A systematic review of environmental covariates and methods for spatial or temporal scrub typhus distribution prediction
文献类型：期刊论文
| 作者 | Wang, Qian10,11; Ma, Tian8,9; Ding, Fang-Yu8,9; Lim, Ahyoung7; Takaya, Saho7; Saraswati, Kartika5,6,11; Hao, Meng-Meng8,9; Jiang, Dong8,9; Fang, Li-Qun4; Sartorius, Benn2,3,10 |
| 刊名 | ENVIRONMENTAL RESEARCH
 |
| 出版日期 | 2024-12-15 |
| 卷号 | 263页码:120067 |
| 关键词 | Scrub typhus Environmental covariates Prediction model Systematic review |
| DOI | 10.1016/j.envres.2024.120067 |
| 产权排序 | 3 |
| 英文摘要 | Background: Scrub typhus is underdiagnosed and underreported but emerging as a global public health problem. To inform future burden and prediction studies we examined through a systematic review the potential effect of environmental covariates on scrub typhus occurrence and the methods which have been used for its prediction. Methods: In this systematic review, we searched PubMed, Scopus, Web of Science, China National Knowledge Infrastructure and other databases, with no language and publication time restrictions, for studies that investigated environmental covariates or utilized methods to predict the spatial or temporal human. Data were manually extracted following a set of queries and systematic analysis was conducted. Results: We included 68 articles published in 1978-2024 with relevant data from 7 countries/regions. Significant environmental risk factors for scrub typhus include temperature (showing positive or inverted-U relationships), precipitation (with positive or inverted-U patterns), humidity (exhibiting complex positive, inverted-U, or Wshaped associations), sunshine duration (with positive, inverted-U associations), elevation, the normalized difference vegetation index (NDVI), and the proportion of cropland. Socioeconomic and biological factors were rarely explored. Autoregressive Integrated Moving Average (ARIMA) (n = 8) and ecological niche modelling (ENM) approach (n = 11) were the most popular methods for predicting temporal trends and spatial distribution of scrub typhus, respectively. Conclusions: Our findings summarized the evidence on environmental covariates affecting scrub typhus occurrence and the methodologies used for predictive modelling. We review the existing knowledge gaps and outline recommendations for future studies modelling disease prediction and burden. |
| WOS关键词 | METEOROLOGICAL FACTORS |
| WOS研究方向 | Environmental Sciences & Ecology ; Public, Environmental & Occupational Health |
| WOS记录号 | WOS:001327981800001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/208223]  |
| 专题 | 资源利用与环境修复重点实验室_外文论文 |
| 通讯作者 | Ding, Fang-Yu |
| 作者单位 | 1.Open Univ, Milton Keynes, England 2.Univ Washington, Sch Med, Dept Hlth Metr Sci, Seattle, WA USA 3.Univ Queensland, Fac Med, Ctr Clin Res UQCCR, Brisbane, Qld, Australia 4.Beijing Inst Microbiol & Epidemiol, State Key Lab Pathogen & Biosecur, Beijing, Peoples R China 5.Univ Indonesia, Fac Med, Oxford Univ Clin Res Unit Indonesia, Jakarta, Indonesia 6.Natl Univ Singapore, Saw Swee Hock Sch Publ Hlth, Singapore, Singapore 7.London Sch Hyg & Trop Med, London, England 8.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China 9.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 10.Wang, Qian; Sartorius, Benn; Day, Nicholas P. J.; Maude, Richard J.] Univ Oxford, Ctr Trop Med & Global Hlth, Nuffield Dept Med, Oxford, England |
| 推荐引用方式 GB/T 7714 | Wang, Qian,Ma, Tian,Ding, Fang-Yu,et al. A systematic review of environmental covariates and methods for spatial or temporal scrub typhus distribution prediction[J]. ENVIRONMENTAL RESEARCH,2024,263:120067. |
| APA | Wang, Qian.,Ma, Tian.,Ding, Fang-Yu.,Lim, Ahyoung.,Takaya, Saho.,...&Maude, Richard J..(2024).A systematic review of environmental covariates and methods for spatial or temporal scrub typhus distribution prediction.ENVIRONMENTAL RESEARCH,263,120067. |
| MLA | Wang, Qian,et al."A systematic review of environmental covariates and methods for spatial or temporal scrub typhus distribution prediction".ENVIRONMENTAL RESEARCH 263(2024):120067. |
入库方式： OAI收割
来源：地理科学与资源研究所
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