Predicting COVID-19 new cases in California with Google Trends data and a machine learning approach.

Background: Google Trends data can be a valuable source of information for health-related issues such as predicting infectious disease trends.

Objectives: To evaluate the accuracy of predicting COVID-19 new cases in California using Google Trends data, we develop and use a GMDH-type neural network model and compare its performance with a LTSM model.

Methods: We predicted COVID-19 new cases using Google query data over three periods.

Estimating Excess Deaths by Race/Ethnicity in the State of California During the COVID-19 Pandemic.

Introduction: To examine excess mortality among minorities in California during the COVID-19 pandemic.

Methods: Using seasonal autoregressive integrated moving average time series, we estimated counterfactual total deaths using historical data (2014-2019) of all-cause mortality by race/ethnicity. Estimates were compared to pandemic mortality trends (January 2020 to January 2021) to predict excess deaths during the pandemic for each race/ethnic group.

Analysis of Excess Deaths During the COVID-19 Pandemic in the State of Florida.

To determine the number of excess deaths (i.e., those exceeding historical trends after accounting for COVID-19 deaths) occurring in Florida during the COVID-19 pandemic.