International COVID-19 mortality forecast visualization: covidcompare.io.
International COVID-19 mortality forecast visualization: covidcompare.io.
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DOI：
10.1093/jamiaopen/ooab113
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发表时间：
2021-10
期刊：
影响因子：
2.1
通讯作者：
Bui AAT
中科院分区：
文献类型：
--
作者：
Akre S;Liu PY;Friedman JR;Bui AAT
新冠肺炎死亡率预测模型提供了有关疫情轨迹的关键信息，政策制定者和公共卫生官员利用这些信息来指导决策。然而，现在存在数以千计的已发布的新冠肺炎死亡率预测，其中许多预测具有自己独特的方法、假设、格式和可视化。因此，很难比较模型，也很难理解模型在哪些情况下执行得最好。在这里，我们描述covidCompare.io的构建和可用性，这是一个网络工具，用于比较众多预测，并提供对每个预测在大流行过程中的表现的洞察。从2020年12月推出到2021年6月，我们已经看到了来自85个国家的4600名独立访问者。与公共卫生专业人员进行的一项研究显示，总体上使用研究后系统可用性问卷正式评估的可用性很高。我们发现，CovidCompare.io是比较国际新冠肺炎死亡率预测模型的有效工具。
COVID-19 mortality forecasting models provide critical information about the trajectory of the pandemic, which is used by policymakers and public health officials to guide decision-making. However, thousands of published COVID-19 mortality forecasts now exist, many with their own unique methods, assumptions, format, and visualization. As a result, it is difficult to compare models and understand under which circumstances a model performs best. Here, we describe the construction and usability of covidcompare.io, a web tool built to compare numerous forecasts and offer insight into how each has performed over the course of the pandemic. From its launch in December 2020 to June 2021, we have seen 4600 unique visitors from 85 countries. A study conducted with public health professionals showed high usability overall as formally assessed using a Post-Study System Usability Questionnaire. We find that covidcompare.io is an impactful tool for the comparison of international COVID-19 mortality forecasting models.
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影响因子：
16.6
作者：
Friedman J;Liu P;Troeger CE;Carter A;Reiner RC Jr;Barber RM;Collins J;Lim SS;Pigott DM;Vos T;Hay SI;Murray CJL;Gakidou E
通讯作者：
Gakidou E
DOI：
10.1016/b978-0-12-802308-2.00008-4
发表时间：
2016-01-01
期刊：
QUANTIFYING THE USER EXPERIENCE: PRACTICAL STATISTICS FOR USER RESEARCH, 2ND EDITION
影响因子：
--
作者：
Sauro, Jeff;Lewis, James R.
通讯作者：
Lewis, James R.
影响因子：
29.9
作者：
Mathieu, Edouard;Ritchie, Hannah;Rodes-Guirao, Lucas
通讯作者：
Rodes-Guirao, Lucas
DOI：
10.1093/jamia/ocab004
发表时间：
2021-02-24
影响因子：
6.4
作者：
Dixon, Brian E.;Grannis, Shaun J.;Embi, Peter J.
通讯作者：
Embi, Peter J.