Using a partial differential equation with Google Mobility data to predict COVID-19 in Arizona.

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• COVID-19 significantly disrupted global life, with Arizona identified as a major US hotspot.
• Accurate forecasting is crucial for effective public health interventions and personal precaution adoption.
• Predicting COVID-19 cases is challenging due to numerous human behavioral factors.

Purpose of the Study:

• To develop and validate a novel forecasting model for COVID-19 cases.
• To integrate human activity data from Google Community Mobility Reports into a predictive model.
• To analyze the combined effects of inter-county spread and human behavior on COVID-19 transmission in Arizona.

Main Methods:

• Development of a specific partial differential equation (PDE) model.
• Validation using county-level COVID-19 data from Arizona (sourced from The New York Times).
• Incorporation of Google Community Mobility Reports to represent human activity.

Main Results:

• The developed PDE model demonstrated high prediction accuracy, exceeding 94%.
• The model effectively captures the influence of human activities and transboundary spread on COVID-19 transmission.
• Analysis of personal precautions like mask-wearing and social distancing provided localized insights.

Conclusions:

• The study presents a validated PDE model for COVID-19 forecasting, incorporating human mobility data.
• The findings offer valuable insights into mitigating COVID-19 spread in Arizona through informed public health strategies.
• This research is the first to apply PDE models for COVID-19 prediction using Google Community Mobility Reports.