COVID-19 and urban security: the power of AI


India distributed more than 10 million Covid-19 jabs on Friday, breaking the country’s previous daily record of 9.2 million. – © AFP

There are a multitude of factors shaping our perceptions and responses to the coronavirus pandemic. Making sense of all the variables is complex, and advances in machine learning can help this process.

Deep Knowledge Group, which is a consortium of business and non-profit organizations active in GovTech, BioTech, FinTech, and SpaceTech, has undertaken a review of the coronavirus and actions taken at the city level.

At the heart of the review are open source COVID-19 production and availability analyzes, such as quantitative analytical frameworks, metrics, and forecasting models. The purpose of the information is that it can be used by government agencies, corporations, financial institutions, universities, and non-profit organizations.

The last opinion is “COVID-19 City Safety T2 / 2021 ranking: Comparative analysis of the municipal response to the pandemic (vaccines, economy, prevention, governance, security) ”. This captures data reviewed and processed through September 2021.

The report analyzes and ranks the economic, societal and health stability of 50 cities and municipalities around the world. In the battle against COVID-19, particularly as the likely next wave of the Delta variant approaches this fall and other waves to come, it is hoped that this will provide a useful picture of which governments may be successful in tackling the next one. wave of COVID-19.

The measures are detailed in some places, such as taking demographics into account. For example, a city that obtains a high score could be a city where population aging is occurring. This is considered to be one of the greatest vulnerabilities of developed regions in the global spread of COVID-19.

The review also provides details by country. This analysis shows that the safest country in terms of COVID-19 measures is Switzerland, followed by Germany and Israel.

The types of measures used for the assessment include the quarantine scale; the governance structure of the country; the types of monitoring systems in place, including for health surveillance; and comprehensive emergency preparedness. These measures are contextualized through a SWOT analysis (strength, weakness, opportunities and threats).

The most vulnerable regions are: Afghanistan, Chad, Mali, Rwanda and South Sudan. These are areas that are relatively impoverished and disturbed by conflict.

It’s part of a new paradigm exploring the application of artificial intelligence to assess and draw more powerful inferences from healthcare-related data. Along with the COVID-19 analysis, this was based on a multiparametric analysis of 20 selected regions, encompassing more than 130 variables. To work through this data, advances in artificial intelligence were needed.

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