Immunological precursors of influenza epidemics]

The authors studied the immuno-epidemiological manifestations of ciculation and variability of the influenza virus during the periods preceding the officiallly recorded rise of the incidence of this disease. The following epidemic precursors were revealed: a) an increase of the number of persons who fell sick with subclinical form of the disease, accompanied by a rise in the population of the antibody level to the type of influenza virus whose latest variant later caused an epidemic morbidity elevation; b) a progressive predominance of the causative agent of the developing epidemic in the etiology of influenza; c) a growth of the collective immunity indices from the "minimal" to the "critical" levels; d) an increase of the sero-conversion multiplicity and of the antibody level in those who sustained the disease during the epidemic development. These precursors could be revealed at the period of from 1 1/2 to 6 months before the beginning of the morbidity growth caused by viruses of endogenous or of exogenous origin.     

Dispersal and spatial scale affect synchrony in spatial population dynamics

A large body of theoretical studies has shown that synchrony among populations is critical for the long-term persistence of species in fragmented habitats. Although the effects of dispersal and environmental factors on synchrony have been investigated theoretically, empirical studies of these relationships have been lacking. We explored the interplay between environmental and demographic factors (fecundity, survival, dispersal) on population synchrony for 53 species of birds. We show that the interspecific differences in mean synchrony were determined by global environmental factors whose action was probably mediated by the abundance of each species. After removing the effect of these global factors on synchrony, the residual synchrony was strongly correlated with dispersal distance. The relationship between dispersal and synchrony was stronger for the species nesting in wet habitats than for those nesting in dry habitats. Our results indicate that different factors synchronize bird populations at different spatial scales, thus highlighting the role of scale in understanding spatial population dynamics and extinction risks.     

The feasibility of forecasting influenza epidemics in Cuba.

A large influenza epidemic took place in Havana during the winter of 1988. The epidemiologic surveillance unit of the Pedro Kourí Institute of Tropical Medicine detected the beginning of the epidemic wave. The Rvachev-Baroyan mathematical model of the geographic spread of an epidemic was used to forecast this epidemic under routine conditions of the public health system. The expected number of individuals who would attend outpatient services, because of influenza-like illness, was calculated and communicated to the health authorities within enough time to permit the introduction of available control measures. The approximate date of the epidemic peak, the daily expected number of individuals attending medical services, and the approximate time of the end of the epidemic wave were estimated. The prediction error was 12%. The model was sufficiently accurate to warrant its use as a practical forecasting tool in the Cuban public health system.     

A geographical spread of vaccine-resistance in avian influenza epidemics

Vaccination can be a useful tool for control of avian influenza outbreaks in poultry, but its use is reconsidered in most of the countries worldwide because of its negative effects on the disease control. One of the most important negative effects is the potential for emergence of vaccine-resistant viruses. Actually, in the vaccination program in China and Mexico, several vaccine-resistant strains were confirmed. Vaccine-resistant strains usually cause a loss of the protection effectiveness of vaccination. Therefore, a vaccination program that engenders the emergence of the resistant strain might promote the spread of the resistant strain and undermine the control of the infectious disease, even if the vaccination protects against the transmission of a vaccine-sensitive strain. We designed and analyzed a deterministic patch-structured model in heterogeneous areas (with or without vaccination) illustrating transmission of vaccine-sensitive and vaccine-resistant strains during a vaccination program. We found that the vaccination program can eradicate the vaccine-sensitive strain but lead to a prevalence of vaccine-resistant strain. Further, interestingly, the replacement of viral strain could occur in another area without vaccination through a migration of non-infectious individuals due to an illegal trade of poultry. It is also a novel result that only a complete eradication of both strains in vaccination area can achieve the complete eradication in another areas. Thus we can obtain deeper understanding of an effect of vaccination for better development of vaccination strategies to control avian influenza spread.     

Dynamics of annual influenza A epidemics with immuno-selection

 The persistence of Influenza A in the human population relies on continual changes in the viral surface antigens allowing the virus to reinfect the same hosts every few years. The epidemiology of such a drifting virus is modeled by a discrete season-to-season map. During the epidemic season only one strain is present and its transmission dynamics follows a standard epidemic model. After the season, cross-immunity to next year's virus is determined from the proportion of hosts that were infected during the season. A partial analysis of this map shows the existence of oscillations where epidemics occur at regular or irregular intervals. Received: 16 February 2001 / Revised version: 11 June 2002 / Published online: 28 February 2003 Key words or phrases: Infectious disease – Influenza drift – Cross-immunity – Seasonal epidemics – Iterated map     

Within-population spatial synchrony in mast seeding of North American oaks

Mast seeding, the synchronous production of large crops of seeds, has been frequently documented in oak species. In this study we used several North American oak data-sets to quantify within-stand (<10 km) synchrony in mast dynamics. Results indicated that intraspecific synchrony in seed production always exceeded interspecific synchrony and was essentially constant over distances ranging from 100 m to 10 km. Asynchrony between species was at least partially attributable to differences in the endogenous dynamics in seed production caused by the varying numbers of years (1 or 2) required to mature seeds. Similarly, the magnitude of intraspecific seed production synchrony was related to intraspecific variation in endogenous dynamics; this intraspecific variation could be caused by spatial variation in habitat conditions. These results indicate that both interspecific and intraspecific variation in the endogenous processes generating variability in seed production may influence the magnitude of spatial synchrony in total (all species) mast production. Such findings may be of significance to understanding interactions between synchrony in mast seeding and animal consumer populations.     

Local spatial and temporal processes of influenza in Pennsylvania, USA: 2003-2009

Background

Influenza is a contagious respiratory disease responsible for annual seasonal epidemics in temperate climates. An understanding of how influenza spreads geographically and temporally within regions could result in improved public health prevention programs. The purpose of this study was to summarize the spatial and temporal spread of influenza using data obtained from the Pennsylvania Department of Health''s influenza surveillance system.

Methodology and Findings

We evaluated the spatial and temporal patterns of laboratory-confirmed influenza cases in Pennsylvania, United States from six influenza seasons (2003–2009). Using a test of spatial autocorrelation, local clusters of elevated risk were identified in the South Central region of the state. Multivariable logistic regression indicated that lower monthly precipitation levels during the influenza season (OR = 0.52, 95% CI: 0.28, 0.94), fewer residents over age 64 (OR = 0.27, 95% CI: 0.10, 0.73) and fewer residents with more than a high school education (OR = 0.76, 95% CI: 0.61, 0.95) were significantly associated with membership in this cluster. In addition, time series analysis revealed a temporal lag in the peak timing of the influenza B epidemic compared to the influenza A epidemic.

Conclusions

These findings illustrate a distinct spatial cluster of cases in the South Central region of Pennsylvania. Further examination of the regional transmission dynamics within these clusters may be useful in planning public health influenza prevention programs.     

Mixed respiratory viral infections during influenza A epidemics

Mixed respiratory viral infections occurring in the course of 8 influenza A epidemics in the Estonian SSR between 1969 and 1978 were investigated. A total of 1638 patients were followed up. The IF method, serological test CFR and HIR and isolation of the virus on tissue cultures and chick embryos were used. Mixed infections were found in 0-77.7% of laboratory-confirmed cases, depending on the epidemic. A combination of influenza A + parainfluenza was observed most frequently during the influenza epidemics in 1971-1977 and a combination of influenza A + influenza B during the 1977-1978 epidemic.     

Spatial pattern switching enables cyclic evolution in spatial epidemics

Infectious diseases often spread as spatial epidemic outbreak waves. A number of model studies have shown that such spatial pattern formation can have important consequences for the evolution of pathogens. Here, we show that such spatial patterns can cause cyclic evolutionary dynamics in selection for the length of the infectious period. The necessary reversal in the direction of selection is enabled by a qualitative change in the spatial pattern from epidemic waves to irregular local outbreaks. The spatial patterns are an emergent property of the epidemic system, and they are robust against changes in specific model assumptions. Our results indicate that emergent spatial patterns can act as a rich source for complexity in pathogen evolution.     

Geographically partitioned spatial synchrony among cyclic moth populations

Many species of forest lepidopterans exhibit regular population cycles, which culminate in outbreak densities at approximately ten-year intervals. Population peaks and mass outbreaks typically occur synchronously and may lead to extensive forest damages over large geographic areas. Here, we report patterns of spatial synchrony among cyclic autumnal moth ( Epirrita autumnata ) populations across Fennoscandia, as inferred from 24 long-term (10–33 years) data sets. The study provides the first formal analysis of spatial synchrony of this pest species which damages mountain birch ( Betula pubescens ssp. czerepanovii ) forests in the sub Arctic. We detected positive cross-correlations in population growth rates between the time series, indicating overall spatial synchrony. However, we found the strongest degree of synchrony within geographically and climatically distinct regional clusters, into which time series were partitioned using cluster analyses. Within regional clusters, moth populations were exposed to the synchronizing effects of common, spatially autocorrelated environmental conditions, i.e. a Moran effect. Consequently, we conclude that a geographically and climatically restricted Moran effect, perhaps interacting with dispersal, is the most likely explanation for the regionally partitioned pattern of synchrony among autumnal moth populations in Fennoscandia. Our results emphasize that high amounts of environmental variation may result in a clear structuring of spatial synchrony at unexpectedly small scales.     

Bootstrap methods for estimating spatial synchrony of fluctuating populations

We describe and examine methods for estimating spatial correlations used in population ecology. We base our analyses on a hypothetical example of a species that has been censured at 30 different locations for 20 years. We assume that the population fluctuations can be described by a simple linear model on logarithmic scale. Stochastic simulations is utilized to check how seven different ways of resampling perform when the goal is to find nominal 95% confidence intervals for the spatial correlation in growth rates at given distances. It turns out that resampling of locations performs badly, with true coverage level as low as 30–40%, especially for small correlations at long distances. Resampling of timepoints performs much better, with coverage varying from 80 to 90%, depending on the strength of density regulation and whether the spatial correlation is estimated for the response variable or for the error terms in the model. Assuming that the underlying model is known, the best results are achieved for parametric bootstrapping, a result that strongly emphasize the importance of defining and estimating a proper population model when studying spatial processes.     

Generalizations of the Moran effect explaining spatial synchrony in population fluctuations

The Moran effect for populations separated in space states that the autocorrelations in the population fluctuations equal the autocorrelation in environmental noise, assuming the same linear density regulation in all populations. Here we generalize the Moran effect to include also nonlinear density regulation with spatial heterogeneity in local population dynamics as well as in the effects of environmental covariates by deriving a simple expression for the correlation between the sizes of two populations, using diffusion approximation to the theta-logistic model. In general, spatial variation in parameters describing the dynamics reduces population synchrony. We also show that the contribution of a covariate to spatial synchrony depends strongly on spatial heterogeneity in the covariate or in its effect on local dynamics. These analyses show exactly how spatial environmental covariation can synchronize fluctuations of spatially segregated populations with no interchange of individuals even if the dynamics are nonlinear.     

Phase transition in spatial epidemics using cellular automata with noise

One of the central issues in studying the complex population patterns observed in nature is the role of stochasticity. In this paper, the effects of additive spatiotemporal random variations—noise—are introduced to an epidemic model. The no-noise model exhibits a phase transition from a disease-free state to an endemic state. However, this phase transition can revert in a resonance-like manner depending on noise intensity when introducing nonzero random variations to the model. On the other hand, given a regime where disease can persist, noise can induce disappearance of the phase transition. The results obtained show that noise plays a tremendous role in the spread of the disease state, which has implications for how we try to prevent, and eventually eradicate, disease.     

Local scale population genetic structure of Entomophthora muscae epidemics

Isolates of the entomopathogenic fungus Entomophthora muscae were obtained from houseflies sampled at five stables on Zealand during an epidemic in fall 2005. DNA fingerprints were generated from single conidia from 40 E. muscae isolates using the PCR-based method of Inter-Simple Sequence Repeats (ISSR). This resulted in fingerprint patterns consisting of about 50 fragments, of which, 14 were polymorphic. From the polymorphic loci we estimated the reproductive mode, genetic differentiation and gene diversity of E. muscae populations using the statistics of index of association, Weir & Cockerhams theta and Nei's analysis of gene diversity in subdivided populations. Our results revealed no significant differences in allele frequencies among the five populations. The index of association test rejected the null hypothesis of random mating, but the test for paired locus compatibility showed weak or no linkage between loci indicating recombination or homoplasy in the dataset. From this study we cannot exclude the possibility that the population genetic structure underlying E. muscae epidemics could be panmictic consisting of several lineages with a high level of reciprocal migration.     

Geographical variation in the spatial synchrony of a forest-defoliating insect: isolation of environmental and spatial drivers

Despite the pervasiveness of spatial synchrony of population fluctuations in virtually every taxon, it remains difficult to disentangle its underlying mechanisms, such as environmental perturbations and dispersal. We used multiple regression of distance matrices (MRMs) to statistically partition the importance of several factors potentially synchronizing the dynamics of the gypsy moth, an invasive species in North America, exhibiting outbreaks that are partially synchronized over long distances (approx. 900 km). The factors considered in the MRM were synchrony in weather conditions, spatial proximity and forest-type similarity. We found that the most likely driver of outbreak synchrony is synchronous precipitation. Proximity played no apparent role in influencing outbreak synchrony after accounting for precipitation, suggesting dispersal does not drive outbreak synchrony. Because a previous modelling study indicated weather might indirectly synchronize outbreaks through synchronization of oak masting and generalist predators that feed upon acorns, we also examined the influence of weather and proximity on synchrony of acorn production. As we found for outbreak synchrony, synchrony in oak masting increased with synchrony in precipitation, though it also increased with proximity. We conclude that precipitation could synchronize gypsy moth populations directly, as in a Moran effect, or indirectly, through effects on oak masting, generalist predators or diseases.