TY - JOUR
T1 - Predicting New Daily COVID-19 Cases and Deaths Using Search Engine Query Data in South Korea from 2020 to 2021
T2 - Infodemiology Study
AU - Husnayain, Atina
AU - Shim, Eunha
AU - Fuad, Anis
AU - Su, Emily Chia Yu
N1 - Funding Information:
This work was supported, via grants to ECYS, by the Ministry of Science and Technology in Taiwan (grants MOST109-2221-E-038-018 and MOST110-2628-E-038-001) and the Higher Education Sprout Project by the Ministry of Education in Taiwan (grant DP2-110-21121-01-A-13). This work was also supported, via a grant to ES, by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (grant 2021R1A6A1A10044154). The sponsors had no role in the research design or content of the manuscript for publication. The authors wish to acknowledge the Center for Systems Science and Engineering at Johns Hopkins University for use of their geographic information system dashboard and providing open-access data on daily cumulative COVID-19 cases and deaths in South Korea. In addition, the authors wish to acknowledge Google and Apple for allowing access to freely available data on community mobility, and NAVER for allowing access to the online search volumes.
Publisher Copyright:
© Atina Husnayain, Eunha Shim, Anis Fuad, Emily Chia-Yu Su. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 22.12.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
PY - 2021/12
Y1 - 2021/12
N2 - Background: Given the ongoing COVID-19 pandemic situation, accurate predictions could greatly help in the health resource management for future waves. However, as a new entity, COVID-19's disease dynamics seemed difficult to predict. External factors, such as internet search data, need to be included in the models to increase their accuracy. However, it remains unclear whether incorporating online search volumes into models leads to better predictive performances for long-term prediction. Objective: The aim of this study was to analyze whether search engine query data are important variables that should be included in the models predicting new daily COVID-19 cases and deaths in short- and long-term periods. Methods: We used country-level case-related data, NAVER search volumes, and mobility data obtained from Google and Apple for the period of January 20, 2020, to July 31, 2021, in South Korea. Data were aggregated into four subsets: 3, 6, 12, and 18 months after the first case was reported. The first 80% of the data in all subsets were used as the training set, and the remaining data served as the test set. Generalized linear models (GLMs) with normal, Poisson, and negative binomial distribution were developed, along with linear regression (LR) models with lasso, adaptive lasso, and elastic net regularization. Root mean square error values were defined as a loss function and were used to assess the performance of the models. All analyses and visualizations were conducted in SAS Studio, which is part of the SAS OnDemand for Academics. Results: GLMs with different types of distribution functions may have been beneficial in predicting new daily COVID-19 cases and deaths in the early stages of the outbreak. Over longer periods, as the distribution of cases and deaths became more normally distributed, LR models with regularization may have outperformed the GLMs. This study also found that models performed better when predicting new daily deaths compared to new daily cases. In addition, an evaluation of feature effects in the models showed that NAVER search volumes were useful variables in predicting new daily COVID-19 cases, particularly in the first 6 months of the outbreak. Searches related to logistical needs, particularly for “thermometer” and “mask strap,” showed higher feature effects in that period. For longer prediction periods, NAVER search volumes were still found to constitute an important variable, although with a lower feature effect. This finding suggests that search term use should be considered to maintain the predictive performance of models. Conclusions: NAVER search volumes were important variables in short- and long-term prediction, with higher feature effects for predicting new daily COVID-19 cases in the first 6 months of the outbreak. Similar results were also found for death predictions.
AB - Background: Given the ongoing COVID-19 pandemic situation, accurate predictions could greatly help in the health resource management for future waves. However, as a new entity, COVID-19's disease dynamics seemed difficult to predict. External factors, such as internet search data, need to be included in the models to increase their accuracy. However, it remains unclear whether incorporating online search volumes into models leads to better predictive performances for long-term prediction. Objective: The aim of this study was to analyze whether search engine query data are important variables that should be included in the models predicting new daily COVID-19 cases and deaths in short- and long-term periods. Methods: We used country-level case-related data, NAVER search volumes, and mobility data obtained from Google and Apple for the period of January 20, 2020, to July 31, 2021, in South Korea. Data were aggregated into four subsets: 3, 6, 12, and 18 months after the first case was reported. The first 80% of the data in all subsets were used as the training set, and the remaining data served as the test set. Generalized linear models (GLMs) with normal, Poisson, and negative binomial distribution were developed, along with linear regression (LR) models with lasso, adaptive lasso, and elastic net regularization. Root mean square error values were defined as a loss function and were used to assess the performance of the models. All analyses and visualizations were conducted in SAS Studio, which is part of the SAS OnDemand for Academics. Results: GLMs with different types of distribution functions may have been beneficial in predicting new daily COVID-19 cases and deaths in the early stages of the outbreak. Over longer periods, as the distribution of cases and deaths became more normally distributed, LR models with regularization may have outperformed the GLMs. This study also found that models performed better when predicting new daily deaths compared to new daily cases. In addition, an evaluation of feature effects in the models showed that NAVER search volumes were useful variables in predicting new daily COVID-19 cases, particularly in the first 6 months of the outbreak. Searches related to logistical needs, particularly for “thermometer” and “mask strap,” showed higher feature effects in that period. For longer prediction periods, NAVER search volumes were still found to constitute an important variable, although with a lower feature effect. This finding suggests that search term use should be considered to maintain the predictive performance of models. Conclusions: NAVER search volumes were important variables in short- and long-term prediction, with higher feature effects for predicting new daily COVID-19 cases in the first 6 months of the outbreak. Similar results were also found for death predictions.
KW - COVID-19
KW - Infodemiology
KW - Internet search
KW - Prediction
KW - South Korea
UR - http://www.scopus.com/inward/record.url?scp=85121980701&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85121980701&partnerID=8YFLogxK
U2 - 10.2196/34178
DO - 10.2196/34178
M3 - Article
C2 - 34762064
AN - SCOPUS:85121980701
SN - 1439-4456
VL - 23
JO - Journal of Medical Internet Research
JF - Journal of Medical Internet Research
IS - 12
M1 - e34178
ER -