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14.08.2020 | COVID-19 | Education & Training Zur Zeit gratis

COVID-19 Prediction Models and Unexploited Data

verfasst von: K. C. Santosh

Erschienen in: Journal of Medical Systems | Ausgabe 9/2020

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Abstract

For COVID-19, predictive modeling, in the literature, uses broadly SEIR/SIR, agent-based, curve-fitting techniques/models. Besides, machine-learning models that are built on statistical tools/techniques are widely used. Predictions aim at making states and citizens aware of possible threats/consequences. However, for COVID-19 outbreak, state-of-the-art prediction models are failed to exploit crucial and unprecedented uncertainties/factors, such as a) hospital settings/capacity; b) test capacity/rate (on a daily basis); c) demographics; d) population density; e) vulnerable people; and f) income versus commodities (poverty). Depending on what factors are employed/considered in their models, predictions can be short-term and long-term. In this paper, we discuss how such continuous and unprecedented factors lead us to design complex models, rather than just relying on stochastic and/or discrete ones that are driven by randomly generated parameters. Further, it is a time to employ data-driven mathematically proved models that have the luxury to dynamically and automatically tune parameters over time.
Literatur
11.
Zurück zum Zitat Charles C Branas, Andrew Rundle, Sen Pei, Wan Yang, Brendan G Carr, Sarah Sims, Alexis Zebrowski, Ronan Doorley, Neil Schluger, James W Quinn, Jeffrey Shaman. “Flattening the curve before it flattens us: hospital critical care capacity limits and mortality from novel coronavirus (SARS-CoV2) cases in US counties”. MedRxiv (Apri 06, 2020) DOI: https://doi.org/10.1101/2020.04.01.20049759 Charles C Branas, Andrew Rundle, Sen Pei, Wan Yang, Brendan G Carr, Sarah Sims, Alexis Zebrowski, Ronan Doorley, Neil Schluger, James W Quinn, Jeffrey Shaman. “Flattening the curve before it flattens us: hospital critical care capacity limits and mortality from novel coronavirus (SARS-CoV2) cases in US counties”. MedRxiv (Apri 06, 2020) DOI: https://​doi.​org/​10.​1101/​2020.​04.​01.​20049759
18.
Zurück zum Zitat Ermanno Cordelli et al. “Time-window SIQR analysis of COVID-19 outbreak and containment measures in Italy” IEEE 33rd International Symposium on Computer Based Medical Systems (CBMS), (2020) Ermanno Cordelli et al. “Time-window SIQR analysis of COVID-19 outbreak and containment measures in Italy” IEEE 33rd International Symposium on Computer Based Medical Systems (CBMS), (2020)
20.
Zurück zum Zitat “Garbage In, Garbage Out: How Anomalies Can Wreck Your Data – Heap – Mobile and Web Analytics”. heapanalytics.com (May 7, 2014). “Garbage In, Garbage Out: How Anomalies Can Wreck Your Data – Heap – Mobile and Web Analytics”. heapanalytics.​com (May 7, 2014).
21.
Zurück zum Zitat Steve Goldstein. “Oops — Rick Perry says broken clock is right once a day”. The New York Post (Retrieved September 19, 2019) Steve Goldstein. “Oops — Rick Perry says broken clock is right once a day”. The New York Post (Retrieved September 19, 2019)
Metadaten
Titel
COVID-19 Prediction Models and Unexploited Data
verfasst von
K. C. Santosh
Publikationsdatum
14.08.2020
Verlag
Springer US
Schlagwort
COVID-19
Erschienen in
Journal of Medical Systems / Ausgabe 9/2020
Print ISSN: 0148-5598
Elektronische ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-020-01645-z

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