Improved Prediction of COVID-19 Transmission and Mortality Using Google Search Trends for Symptoms in the United States
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources. Due to the exponential spread of the COVID-19 infection worldwide, several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the literature. To accelerate scientific and public health insights into the spread and impact of COVID-19, Google released the Google COVID-19 search trends symptoms open-access dataset. Our objective is to develop 7 and 14 -day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related symptoms. Specifically, we propose a stacked long short-term memory (SLSTM) architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms dataset. Considering the SLSTM networks trained using historical data only as the base models, our base models for 7 and 14 -day-ahead forecasting of COVID cases had the mean absolute percentage error (MAPE) values of 6.6% and 8.8%, respectively. On the other side, our proposed models had improved MAPE values of 3.2% and 5.6%, respectively. For 7 and 14 -day-ahead forecasting of COVID-19 deaths, the MAPE values of the base models were 4.8% and 11.4%, while the improved MAPE values of our proposed models were 4.7% and 7.8%, respectively. We found that the Google search trends for “pneumonia,” “shortness of breath,” and “fever” are the most informative search trends for predicting COVID-19 transmission. We also found that the search trends for “hypoxia” and “fever” were the most informative trends for forecasting COVID-19 mortality.
Article activity feed
-
SciScore for 10.1101/2021.03.14.21253554: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources For these four prediction tasks, we experimented with the following types of input features: i) historical data; ii) individual Google search trend for nine COVID-19 related symptoms; iii) historical data and single Google search trend for nine COVID-19 related symptoms; iv) historical data and top two Google search trends for nine COVID-19 related symptoms determined using the validation set in step 3 experiments; v) historical data and top three Google search trends for nine COVID-19 related symptoms determined using the validation set in step 3 experiments. Googlesuggested: (Google, …SciScore for 10.1101/2021.03.14.21253554: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources For these four prediction tasks, we experimented with the following types of input features: i) historical data; ii) individual Google search trend for nine COVID-19 related symptoms; iii) historical data and single Google search trend for nine COVID-19 related symptoms; iv) historical data and top two Google search trends for nine COVID-19 related symptoms determined using the validation set in step 3 experiments; v) historical data and top three Google search trends for nine COVID-19 related symptoms determined using the validation set in step 3 experiments. Googlesuggested: (Google, RRID:SCR_017097)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:To overcome this limitation, we are interested in developing state-specific models, which is the subject of our ongoing work.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
-
