Using Data Science to monitor the pandemic with a single number: the\n Synthetic COVID Index

2020/12/20 by Raffaele Zenti, Zenti, Raffaele
Computer Science · Mathematics · Medicine · #Advanced Statistical Methods and Models #Anomaly Detection Techniques and Applications #COVID-19 diagnosis using AI #COVID-19 epidemiological studies #Computers and Society (cs.CY) #FOS: Computer and information sciences #K.4.0 #K.4.1

paper · pdf · doi:10.48550/arxiv.2101.02013

openalex publication_date 2020/12/20 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28

Abstract

Rapid and affordable methods of summarizing the multitude of data relating to\nthe pandemic can be useful to health authorities and policy makers who are\ndealing with the COVID-19 pandemic at various levels in the territories\naffected by SARSCoV-2. This is the goal of the Synthetic COVID Index, an index\nbased on an ensemble of Unsupervised Machine Learning techniques which focuses\non the identification of a latent variable present in data that contains\nmeasurement errors. This estimated latent variable can be interpreted as "the\nstrength of the pandemic". An application to the Italian case shows how the\nindex is able to provide a concise representation of the situation.\n

Related

• A COVINDEX based on a GAM beta regression model with an application to the COVID-19 pandemic in Italy
• Data-Driven Methods to Monitor, Model, Forecast and Control Covid-19 Pandemic: Leveraging Data Science, Epidemiology and Control Theory
• Analyzing the State of COVID-19: Real-time Visual Data Analysis, Short-Term Forecasting, and Risk Factor Identification
• Data-driven Analytical Models of COVID-2019 for Epidemic Prediction, Clinical Diagnosis, Policy Effectiveness and Contact Tracing: A Survey
• Monitoring a developing pandemic with available data