There are a host of factors that shape our perceptions and responses to the coronavirus pandemic. To make sense of all of the variables is complex, and advances with machine learning can assist with this process.
Deep Knowledge Group, which is a consortium of commercial and non-profit organizations active in GovTech, BioTech, FinTech, and SpaceTech, has undertaken a review of coronavirus and the measures being undertaken at the city level.
Central to the review is the production and availability open-source COVID-19 analytics, such as quantitative analytical frameworks, metrics, and forecasting models. The aim of the information is so that it can be used by government agencies, corporations, financial institutions, universities, and non-profit organizations.
The latest review is “COVID-19 City Safety Ranking Q2/2021: Benchmarking of Municipal Pandemic Response (Vaccines, Economy, Prevention, Governance, Safety)”. This captures data reviewed and processed up until September 2021.
The report analyzes and ranks the economic, societal, and health stability of 50 cities and municipalities globally. In the battle against COVID-19, especially in the run-up to the likely forthcoming wave of the Delta variant this autumn and other future waves, it is hoped this will provide a useful picture of which governments are likely to be successful at fighting the next wave of COVID-19.
The measures are detailed in places, such as accounting for demographics. For instance, a city scoring high could be one where population aging occurring. This is regarded as one of the greatest vulnerability of developed regions in the global progression of COVID-19.
The review also provides detail 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 scale of quarantine; the governance structure for the country; the types of monitoring systems in place, including for health surveillance; and overall emergency preparedness. These measures are contextualized through SWOT (strength, weakness, opportunities and threats) analysis.
The most vulnerable regions are: Afghanistan, Chad, Mali, Rwanda, and South Sudan. Each are areas that are relatively impoverished and disrupted by conflict.
This forms part of a new paradigm exploring the application of artificial intelligence for assessing and drawing more powerful inferences from healthcare related data. With the COVID-19 analysis this was based om multiparameter analysis of 20 selected regions, encompassing more than 130 variables. To work through this data advances in artificial intelligence were required.
