Collinge offer interesting views into complex disease transmission systems. Although a parallel chapter summarizing the complex community and environmental interactions underlying hantavirus transmission would have been a nice complement, the biggest shortfall is that few of the chapters manage to link strongly to the theoretical ecologic framework offered in the chapter by R.
More generally, the book is attractively composed and appears to be bound well and printed on quality paper. For the size and content, though, the price is quite high—I suspect that this volume will be a valued addition to any library but is perhaps unlikely to be purchased by many people. This book will, I hope, be a first step toward a new synthesis of 2 seemingly distant but intimately related fields of inquiry, and at the very least represents an intriguing compendium of well-developed case studies of the complexities of disease systems.
Suggested citation for this article: Peterson AT. Disease ecology: community structure and pathogen dynamics [book review].
Emerg Infect Dis [serial on the Internet]. National Center for Biotechnology Information , U. Journal List Emerg Infect Dis v. Emerg Infect Dis. Reviewed by A. Townsend Peterson. Author information Copyright and License information Disclaimer. Corresponding author. Address for correspondence: A. Copyright notice. This is defined as the relative abundance of the most abundant strain see Materials and methods section. The Berger-Parker index is always higher in a heterogeneous network, even when the comparison is made at fixed values of richness Fig 2C.
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An alternative indicator, the Shannon evenness, shows a similar behavior as displayed in S3 Fig. B Berger-Parker index, i. C Berger-Parker index vs richness. Parameters are the same as in Fig 1. The fraction of strains going extinct also depends on stochastic effects in a finite size population. We indeed found that increasing network size, when temporal and topological properties were the same, led to an increase in both persistence time and richness S4 Fig. This shows that interference among transmission chains is reduced in larger populations.
Eventually, we tested whether additional mechanisms of strain injection were leading to different results.
In S6 Fig we assumed new strains to infect susceptible nodes already present in the system with rate q s , mimicking in this way transmissions originating from an external source, as it can happen in real cases. The plot of S6 Fig shows the same qualitative behavior described here.
The closer p IN is to 1, the stronger the repartition in communities is. The limited role of community structure is also confirmed by the fact that once this feature is combined with heterogeneous activation—in a model with the activation scheme of HET and the stub-matching of COM—the latter property has the dominant effect and the richness decreases S1 Fig. Solid lines refer to the right y-axis, while dashed ones to the left y-axis. A value of the IPR close to 1 indicates localization over one community. The effect of this second mechanism is shown in S6 Fig.
The relation between richness and prevalence remains the same when adding the injection of new strains due to the transmission from an external source. This mechanism further increases the richness. We tested the consequences of communities in strain dominance by plotting the Berger-Parker index in Fig 3C.
The increase in strain diversity is due to the reduced competition among strains introduced in different communities. When coupling among communities is low, indeed, strains may spend the majority of time within the community they were injected in, thus avoiding strains injected in other communities. Fig 3D confirms this hypothesis by showing the Inverse Participation Ratio IPR [ 55 ] that quantifies uniformity in the repartition of abundance across communities.
Values close to zero indicate uniform repartition, while, conversely, values close to 1 indicate that, on average, a strain is confined within a single community for most of the time more details are reported in the Materials and methods section. The strength of the community structure does not affect the repartition of the total prevalence squares in the plot , however it alters the average IPR value computed from the abundance of single strains, thus strains become more localized as p IN increases.
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Notice that a certain degree of localization is present also in the homogeneous network, due to those strains causing very few generations before going extinct. As a sensitivity analysis we tested whether the main results obtained so far are the same in a more realistic situation where additional heterogeneous properties of nodes are accounted for. We consider the case in which infectious duration varies across individuals, as happens for S.
S7 Fig shows that the inclusion of three classes differing in recovery rate reduces richness and increases the Berger-Parker index with respect to the homogeneous recovery. However, the effects discussed so far—e. Node turnover represents another important property of a network that may impact the ecological dynamics of strains for two reasons: incoming individuals contribute to richness by injecting new strains; on the other hand, the removal from the population of infected nodes breaks transmission chains and hampers the persistence of strains.
The figure, obtained with the HOM model, shows two distinct regimes.
This behavior can be explained by looking at the balance between injection and extinction that determines the equilibrium value of richness,. This general behavior was not altered by the accounting for introductions by transmissions from an external source as shown in S6 Fig. Contour plots are shown in both figures.
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We derive an approximate formula for T pers considering an emerging strain competing with a single effective strain formed by all other strains grouped together. This formulation, enabled by the neutral hypothesis, makes it possible to write the master equation describing the dynamics and to use the Fokker-Planck approximation to derive persistence times see Materials and methods section. The quantitative match for other values of p s is reported in S9 Fig.
Unlike richness, Berger-Parker index always increases monotonically with the length of stay— Fig 4B. This behavior is due to the correlation of this indicator with average abundance, similarly to what we discussed in the previous section. We conclude by analyzing the real-case example of the S. We used close-proximity-interaction CPI data recorded in a long-term health-care facility during 4 months by the i-Bird study [ 16 , 28 , 31 ]. These describe a high-resolution dynamical network whose complex structure reflects the hospital organization, the subdivision in wards and the admission and discharge of patients [ 58 ].
Disease Ecology: Community structure and pathogen dynamics
Together with the measurements of contacts, weekly nasal swabs were routinely performed to monitor the S. The modeling framework considered here well applies to this case. The SIS model is widely adopted for modeling the S. The dynamic CPI network was previously shown to be associated with paths of strain propagation [ 16 ]. In addition, new strains are introduced in the population carried by incoming patients, or through contacts with persons not taking part in the study. Fig 5A shows weekly carriage and its breakdown in different strains.
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Simulation results are reported in Fig 5B , that displays the impact of transmission and introduction rate on richness and prevalence. When q s is low we find a positive trend between richness and prevalence, consistently with the synthetic case. For larger values of q s the trend appears instead different. As transmissibility increases, richness initially grows with prevalence and then decreases after a certain point. This behavior is the same as observed in S6 Fig and stems from the reduction of susceptible nodes, that causes a decline in the expected injection rate—see Materials and methods section.
A Weekly carriage data measured during the i-Bird experiment. Each S.