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

JMIR Theme Issue: COVID-19 Special Issue (2502) Big Data (286) Infectious Diseases (non-STD/STI) (1662) Outbreak and Pandemic Preparedness and Management (1785) Statistical Methods for Surveillance and Population Health (174) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1532)

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 (6) 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

Conference Proceedings

  1. Wong J, Alla K, Dominic P. 2020 International Conference on Computational Intelligence (ICCI). A Proposed Framework for Identifying the Role of Data Science in Handling Future Pandemics for Malaysian SMEs through Technology Acceptance Model View