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Introduction to COVID-19 epidemiological modeling

DOCUMENT DESCRIPTION

An overview of epidemiological SIR modeling for COVID-19, and introduction to relevant statistical theories and terms. And an overview of spatiotemporal datasets, which serve as the foundation of almost all spatial analysis including RMDS Lab's Project Coronavirus. Gain an understanding of how mapping acts as a powerful tool in visualizing and informing the trend of coronavirus worldwide.

RMDS Lab is a pioneer in developing data science and AI ecosystems, and in offering extensive assistance for data science with its unique RM4Es framework, ResearchMap, and ecosystem approach. We exist to empower our clients worldwide to achieve successful, data-driven results. With resources and tools in our robust scientific community, we have the capability to advance your project impact through data impact consultation and AI-powered services that evaluates your needs, deploys the necessary solutions, and maximize the effectiveness.

RMDS expands your company's reach to find new technologies and resources with the goal of reshaping how commercial clients, researchers and data scientists with industry experience work together to develop high-impact data science solutions.

We have a long-standing Ecosystem of data scientists, engineers, and subject matter experts, eager to be your trusted partner, enhance your research, increase the success rate of your work, and leverage the power of analytics. Each of our consultant teams has experience delivering and commercializing E2E data science solutions and software.

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Source: Overview: Epidemiological SIR Modeling for COVID-19 Outbreak PDF document

Overview: Epidemiological SIR Modeling for COVID-19 Outbreak

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This business document is categorized under the function(s):

Information Technology Health & Safety

It applies to Healthcare & Life Sciences

File Type: PDF (pdf)

File Size: 1.8 MB

Number of Pages: 33

Related Topic(s): COVID-19 Data Science

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Initial upload date (first version): May 6, 2021
Most recent version published: May 7, 2021

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