Since the onset of the COVID-19 pandemic, the correlation between the spread of the SARS-Cov-2 virus and a number of epidemiological parameters has been a key tool for understanding the dynamics of its flow. This information has assisted local authorities in making policy decisions for the containment of its expansion. Several methods have been used including topographical data, artificial intelligence and machine learning data, and epidemiological tools to analyze factors facilitating the spread of epidemic at a local and global scale. The aim of this study is to use a new tool to assess and categorize the incoming epidemiological data regarding the spread of the disease as per population densities, spatial and topographical morphologies, social and financial activities, population densities and mobility between regions. These data will be appraised as risk factors in the spread of the disease on a local and a global scale.
TopIntroduction
The extreme consequences caused by the SARS-CoV-2 coronavirus Pandemic, have revealed many of the weaknesses of local governments to contain the spread of this disease, and to control its outbreak and transmission (Anderson M, Mckee M, Mossialos E. 2020). The luck of common platforms to share information as close to real time as possible in neighboring countries, a common and cohesive strategy among them to monitor the outbreak, the concentration of innovative tools in the powerful areas as opposed to the less powerful ones, luck of funds, luck of sophistication and familiarity with technology, as well as previous problematic political, social and economic relations have created many boundaries and many geographical exclusions in data sharing and therefore many outdated reactions related to the virus (Thomas Carothers, David Wong, 2020).
On the other hand, globally, the ongoing events have pushed cities to invent new innovative ideas that ensure immediate results and bring long term changes in technology, and innovation (OECD, 2021). These tools assist policy makers in their decisions related to the quarantine measures, smart working, social distances among others, altering the openness and agility of research ecosystems. Many tools and efforts are now mixed as a means to track down the virus’ outbreak and contribute to a controllable situation. For example, technologies that allow for surveys in the roads such as drones and movement sensors allowed for the creation of a number of high quality aerial images that can depict many layers of information related to urban equipment, social practices and urban analysis recognition (Kamel Boulos, M.N, Geraghty, E.M., 2020). Others, such as smartphone applications, allowed for new possibilities of data exchange based on participatory processes and user’s engagement to exchange information related to the virus spread. Large institutions, such as the UN have created a number of toolkits such as the UNDP Accelerator Lab (UNDP, 2020) that uses robots to support the detection of COVID-19 cases. Similarly, new medical technologies which offer a number of facilitations based on face recognition and individual health data allow for access to share the whole country's medical experience (UNAIDS, 2020). In other words, it seems that previously strong limitations related to communicating personal and community data, now are being solved and replaced by new innovative tools that contribute to targets for urban health.
However, despite the urgency and importance of these actions, their creation and establishment arrived rather late in many places of the world and even after their reach, they did not guarantee a unified methodology to track data and to collect precise information. On the contrary, the current landscape of receiving epidemiological data and the way these affect cities has remained obscured and blurred, not being able to assist global institutions reach in a reasonable time scale every place of the world.