University Preparatory Academy, San Jose, CA 95125, USA.
Monta Vista High School, Cupertino, CA 95014, USA.
Infect Genet Evol. 2021 Mar;88:104701. doi: 10.1016/j.meegid.2020.104701. Epub 2020 Dec 31.
Given the pertinence and acceleration of the spread of COVID-19, there is an increased need for the replicability of data models to verify the veracity of models and visualize important data. Most of these visualizations lack reproducibility, credibility, or accuracy, and are static, which makes it difficult to analyze the spread over time. Furthermore, most current visualizations depicting the spread of COVID-19 are at a global or country level, meaning there is a dearth of regional analysis within a country. Keeping these issues in mind, a replicable, efficient, and simple method to generate regional COVID-19 visualizations mapped with time was created by using the KNIME software, an open-source data analytics platform that can create user-friendly applications or workflows. For this analysis, Albania, Sweden, Ukraine, Denmark, Russia, India, and Australia were closely observed. Among the maps generated for the aforementioned countries, it was noticed that regions with a high population or high population density were often the epicenters within their respective country. The regions caused the virus to spread to their neighboring regions: kickstarting the "domino effect", leading to the infection of another region until the country is overwhelmed with cases-what we call a proximity trend. These dynamic maps are crucial to fighting the pandemic because they can provide insight as to how COVID-19 spreads by providing researchers or officials with an accurate and insightful tool to aid their analysis. By being able to visualize the spread, health and government officials can dive deeper to identify the sources of transmission and attempt to stop or reverse them accordingly.
鉴于 COVID-19 的传播相关性和加速性，越来越需要对数据模型进行复制，以验证模型的真实性并可视化重要数据。这些可视化大多数缺乏可重复性、可信度或准确性，并且是静态的，这使得难以随时间分析传播情况。此外，大多数当前描述 COVID-19 传播的可视化都是在全球或国家层面进行的，这意味着在一个国家内部缺乏区域分析。考虑到这些问题，使用 KNIME 软件创建了一种可复制、高效且简单的方法来生成带有时间映射的区域 COVID-19 可视化，KNIME 是一个开源数据分析平台，可以创建用户友好的应用程序或工作流程。在对上述国家进行分析时，人们注意到，人口众多或人口密度较高的地区通常是各自国家的中心。这些地区导致病毒传播到邻近地区：引发了“多米诺骨牌效应”，导致另一个地区感染，直到该国病例泛滥——我们称之为接近趋势。这些动态地图对于抗击大流行至关重要，因为它们可以提供有关 COVID-19 如何传播的见解，为研究人员或官员提供准确而有见地的工具来帮助他们进行分析。通过可视化传播，卫生和政府官员可以更深入地了解传播源，并尝试相应地阻止或扭转它们。
