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Network-augmented compartmental models to track asymptomatic disease spread
SUMMARY: A major challenge in understanding the spread of certain newly emerging viruses is the presence of asymptomatic cases. Their prevalence is hard to measure in the absence of testing tools, and yet the information is critical for tracking disease spread and shaping public health policies. Her...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10354004/ https://www.ncbi.nlm.nih.gov/pubmed/37476534 http://dx.doi.org/10.1093/bioadv/vbad082 |
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author | Dabke, Devavrat Vivek Karntikoon, Kritkorn Aluru, Chaitanya Singh, Mona Chazelle, Bernard |
author_facet | Dabke, Devavrat Vivek Karntikoon, Kritkorn Aluru, Chaitanya Singh, Mona Chazelle, Bernard |
author_sort | Dabke, Devavrat Vivek |
collection | PubMed |
description | SUMMARY: A major challenge in understanding the spread of certain newly emerging viruses is the presence of asymptomatic cases. Their prevalence is hard to measure in the absence of testing tools, and yet the information is critical for tracking disease spread and shaping public health policies. Here, we introduce a framework that combines classic compartmental models with travel networks and we use it to estimate asymptomatic rates. Our platform, traSIR (“tracer”), is an augmented susceptible-infectious-recovered (SIR) model that incorporates multiple locations and the flow of people between them; it has a compartment model for each location and estimates of commuting traffic between compartments. TraSIR models both asymptomatic and symptomatic infections, as well as the dampening effect symptomatic infections have on traffic between locations. We derive analytical formulae to express the asymptomatic rate as a function of other key model parameters. Next, we use simulations to show that empirical data fitting yields excellent agreement with actual asymptomatic rates using only information about the number of symptomatic infections over time and compartments. Finally, we apply our model to COVID-19 data consisting of reported daily infections in the New York metropolitan area and estimate asymptomatic rates of COVID-19 to be ∼34%, which is within the 30–40% interval derived from widespread testing. Overall, our work demonstrates that traSIR is a powerful approach to express viral propagation dynamics over geographical networks and estimate key parameters relevant to virus transmission. AVAILABILITY AND IMPLEMENTATION: No public repository. |
format | Online Article Text |
id | pubmed-10354004 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-103540042023-07-20 Network-augmented compartmental models to track asymptomatic disease spread Dabke, Devavrat Vivek Karntikoon, Kritkorn Aluru, Chaitanya Singh, Mona Chazelle, Bernard Bioinform Adv Original Article SUMMARY: A major challenge in understanding the spread of certain newly emerging viruses is the presence of asymptomatic cases. Their prevalence is hard to measure in the absence of testing tools, and yet the information is critical for tracking disease spread and shaping public health policies. Here, we introduce a framework that combines classic compartmental models with travel networks and we use it to estimate asymptomatic rates. Our platform, traSIR (“tracer”), is an augmented susceptible-infectious-recovered (SIR) model that incorporates multiple locations and the flow of people between them; it has a compartment model for each location and estimates of commuting traffic between compartments. TraSIR models both asymptomatic and symptomatic infections, as well as the dampening effect symptomatic infections have on traffic between locations. We derive analytical formulae to express the asymptomatic rate as a function of other key model parameters. Next, we use simulations to show that empirical data fitting yields excellent agreement with actual asymptomatic rates using only information about the number of symptomatic infections over time and compartments. Finally, we apply our model to COVID-19 data consisting of reported daily infections in the New York metropolitan area and estimate asymptomatic rates of COVID-19 to be ∼34%, which is within the 30–40% interval derived from widespread testing. Overall, our work demonstrates that traSIR is a powerful approach to express viral propagation dynamics over geographical networks and estimate key parameters relevant to virus transmission. AVAILABILITY AND IMPLEMENTATION: No public repository. Oxford University Press 2023-07-03 /pmc/articles/PMC10354004/ /pubmed/37476534 http://dx.doi.org/10.1093/bioadv/vbad082 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Dabke, Devavrat Vivek Karntikoon, Kritkorn Aluru, Chaitanya Singh, Mona Chazelle, Bernard Network-augmented compartmental models to track asymptomatic disease spread |
title | Network-augmented compartmental models to track asymptomatic disease spread |
title_full | Network-augmented compartmental models to track asymptomatic disease spread |
title_fullStr | Network-augmented compartmental models to track asymptomatic disease spread |
title_full_unstemmed | Network-augmented compartmental models to track asymptomatic disease spread |
title_short | Network-augmented compartmental models to track asymptomatic disease spread |
title_sort | network-augmented compartmental models to track asymptomatic disease spread |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10354004/ https://www.ncbi.nlm.nih.gov/pubmed/37476534 http://dx.doi.org/10.1093/bioadv/vbad082 |
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