Snow tussocks, chaos, and the evolution of mast seeding

One hitherto intractable problem in studying mast seeding (synchronous intermittent heavy flowering by a population of perennial plants) is determining the relative roles of weather, plant reserves, and evolutionary selective pressures such as predator satiation. We parameterize a mechanistic resource-based model for mast seeding in Chionochloa pallens (Poaceae) using a long-term individually structured data set. Each plant's energy reserves were reconstructed using annual inputs (growing degree days), outputs (flowering), and a novel regression technique. This allowed the estimation of the parameters that control internal plant resource dynamics, and thereby allowed different models for masting to be tested against each other. Models based only on plant size, season degree days, and/or climatic cues (warm January temperatures) fail to reproduce the pattern of autocovariation in individual flowering and the high levels of flowering synchrony seen in the field. This shows that resource-matching or simple cue-based models cannot account for this example of mast seeding. In contrast, the resource-based model pulsed by a simple climate cue accurately describes both individual-level and population-level aspects of the data. The fitted resource-based model, in the absence of environmental forcing, has chaotic (but often statistically periodic) dynamics. Environmental forcing synchronizes individual reproduction, and the models predict highly variable seed production in close agreement with the data. An evolutionary model shows that the chaotic internal resource dynamics, as predicted by the fitted model, is selectively advantageous provided that adult mortality is low and seeds survive for more than 1 yr, both of which are true for C. pallens. Highly variable masting and chaotic dynamics appear to be advantageous in this case because they reduce seed losses to specialist seed predators, while balancing the costs of missed reproductive events

Pastoral production is associated with increased peste des petits ruminants seroprevalence in northern Tanzania across sheep, goats and cattle

Peste des petits ruminants virus (PPRV) causes a contagious disease of high morbidity and mortality in small ruminant populations globally. Using cross-sectional serosurvey data collected in 2016, our study investigated PPRV seroprevalence and risk factors among sheep, goats and cattle in 20 agropastoral (AP) and pastoral (P) villages in northern Tanzania. Overall observed seroprevalence was 21.1% (95% exact confidence interval (CI) 20.1–22.0) with 5.8% seroprevalence among agropastoral (95% CI 5.0–6.7) and 30.7% among pastoral villages (95% CI 29.3–32.0). Seropositivity varied significantly by management (production) system. Our study applied the catalytic framework to estimate the force of infection. The associated reproductive numbers (R0) were estimated at 1.36 (95% CI 1.32–1.39), 1.40 (95% CI 1.37–1.44) and 1.13 (95% CI 1.11–1.14) for sheep, goats and cattle, respectively. For sheep and goats, these R0 values are likely underestimates due to infection-associated mortality. Spatial heterogeneity in risk among pairs of species across 20 villages was significantly positively correlated (R2: 0.59–0.69), suggesting either cross-species transmission or common, external risk factors affecting all species. The non-negligible seroconversion in cattle may represent spillover or cattle-to-cattle transmission and must be investigated further to understand the role of cattle in PPRV transmission ahead of upcoming eradication efforts

Analysis of a spatial Lotka-Volterra model with a finite range predator-prey interaction

We perform an analysis of a recent spatial version of the classical Lotka-Volterra model, where a finite scale controls individuals' interaction. We study the behavior of the predator-prey dynamics in physical spaces higher than one, showing how spatial patterns can emerge for some values of the interaction range and of the diffusion parameter.Comment: 7 pages, 7 figure

Measles metapopulation dynamics: A gravity model for epidemiological coupling and dynamics

10.1086/422341American Naturalist1642267-281AMNT

Peak shift and epidemiology in a seasonal host–nematode system

Insight into the dynamics of parasite–host relationships of higher vertebrates requires an understanding of two important features: the nature of transmission and the development of acquired immunity in the host. A dominant hypothesis proposes that acquired immunity develops with the cumulative exposure to infection, and consequently predicts a negative relationship between peak intensity of infection and host age at this peak. Although previous studies have found evidence to support this hypothesis through between-population comparisons, these results are confounded by spatial effects. In this study, we examined the dynamics of infection of the nematode Trichostrongylus retortaeformis within a natural population of rabbits sampled monthly for 26 years. The rabbit age structure was reconstructed using body mass as a proxy for age, and the host age–parasite intensity relationship was examined for each rabbit cohort born from February to August. The age–intensity curves exhibited a typical concave shape, and a significant negative relationship was found between peak intensity of infection and host age at this peak. Adult females showed a distinct periparturient rise in T. retortaeformis infection, with higher intensities in breeding adult females than adult males and non-breeding females. These findings are consistent with the hypothesis of an acquired immune response of the host to a parasite infection, supporting the principle that acquired immunity can be modelled using the cumulative exposure to infection. These findings also show that seasonality can be an important driver of host–parasite interactions

The epidemiology of rubella in Mexico: seasonality, stochasticity and regional variation

The factors underlying the temporal dynamics of rubella outside of Europe and North America are not well known. Here we used 20 years of incidence reports from Mexico to identify variation in seasonal forcing and magnitude of transmission across the country and to explore determinants of inter-annual variability in epidemic magnitude in rubella. We found considerable regional variation in both magnitude of transmission and amplitude of seasonal variation in transmission. Several lines of evidence pointed to stochastic dynamics as an important driver of multi-annual cycles. Since average age of infection increased with the relative importance of stochastic dynamics, this conclusion has implications for the burden of congenital rubella syndrome. We discuss factors underlying regional variation, and implications of the importance of stochasticity for vaccination implementation

Implications of spatially heterogeneous vaccination coverage for the risk of congenital rubella syndrome in South Africa

Rubella is generally a mild childhood disease, but infection during early pregnancy may cause spontaneous abortion or congenital rubella syndrome (CRS), which may entail a variety of birth defects. Since vaccination at levels short of those necessary to achieve eradication may increase the average age of infection, and thus potentially the CRS burden, introduction of the vaccine has been limited to contexts where coverage is high. Recent work suggests that spatial heterogeneity in coverage should also be a focus of concern. Here, we use a detailed dataset from South Africa to explore the implications of heterogeneous vaccination for the burden of CRS, introducing realistic vaccination scenarios based on reported levels of measles vaccine coverage. Our results highlight the potential impact of country-wide reductions of incidence of rubella on the local CRS burdens in districts with small population sizes. However, simulations indicate that if rubella vaccination is introduced with coverage reflecting current estimates for measles coverage in South Africa, the burden of CRS is likely to be reduced overall over a 30 year time horizon by a factor of 3, despite the fact that this coverage is lower than the traditional 80 per cent rule of thumb for vaccine introduction, probably owing to a combination of relatively low birth and transmission rates. We conclude by discussing the likely impact of private-sector vaccination

Time is of the essence: exploring a measles outbreak response vaccination in Niamey, Niger.

The current World Health Organization recommendations for response during measles epidemics focus on case management rather than outbreak response vaccination (ORV) campaigns, which may occur too late to impact morbidity and mortality and have a high cost per case prevented. Here, we explore the potential impact of an ORV campaign conducted during the 2003-2004 measles epidemic in Niamey, Niger. We measured the impact of this intervention and also the potential impact of alternative strategies. Using a unique geographical, epidemiologic and demographic dataset collected during the epidemic, we developed an individual-based simulation model. We estimate that a median of 7.6% [4.9-8.9] of cases were potentially averted as a result of the outbreak response, which vaccinated approximately 57% (84563 of an estimated 148600) of children in the target age range (6-59 months), 23 weeks after the epidemic started. We found that intervening early (up to 60 days after the start of the epidemic) and expanding the age range to all children aged 6 months to 15 years may lead to a much larger (up to 90%) reduction in the number of cases in a West African urban setting like Niamey. Our results suggest that intervening earlier even with lower target coverage (approx. 60%), but a wider age range, may be more effective than intervening later with high coverage (more than 90%) in similar settings. This has important implications for the implementation of reactive vaccination interventions as they can be highly effective if the response is fast with respect to the spread of the epidemic

Synergistic interventions to control COVID-19:Mass testing and isolation mitigates reliance on distancing

Stay-at-home orders and shutdowns of non-essential businesses are powerful, but socially costly, tools to control the pandemic spread of SARS-CoV-2. Mass testing strategies, which rely on widely administered frequent and rapid diagnostics to identify and isolate infected individuals, could be a potentially less disruptive management strategy, particularly where vaccine access is limited. In this paper, we assess the extent to which mass testing and isolation strategies can reduce reliance on socially costly non-pharmaceutical interventions, such as distancing and shutdowns. We develop a multi-compartmental model of SARS-CoV-2 transmission incorporating both preventative non-pharmaceutical interventions (NPIs) and testing and isolation to evaluate their combined effect on public health outcomes. Our model is designed to be a policy-guiding tool that captures important realities of the testing system, including constraints on test administration and non-random testing allocation. We show how strategic changes in the characteristics of the testing system, including test administration, test delays, and test sensitivity, can reduce reliance on preventative NPIs without compromising public health outcomes in the future. The lowest NPI levels are possible only when many tests are administered and test delays are short, given limited immunity in the population. Reducing reliance on NPIs is highly dependent on the ability of a testing program to identify and isolate unreported, asymptomatic infections. Changes in NPIs, including the intensity of lockdowns and stay at home orders, should be coordinated with increases in testing to ensure epidemic control; otherwise small additional lifting of these NPIs can lead to dramatic increases in infections, hospitalizations and deaths. Importantly, our results can be used to guide ramp-up of testing capacity in outbreak settings, allow for the flexible design of combined interventions based on social context, and inform future cost-benefit analyses to identify efficient pandemic management strategies