About: Simulation of COVID-19 Incubation Period and the Effect of Probability Distribution Functionon Model Training Using MIMANSA

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About: Simulation of COVID-19 Incubation Period and the Effect of Probability Distribution Functionon Model Training Using MIMANSA Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Simulation of COVID-19 Incubation Period and the Effect of Probability Distribution Functionon Model Training Using MIMANSA
Creator	Kulkarni, Padmaj Patel, Abhilasha Vaidya, Vinay Welling, Arpita
source	MedRxiv
abstract	Coronavirus disease 2019 (COVID-19) has infected people all over the world. While scientists are busy finding a vaccine and medicine, it becomes difficult to control the spread and manage patients. Mathematical models help one get a better feel for the challenges in patient management. With this in mind, our team developed a model called Multilevel Integrated Model with a Novel Systems Approach (MIMANSA). MIMANSA is a multi-parametric model. One of the challenges in the design of MIMANSA was to simulate the incubation period of coronavirus. The incubation period decides when virus-infected patients would show symptoms. The probability distribution function (PDF), when applied to the number of virus-infected cases, gives a good representation of the process of the incubation period. The probability distribution functions can take various forms. In this paper, we explore a variety of PDFs and their impact on parameter estimation in the MIMANSA model. For our experiments, we used Weibull, Gaussian, uniform, and Gamma distribution. To ensure a fair comparison of Weibull, Gaussian, and Gamma distribution, we matched the peak value of the distribution. Our results show that the Weibull distribution with shape 7.7 and scale 7 for 14 days gives a better training model and predictions.
has issue date	2020-06-20(xsd:dateTime)
bibo:doi	10.1101/2020.06.18.20134460
has license	medrxiv
sha1sum (hex)	3bb89ce9a98827c1b381df956381bc1d2f02f6ca
schema:url	https://doi.org/10.1101/2020.06.18.20134460
resource representing a document's title	Simulation of COVID-19 Incubation Period and the Effect of Probability Distribution Functionon Model Training Using MIMANSA
resource representing a document's body	covid:3bb89ce9a98827c1b381df956381bc1d2f02f6ca#body_text
is schema:about of	named entity 'While' named entity 'MIMANSA' named entity 'Coronavirus disease 2019' named entity 'MIMANSA' named entity 'Gamma distribution' »more»

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