Spatial, seasonal and climatic predicitve models of Rift Valley Fever disease across Africa

Understanding the emergence and subsequent spread of human infectious diseases is a critical global challenge, especially for high-impact zoonotic and vector-borne diseases. Global climate and land-use change are likely to alter host and vector distributions, but understanding the impact of these changes on the burden of infectious diseases is difficult. Here, we use a Bayesian spatial model to investigate environmental drivers of one of the most important diseases in Africa, Rift Valley fever (RVF). The model uses a hierarchical approach to determine how environmental drivers vary both spatially and seasonally, and incorporates the effects of key climatic oscillations, to produce a continental risk map of RVF in livestock (as a proxy for human RVF risk). We find RVF risk has a distinct seasonal spatial pattern influenced by climatic variation, with the majority of cases occurring in South Africa and Kenya in the first half of an El Niño year. Irrigation, rainfall and human population density were the main drivers of RVF cases, independent of seasonal, climatic or spatial variation. By accounting more subtly for the patterns in RVF data, we better determine the importance of underlying environmental drivers, and also make space- and time-sensitive predictions to better direct future surveillance resources. This article is part of the themed issue ‘One Health for a changing world: zoonoses, ecosystems and human well-being’

Using environmental niche modelling to investigate the importance of ambient temperature in human-crocodilian attack occurrence for two species of crocodilian

Crocodilians are responsible for more attacks on people than any other large predator, which has important implications for human safety and crocodilian conservation. Understanding the drivers of crocodilian attacks on people could help minimise future attacks and inform conflict management. Crocodilian attacks follow a seasonal pattern for many species; however, there has been limited analyses of the relationship between fine-scale contemporaneous environmental conditions and atack occurrence. Here, we use methods from environmental niche modelling to explore the relationships between abiotic predictors and human attack occurrence at a daily temporal resolution for two species: the Nile crocodile (Crocodylus niloticus) in South Africa and Swaziland (renamed Eswatini), and the American alligator (Alligator mississippiensis) in Florida. Our results indicate that ambient daily temperature in the most important abiotic temporal predictor of attack occurrence for both species, with attack likelihood increasing sharply at temperatures above 18°C and peaking at 28°C. It is likely that this relationship is explained partially by human propensity to spend time in and around water in warmer weather, but also by the effect of temperature on crocodilian hunting behaviour and physiology, especially the ability to digest food. We discuss the potential of our findings to contribute to the management of crocodilians, with benefits for human safety and conservation, as well as the application of environmental niche modelling to analysing human conflict with other species, including ectotherms and endotherms

Radiogenic and Muon-Induced Backgrounds in the LUX Dark Matter Detector

The Large Underground Xenon (LUX) dark matter experiment aims to detect rare low-energy interactions from Weakly Interacting Massive Particles (WIMPs). The radiogenic backgrounds in the LUX detector have been measured and compared with Monte Carlo simulation. Measurements of LUX high-energy data have provided direct constraints on all background sources contributing to the background model. The expected background rate from the background model for the 85.3 day WIMP search run is $(2.6\pm0.2_{\textrm{stat}}\pm0.4_{\textrm{sys}})\times10^{-3}$~events~keV$_{ee}^{-1}$~kg$^{-1}$~day$^{-1}$ in a 118~kg fiducial volume. The observed background rate is $(3.6\pm0.4_{\textrm{stat}})\times10^{-3}$~events~keV$_{ee}^{-1}$~kg$^{-1}$~day$^{-1}$, consistent with model projections. The expectation for the radiogenic background in a subsequent one-year run is presented.Comment: 18 pages, 12 figures / 17 images, submitted to Astropart. Phy

Signal yields, energy resolution, and recombination fluctuations in liquid xenon

This work presents an analysis of monoenergetic electronic recoil peaks in the dark-matter-search and calibration data from the first underground science run of the Large Underground Xenon (LUX) detector. Liquid xenon charge and light yields for electronic recoil energies between 5.2 and 661.7 keV are measured, as well as the energy resolution for the LUX detector at those same energies. Additionally, there is an interpretation of existing measurements and descriptions of electron-ion recombination fluctuations in liquid xenon as limiting cases of a more general liquid xenon re- combination fluctuation model. Measurements of the standard deviation of these fluctuations at monoenergetic electronic recoil peaks exhibit a linear dependence on the number of ions for energy deposits up to 661.7 keV, consistent with previous LUX measurements between 2-16 keV with $^3$H. We highlight similarities in liquid xenon recombination for electronic and nuclear recoils with a comparison of recombination fluctuations measured with low-energy calibration data.Comment: 11 pages, 12 figures, 3 table