The Reference Model: An Initial Use Case for COVID-19
The outbreak of the coronavirus disease-19 (COVID-19) pandemic has created much speculation on the behavior of the disease. Some of the questions that have been asked can be addressed by computational modeling based on the use of high-performance computing (HPC) and machine learning techniques. The...
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| Vydané v: | Curēus (Palo Alto, CA) Ročník 12; číslo 7; s. e9455 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Palo Alto
Springer Nature B.V
29.07.2020
Cureus |
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| ISSN: | 2168-8184, 2168-8184 |
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| Abstract | The outbreak of the coronavirus disease-19 (COVID-19) pandemic has created much speculation on the behavior of the disease. Some of the questions that have been asked can be addressed by computational modeling based on the use of high-performance computing (HPC) and machine learning techniques. The Reference Model previously used such techniques to model diabetes. The Reference Model is now used to answer a few questions on COVID-19, while changing the traditional susceptible-infected-recovered (SIR) model approach. This adaptation allows us to answer questions such as the probability of transmission per encounter, disease duration, and mortality rate. The Reference Model uses data on US infection and mortality from 52 states and territories combining multiple assumptions of human interactions to compute the best fitting parameters that explain the disease behavior for given assumptions and accumulated data from April 2020 to June 2020.This is a preliminary report aimed at demonstrating the possible use of computational models based on computing power to aid comprehension of disease characteristics. This infrastructure can accumulate models and assumptions from multiple contributors. |
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| AbstractList | The outbreak of the coronavirus disease-19 (COVID-19) pandemic has created much speculation on the behavior of the disease. Some of the questions that have been asked can be addressed by computational modeling based on the use of high-performance computing (HPC) and machine learning techniques. The Reference Model previously used such techniques to model diabetes. The Reference Model is now used to answer a few questions on COVID-19, while changing the traditional susceptible-infected-recovered (SIR) model approach. This adaptation allows us to answer questions such as the probability of transmission per encounter, disease duration, and mortality rate. The Reference Model uses data on US infection and mortality from 52 states and territories combining multiple assumptions of human interactions to compute the best fitting parameters that explain the disease behavior for given assumptions and accumulated data from April 2020 to June 2020. This is a preliminary report aimed at demonstrating the possible use of computational models based on computing power to aid comprehension of disease characteristics. This infrastructure can accumulate models and assumptions from multiple contributors.The outbreak of the coronavirus disease-19 (COVID-19) pandemic has created much speculation on the behavior of the disease. Some of the questions that have been asked can be addressed by computational modeling based on the use of high-performance computing (HPC) and machine learning techniques. The Reference Model previously used such techniques to model diabetes. The Reference Model is now used to answer a few questions on COVID-19, while changing the traditional susceptible-infected-recovered (SIR) model approach. This adaptation allows us to answer questions such as the probability of transmission per encounter, disease duration, and mortality rate. The Reference Model uses data on US infection and mortality from 52 states and territories combining multiple assumptions of human interactions to compute the best fitting parameters that explain the disease behavior for given assumptions and accumulated data from April 2020 to June 2020. This is a preliminary report aimed at demonstrating the possible use of computational models based on computing power to aid comprehension of disease characteristics. This infrastructure can accumulate models and assumptions from multiple contributors. The outbreak of the coronavirus disease-19 (COVID-19) pandemic has created much speculation on the behavior of the disease. Some of the questions that have been asked can be addressed by computational modeling based on the use of high-performance computing (HPC) and machine learning techniques. The Reference Model previously used such techniques to model diabetes. The Reference Model is now used to answer a few questions on COVID-19, while changing the traditional susceptible-infected-recovered (SIR) model approach. This adaptation allows us to answer questions such as the probability of transmission per encounter, disease duration, and mortality rate. The Reference Model uses data on US infection and mortality from 52 states and territories combining multiple assumptions of human interactions to compute the best fitting parameters that explain the disease behavior for given assumptions and accumulated data from April 2020 to June 2020.This is a preliminary report aimed at demonstrating the possible use of computational models based on computing power to aid comprehension of disease characteristics. This infrastructure can accumulate models and assumptions from multiple contributors. The outbreak of the coronavirus disease-19 (COVID-19) pandemic has created much speculation on the behavior of the disease. Some of the questions that have been asked can be addressed by computational modeling based on the use of high-performance computing (HPC) and machine learning techniques. The Reference Model previously used such techniques to model diabetes. The Reference Model is now used to answer a few questions on COVID-19, while changing the traditional susceptible-infected-recovered (SIR) model approach. This adaptation allows us to answer questions such as the probability of transmission per encounter, disease duration, and mortality rate. The Reference Model uses data on US infection and mortality from 52 states and territories combining multiple assumptions of human interactions to compute the best fitting parameters that explain the disease behavior for given assumptions and accumulated data from April 2020 to June 2020. This is a preliminary report aimed at demonstrating the possible use of computational models based on computing power to aid comprehension of disease characteristics. This infrastructure can accumulate models and assumptions from multiple contributors. |
| Author | Barhak, Jacob |
| AuthorAffiliation | 1 Software Developer and Computational Disease Modeler, Jacob Barhak - Sole Proprietor, Austin, USA |
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| Cites_doi | 10.1126/science.abb3221 10.46234/ccdcw2020.032 10.15585/mmwr.mm6912e2 10.1016/j.chaos.2020.110023 10.1098/rspb.1997.0131 10.1016/j.socnet.2007.04.005 10.1017/ice.2020.116 10.21203/rs.3.rs-34092/v1 |
| ContentType | Journal Article |
| Copyright | Copyright © 2020, Barhak et al. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2020, Barhak et al. Copyright © 2020, Barhak et al. 2020 Barhak et al. |
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| References_xml | – year: 2020 ident: ref10 article-title: Modeling COVID-19 in Cape Verde Islands - an application of SIR model publication-title: arXiv.org – ident: ref1 – ident: ref7 – year: 2020 ident: ref9 article-title: The first 100 days: modeling the evolution of the COVID-19 pandemic publication-title: arXiv.org – volume: 368 year: 2020 ident: ref3 article-title: Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2) publication-title: Science doi: 10.1126/science.abb3221 – ident: ref24 – ident: ref28 doi: 10.46234/ccdcw2020.032 – ident: ref22 – volume: 42 year: 2020 ident: ref17 article-title: The Reference Model for disease progression handles human interpretation publication-title: MODSIM World – ident: ref27 – ident: ref19 – year: 2018 ident: ref23 article-title: Evolutionary computation examples with Inspyred – volume: 69 year: 2020 ident: ref25 article-title: Severe outcomes among patients with coronavirus disease 2019 (COVID-19) — United States, February 12-March 16, 2020 publication-title: MMWR Morb Mortal Wkly Rep doi: 10.15585/mmwr.mm6912e2 – ident: ref15 – ident: ref30 – volume: 138 year: 2020 ident: ref4 article-title: Modeling and prediction of COVID-19 pandemic using Gaussian mixture model publication-title: Chaos Soliton Fract doi: 10.1016/j.chaos.2020.110023 – ident: ref6 – volume: 264 year: 1997 ident: ref21 article-title: Who mixes with whom? A method to determine the contact patterns of adults that may lead to the spread of airborne infections publication-title: Proc R Soc Lond B doi: 10.1098/rspb.1997.0131 – year: 2020 ident: ref2 article-title: Modeling ambient temperature and relative humidity sensitivity of respiratory droplets and their role in Covid-19 outbreaks publication-title: arXiv.org – ident: ref26 – year: 2020 ident: ref11 article-title: Modeling the spread of infectious disease in urban areas with travel contagion publication-title: arXiv.org – volume: 29 year: 2007 ident: ref20 article-title: Mixing patterns between age groups in social networks publication-title: Soc Networks doi: 10.1016/j.socnet.2007.04.005 – ident: ref8 – year: 2020 ident: ref13 article-title: Master Question List for COVID-19 (caused by SARS-CoV-2): Weekly Report, 26 May 2020 – ident: ref18 – volume: 41 year: 2020 ident: ref29 article-title: Level of underreporting including underdiagnosis before the first peak of COVID-19 in various countries: preliminary retrospective results based on wavelets and deterministic modeling publication-title: Infection control and hospital epidemiology doi: 10.1017/ice.2020.116 – year: 2020 ident: ref5 article-title: Forecast predictions for the COVID-19 pandemic in Brazil by statistical modeling using the Weibull distribution for daily new cases and deaths publication-title: Braz J Microbiol doi: 10.21203/rs.3.rs-34092/v1 – year: 2019 ident: ref14 article-title: Standardizing clinical data with Python – ident: ref16 – ident: ref12 |
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| Title | The Reference Model: An Initial Use Case for COVID-19 |
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