The spatial resolution of epidemic peaks.

The emergence of novel respiratory pathogens can challenge the capacity of key health care resources, such as intensive care units, that are constrained to serve only specific geographical populations. An ability to predict the magnitude and timing of peak incidence at the scale of a single large po...

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Autores principales:	Harriet L Mills, Steven Riley
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/6f00f941d2d644f9828e1160b16aab2d
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spelling	oai:doaj.org-article:6f00f941d2d644f9828e1160b16aab2d2021-11-18T05:52:59ZThe spatial resolution of epidemic peaks.1553-734X1553-735810.1371/journal.pcbi.1003561https://doaj.org/article/6f00f941d2d644f9828e1160b16aab2d2014-04-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24722420/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The emergence of novel respiratory pathogens can challenge the capacity of key health care resources, such as intensive care units, that are constrained to serve only specific geographical populations. An ability to predict the magnitude and timing of peak incidence at the scale of a single large population would help to accurately assess the value of interventions designed to reduce that peak. However, current disease-dynamic theory does not provide a clear understanding of the relationship between: epidemic trajectories at the scale of interest (e.g. city); population mobility; and higher resolution spatial effects (e.g. transmission within small neighbourhoods). Here, we used a spatially-explicit stochastic meta-population model of arbitrary spatial resolution to determine the effect of resolution on model-derived epidemic trajectories. We simulated an influenza-like pathogen spreading across theoretical and actual population densities and varied our assumptions about mobility using Latin-Hypercube sampling. Even though, by design, cumulative attack rates were the same for all resolutions and mobilities, peak incidences were different. Clear thresholds existed for all tested populations, such that models with resolutions lower than the threshold substantially overestimated population-wide peak incidence. The effect of resolution was most important in populations which were of lower density and lower mobility. With the expectation of accurate spatial incidence datasets in the near future, our objective was to provide a framework for how to use these data correctly in a spatial meta-population model. Our results suggest that there is a fundamental spatial resolution for any pathogen-population pair. If underlying interactions between pathogens and spatially heterogeneous populations are represented at this resolution or higher, accurate predictions of peak incidence for city-scale epidemics are feasible.Harriet L MillsSteven RileyPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 4, p e1003561 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Harriet L Mills Steven Riley The spatial resolution of epidemic peaks.
description	The emergence of novel respiratory pathogens can challenge the capacity of key health care resources, such as intensive care units, that are constrained to serve only specific geographical populations. An ability to predict the magnitude and timing of peak incidence at the scale of a single large population would help to accurately assess the value of interventions designed to reduce that peak. However, current disease-dynamic theory does not provide a clear understanding of the relationship between: epidemic trajectories at the scale of interest (e.g. city); population mobility; and higher resolution spatial effects (e.g. transmission within small neighbourhoods). Here, we used a spatially-explicit stochastic meta-population model of arbitrary spatial resolution to determine the effect of resolution on model-derived epidemic trajectories. We simulated an influenza-like pathogen spreading across theoretical and actual population densities and varied our assumptions about mobility using Latin-Hypercube sampling. Even though, by design, cumulative attack rates were the same for all resolutions and mobilities, peak incidences were different. Clear thresholds existed for all tested populations, such that models with resolutions lower than the threshold substantially overestimated population-wide peak incidence. The effect of resolution was most important in populations which were of lower density and lower mobility. With the expectation of accurate spatial incidence datasets in the near future, our objective was to provide a framework for how to use these data correctly in a spatial meta-population model. Our results suggest that there is a fundamental spatial resolution for any pathogen-population pair. If underlying interactions between pathogens and spatially heterogeneous populations are represented at this resolution or higher, accurate predictions of peak incidence for city-scale epidemics are feasible.
format	article
author	Harriet L Mills Steven Riley
author_facet	Harriet L Mills Steven Riley
author_sort	Harriet L Mills
title	The spatial resolution of epidemic peaks.
title_short	The spatial resolution of epidemic peaks.
title_full	The spatial resolution of epidemic peaks.
title_fullStr	The spatial resolution of epidemic peaks.
title_full_unstemmed	The spatial resolution of epidemic peaks.
title_sort	spatial resolution of epidemic peaks.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/6f00f941d2d644f9828e1160b16aab2d
work_keys_str_mv	AT harrietlmills thespatialresolutionofepidemicpeaks AT stevenriley thespatialresolutionofepidemicpeaks AT harrietlmills spatialresolutionofepidemicpeaks AT stevenriley spatialresolutionofepidemicpeaks
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The spatial resolution of epidemic peaks.

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