传染病(医学专业)
预测(人工智能)
疟疾
传染病的数学模型
生物
脊髓灰质炎
公共卫生
管理科学
风险分析(工程)
疾病
计算机科学
病毒学
免疫学
医学
经济
人工智能
护理部
病理
作者
Hans Heesterbeek,Roy M. Anderson,Viggo Andreasen,Shweta Bansal,Daniela De Angelis,Chris Dye,Ken Eames,W. John Edmunds,Simon D. W. Frost,Sebastian Funk,T. Déirdre Hollingsworth,Thomas House,Valerie Isham,Petra Klepac,Justin Lessler,Alun L. Lloyd,C. Jessica E. Metcalf,Denis Mollison,Lorenzo Pellis,Juliet R. C. Pulliam,M. G. Roberts,Cécile Viboud
出处
期刊:Science
[American Association for the Advancement of Science (AAAS)]
日期:2015-03-13
卷期号:347 (6227)
被引量:546
标识
DOI:10.1126/science.aaa4339
摘要
Mathematical modeling of infectious diseases The spread of infectious diseases can be unpredictable. With the emergence of antibiotic resistance and worrying new viruses, and with ambitious plans for global eradication of polio and the elimination of malaria, the stakes have never been higher. Anticipation and measurement of the multiple factors involved in infectious disease can be greatly assisted by mathematical methods. In particular, modeling techniques can help to compensate for imperfect knowledge, gathered from large populations and under difficult prevailing circumstances. Heesterbeek et al. review the development of mathematical models used in epidemiology and how these can be harnessed to develop successful control strategies and inform public health policy. Science , this issue 10.1126/science.aaa4339
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