Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data-Reference-Cited by-同舟云学术

Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data

Published:2019-12 Issue:2 Volume:67 Page:935-946
ISSN:1865-1674
Container-title:Transboundary and Emerging Diseases
language:en
Short-container-title:Transbound Emerg Dis

Author:

Liang Ruirui¹^ORCID,Lu Yi¹^ORCID,Qu Xiaosheng²^ORCID,Su Qiang¹³⁴^ORCID,Li Chunxia¹^ORCID,Xia Sijing¹^ORCID,Liu Yongxin¹^ORCID,Zhang Qiang⁵^ORCID,Cao Xin⁶^ORCID,Chen Qin¹^ORCID,Niu Bing¹^ORCID

Affiliation:

1. School of Life Sciences Shanghai University Shanghai China

2. National Engineering Laboratory of Southwest Endangered Medicinal Resources Development Guangxi Botanical Garden of Medicinal Plants Nanning China

3. Computing Center of Guangxi Nanning China

4. Guangxi Institute for Food and Drug Control Nanning China

5. Technical Center For Animal, Plant and Food Inspection and Quarantine of Shanghai Customs Shanghai China

6. Institute of Clinical Science Shanghai Medical College Zhongshan Hospital Fudan University Shanghai China

Publisher

Wiley

Subject

General Veterinary,General Immunology and Microbiology,General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/tbed.13424

Reference43 articles.

1. Prediction of Shigellosis outcomes in Israel using machine learning classifiers

2. The global distribution and burden of dengue

3. A random forest guided tour

4. Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines

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