Developing predictive models for COVID-19 positive tests based on the XGBoost and random forest algorithms with internet search data

The XGBoost model with the lagged BSI can predict COVID-19 epidemics, which make it a powerful addition to the traditional surveillance systems.

Authors

Zhicheng Du (ORCID: https://orcid.org/0000-0002-1155-8443)
Wangjian Zhang (ORCID: https://orcid.org/0000-0001-9655-6385)
Haolin Chen (ORCID: https://orcid.org/0000-0003-2689-1369)
Jinwei Chen
Yikun Chang
Xiaoxuan Chen (ORCID: https://orcid.org/0000-0002-5559-9183)
Jie Sun (ORCID: https://orcid.org/0000-0001-5825-9830)
Hui Tang (ORCID: https://orcid.org/0000-0002-8060-4896)
Yueqian Wu
Songwang Cai (ORCID: https://orcid.org/0000-0001-7393-1242)
Gonghua Wu
Yuantao Hao
Yuyi Liao

Institutions

Sun Yat-sen University (CN)
Peking University (CN)
Guangzhou Center for Disease Control and Prevention (CN)
Ministry of Education (RO)

Publication Details

Journal: BMC Public Health
Published: 2025-11-28
DOI: https://doi.org/10.1186/s12889-025-25569-w
Primary Topic: Data-Driven Disease Surveillance
Type: article
Field-Weighted Citation Impact: 0.00

Funders

Baidu
National Natural Science Foundation of China

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article

Developing predictive models for COVID-19 positive tests based on the XGBoost and random forest algorithms with internet search data

Zhicheng Du, Wangjian Zhang, Haolin Chen, Jinwei Chen et al.

Nov 28, 2025

BMC Public Health

Data-Driven Disease Surveillance

article

Developing predictive models for COVID-19 positive tests based on the XGBoost and random forest algorithms with internet search data

Zhicheng Du, Wangjian Zhang, Haolin Chen, Jinwei Chen, Yikun Chang, Xiaoxuan Chen, Jie Sun, Hui Tang, Yueqian Wu, Songwang Cai, Gonghua Wu, Yuantao Hao, Yuyi Liao

November 28, 2025article en

Abstract

The XGBoost model with the lagged BSI can predict COVID-19 epidemics, which make it a powerful addition to the traditional surveillance systems.

BMC Public HealthVol. 25(1)

Sun Yat-sen University (CN), Peking University (CN), Guangzhou Center for Disease Control and Prevention (CN), Ministry of Education (RO)

Baidu, National Natural Science Foundation of China

Openalex Percentile: Top 51%

Data-Driven Disease Surveillance

DOI Open Access PDF OpenAlex

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Developing predictive models for COVID-19 positive tests based on the XGBoost and random forest algorithms with internet search data — Zhicheng Du, Wangjian Zhang, et al. · BMC Public Health (2025) | TGRS Research Map | TGRS