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Modeling and tracking Covid-19 cases using Big Data analytics on HPCC system platformm.

Flavio Villanustre1, Arjuna Chala1, Roger Dev1

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Summary
This summary is machine-generated.

This project models coronavirus (Covid-19) spread using big data analytics and the HPCC Systems platform. The developed tracker provides real-time data and analysis to help reduce infections and inform public health efforts.

Keywords:
Big DataCovid-19HPCC systemModeling Corona spreadSARS-Cov-2Spreading indicators

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Area of Science:

  • Epidemiology and Public Health
  • Big Data Analytics
  • Computational Modeling

Background:

  • The novel coronavirus (Covid-19) pandemic poses a significant global health threat, overwhelming healthcare systems and necessitating accurate spread prediction.
  • Existing mathematical models face challenges in early outbreak stages due to scarce data and complex transmission dynamics.
  • Effective pandemic management requires timely, multi-level data analysis to inform public health interventions and mitigate societal panic.

Purpose of the Study:

  • To develop an innovative big data analytics model for predicting Covid-19 spread.
  • To create a publicly accessible Covid-19 tracker for real-time pandemic monitoring and analysis.
  • To leverage experience from modeling previous epidemics, such as Ebola, to enhance Covid-19 spread projections.

Main Methods:

  • Utilized big data analytics techniques and tools, specifically the HPCC Systems platform, for data ingestion, processing, and delivery.
  • Integrated a classical Susceptible-Infected-Recovered (SIR) epidemiological model with a causal model for spreading indicators.
  • Developed a multi-level Covid-19 tracker presenting data from global to county levels, including statistical analysis and contagion risk assessments.

Main Results:

  • Successfully modeled coronavirus spread using big data analytics, building upon prior Ebola spread modeling experience.
  • The HPCC Systems Covid-19 tracker provides timely, multi-level insights into pandemic status and virus spreading indicators.
  • The publicly released website and open-source project have attracted significant traffic, demonstrating the utility of the developed system.

Conclusions:

  • The developed big data analytics model and HPCC Systems Covid-19 tracker offer a robust solution for monitoring and understanding pandemic spread.
  • The system's ability to provide detailed, real-time data analysis aids in projecting resource demands and informing public health strategies.
  • Open-sourcing the project and releasing the tracker publicly facilitates broader access to critical pandemic information and collaborative efforts.