A general framework for spatio-temporal modeling of epidemics with multiple epicenters: Application to an aerially dispersed plant pathogen

Authors: OJWANG, AWINO - North Carolina State University; RUIZ, TREVOR - Oregon State University; BHATTACHARYYA, SHARMODEEP - Oregon State University; CHATTERJEE, SHIRSHENDU - City University Of New York; OJIAMBO, PETER - North Carolina State University; Gent, David - Dave

Submitted to: Frontiers in Applied Mathematics and Statistics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/5/2021
Publication Date: 11/15/2021
Citation: Ojwang, A.M., Ruiz, T., Bhattacharyya, S., Chatterjee, S., Ojiambo, P.S., Gent, D.H. 2021. A general framework for spatio-temporal modeling of epidemics with multiple epicenters: Application to an aerially dispersed plant pathogen. Frontiers in Applied Mathematics and Statistics. 7. Article 721352. https://doi.org/10.3389/fams.2021.721352.
DOI: https://doi.org/10.3389/fams.2021.721352

Interpretive Summary: The dynamics of the spread of disease are influenced by numerous factors, including how the causal disease organism is dispersed, weather conditions, and where disease is already present in the landscape. In this research, we developed a flexible class of spatio-temporal models that can account for the presence of multiple sources of disease and spread that is not uniform but rather directional due to wind. We use the disease cucurbit downy mildew to formulate a data-driven procedure that is amenable to modeling disease spread at the continental scale when multiple sources of disease are present and wind influences the pattern of disease spread. We found a small but consistent reduction in prediction errors by incorporating directionality of disease spread due to wind. We did not find evidence of an annually occurring, alternative source of disease in northern latitudes. However, we found signal indicating a source of the disease on both the eastern and western edge of the Gulf of Mexico. This modeling framework is tractable for estimating the generalized location and velocity of a disease front when available data is relatively sparse. These attributes make this modeling approach useful for a broad range of ecological data sets where multiple sources of disease, or other organisms, may exist and when disease spread is directional.

Technical Abstract: 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 spatio-temporal models that can 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 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 invasions at the continental scale. The 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 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, or other organisms, may exist and whose subsequent spread is directional.