Abstract
Abstract Vaccination is the primary strategy to prevent COVID-19 illness and hospitalization. However, supplies are scarce and due to the regional mutations of the virus, new vaccines or booster shots will need to be administered potentially regularly. Hence, the prediction of the rate of growth of COVID-19 cases is paramount to ensuring the ample supply of vaccines as well as for local, state, and federal government measures to ensure the availability of hospital beds, supplies, and staff. eVision is an epidemic forecaster aimed at combining Machine Learning (ML) - in the form of a Long Short-Term Memory (LSTM) Recursive Neural Network (RNN) - and search engine statistics, in order to make accurate predictions about the weekly number of cases for highly communicable diseases. By providing eVision with the relative popularity of carefully selected keywords searched via Google along with the number of positive cases reported from the US Centers for Disease Control and Prevention (CDC) and/or the World Health Organization (WHO) the model can make highly accurate predictions about the trend of the outbreak by learning the relationship between the two trends. Thus, in order to predict the trend of the outbreak in a specific region, eVision is provided with a weekly count of the number of COVID-19 cases in a region along with statistics surrounding common symptom search phrases such as “loss of smell” and “loss of taste” that have been searched on Google in that region since the start of the pandemic. eVision has, for instance, been able to achieve an accuracy of %89 for predicting the trend of the COVID-19 outbreak in the United States
Highlights
Vaccination is the primary strategy to prevent COVID-19 illness and hospitalization
An Long Short-Term Memory (LSTM) neural network was constructed using functions provided by MATLAB's Deep Learning toolkit
Since unlike Influenza case data, COVID-19 data was made available by the Centers for Disease Control and Prevention (CDC) and World Health Organization (WHO) in close to real time, predictions were able to be made at one week in advance
Summary
Vaccination is the primary strategy to prevent COVID-19 illness and hospitalization. supplies are scarce and due to the regional mutations of the virus, new vaccines or booster shots will need to be administered potentially regularly. Since most of the forecasting models created to track the COVID-19 pandemic, estimate the number of infected people over time under given conditions based on epidemiological models [6], they have missed the insight discovered by eVision [7] previously on influenza forecasting: by providing the ML model with the relative popularity of carefully selected keywords searched via Google along with the number of positive cases reported from the US Centers for Disease Control and Prevention (CDC) and/or the World Health Organization (WHO) the model can make highly accurate predictions about the trend of the outbreak by learning the relationship between the two trends [8]. This paper is to report on the adaption and usage of eVision for COVID19 trend predictions which have yielded an accuracy of %89
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