Predicting influenza trends in the context of post-COVID immunity gaps in Macao, China

Ying Liu; Kefeng Li; Hoiman Ng; Qianhong Ye; Henry H Y Tong

doi:10.1093/jtm/taaf035

Predicting influenza trends in the context of post-COVID immunity gaps in Macao, China

J Travel Med. 2025 Jul 1;32(5):taaf035. doi: 10.1093/jtm/taaf035.

Authors

Ying Liu^{1

2}, Kefeng Li¹, Hoiman Ng³, Qianhong Ye⁴, Henry H Y Tong^{1

4}

Affiliations

¹ Faculty of Applied Sciences, Macao Polytechnic University, Rua de Luís Gonzaga Gomes, Macau SAR 99078, China.
² School of International Business, Xiamen University Tan Kah Kee College, China Merchants Zhangzhou Development Zone, Zhangzhou 363105, China.
³ Clinical Laboratory, Kiang Wu Hospital, Rua de Coelho do Amaral, Santo António District, Macau SAR 99078, China.
⁴ Faculty of Health Sciences and Sports, Macao Polytechnic University, Rua de Coelho do Amaral, Santo António District, Macau SAR 99078, China.

Abstract

We used Google and Baidu search data with machine learning to predict flu trends in Macao. ‘Cold’ was the strongest early indicator, with XGBoost showing the best performance. Findings support enhanced flu surveillance amid rising risks from post-COVID immunity debt and growing tourist inflow.

Keywords: Baidu index; Google trends; Predict; influenza; search term.

Abstract

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