流行病建模的主要挑战——迄今为止,我们从COVID-19流行病中学到了什么?

IF 1.6 4区 医学 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Zdravstveno Varstvo Pub Date : 2020-06-25 eCollection Date: 2020-09-01 DOI:10.2478/sjph-2020-0015
Ivan Eržen, Tina Kamenšek, Miha Fošnarič, Janez Žibert
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引用次数: 9

摘要

数学建模可用于预测传染病的发展,使我们能够显示流行病的可能结果,并帮助为公共卫生干预提供信息。不同的建模技术被用于预测和模拟COVID-19的传播,但它们对流行病学家和决策者并不总是有用。为了提高建模结果的可靠性,非常重要的是要严格评估所使用的数据,并检查是否适当考虑了疾病在人群中传播的不同方式。要建立一个足够可靠、适合某一国家或地区流行病学现状的流行病学模型,在建模过程中必须满足一定的标准。可能有必要结合使用两种或两种以上不同类型的模型,以便涵盖流行病建模的所有方面。如果我们希望流行病学模型成为抗击这一流行病的有用工具,我们就需要让流行病学、数据科学和统计学方面的专家参与进来。
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Key Challenges in Modelling an Epidemic - What have we Learned from the COVID-19 Epidemic so Far.

Mathematical modelling can be useful for predicting how infectious diseases progress, enabling us to show the likely outcome of an epidemic and help inform public health interventions. Different modelling techniques have been used to predict and simulate the spread of COVID-19, but they have not always been useful for epidemiologists and decision-makers. To improve the reliability of the modelling results, it is very important to critically evaluate the data used and to check whether or not due regard has been paid to the different ways in which the disease spreads through the population. As building an epidemiological model that is reliable enough and suits the current epidemiological situation within a country or region, certain criteria must be met in the modelling process. It might be necessary to use a combination of two or more different types of models in order to cover all aspects of epidemic modelling. If we want epidemiological models to be a useful tool in combating the epidemic, we need to engage experts from epidemiology, data science and statistics.

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来源期刊
Zdravstveno Varstvo
Zdravstveno Varstvo PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
3.00
自引率
20.00%
发文量
30
审稿时长
23 weeks
期刊最新文献
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