Associations Between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States

Abbas, Mostafa M.; Morland, Thomas B.; Hall, Eric S.; EL-Manzalawy, Yasser

doi:10.1101/2021.02.22.21252254

Cited by 8 publications

(5 citation statements)

References 32 publications

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“…It is worse noting that our results demonstrated the added value of using Google search trends for COVID-19 related symptoms (i.e., fever, pneumonia, shortness of breath, and hypoxia) combined with historical data in forecasting COVID-19 confirmed and death cases. However, despite their strong and significant correlations with COVID-19 spread and death trajectories reported in [41], we found that SLSTM models trained using any of these symptoms alone yielded models with poor performance. In addition to the improvement in predictive performance obtained via using Google symptoms time series along with the historical data in training our models, another significant gain is improving the generalizability of the models on both validation and test sets (i.e., the best performing model on the validation set is the model with the best performance on the test set).…”

Section: Analysis Of State-level Predictions Of the Best Performing Modelsmentioning

confidence: 55%

“…We have developed deep learning models for forecasting COVID-19 infection and mortality in the US using historical data and Google search trends for COVID-19 related symptoms. Out of 422 symptoms included in the Google COVID-19 symptoms database [43], we have focused on the nine symptoms identified in [41] using dynamic correlation analysis. We then re-ranked these nine symptoms based on the performance of deep learning models trained using historical data and every single symptom.…”

Section: Discussionmentioning

confidence: 99%

“…The data includes search trends for 422 symptoms that might be related to COVID-19. However, we limited our experiments to the nine symptoms suggested by the exploratory functional data analysis [44] provided in [41]. Therefore, our final symptoms dataset includes time series for the following nine symptoms: hypoxemia, ageusia, anosmia, dysgeusia, hypoxia, fever, pneumonia, chills, and shortness of breath (SOB).…”

Section: Datamentioning

confidence: 99%

“…We proceed with reporting our experimental results for testing two hypotheses regarding the Google search trends for nine COVID-19 related symptoms: i) SLSTM models trained using any single symptom can predict COVID-19 confirmed and death cases; ii) SLSTM models trained using any single symptom combined with COVID-19 historical data can better predict COVID-19 confirmed and death cases compared with SLSTM models trained using historical data only. Our rationale is that these nine symptoms had been shown to have strong associations with both of COVID-19 transmission and mortality [41] and, therefore, could be used for forecasting COVID-19 transmission and mortality or for improving the performance of the models developed for the four tasks considered in this study.…”

Section: Improved Prediction Of Covid-19 Confirmed and Death Cases Using Google Search Trends For Covid-19 Symptomsmentioning

confidence: 99%

“…Recently, Abbas et al [41] have shown that Google search trends for nine COVID-19 related symptoms (namely, hypoxemia, ageusia, anosmia, dysgeusia, hypoxia, fever, pneumonia, chills, and shortness of breath (SOB)) are strongly associated with COVID-19 confirmed as well as death cases in the US. Results of their analysis suggested that these Google trends can be used (in combination with COVID-19 historical data) to forecast COVID-19 spread and mortality up to three weeks ahead in time.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of COVID-19 Transmission in the United States Using Google Search Trends

Alruily¹,

Ezz²,

Mostafa³

et al. 2022

Computers, Materials &Amp; Continua

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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 14day-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.

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Section: Analysis Of State-level Predictions Of the Best Performing Modelsmentioning

confidence: 55%

Section: Discussionmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Improved Prediction Of Covid-19 Confirmed and Death Cases Using Google Search Trends For Covid-19 Symptomsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of COVID-19 Transmission in the United States Using Google Search Trends

Alruily¹,

Ezz²,

Mostafa³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

Improved Prediction of COVID-19 Transmission and Mortality Using Google Search Trends for Symptoms in the United States

Alruily

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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.

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Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

Clark,

Neumann,

Hopkins

et al. 2024

JMIR Public Health Surveill

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Background Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts. Objective This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods. Methods A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers. Results Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses. Conclusions The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.

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Associations Between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States

Cited by 8 publications

References 32 publications

Prediction of COVID-19 Transmission in the United States Using Google Search Trends

Prediction of COVID-19 Transmission in the United States Using Google Search Trends

Improved Prediction of COVID-19 Transmission and Mortality Using Google Search Trends for Symptoms in the United States

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

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