Population dispersion and equilibrium infection frequency in a spatial epidemic

Duryea, Maria; Caraco, Thomas; Gardner, Geoffrey; Maniatty, William

doi:10.1016/s0167-2789(99)00059-7

Cited by 32 publications

(28 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each of these processes adds withinhost competition to between-host competition, complicating the evolutionary dynamics (Adler and Mosquera, 2000). Furthermore, since pathogen transmission often occurs only across local neighborhoods (Satō et al, 1994;Duryea et al, 1999;Caraco et al, 2001) a complete theory for evolution of disease requires modeling of spatial processes (Claessen and de Roos, 1995;Caraco et al, 2006;Boots and Mealor, 2007) or contact-network transmission (Keeling, 2005). Not surprisingly, development of hypotheses concerning dependence between traits of pathogens has outpaced empirical tests, but the latter are increasing (e.g., Ebert, 1994;Ebert and Mangin, 1997;Mackinnon and Read, 1999;McKean and Nunney, 2005).…”

Section: Discussionmentioning

confidence: 99%

Free-living pathogens: Life-history constraints and strain competition

Caraco

Wang

2008

Journal of Theoretical Biology

Self Cite

View full text Add to dashboard Cite

Many pathogen life histories include a free-living stage, often with anatomical and physiological adaptations promoting persistence outside of host tissues. More durable particles presumably require that the pathogen metabolize more resources per particle. Therefore, we hypothesize functional dependencies, pleiotropic constraints, between the rate at which free-living particles decay outside of host tissues and other pathogen traits, including virulence, the probability of infecting a host upon contact, and pathogen reproduction within host tissues. Assuming that pathogen strains compete for hosts preemptively, we find patterns in trait dependencies predicting whether or not strain competition favors a highly persistent free-living stage.

show abstract

Section: Discussionmentioning

confidence: 99%

Free-living pathogens: Life-history constraints and strain competition

Caraco

Wang

2008

Journal of Theoretical Biology

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the absence of the invader, the resident's global density will be approximated by ρ * r = (1 -µ/α r ). Suppose that an individual of the invasive species is introduced, via dispersal, at an open site y at time t. Approximating the local state frequencies by global densities (e.g., Duryea et al, 1999), the probability that no site is open (for local propagation) among the neighbors of site y is close to (ρ , since each individual has the same exponentially distributed waiting time for mortality. Larger neighborhoods increase the probability that the immigrant will find a neighboring site open, but do not necessarily increase the chance that the invader will propagate into an open site.…”

Section: Local Dynamicsmentioning

confidence: 99%

Spatial dynamics of invasion: the geometry of introduced species

Korniss

Caraco

2005

Journal of Theoretical Biology

Self Cite

View full text Add to dashboard Cite

Many exotic species combine low probability of establishment at each introduction with rapid population growth once introduction does succeed. To analyze this phenomenon, we note that invaders often cluster spatially when rare, and consequently an introduced exotic's population dynamics should depend on locally structured interactions. Ecological theory for spatially structured invasion relies on deterministic approximations, and determinism does not address the observed uncertainty of the exotic-introduction process. We take a new approach to the population dynamics of invasion and, by extension, to the general question of invasibility in any spatial ecology. We apply the physical theory for nucleation of spatial systems to a lattice-based model of competition between plant species, a resident and an invader, and the analysis reaches conclusions that differ qualitatively from the standard ecological theories. Nucleation theory distinguishes between dynamics of single-cluster and multi-cluster invasion. Low introduction rates and small system size produce single-cluster dynamics, where success or failure of introduction is inherently stochastic. Single-cluster invasion occurs only if the cluster reaches a critical size, typically preceded by a number of failed attempts. For this case, we identify the functional form of the probability distribution of time elapsing until invasion succeeds. Although multi-cluster invasion for sufficiently large systems exhibits spatial averaging and almostdeterministic dynamics of the global densities, an analytical approximation from nucleation theory, known as Avrami's law, describes our simulation results far better than standard ecological approximations.

show abstract

“…Both cellular and geographic automata provide an alternative to differential equation based epidemic models. These models treat time as discrete and interactions as localized [33] and have been applied to a wide range of disease spread problems [1,2,9,17,32,36]. Susceptible-infected-recovered models are often built into geographic automata to examine the spatial and temporal propagation of epidemics [2,13,17,23,32,33].…”

Section: Introductionmentioning

confidence: 99%

Representation of animal distributions in space: how geostatistical estimates impact simulation modeling of foot-and-mouth disease spread

2008

View full text Add to dashboard Cite

-Modeling potential disease spread in wildlife populations is important for predicting, responding to and recovering from a foreign animal disease incursion. To make spatial epidemic predictions, the target animal species of interest must first be represented in space. We conducted a series of simulation experiments to determine how estimates of the spatial distribution of white-tailed deer impact the predicted magnitude and distribution of foot-and-mouth disease (FMD) outbreaks. Outbreaks were simulated using a susceptible-infected-recovered geographic automata model. The study region was a 9-county area (24 000 km 2 ) of southern Texas. Methods used for creating deer distributions included dasymetric mapping, kriging and remotely sensed image analysis. The magnitudes and distributions of the predicted outbreaks were evaluated by comparing the median number of deer infected and median area affected (km 2 ), respectively. The methods were further evaluated for similar predictive power by comparing the model predicted outputs with unweighted pair group method with arithmetic mean (UPGMA) clustering. There were significant differences in the estimated number of deer in the study region, based on the geostatistical estimation procedure used (range: 385 939-768 493). There were also substantial differences in the predicted magnitude of the FMD outbreaks (range: 1 563-8 896) and land area affected (range: 56-447 km 2 ) for the different estimated animal distributions. UPGMA clustering indicated there were two main groups of distributions, and one outlier. We recommend that one distribution from each of these two groups be used to model the range of possible outbreaks. Methods included in cluster 1 (such as county-level disaggregation) could be used in conjunction with any of the methods in cluster 2, which included kriging, NDVI split by ecoregion, or disaggregation at the regional level, to represent the variability in the model predicted outbreak distributions. How animal populations are represented needs to be considered in all spatial disease spread models.spatial modeling / epidemic modeling / deer density / sensitivity analysis

show abstract

Population dispersion and equilibrium infection frequency in a spatial epidemic

Cited by 32 publications

References 31 publications

Free-living pathogens: Life-history constraints and strain competition

Free-living pathogens: Life-history constraints and strain competition

Spatial dynamics of invasion: the geometry of introduced species

Representation of animal distributions in space: how geostatistical estimates impact simulation modeling of foot-and-mouth disease spread

Contact Info

Product

Resources

About