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Data Driven decision support during COVID.
Street, Michael; Mestric, Ivana Ilic; Ndoni, Adelica; Lenk, Peter; Teufert, John; Figueiredo, Nuno.
  • Street M; NATO Communication and Information Agency, The Hague, Netherlands.
  • Mestric II; NATO Communication and Information Agency, The Hague, Netherlands.
  • Ndoni A; NATO Communication and Information Agency, The Hague, Netherlands.
  • Lenk P; NATO Communication and Information Agency, The Hague, Netherlands.
  • Teufert J; NATO Communication and Information Agency, The Hague, Netherlands.
  • Figueiredo N; NATO Communication and Information Agency, The Hague, Netherlands.
Procedia Comput Sci ; 205: 117-126, 2022.
Article in English | MEDLINE | ID: covidwho-2042094
ABSTRACT
This paper outlines the development and use of a tool suite developed by the NCI Agency to provide situational awareness and decision support during the current Covid-19. The tool suite was developed to understand how Covid-19 could impact the provision of communication and information services (CIS) to NATO, and so understand where risks to NATO operational functions might occur. The tool suite combines open source data on instances of Covid-19 globally along with internal information about the impact of Covid-19 on NCI Agency staff and the services they deliver to the NATO enterprise. It supports business impact assessments due to Covid-19; showing trends, age demographics, and providing early indications of critical services that may be affected, sites that may be affected, etc. The tool suite is an example of data science techniques supporting data driven decision making within a military organization.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Procedia Comput Sci Year: 2022 Document Type: Article Affiliation country: J.procs.2022.09.013

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Procedia Comput Sci Year: 2022 Document Type: Article Affiliation country: J.procs.2022.09.013