STROVE: spatial data infrastructure enabled cloud-fog-edge computing framework for combating COVID-19 pandemic.

The outbreak of 2019 novel coronavirus (COVID-19) has triggered unprecedented challenges and put the whole world in a parlous condition. The impacts of COVID-19 is a matter of grave concern in terms of fatality rate, socio-economical condition, health infrastructure. It is obvious that only pharmaceutical solutions (vaccine) cannot eradicate this pandemic completely, and effective strategies regarding lockdown measures, restricted mobility, emergency services to users-in brief data-driven decision system is of utmost importance. This necessitates an efficient data analytics framework, data infrastructure to store, manage pandemic related information, and distributed computing platform to support such data-driven operations. In the past few decades, Internet of Things-based devices and applications have emerged significantly in various sectors including healthcare and time-critical applications. To be specific, health-sensors help to accumulate health-related parameters at different time-instances of a day, the movement sensors keep track of mobility traces of the user, and helps to assist them in varied conditions. The smartphones are equipped with several such sensors and the ability of low-cost connected sensors to cover large areas makes it the most useful component to combat pandemics such as COVID-19. However, analysing and managing the huge amount of data generated by these sensors is a big challenge. In this paper we have proposed a unified framework which has three major components: (i) Spatial Data Infrastructure to manage, store, analyse and share spatio-temporal information with stakeholders efficiently, (ii) Cloud-Fog-Edge-based hierarchical architecture to support preliminary diagnosis, monitoring patients' mobility, health parameters and activities while they are in quarantine or home-based treatment, and (iii) Assisting users in varied emergency situation leveraging efficient data-driven techniques at low-latency and energy consumption. The mobility data analytics along with SDI is required to interpret the movement dynamics of the region and correlate with COVID-19 hotspots. Further, Cloud-Fog-Edge-based system architecture is required to provision healthcare services efficiently and in timely manner. The proposed framework yields encouraging results in taking decisions based on the COVID-19 context and assisting users effectively by enhancing accuracy of detecting suspected infected people by ∼ 24% and reducing delay by ∼ 55% compared to cloud-only system.

Data Mining in Healthcare and Prediction Model Using Data Mining Technique on COVID-19

The number of COVID-19 cases has reached millions globally, and those taking on the conflict against the pandemic have been roused to actualize inventive strategies to help foresee the spread of the outbreak. It has been seen from the past few months that various concerns are engaged with the COVID-19 retaliation over a globe embracing data mining tools and techniques to help with breaking down the spread of the virus. Such forward-thinking methods have been developed in pervasiveness, as different concerns have made their product and information accessible for free. The main purpose of data mining, whether its actuality charity in healthcare or business, is to recognize beneficial and reasonable arrangements by examining big circles of statistics. In this chapter, we provide a prediction, conferring the probable impact of data mining to combat against COVID-19 and the current restraints on these aids. © 2022, Springer Nature Switzerland AG.