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Data-driven methods for present and future pandemics: Monitoring, modelling and managing
Teodoro Alamo1, Daniel G Reina2, Pablo Millán Gata3
1Departamento de Ingeniería de Sistemas y Automática, Universidad de Sevilla, Escuela Superior de Ingenieros, Sevilla, Spain.
Annual Reviews in Control
|July 5, 2021
Summary
This study outlines a data-driven roadmap for pandemic control, integrating data science, epidemiology, and systems theory. It details methods for monitoring, modeling, and managing epidemics effectively.
Area of Science:
- Epidemiology and Public Health
- Data Science and Computational Biology
- Systems and Control Theory
Background:
- Pandemics necessitate integrated approaches combining diverse scientific disciplines.
- Data-driven methodologies offer powerful tools for understanding and controlling infectious disease outbreaks.
- Existing strategies often lack a holistic framework for epidemic management.
Purpose of the Study:
- To present a comprehensive roadmap for data-driven pandemic modeling and control.
- To integrate data science, epidemiology, and systems-and-control theory for epidemic analysis.
- To explore the application of data-driven schemes in monitoring, modeling, and managing pandemics.
Main Methods:
- Review of data-driven methodologies for epidemiological data analysis.
- Development of a 3M-analysis framework: Monitoring, Modeling, and Managing.
- Examination of theoretical approaches and their application to past and future epidemics.
Main Results:
- A structured roadmap from data access to epidemic control is proposed.
- The 3M-analysis framework addresses key pandemic challenges: monitoring, forecasting, and decision-making.
- Data-driven strategies show potential for assessing countermeasures and suppressing contagion.
Conclusions:
- A multidisciplinary, data-driven approach is crucial for effective pandemic response.
- The proposed framework enhances capabilities in epidemic monitoring, modeling, and management.
- This work provides a foundation for leveraging data science in combating infectious diseases.
Keywords:
Epidemic controlEpidemiological modelsForecastingMachine learningModel predictive controlOptimal controlPandemic controlSurveillance systemsMore Related Videos
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