Network models and the interpretation of prolonged infection plateaus in the COVID19 pandemic
Non-pharmaceutical intervention measures, such as social distancing, have so far been the only means to slow the spread of SARS-CoV-2. In the United States, strict social distancing during the first wave of virus spread has resulted in different types of infection dynamics. In some states, such as N...
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doaj-0b0d74ed214b4bd19fc0cb09e99bccf12021-06-09T05:57:30ZengElsevierEpidemics1755-43652021-06-0135100463Network models and the interpretation of prolonged infection plateaus in the COVID19 pandemicNatalia L. Komarova0Asma Azizi1Dominik Wodarz2Department of Mathematics, University of California Irvine, Irvine, CA 92697, United StatesDepartment of Mathematics, University of California Irvine, Irvine, CA 92697, United StatesDepartment of Population Health and Disease Prevention, Program in Public Health, Susan and Henry Samueli College of Health Science, University of California Irvine, Irvine, CA, 92697, United States; Corresponding author.Non-pharmaceutical intervention measures, such as social distancing, have so far been the only means to slow the spread of SARS-CoV-2. In the United States, strict social distancing during the first wave of virus spread has resulted in different types of infection dynamics. In some states, such as New York, extensive infection spread was followed by a pronounced decline of infection levels. In other states, such as California, less infection spread occurred before strict social distancing, and a different pattern was observed. Instead of a pronounced infection decline, a long-lasting plateau is evident, characterized by similar daily new infection levels. Here we show that network models, in which individuals and their social contacts are explicitly tracked, can reproduce the plateau if network connections are cut due to social distancing measures. The reason is that in networks characterized by a 2D spatial structure, infection tends to spread quadratically with time, but as edges are randomly removed, the infection spreads along nearly one-dimensional infection “corridors”, resulting in plateau dynamics. Further, we show that plateau dynamics are observed only if interventions start sufficiently early; late intervention leads to a “peak and decay” pattern. Interestingly, the plateau dynamics are predicted to eventually transition into an infection decline phase without any further increase in social distancing measures. Additionally, the models suggest that a second wave becomes significantly less pronounced if social distancing is only relaxed once the dynamics have transitioned to the decline phase. The network models analyzed here allow us to interpret and reconcile different infection dynamics during social distancing observed in various US states.http://www.sciencedirect.com/science/article/pii/S1755436521000220COVID19Mathematical modelsNetwork modelsSocial distancingPlateausSARS-CoV-2 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Natalia L. Komarova Asma Azizi Dominik Wodarz |
spellingShingle |
Natalia L. Komarova Asma Azizi Dominik Wodarz Network models and the interpretation of prolonged infection plateaus in the COVID19 pandemic Epidemics COVID19 Mathematical models Network models Social distancing Plateaus SARS-CoV-2 |
author_facet |
Natalia L. Komarova Asma Azizi Dominik Wodarz |
author_sort |
Natalia L. Komarova |
title |
Network models and the interpretation of prolonged infection plateaus in the COVID19 pandemic |
title_short |
Network models and the interpretation of prolonged infection plateaus in the COVID19 pandemic |
title_full |
Network models and the interpretation of prolonged infection plateaus in the COVID19 pandemic |
title_fullStr |
Network models and the interpretation of prolonged infection plateaus in the COVID19 pandemic |
title_full_unstemmed |
Network models and the interpretation of prolonged infection plateaus in the COVID19 pandemic |
title_sort |
network models and the interpretation of prolonged infection plateaus in the covid19 pandemic |
publisher |
Elsevier |
series |
Epidemics |
issn |
1755-4365 |
publishDate |
2021-06-01 |
description |
Non-pharmaceutical intervention measures, such as social distancing, have so far been the only means to slow the spread of SARS-CoV-2. In the United States, strict social distancing during the first wave of virus spread has resulted in different types of infection dynamics. In some states, such as New York, extensive infection spread was followed by a pronounced decline of infection levels. In other states, such as California, less infection spread occurred before strict social distancing, and a different pattern was observed. Instead of a pronounced infection decline, a long-lasting plateau is evident, characterized by similar daily new infection levels. Here we show that network models, in which individuals and their social contacts are explicitly tracked, can reproduce the plateau if network connections are cut due to social distancing measures. The reason is that in networks characterized by a 2D spatial structure, infection tends to spread quadratically with time, but as edges are randomly removed, the infection spreads along nearly one-dimensional infection “corridors”, resulting in plateau dynamics. Further, we show that plateau dynamics are observed only if interventions start sufficiently early; late intervention leads to a “peak and decay” pattern. Interestingly, the plateau dynamics are predicted to eventually transition into an infection decline phase without any further increase in social distancing measures. Additionally, the models suggest that a second wave becomes significantly less pronounced if social distancing is only relaxed once the dynamics have transitioned to the decline phase. The network models analyzed here allow us to interpret and reconcile different infection dynamics during social distancing observed in various US states. |
topic |
COVID19 Mathematical models Network models Social distancing Plateaus SARS-CoV-2 |
url |
http://www.sciencedirect.com/science/article/pii/S1755436521000220 |
work_keys_str_mv |
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