# STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework for Pandemic Monitoring and Management

arXiv: https://arxiv.org/abs/2104.01600 · HTML: https://mlsanity.com/paper/2104.01600/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 69 references that are in the map.
- First posted on arXiv 4 Apr 2021.

## Builds on (most cited first, 15 of 15)

- [Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs](https://mlsanity.com/paper/1705.05742/) (2017)
- [Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2007.03113/) (2020)
- [Building a PubMed knowledge graph](https://mlsanity.com/paper/2005.04308/) (2020)
- [COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation](https://mlsanity.com/paper/2007.00576/) (2020)
- [Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images](https://mlsanity.com/paper/2004.05717/) (2020)
- [CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization](https://mlsanity.com/paper/2006.09595/) (2020)
- [FogBus: A Blockchain-based Lightweight Framework for Edge and Fog Computing](https://mlsanity.com/paper/1811.11978/) (2018)
- [Coronavirus Knowledge Graph: A Case Study](https://mlsanity.com/paper/2007.10287/) (2020)
- [Human Mobility in Opportunistic Networks: Characteristics, Models and Prediction Methods](https://mlsanity.com/paper/2008.05611/) (2020)
- [Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions](https://mlsanity.com/paper/2005.10460/) (2020)
- [A Drone-based Networked System and Methods for Combating Coronavirus Disease (COVID-19) Pandemic](https://mlsanity.com/paper/2006.06943/) (2020)
- [A Privacy-preserving Mobile and Fog Computing Framework to Trace and Prevent COVID-19 Community Transmission](https://mlsanity.com/paper/2006.13364/) (2020)
- [Country-wide mobility changes observed using mobile phone data during COVID-19 pandemic](https://mlsanity.com/paper/2008.10064/) (2020)
- [Leveraging Artificial Intelligence to Analyze the COVID-19 Distribution Pattern based on Socio-economic Determinants](https://mlsanity.com/paper/2102.06656/) (2021)
- [Efficient Suspected Infected Crowds Detection Based on Spatio-Temporal Trajectories](https://mlsanity.com/paper/2004.06653/) (2020)

## Abstract (arXiv)

Several researches and evidence show the increasing likelihood of pandemics (large-scale outbreaks of infectious disease) which has far reaching sequels in all aspects of human lives ranging from rapid mortality rates to economic and social disruption across the world. In the recent time, COVID-19 (Coronavirus Disease 2019) pandemic disrupted normal human lives, and motivated by the urgent need of combating COVID-19, researchers have put significant efforts in modelling and analysing the disease spread patterns for effective preventive measures (in addition to developing pharmaceutical solutions, like vaccine). In this regards, it is absolutely necessary to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous datasources to deliver insights in improving administrative policy and enhance the preparedness to combat the pandemic. Specifically, human mobility, travel history and other transport statistics have significant impacts on the spread of any infectious disease. In this direction, this paper proposes a spatio-temporal knowledge mining framework, named STOPPAGE to model the impact of human mobility and other contextual information over large geographic area in different temporal scales. The framework has two major modules: (i) Spatio-temporal data and computing infrastructure using fog/edge based architecture; and (ii) Spatio-temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. Typically, we develop a Pandemic-knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hot-spot zones; and provide necessary support in home-health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real-life datasets related to COVID-19 in India illustrate the efficacy of the proposed methods.