart overview of the Invention

The patent application outlines a machine learning framework designed to predict COVID-19 cases and deaths. This system leverages multiple open data sources to gather relevant information about the pandemic's progression in specific geographic areas, such as cities or regions. The data includes localized COVID-19 cases and deaths, demographics, socioeconomic factors, non-medical interventions, and mobility data from cell phones or GPS. The model forecasts future cases and deaths at these localized levels and suggests necessary healthcare resources.

Field of Application

The invention focuses on systems and methods for forecasting diseases within a population, specifically targeting COVID-19. Accurate local-level predictions are crucial for decision-making by healthcare professionals, public sector entities, and private organizations to prepare for future outbreaks effectively. The framework's adaptability allows it to be applied to various countries and regions beyond the United States.

Methodology

The process involves obtaining data from multiple online databases, preprocessing this data, and extracting feature vectors that include mobility data, stringency measures, COVID-19 statistics, and demographic information. The extracted features are used to train a machine learning model, which is then validated to predict future COVID-19 cases and deaths at localized levels. The method also includes allocating healthcare resources based on these predictions.

Features and Data Utilization

The system uses various feature vectors such as vaccination data, socio-economic factors, mobility information, and stringency measures like lockdowns or curfews. Time-related data including day of the year and holidays are also considered. The machine learning model employed can be an ensemble model like XGBoost, utilizing gradient boosted decision tree algorithms for enhanced prediction accuracy.

Implementation Details

The machine learning framework can be implemented on various computer systems ranging from desktop computers to cloud infrastructures like Amazon Web Services. The system's architecture allows communication between subsystems via a system bus, enabling efficient processing of instructions and data exchange. Software components can be developed using programming languages such as Java or Python and stored on various computer-readable media for execution.