Characterizing spatiotemporal variation in transmission heterogeneity during the 2022 mpox outbreak in the USA
Abstract Understanding how transmission heterogeneity varies over the course of an enduring infectious disease outbreak improves understanding of observed disease dynamics and informs public health strategy. We quantified the spatiotemporal variation in transmission heterogeneity for the 2022 mpox outbreak in the US using the dispersion parameter of the offspring distribution,k. Our methods fit negative binomial distributions to transmission chain offspring distributions informed by a large mpox contact tracing dataset. We found that estimates of transmission heterogeneity varied across the outbreak, but overall estimated transmission heterogeneity was low. When testing our methods on simulated data, estimate accuracy depended on contact tracing data accuracy and completeness. Because the actual contact tracing data had high incompleteness, the values ofkestimated from the empirical data may therefore be artificially high. Through simulation, we explore a method to correct estimatedkfor data incompleteness and, further, explore baseline expectations for temporal dynamics ofk..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 03. Apr. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Love, Jay [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2023.05.10.23289580 |
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| PPN (Katalog-ID): |
XBI039571467 |
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| 245 | 1 | 0 | |a Characterizing spatiotemporal variation in transmission heterogeneity during the 2022 mpox outbreak in the USA |
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| 520 | |a Abstract Understanding how transmission heterogeneity varies over the course of an enduring infectious disease outbreak improves understanding of observed disease dynamics and informs public health strategy. We quantified the spatiotemporal variation in transmission heterogeneity for the 2022 mpox outbreak in the US using the dispersion parameter of the offspring distribution,k. Our methods fit negative binomial distributions to transmission chain offspring distributions informed by a large mpox contact tracing dataset. We found that estimates of transmission heterogeneity varied across the outbreak, but overall estimated transmission heterogeneity was low. When testing our methods on simulated data, estimate accuracy depended on contact tracing data accuracy and completeness. Because the actual contact tracing data had high incompleteness, the values ofkestimated from the empirical data may therefore be artificially high. Through simulation, we explore a method to correct estimatedkfor data incompleteness and, further, explore baseline expectations for temporal dynamics ofk. | ||
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| 700 | 1 | |a Spicknall, Ian H. |e verfasserin |4 aut | |
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