Pandemic model with data-driven phase detection, a study using COVID-19 data

TL;DR

This paper introduces a data-driven COVID-19 pandemic model that detects change points in disease spread parameters, improving understanding and decision-making by adapting to sudden shifts in pandemic dynamics.

Contribution

The work presents a novel piecewise autonomous ODE model that integrates change detection, interprets parameters, and identifies key factors influencing pandemic evolution.

Findings

Model accurately detects change points in COVID-19 data

Parameters related to social behavior significantly impact disease spread

Provides improved pandemic understanding for better decision-making

Abstract

The recent COVID-19 pandemic has promoted vigorous scientific activity in an effort to understand, advice and control the pandemic. Data is now freely available at a staggering rate worldwide. Unfortunately, this unprecedented level of information contains a variety of data sources and formats, and the models do not always conform to the description of the data. Health officials have recognized the need for more accurate models that can adjust to sudden changes, such as produced by changes in behavior or social restrictions. In this work we formulate a model that fits a ``SIR''-type model concurrently with a statistical change detection test on the data. The result is a piece wise autonomous ordinary differential equation, whose parameters change at various points in time (automatically learned from the data). The main contributions of our model are: (a) providing interpretation of the parameters, (b) determining which parameters of the model are more important to produce changes in the spread of the disease, and (c) using data-driven discovery of sudden changes in the evolution of the pandemic. Together, these characteristics provide a new model that better describes the situation and thus, provides better quality of information for decision making.