A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States.
To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individ...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-11-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011610&type=printable |
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| author | John M Drake Andreas Handel Éric Marty Eamon B O'Dea Tierney O'Sullivan Giovanni Righi Andrew T Tredennick |
| author_facet | John M Drake Andreas Handel Éric Marty Eamon B O'Dea Tierney O'Sullivan Giovanni Righi Andrew T Tredennick |
| author_sort | John M Drake |
| collection | DOAJ |
| description | To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individuals, and hospitalized cases, and features realistic interval distributions for presymptomatic and symptomatic periods, time varying rates of case detection, diagnosis, and mortality. The model accounts for the effects on transmission of human mobility using anonymized mobility data collected from cellular devices, and of difficult to quantify environmental and behavioral factors using a latent process. The baseline transmission rate is the product of a human mobility metric obtained from data and this fitted latent process. We fit the model to incident case and death reports for each state in the USA and Washington D.C., using likelihood Maximization by Iterated particle Filtering (MIF). Observations (daily case and death reports) are modeled as arising from a negative binomial reporting process. We estimate time-varying transmission rate, parameters of a sigmoidal time-varying fraction of hospitalized cases that result in death, extra-demographic process noise, two dispersion parameters of the observation process, and the initial sizes of the latent, asymptomatic, and symptomatic classes. In a retrospective analysis covering March-December 2020, we show how mobility and transmission strength became decoupled across two distinct phases of the pandemic. The decoupling demonstrates the need for flexible, semi-parametric approaches for modeling infectious disease dynamics in real-time. |
| format | Article |
| id | doaj-art-ceb1464458014c50bd8371843335fa25 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2023-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-ceb1464458014c50bd8371843335fa252025-02-12T05:30:33ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-11-011911e101161010.1371/journal.pcbi.1011610A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States.John M DrakeAndreas HandelÉric MartyEamon B O'DeaTierney O'SullivanGiovanni RighiAndrew T TredennickTo support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individuals, and hospitalized cases, and features realistic interval distributions for presymptomatic and symptomatic periods, time varying rates of case detection, diagnosis, and mortality. The model accounts for the effects on transmission of human mobility using anonymized mobility data collected from cellular devices, and of difficult to quantify environmental and behavioral factors using a latent process. The baseline transmission rate is the product of a human mobility metric obtained from data and this fitted latent process. We fit the model to incident case and death reports for each state in the USA and Washington D.C., using likelihood Maximization by Iterated particle Filtering (MIF). Observations (daily case and death reports) are modeled as arising from a negative binomial reporting process. We estimate time-varying transmission rate, parameters of a sigmoidal time-varying fraction of hospitalized cases that result in death, extra-demographic process noise, two dispersion parameters of the observation process, and the initial sizes of the latent, asymptomatic, and symptomatic classes. In a retrospective analysis covering March-December 2020, we show how mobility and transmission strength became decoupled across two distinct phases of the pandemic. The decoupling demonstrates the need for flexible, semi-parametric approaches for modeling infectious disease dynamics in real-time.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011610&type=printable |
| spellingShingle | John M Drake Andreas Handel Éric Marty Eamon B O'Dea Tierney O'Sullivan Giovanni Righi Andrew T Tredennick A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States. PLoS Computational Biology |
| title | A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States. |
| title_full | A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States. |
| title_fullStr | A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States. |
| title_full_unstemmed | A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States. |
| title_short | A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States. |
| title_sort | data driven semi parametric model of sars cov 2 transmission in the united states |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011610&type=printable |
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