A General Framework for Spatio-Temporal Modeling of Epidemics With Multiple Epicenters: Application to an Aerially Dispersed Plant Pathogen

The spread dynamics of long-distance-dispersed pathogens are influenced by the dispersal characteristics of a pathogen, anisotropy due to multiple factors, and the presence of multiple sources of inoculum. In this research, we developed a flexible class of phenomenological spatio-temporal models tha...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Awino M. E. Ojwang', Trevor Ruiz, Sharmodeep Bhattacharyya, Shirshendu Chatterjee, Peter S. Ojiambo, David H. Gent
Formato: article
Lenguaje:EN
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://doaj.org/article/a5a85ef414834b5681540250abcf16c6
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:a5a85ef414834b5681540250abcf16c6
record_format dspace
spelling oai:doaj.org-article:a5a85ef414834b5681540250abcf16c62021-11-15T12:59:09ZA General Framework for Spatio-Temporal Modeling of Epidemics With Multiple Epicenters: Application to an Aerially Dispersed Plant Pathogen2297-468710.3389/fams.2021.721352https://doaj.org/article/a5a85ef414834b5681540250abcf16c62021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fams.2021.721352/fullhttps://doaj.org/toc/2297-4687The spread dynamics of long-distance-dispersed pathogens are influenced by the dispersal characteristics of a pathogen, anisotropy due to multiple factors, and the presence of multiple sources of inoculum. In this research, we developed a flexible class of phenomenological spatio-temporal models that extend a modeling framework used in plant pathology applications to account for the presence of multiple sources and anisotropy of biological species that can govern disease gradients and spatial spread in time. We use the cucurbit downy mildew pathosystem (caused by Pseudoperonospora cubensis) to formulate a data-driven procedure based on the 2008 to 2010 historical occurrence of the disease in the U.S. available from standardized sentinel plots deployed as part of the Cucurbit Downy Mildew ipmPIPE program. This pathosystem is characterized by annual recolonization and extinction cycles, generating annual disease invasions at the continental scale. This data-driven procedure is amenable to fitting models of disease spread from one or multiple sources of primary inoculum and can be specified to provide estimates of the parameters by regression methods conditional on a function that can accommodate anisotropy in disease occurrence data. Applying this modeling framework to the cucurbit downy mildew data sets, we found a small but consistent reduction in temporal prediction errors by incorporating anisotropy in disease spread. Further, we did not find evidence of an annually occurring, alternative source of P. cubensis in northern latitudes. However, we found a signal indicating an alternative inoculum source on the western edge of the Gulf of Mexico. This modeling framework is tractable for estimating the generalized location and velocity of a disease front from sparsely sampled data with minimal data acquisition costs. These attributes make this framework applicable and useful for a broad range of ecological data sets where multiple sources of disease may exist and whose subsequent spread is directional.Awino M. E. Ojwang'Trevor RuizSharmodeep BhattacharyyaShirshendu ChatterjeePeter S. OjiamboDavid H. GentFrontiers Media S.A.articlelong-distance dispersalanisotrophyspatio-temporal modelsinoculum sourcecucurbit downy mildewApplied mathematics. Quantitative methodsT57-57.97Probabilities. Mathematical statisticsQA273-280ENFrontiers in Applied Mathematics and Statistics, Vol 7 (2021)
institution DOAJ
collection DOAJ
language EN
topic long-distance dispersal
anisotrophy
spatio-temporal models
inoculum source
cucurbit downy mildew
Applied mathematics. Quantitative methods
T57-57.97
Probabilities. Mathematical statistics
QA273-280
spellingShingle long-distance dispersal
anisotrophy
spatio-temporal models
inoculum source
cucurbit downy mildew
Applied mathematics. Quantitative methods
T57-57.97
Probabilities. Mathematical statistics
QA273-280
Awino M. E. Ojwang'
Trevor Ruiz
Sharmodeep Bhattacharyya
Shirshendu Chatterjee
Peter S. Ojiambo
David H. Gent
A General Framework for Spatio-Temporal Modeling of Epidemics With Multiple Epicenters: Application to an Aerially Dispersed Plant Pathogen
description The spread dynamics of long-distance-dispersed pathogens are influenced by the dispersal characteristics of a pathogen, anisotropy due to multiple factors, and the presence of multiple sources of inoculum. In this research, we developed a flexible class of phenomenological spatio-temporal models that extend a modeling framework used in plant pathology applications to account for the presence of multiple sources and anisotropy of biological species that can govern disease gradients and spatial spread in time. We use the cucurbit downy mildew pathosystem (caused by Pseudoperonospora cubensis) to formulate a data-driven procedure based on the 2008 to 2010 historical occurrence of the disease in the U.S. available from standardized sentinel plots deployed as part of the Cucurbit Downy Mildew ipmPIPE program. This pathosystem is characterized by annual recolonization and extinction cycles, generating annual disease invasions at the continental scale. This data-driven procedure is amenable to fitting models of disease spread from one or multiple sources of primary inoculum and can be specified to provide estimates of the parameters by regression methods conditional on a function that can accommodate anisotropy in disease occurrence data. Applying this modeling framework to the cucurbit downy mildew data sets, we found a small but consistent reduction in temporal prediction errors by incorporating anisotropy in disease spread. Further, we did not find evidence of an annually occurring, alternative source of P. cubensis in northern latitudes. However, we found a signal indicating an alternative inoculum source on the western edge of the Gulf of Mexico. This modeling framework is tractable for estimating the generalized location and velocity of a disease front from sparsely sampled data with minimal data acquisition costs. These attributes make this framework applicable and useful for a broad range of ecological data sets where multiple sources of disease may exist and whose subsequent spread is directional.
format article
author Awino M. E. Ojwang'
Trevor Ruiz
Sharmodeep Bhattacharyya
Shirshendu Chatterjee
Peter S. Ojiambo
David H. Gent
author_facet Awino M. E. Ojwang'
Trevor Ruiz
Sharmodeep Bhattacharyya
Shirshendu Chatterjee
Peter S. Ojiambo
David H. Gent
author_sort Awino M. E. Ojwang'
title A General Framework for Spatio-Temporal Modeling of Epidemics With Multiple Epicenters: Application to an Aerially Dispersed Plant Pathogen
title_short A General Framework for Spatio-Temporal Modeling of Epidemics With Multiple Epicenters: Application to an Aerially Dispersed Plant Pathogen
title_full A General Framework for Spatio-Temporal Modeling of Epidemics With Multiple Epicenters: Application to an Aerially Dispersed Plant Pathogen
title_fullStr A General Framework for Spatio-Temporal Modeling of Epidemics With Multiple Epicenters: Application to an Aerially Dispersed Plant Pathogen
title_full_unstemmed A General Framework for Spatio-Temporal Modeling of Epidemics With Multiple Epicenters: Application to an Aerially Dispersed Plant Pathogen
title_sort general framework for spatio-temporal modeling of epidemics with multiple epicenters: application to an aerially dispersed plant pathogen
publisher Frontiers Media S.A.
publishDate 2021
url https://doaj.org/article/a5a85ef414834b5681540250abcf16c6
work_keys_str_mv AT awinomeojwang ageneralframeworkforspatiotemporalmodelingofepidemicswithmultipleepicentersapplicationtoanaeriallydispersedplantpathogen
AT trevorruiz ageneralframeworkforspatiotemporalmodelingofepidemicswithmultipleepicentersapplicationtoanaeriallydispersedplantpathogen
AT sharmodeepbhattacharyya ageneralframeworkforspatiotemporalmodelingofepidemicswithmultipleepicentersapplicationtoanaeriallydispersedplantpathogen
AT shirshenduchatterjee ageneralframeworkforspatiotemporalmodelingofepidemicswithmultipleepicentersapplicationtoanaeriallydispersedplantpathogen
AT petersojiambo ageneralframeworkforspatiotemporalmodelingofepidemicswithmultipleepicentersapplicationtoanaeriallydispersedplantpathogen
AT davidhgent ageneralframeworkforspatiotemporalmodelingofepidemicswithmultipleepicentersapplicationtoanaeriallydispersedplantpathogen
AT awinomeojwang generalframeworkforspatiotemporalmodelingofepidemicswithmultipleepicentersapplicationtoanaeriallydispersedplantpathogen
AT trevorruiz generalframeworkforspatiotemporalmodelingofepidemicswithmultipleepicentersapplicationtoanaeriallydispersedplantpathogen
AT sharmodeepbhattacharyya generalframeworkforspatiotemporalmodelingofepidemicswithmultipleepicentersapplicationtoanaeriallydispersedplantpathogen
AT shirshenduchatterjee generalframeworkforspatiotemporalmodelingofepidemicswithmultipleepicentersapplicationtoanaeriallydispersedplantpathogen
AT petersojiambo generalframeworkforspatiotemporalmodelingofepidemicswithmultipleepicentersapplicationtoanaeriallydispersedplantpathogen
AT davidhgent generalframeworkforspatiotemporalmodelingofepidemicswithmultipleepicentersapplicationtoanaeriallydispersedplantpathogen
_version_ 1718428450985869312