A novel data driven anticipatory framework for the communicable syndrome

Preceding pandemic trends uncovered the weakness of existing healthcare management. As a result, there is an urgent need to build a resilient pandemic-lockdown model to relieve health systems from crashing and equip them with the latest tools and techniques for rapid actions to manage future pandemics efficiently. Therefore, this research work aims at proposing an adaptable framework called the Automated Selection of Streets for Lockdown (ASSLD) to mitigate the exponential progression of the epidemic from the perspective of urban resilience. Intuitive selection of pandemic-affected streets was accomplished by first applying the shortest path algorithm over the respective spatial network and then validating the selection through spatial analysis using space syntax syntactic measures. Medical records for victims of a preceding contagious viral infection were used as a dataset to prepare test beds against which the ASSLD model was regressively validated. Multiple linear regression was used to analyse the impact of space syntax syntactic measures along with socio-economic factors on the number of replicating victims. The findings indicated that space syntax syntactic variables along with certain socioeconomic factors, provided more granular means of measuring the spatial elements (streets) as diffusion hubs for a communicable disease. This paper presents in detail proof of concept with results applied to actual data obtained from a preceding communicable syndrome outbreak.