A participatory modelling approach for investigating the spread of COVID-19 in countries of the Eastern Mediterranean Region to support public health decision-making.
A participatory modelling approach for investigating the spread of COVID-19 in countries of the Eastern Mediterranean Region to support public health decision-making.
复制标题
在东地中海区域各国调查COVID-19传播以支持公共卫生决策的参与式建模方法。
DOI：
10.1136/bmjgh-2021-005207
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发表时间：
2021-03
影响因子：
8.1
通讯作者：
Nabeth P
中科院分区：
文献类型：
--
作者：
Adib K;Hancock PA;Rahimli A;Mugisa B;Abdulrazeq F;Aguas R;White LJ;Hajjeh R;Al Ariqi L;Nabeth P
在 COVID-19 大流行初期，世卫组织东地中海区域办事处就认识到流行病学模型对于预测 COVID-19 大流行的进展以支持指导实施应对措施的决策的重要性。我们建立了一个建模支持团队，以促进流行病学模型分析在东地中海地区（EMR）国家的应用。在这里，我们提出了一种创新的、逐步的方法来对 COVID-19 大流行进行参与式建模，该方法让各国的决策者和公共卫生专业人员参与了建模过程的所有阶段。我们的方法包括首先确定相关政策问题，收集特定国家的数据，从决策者的角度解释模型结果，以及传达模型的不确定性。我们使用了一种简单的建模方法，该方法适合我们地区流行病学数据的短缺和建模能力有限的情况。我们讨论了使用模型为 WHO EMR 中的 COVID-19 控制提供快速决策指导的好处，以及我们在传达与模型结果相关的不确定性、综合和比较多种建模方法的结果以及对脆弱和受冲突影响国家建模方面遇到的挑战。
Early on in the COVID-19 pandemic, the WHO Eastern Mediterranean Regional Office recognised the importance of epidemiological modelling to forecast the progression of the COVID-19 pandemic to support decisions guiding the implementation of response measures. We established a modelling support team to facilitate the application of epidemiological modelling analyses in the Eastern Mediterranean Region (EMR) countries. Here, we present an innovative, stepwise approach to participatory modelling of the COVID-19 pandemic that engaged decision-makers and public health professionals from countries throughout all stages of the modelling process. Our approach consisted of first identifying the relevant policy questions, collecting country-specific data and interpreting model findings from a decision-maker’s perspective, as well as communicating model uncertainty. We used a simple modelling methodology that was adaptable to the shortage of epidemiological data, and the limited modelling capacity, in our region. We discuss the benefits of using models to produce rapid decision-making guidance for COVID-19 control in the WHO EMR, as well as challenges that we have experienced regarding conveying uncertainty associated with model results, synthesising and comparing results across multiple modelling approaches, and modelling fragile and conflict-affected states.
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影响因子：
8.1
作者：
Aguas R;White L;Hupert N;Shretta R;Pan-Ngum W;Celhay O;Moldokmatova A;Arifi F;Mirzazadeh A;Sharifi H;Adib K;Sahak MN;Franco C;Coutinho R;CoMo Consortium
通讯作者：
CoMo Consortium
影响因子：
3.5
作者：
Eubank, S.;Eckstrand, I;Barrett, C. L.
通讯作者：
Barrett, C. L.
影响因子：
3.5
作者：
Calder M;Craig C;Culley D;de Cani R;Donnelly CA;Douglas R;Edmonds B;Gascoigne J;Gilbert N;Hargrove C;Hinds D;Lane DC;Mitchell D;Pavey G;Robertson D;Rosewell B;Sherwin S;Walport M;Wilson A
通讯作者：
Wilson A
影响因子：
4.9
作者：
Basco-Carrera, Laura;Warren, Andrew;Giardino, Alessio
通讯作者：
Giardino, Alessio
影响因子：
15.8
作者：
Truelove, Shaun;Abrahim, Orit;Spiegel, Paul
通讯作者：
Spiegel, Paul