About: A data first approach to modelling Covid-19

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	A data first approach to modelling Covid-19
Creator	Prasad, Jayanti
source	MedRxiv
abstract	The primary data for Covid-19 pandemic is in the form of time series for the number of confirmed, recovered and dead cases. This data is updated every day and is available for most countries from multiple sources. In this work we present a two step procedure for model fitting to Covid-19 data. In the first step, time dependent transmission coefficients are constructed directly from the data and, in the second step, measures of those (minimum, maximum, mean, median etc.,) are used to set priors for fitting models to data. We call this approach a %22data driven approach%22 or %22data first approach%22. This scheme is complementary to Bayesian approach and can be used with or without that for parameter estimation. We use the procedure to fit a set of SIR and SIRD models, with time dependent contact rate, to Covid-19 data for a set of 45 most affected countries. We find that SIR and SIRD models with constant transmission coefficients cannot fit Covid-19 data for most countries (mainly because social distancing, lockdown etc., make those time dependent). We find that any time dependent contact rate, which falls gradually with time, can help to fit SIR and SIRD models for most of the countries. We also present constraints on transmission coefficients and basic reproduction number R0~ as well as effective reproduction number R(t). The main contributions of our work are as follows. (1) presenting a two step procedure for model fitting to Covid-19 data (2) constraining transmission coefficients as well as R0~ and R(t), for a set of most affected countries and (3) releasing a python package PyCov19 that can used to fit a set of compartmental models with time varying coefficients to Covid-19 data.
has issue date	2020-05-26(xsd:dateTime)
bibo:doi	10.1101/2020.05.22.20110171
has license	medrxiv
sha1sum (hex)	073456afdd52e85655dc9a207c9c6448144f12ac
schema:url	https://doi.org/10.1101/2020.05.22.20110171
resource representing a document's title	A data first approach to modelling Covid-19
resource representing a document's body	covid:073456afdd52e85655dc9a207c9c6448144f12ac#body_text
is schema:about of	named entity 'driven' named entity 'Bayesian' named entity 'procedure' named entity 'time' named entity 'number' »more»

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