Robust models of disease heterogeneity and control, with application to the SARS-CoV-2 epidemic.
In light of the continuing emergence of new SARS-CoV-2 variants and vaccines, we create a robust simulation framework for exploring possible infection trajectories under various scenarios. The situations of primary interest involve the interaction between three components: vaccination campaigns, non...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLOS Global Public Health |
| Online Access: | https://doi.org/10.1371/journal.pgph.0000412 |
| _version_ | 1797703388953051136 |
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| author | Kory D Johnson Annemarie Grass Daniel Toneian Mathias Beiglböck Jitka Polechová |
| author_facet | Kory D Johnson Annemarie Grass Daniel Toneian Mathias Beiglböck Jitka Polechová |
| author_sort | Kory D Johnson |
| collection | DOAJ |
| description | In light of the continuing emergence of new SARS-CoV-2 variants and vaccines, we create a robust simulation framework for exploring possible infection trajectories under various scenarios. The situations of primary interest involve the interaction between three components: vaccination campaigns, non-pharmaceutical interventions (NPIs), and the emergence of new SARS-CoV-2 variants. Additionally, immunity waning and vaccine boosters are modeled to account for their growing importance. New infections are generated according to a hierarchical model in which people have a random, individual infectiousness. The model thus includes super-spreading observed in the COVID-19 pandemic which is important for accurate uncertainty prediction. Our simulation functions as a dynamic compartment model in which an individual's history of infection, vaccination, and possible reinfection all play a role in their resistance to further infections. We present a risk measure for each SARS-CoV-2 variant, [Formula: see text], that accounts for the amount of resistance within a population and show how this risk changes as the vaccination rate increases. [Formula: see text] highlights that different variants may become dominant in different countries-and in different times-depending on the population compositions in terms of previous infections and vaccinations. We compare the efficacy of control strategies which act to both suppress COVID-19 outbreaks and relax restrictions when possible. We demonstrate that a controller that responds to the effective reproduction number in addition to case numbers is more efficient and effective in controlling new waves than monitoring case numbers alone. This not only reduces the median total infections and peak quarantine cases, but also controls outbreaks much more reliably: such a controller entirely prevents rare but large outbreaks. This is important as the majority of public discussions about efficient control of the epidemic have so far focused primarily on thresholds for case numbers. |
| first_indexed | 2024-03-12T05:04:51Z |
| format | Article |
| id | doaj.art-7189a53c222b446ead274aec284e7320 |
| institution | Directory Open Access Journal |
| issn | 2767-3375 |
| language | English |
| last_indexed | 2024-03-12T05:04:51Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Global Public Health |
| spelling | doaj.art-7189a53c222b446ead274aec284e73202023-09-03T09:00:36ZengPublic Library of Science (PLoS)PLOS Global Public Health2767-33752022-01-0125e000041210.1371/journal.pgph.0000412Robust models of disease heterogeneity and control, with application to the SARS-CoV-2 epidemic.Kory D JohnsonAnnemarie GrassDaniel ToneianMathias BeiglböckJitka PolechováIn light of the continuing emergence of new SARS-CoV-2 variants and vaccines, we create a robust simulation framework for exploring possible infection trajectories under various scenarios. The situations of primary interest involve the interaction between three components: vaccination campaigns, non-pharmaceutical interventions (NPIs), and the emergence of new SARS-CoV-2 variants. Additionally, immunity waning and vaccine boosters are modeled to account for their growing importance. New infections are generated according to a hierarchical model in which people have a random, individual infectiousness. The model thus includes super-spreading observed in the COVID-19 pandemic which is important for accurate uncertainty prediction. Our simulation functions as a dynamic compartment model in which an individual's history of infection, vaccination, and possible reinfection all play a role in their resistance to further infections. We present a risk measure for each SARS-CoV-2 variant, [Formula: see text], that accounts for the amount of resistance within a population and show how this risk changes as the vaccination rate increases. [Formula: see text] highlights that different variants may become dominant in different countries-and in different times-depending on the population compositions in terms of previous infections and vaccinations. We compare the efficacy of control strategies which act to both suppress COVID-19 outbreaks and relax restrictions when possible. We demonstrate that a controller that responds to the effective reproduction number in addition to case numbers is more efficient and effective in controlling new waves than monitoring case numbers alone. This not only reduces the median total infections and peak quarantine cases, but also controls outbreaks much more reliably: such a controller entirely prevents rare but large outbreaks. This is important as the majority of public discussions about efficient control of the epidemic have so far focused primarily on thresholds for case numbers.https://doi.org/10.1371/journal.pgph.0000412 |
| spellingShingle | Kory D Johnson Annemarie Grass Daniel Toneian Mathias Beiglböck Jitka Polechová Robust models of disease heterogeneity and control, with application to the SARS-CoV-2 epidemic. PLOS Global Public Health |
| title | Robust models of disease heterogeneity and control, with application to the SARS-CoV-2 epidemic. |
| title_full | Robust models of disease heterogeneity and control, with application to the SARS-CoV-2 epidemic. |
| title_fullStr | Robust models of disease heterogeneity and control, with application to the SARS-CoV-2 epidemic. |
| title_full_unstemmed | Robust models of disease heterogeneity and control, with application to the SARS-CoV-2 epidemic. |
| title_short | Robust models of disease heterogeneity and control, with application to the SARS-CoV-2 epidemic. |
| title_sort | robust models of disease heterogeneity and control with application to the sars cov 2 epidemic |
| url | https://doi.org/10.1371/journal.pgph.0000412 |
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