This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2394) Big Data (220) Infectious Diseases (non-STD/STI) (1490) Outbreak and Pandemic Preparedness and Management (1679) Statistical Methods for Surveillance and Population Health (148) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1484)

Published on in Vol 22, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21081, first published .
How Data Analytics and Big Data Can Help Scientists in Managing COVID-19 Diffusion: Modeling Study to Predict the COVID-19 Diffusion in Italy and the Lombardy Region

How Data Analytics and Big Data Can Help Scientists in Managing COVID-19 Diffusion: Modeling Study to Predict the COVID-19 Diffusion in Italy and the Lombardy Region

How Data Analytics and Big Data Can Help Scientists in Managing COVID-19 Diffusion: Modeling Study to Predict the COVID-19 Diffusion in Italy and the Lombardy Region

Authors of this article:

Davide Tosi1 Author Orcid Image ;   Alessandro Campi2 Author Orcid Image
Article Authors Cited by (16) Tweetations (7) Metrics

Journals

  1. Tosi D, Chiappa M. Understanding the Geographical Spread of COVID-19 in Relation with Goods Regional Routes and Governmental Decrees: The Lombardy Region Case Study. SN Computer Science 2021;2(3) View
  2. Lee K, Chien T, Yeh Y, Chou W, Wang H. An online time-to-event dashboard comparing the effective control of COVID-19 among continents using the inflection point on an ogive curve. Medicine 2021;100(10):e24749 View
  3. Tosi D, Campi A. How Schools Affected the COVID-19 Pandemic in Italy: Data Analysis for Lombardy Region, Campania Region, and Emilia Region. Future Internet 2021;13(5):109 View
  4. Wang L, Alexander C. COVID-19: a pandemic challenging healthcare systems. IISE Transactions on Healthcare Systems Engineering 2021:1 View
  5. Baksh S, Volodko N, Soucie M, Geier S, Diep A, Rozak K, Chan T, Mustapha J, Lai R, Estey M, Verity B, Lee M. Extractionless nucleic acid detection: a high capacity solution to COVID-19 testing. Diagnostic Microbiology and Infectious Disease 2021;101(2):115458 View
  6. Riswantini D, Nugraheni E, Arisal A, Khotimah P, Munandar D, Suwarningsih W. Big Data Research in Fighting COVID-19: Contributions and Techniques. Big Data and Cognitive Computing 2021;5(3):30 View
  7. Branda F, Tosi D. Comparing Worldwide, National, and Independent Notifications about Adverse Drug Reactions Due to COVID-19 Vaccines. Information 2022;13(7):329 View
  8. Lazzaroni E, Tosi D, Pontiggia S, Ermolli R, Borghesi L, Rigamonti V, Frisone E, Piconi S. Early psychological intervention in adult patients after hospitalization during COVID-19 pandemia. A single center observational study. Frontiers in Psychology 2022;13 View
  9. Cappi R, Casini L, Tosi D, Roccetti M. Questioning the seasonality of SARS-COV-2: a Fourier spectral analysis. BMJ Open 2022;12(4):e061602 View
  10. Casini L, Roccetti M. A Bayesian Analysis of the Inversion of the SARS-COV-2 Case Rate in the Countries of the 2020 European Football Championship. Future Internet 2021;13(8):212 View
  11. Corsini C. I costi della scuola in presenza a tutti i costi: una riflessione pedagogica. CADMO 2020;(2):82 View
  12. Sariyer G, Mangla S, Kazancoglu Y, Jain V, Ataman M. Data-driven decision making for modelling covid-19 and its implications: A cross-country study. Technological Forecasting and Social Change 2023;197:122886 View
  13. Roccetti M. Drawing a parallel between the trend of confirmed COVID-19 deaths in the winters of 2022/2023 and 2023/2024 in Italy, with a prediction. Mathematical Biosciences and Engineering 2024;21(3):3742 View

Books/Policy Documents

  1. Kalina J. Digital Innovation for Healthcare in COVID-19 Pandemic: Strategies and Solutions. View
  2. Li R. Proceedings of the 2022 3rd International Conference on Big Data Economy and Information Management (BDEIM 2022). View