Piedmont COVID-19 Data Modelling and Management

OVERVIEW

This repository contains the code, documentation manual and data visualisations for the design and operation of the Piedmont COVID-19 surveillance data modelling and management pipeline developed in collaboration with the Piedmont Epidemiological Service (SEPI).

For privacy purposes all the data in this repository are either fake (i.e. invented) or synthetic (i.e. simulated) in order to be structurally equivalent to the original individual-level data to accurately showcase the functionalities of the data modelling and management pipeline.

The only reference to the real data can be found in the plots located in the images/real-output folder.

HOW TO ACCESS

If you would like to access the real Piedmont COVID-19 surveillance data covering the year 2020 for your research project (i.e. sequences, incidences and empirical time delay distributions visualised here), please feel free to contact us by sending us an email.

HOW TO CITE

If you use these contents in your work, please cite this repository using the metadata in CITATION.bib.

REFERENCES

Data

1. CSI Piemonte (2020) Piedmont Region COVID-19 Data Management Platform. CSI Piemonte
2. CSI Piemonte (2020) GESCOVID19: COVID-19 Data Management Platform in Piedmont. GitHub
3. Leproni (2021) The Piedmont Region COVID-19 Platform. CSI Piemonte
4. Moroni and Monticone (2022) Italian COVID-19 Integrated Surveillance Dataset. Zenodo

Software

1. Monticone and Moroni (2022) ICD_GEMs.jl: A Julia Package to Translate Between ICD-9 and ICD-10 Codes. Zenodo
2. Monticone and Moroni (2022) UnrollingAverages.jl: A Julia Package to Deconvolve Time Series Data.. Zenodo

Papers

• Del Manso et al. (2020) COVID-19 integrated surveillance in Italy: outputs and related activities. Epidemiologia & Prevenzione
• Milani et al. (2021). Characteristics of patients affecting the duration of positivity at SARS-CoV-2: a cohort analysis of the first wave of epidemic in Italy. Epidemiologia & Prevenzione
• Starnini et al. (2021) Impact of data accuracy on the evaluation of COVID-19 mitigation policies. Data & Policy, 3, E28.
• Zhang et al. (2021) Data science approaches to confronting the COVID-19 pandemic: a narrative review. Philosophical Transactions of the Royal Society A
• Vasiliauskaite et al. (2021) On some fundamental challenges in monitoring epidemics. Philosophical Transactions of the Royal Society A
• Badker et al. (2021) Challenges in reported COVID-19 data: best practices and recommendations for future epidemics. BMJ Global Health
• Shadbolt et al. (2022) The Challenges of Data in Future Pandemics. Epidemics