Interdisciplinary Perspectives on Infectious Diseases / 2011 / Article / Tab 1

Research Article

Pathogens, Social Networks, and the Paradox of Transmission Scaling

Table 1

Empirical examples from the published literature of beta and measured in host populations differing in size, indicating the empirical observations and the likely mean-field scaling model.

Host-pathogen systemEmpirical observationsModel supportedReference

Humans-measles
Humans-pertussis
Humans-diphtheria
Humans-scarlet fever
Found to be relatively invariant across population sizes.Frequency dependent[15]

Humans-smallpoxTransmission was inverse of population sizeFrequency dependent[58]

House finches-mycoplasmaTransmission was independent of flock sizesFrequency dependent[59]

Pigs-Aujeszky’s disease virus (ADV) was invariant across different population sizesFrequency dependent[21]

Harbor seals-phocine distemper virus (PDV)Density-dependent scaling did not explain differences in transmission between different-sized seal haul-out sitesFrequency dependent[20]

Rana mucosa-chytridiomycosisTransmission rate increases and saturates with density of infected individualsFrequency dependent[33]

Tasmanian devil—devil facial tumor diseaseMaintenance of high prevalence following population declineFrequency dependent[34]

Brushtail possums-leptospira interogansDensity-dependent model fit experimental infection ratesDensity dependent[60]

Elk-brucellosisPopulation density was associated with an increase in seroprevalence but could not differentiate among linear and nonlinear effects of host density.Nonlinear
Density dependent
[61]

Rodents-cowpoxBoth models fit to incidence time series; support for both equivocal.Frequency and density dependent[22]

Rodents-cowpoxTransmission term lies between density- and frequency-dependent and varies seasonally.Model is intermediate[11]

Indian meal moth-granulosis virusA decline in transmission with increasing density of infectious cadaversNeither[26]

Possum-tuberculosisTransmission did not fit frequency- or density-dependent modelsNeither[62]

Tiger salamander-Abystomatigrinum virusTransmission was best modeled by a power or negative binomial function, that is, nonlinear density dependence.Neither[63]

Badgers-Mycobacterium bovis Negative relationship between host abundance and infection prevalenceNeither[64]

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