In light of bee declines, the importance of pollination services from managed and native bees to our agriculture and economy is of great political, scientific and public interest. Viruses, first observed in honeybees, have been documented in bumblebees and the prevalence and load of some RNA viruses have been associated with managed honeybees. Shared flowers may be the bridge across which viruses pass between bees but no study has yet demonstrated that bumblebees can pick up viruses while foraging on contaminated flowers. Here, through a series of mechanistic laboratory experiments and mathematical modelling, we ask whether viruses can be transmitted between bee genera on shared flowers and how transmission can be effectively mitigated. We demonstrated that deformed wing virus (DWV) can be transmitted from infected honeybees to bumblebees through the use of shared red clover. We were also able to show that the route may work in reverse and bumblebees could contribute to the spread as well. Our model showed that reducing vector‐mediated transmission in honeybee colonies could potentially lead to a far greater reduction in bumblebee infection than simply reducing the number of honeybees. Additionally, we identified a dilution effect, whereby increasing floral abundance reduced transmission. Synthesis and applications. In this study, we showed that DWV may be spread between bee genera through the shared use of flowers. Through mathematical simulation, we identified two practical management options for reducing spread. The combination of treating honeybees effectively for the Varroa mite, a known vector of DWV, and increasing floral abundance where honeybees and native pollinators share the landscape were shown to reduce the spread of DWV within bee communities in simulations.

Controlling the spread of COVID-19 -- even after a licensed vaccine is available -- requires the effective use of non-pharmaceutical interventions, e.g., physical distancing, limits on group sizes, mask wearing, etc.. To date, such interventions have neither been uniformly nor systematically implemented in most countries. For example, even when under strict stay-at-home orders, numerous jurisdictions granted exceptions and/or were in close proximity to locations with entirely different regulations in place. Here, we investigate the impact of such geographic inconsistencies in epidemic control policies by coupling search and mobility data to a simple mathematical model of SARS-COV2 transmission. Our results show that while stay-at-home orders decrease contacts in most areas of the United States of America (US), some specific activities and venues often see an increase in attendance. Indeed, over the month of March 2020, between 10 and 30% of churches in the US saw increases in attendance; even as the total number of visits to churches declined nationally. This heterogeneity, where certain venues see substantial increases in attendance while others close, suggests that closure can cause individuals to find an open venue, even if that requires longer-distance travel. And, indeed, the average distance travelled to churches in the US rose by 13% over the same period. Strikingly, our mathematical model reveals that, across a broad range of model parameters, partial measures can often be worse than no measures at all. In the most severe cases, individuals not complying with policies by traveling to neighboring jurisdictions can create epidemics when the outbreak would otherwise have been controlled. Taken together, our data analysis and modelling results highlight the potential unintended consequences of inconsistent epidemic control policies and stress the importance of balancing the societal needs of a population with the risk of an outbreak growing into a large epidemic.

A key challenge to make effective use of evolutionary algorithms is to choose appropriate settings for their parameters. However, the appropriate parameter setting generally depends on the structure of the optimisation problem, which is often unknown to the user. Non-deterministic parameter control mechanisms adjust parameters using information obtained from the evolutionary process. Self-adaptation -where parameter settings are encoded in the chromosomes of individuals and evolve through mutation and crossover -is a popular parameter control mechanism in evolutionary strategies. However, there is little theoretical evidence that self-adaptation is effective, and self-adaptation has largely been ignored by the discrete evolutionary computation community.Here we show through a theoretical runtime analysis that a non-elitist, discrete evolutionary algorithm which self-adapts its mutation rate not only outperforms EAs which use static mutation rates on LeadingOnes k , but also improves asymptotically on an EA using a state-of-the-art control mechanism. The structure of this problem depends on a parameter k, which is a priori unknown to the algorithm, and which is needed to appropriately set a fixed mutation rate. The self-adaptive EA achieves the same asymptotic runtime as if this parameter was known to the algorithm beforehand, which is an asymptotic speedup for this problem compared to all other EAs previously studied. An experimental study of

Widespread application of insecticide remains the primary form of control for Chagas disease in Central America, despite only temporarily reducing domestic levels of the endemic vector Triatoma dimidiata and having little long-term impact. Recently, an approach emphasizing community feedback and housing improvements has been shown to yield lasting results. However, the additional resources and personnel required by such an intervention likely hinders its widespread adoption. One solution to this problem would be to target only a subset of houses in a community while still eliminating enough infestations to interrupt disease transfer. Here we develop a sequential sampling framework that adapts to information specific to a community as more houses are visited, thereby allowing us to efficiently find homes with domiciliary vectors while minimizing sampling bias. The method fits Bayesian geostatistical models to make spatially informed predictions, while gradually transitioning from prioritizing houses based on prediction uncertainty to targeting houses with a high risk of infestation. A key feature of the method is the use of a single exploration parameter, α, to control the rate of transition between these two design targets. In a simulation study using empirical data from five villages in southeastern Guatemala, we test our method using a range of values for α, and find it can consistently select fewer homes than random sampling, while still bringing the village infestation rate below a given threshold. We further find that when additional socioeconomic information is available, much larger savings are possible, but that meeting the target infestation rate is less consistent, particularly among the less exploratory strategies. Our results suggest new options for implementing long-term T. dimidiata control.

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