Prediction of risk factors for scrub typhus from 2006 to 2019 based on random forest model in Guangzhou, China

被引:3
作者
Huang, Xiaobin [1 ,2 ]
Xie, Binbin [3 ]
Long, Jiali [2 ]
Chen, Haiyan [2 ]
Zhang, Hao [2 ]
Fan, Lirui [2 ]
Chen, Shouyi [2 ]
Chen, Kuncai [2 ]
Wei, Yuehong [2 ]
机构
[1] Sun Yat Sen Univ, Sch Publ Hlth, Guangzhou, Peoples R China
[2] Guangzhou Ctr Dis Control & Prevent, Dept Parasit Dis & Endem Dis Control & Prevent, Guangzhou, Peoples R China
[3] Hainan Trop Dis Res Ctr, Dept Surveillance & Control, Haikou, Peoples R China
关键词
prediction; random forest model; risk factors; scrub typhus; METEOROLOGICAL FACTORS; PESCADORES ISLANDS; HEMORRHAGIC-FEVER; RENAL SYNDROME; OUTBREAK; ORIENTIA; CLIMATE; RICKETTSIOSES; TRANSMISSION; ASSOCIATION;
D O I
10.1111/tmi.13896
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Objectives: Scrub typhus is an increasingly serious public health problem, which is becoming the most common vector-borne disease in Guangzhou. This study aimed to analyse the correlation between scrub typhus incidence and potential factors and rank the importance of influential factors.Methods: We collected monthly scrub typhus cases, meteorological variables, rodent density (RD), Normalised Difference Vegetation Index (NDVI), and land use type in Guangzhou from 2006 to 2019. Correlation analysis and a random forest model were used to identify the risk factors for scrub typhus and predict the importance rank of influencing factors related to scrub typhus incidence.Results: The epidemiological results of the scrub typhus cases in Guangzhou between 2006 and 2019 showed that the incidence rate was on the rise. The results of correlation analysis revealed that a positive relationship between scrub typhus incidence and meteorological factors of mean temperature (T-mean), accumulative rainfall (RF), relative humidity (RH), sunshine hours (SH), and NDVI, RD, population density, and green land coverage area (all p < 0.001). Additionally, we tested the relationship between the incidence of scrub typhus and the lagging meteorological factors through cross-correlation function, and found that incidence was positively correlated with 1-month lag T-mean, 2-month lag RF, 2-month lag RH, and 6-month lag SH (all p < 0.001). Based on the random forest model, we found that the T-mean was the most important predictor among the influential factors, followed by NDVI.Conclusions: Meteorological factors, NDVI, RD, and land use type jointly affect the incidence of scrub typhus in Guangzhou. Our results provide a better understanding of the influential factors correlated with scrub typus, which can improve our capacity for biological monitoring and help public health authorities to formulate disease control strategies.
引用
收藏
页码:551 / 561
页数:11
相关论文
共 50 条
  • [41] Research on Wind Power Prediction Model Based on Random Forest and SVR
    Wang Z.
    Chi D.
    [J]. EAI Endorsed Transactions on Energy Web, 2024, 11 : 1 - 8
  • [42] A Signal Strength Fluctuation Prediction Model Based on the Random Forest Algorithm
    Dohnalek, P.
    Dvorsky, M.
    Gajdos, P.
    Michalek, L.
    Sebesta, R.
    Voznak, M.
    [J]. ELEKTRONIKA IR ELEKTROTECHNIKA, 2014, 20 (05) : 123 - 126
  • [43] Identification and Analysis of Industrial Land in China Based on the Point of Interest Data and Random Forest Model
    He, Qingsong
    Tang, Xinyu
    [J]. FRONTIERS IN ENVIRONMENTAL SCIENCE, 2022, 10
  • [44] Retrospective Study on the Influencing Factors and Prediction of Hospitalization Expenses for Chronic Renal Failure in China Based on Random Forest and LASSO Regression
    Dai, Pingping
    Chang, Weifu
    Xin, Zirui
    Cheng, Haiwei
    Ouyang, Wei
    Luo, Aijing
    [J]. FRONTIERS IN PUBLIC HEALTH, 2021, 9 : 678276
  • [45] Changes in seasonality and sex ratio of scrub typhus: a case study of South Korea from 2003 to 2019 based on wavelet transform analysis
    Kim, Jeehyun
    Vounatsou, Penelope
    Chun, Byung Chul
    [J]. BMC INFECTIOUS DISEASES, 2024, 24 (01)
  • [46] District prediction of cholera risk in China based on environmental factors
    Xu Min
    Cao ChunXiang
    Wang DuoChun
    Kan Biao
    Jia HuiCong
    Xu YunFei
    Li XiaoWen
    [J]. CHINESE SCIENCE BULLETIN, 2013, 58 (23): : 2798 - 2804
  • [47] Prediction of the spatial distribution of soil arthropods using a random forest model: A case study in Changtu County, Northeast China
    Guo, Xiaoyu
    Bian, Zhenxing
    Wang, Shuai
    Wang, Qiubing
    Zhang, Yufei
    Zhou, Jun
    Lin, Lin
    [J]. AGRICULTURE ECOSYSTEMS & ENVIRONMENT, 2020, 292
  • [48] Early Prediction of the 2019 Novel Coronavirus Outbreak in the Mainland China Based on Simple Mathematical Model
    Zhong, Linhao
    Mu, Lin
    Li, Jing
    Wang, Jiaying
    Yin, Zhe
    Liu, Darong
    [J]. IEEE ACCESS, 2020, 8 : 51761 - 51769
  • [49] Factors Associated with Severe Adenovirus Pneumonia in Children: A Retrospective Study from Guangzhou, China (2017-2019)
    Hong, Yan
    Wang, Qiang
    Song, Yongling
    Liu, Guangming
    Shen, Jun
    [J]. MEDICAL SCIENCE MONITOR, 2023, 29
  • [50] Flight trip fuel volume prediction based on random forest with adjustment to risk preference
    Wu Z.
    Zhang N.
    Gao K.
    Peng R.
    [J]. Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica, 2022, 43 (02):