About: Estimating the Changing Infection Rate of COVID-19 Using Bayesian Models of Mobility

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Estimating the Changing Infection Rate of COVID-19 Using Bayesian Models of Mobility
Creator	Ben, Xue Liu, Luyang O'banion, Shawn Research, Google Hoffman, Matthew Lao, Junpeng Sandler, Mark Saurous, Rif Vikram, Sharad
Source	MedRxiv
abstract	In order to prepare for and control the continued spread of the COVID-19 pandemic while minimizing its economic impact, the world needs to be able to estimate and predict COVID-19's spread. Unfortunately, we cannot directly observe the prevalence or growth rate of COVID-19; these must be inferred using some kind of model. We propose a hierarchical Bayesian extension to the classic susceptible-exposed-infected-removed (SEIR) compartmental model that adds compartments to account for isolation and death and allows the infection rate to vary as a function of both mobility data collected from mobile phones and a latent time-varying factor that accounts for changes in behavior not captured by mobility data. Since confirmed-case data is unreliable, we infer the model's parameters conditioned on deaths data. We replace the exponential-waiting-time assumption of classic compartmental models with Erlang distributions, which allows for a more realistic model of the long lag between exposure and death. The mobility data gives us a leading indicator that can quickly detect changes in the pandemic's local growth rate and forecast changes in death rates weeks ahead of time. This is an analysis of observational data, so any causal interpretations of the model's inferences should be treated as suggestive at best; nonetheless, the model's inferred relationship between different kinds of trips and the infection rate do suggest some possible hypotheses about what kinds of activities might contribute most to COVID-19's spread.
has issue date	2020-08-07(xsd:dateTime)
bibo:doi	10.1101/2020.08.06.20169664
has license	medrxiv
sha1sum (hex)	85ffce9a76015f426d06e1850bdf56ee43ba2ea4
schema:url	https://doi.org/10.1101/2020.08.06.20169664
resource representing a document's title	Estimating the Changing Infection Rate of COVID-19 Using Bayesian Models of Mobility
resource representing a document's body	covid:85ffce9a76015f426d06e1850bdf56ee43ba2ea4#body_text
is schema:about of	named entity 'USING' named entity 'HIERARCHICAL' named entity 'REMOVED' named entity 'LOCAL' named entity 'WAITING' named entity 'CONTRIBUTE ' named entity 'ITS' named entity 'COMPARTMENTAL MODEL' named entity 'DATA COLLECTED' named entity 'ABOUT' named entity 'PREDICT' named entity 'CONDITIONED' named entity 'ABLE TO' named entity 'GROWTH RATE' named entity 'KINDS' named entity 'growth rate' named entity 'compartmental models' named entity 'infection rate' named entity 'observational data' named entity 'Bayesian' named entity 'infection' named entity 'compartmental model' named entity 'time-series' named entity 'COVID-19' named entity 'differential equations' named entity 'log-likelihood' named entity 'rate parameter' named entity 'compartmental model' named entity 'prior distribution' named entity 'latent factors' named entity 'Bayesian models' named entity 'positive and negative' named entity 'compartmental model' named entity 'infection rate' named entity 'compartmental model' named entity 'infection rate' named entity 'human behavior' named entity 'Runge-Kutta' named entity 'hindcast' named entity 'log-likelihood' named entity 'COVID-19' named entity 'infection rate' named entity 'infection rate' named entity 'log-likelihood' named entity 'reproductive number' named entity 'infection rate' named entity 'COVID-19' named entity 'reproductive number' named entity 'infection rate' named entity 'New York City' named entity 'asymptomatic' named entity 'Google' named entity 'infection' named entity 'COVID-19' named entity 'latent variables' named entity 'prior distributions' named entity 'compartmental model' named entity 'compartmental model' named entity 'infection rate' named entity 'sample city' named entity 'compartmental models' named entity 'quarantine' named entity 'compartmental model' named entity 'Softplus' named entity 'human-to-human' named entity 'compartmental model' named entity 'compartmental model' named entity 'compartmental model' named entity 'COVID-19' named entity 'infection' named entity 'SEIR' named entity 'generalized linear model' named entity 'prior knowledge' named entity 'infection rate' named entity 'infection rate' named entity 'SEIR' named entity 'infection'

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