Quantifying airborne dispersal routes of pathogens over continents to safeguard global wheat supply.

Infectious crop diseases spreading over large agricultural areas pose a threat to food security. Aggressive strains of the obligate pathogenic fungus Puccinia graminis f.sp. tritici (Pgt), causing the crop disease wheat stem rust, have been detected in East Africa and the Middle East, where they lead to substantial economic losses and threaten livelihoods of farmers. The majority of commercially grown wheat cultivars worldwide are susceptible to these emerging strains, which pose a risk to global wheat production, because the fungal spores transmitting the disease can be wind-dispersed over regions and even continents 1-11 . Targeted surveillance and control requires knowledge about airborne dispersal of pathogens, but the complex nature of long-distance dispersal poses significant challenges for quantitative research 12-14 . We combine international field surveys, global meteorological data, a Lagrangian dispersion model and high-performance computational resources to simulate a set of disease outbreak scenarios, tracing billions of stochastic trajectories of fungal spores over dynamically changing host and environmental landscapes for more than a decade. This provides the first quantitative assessment of spore transmission frequencies and amounts amongst all wheat producing countries in Southern/East Africa, the Middle East and Central/South Asia. We identify zones of high air-borne connectivity that geographically correspond with previously postulated wheat rust epidemiological zones (characterized by endemic disease and free movement of inoculum) 10,15 , and regions with genetic similarities in related pathogen populations 16,17 . We quantify the circumstances (routes, timing, outbreak sizes) under which virulent pathogen strains such as 'Ug99' 5,6 pose a threat from long-distance dispersal out of East Africa to the large wheat producing areas in Pakistan and India. Long-term mean spore dispersal trends (predominant direction, frequencies, amounts) are summarized for all countries in the domain (Supplementary Data). Our mechanistic modelling framework can be applied to other geographic areas, adapted for other pathogens and used to provide risk assessments in real-time 3 .

Large-Scale Atmospheric Dispersal Simulations Identify Likely Airborne Incursion Routes of Wheat Stem Rust Into Ethiopia.

In recent years, severe wheat stem rust epidemics hit Ethiopia, sub-Saharan Africa's largest wheat-producing country. These were caused by race TKTTF (Digalu race) of the pathogen Puccinia graminis f. sp. tritici, which, in Ethiopia, was first detected at the beginning of August 2012. We use the incursion of this new pathogen race as a case study to determine likely airborne origins of fungal spores on regional and continental scales by means of a Lagrangian particle dispersion model (LPDM). Two different techniques, LPDM simulations forward and backward in time, are compared. The effects of release altitudes in time-backward simulations and P. graminis f. sp. tritici urediniospore viability functions in time-forward simulations are analyzed. Results suggest Yemen as the most likely origin but, also, point to other possible sources in the Middle East and the East African Rift Valley. This is plausible in light of available field surveys and phylogenetic data on TKTTF isolates from Ethiopia and other countries. Independent of the case involving TKTTF, we assess long-term dispersal trends (>10 years) to obtain quantitative estimates of the risk of exotic P. graminis f. sp. tritici spore transport (of any race) into Ethiopia for different 'what-if' scenarios of disease outbreaks in potential source countries in different months of the wheat season.

An assessment of the doses received by members of the public in Japan following the nuclear accident at Fukushima Daiichi nuclear power plant.

The earthquake and tsunami on 11 March 2011, centred off the east coast of Japan, caused considerable destruction and substantial loss of life along large swathes of the Japanese coastline. The tsunami damaged the Fukushima Daiichi nuclear power plant (NPP), resulting in prolonged releases of radioactive material into the environment. This paper assesses the doses received by members of the public in Japan. The assessment is based on an estimated source term and atmospheric dispersion modelling rather than monitoring data. It is evident from this assessment that across the majority of Japan the estimates of dose are very low, for example they are estimated to be less than the annual average dose from natural background radiation in Japan. Even in the regions local to Fukushima Daiichi NPP (and not affected by any form of evacuation) the maximum lifetime effective dose is estimated to be well below the cumulative natural background dose over the same period. The impact of the urgent countermeasures on the estimates of dose was considered. And the relative contribution to dose from the range of exposure pathways and radionuclides were evaluated. Analysis of estimated doses focused on the geographic irregularity and the impact of the meteorological conditions. For example the dose to an infant's thyroid received over the first year was estimated to be greater in Hirono than in the non-evacuated region of Naraha, despite Hirono being further from the release location. A number of factors were identified and thought to contribute towards this outcome, including the local wind pattern which resulted in the recirculation of part of the release. The non-uniform nature of dose estimates strengthens the case for evaluations based on dispersion modelling.

Key factors in imprecision in radiological emergency response assessments using the NAME model.

In the very early stages of response to an accidental release of radioactivity leading to environmental contamination, it is likely that only limited measurements of radioactivity in the local environment will be available on which to base decisions concerning protection measures and radiation monitoring activities. Model predictions will be used to aid understanding of the radiological situation and to form a basis for emergency health protection decisions. This paper presents an analysis of the relative importance of contributors to the imprecision associated with emergency response calculations based on a few off-site measurements, using predictions from the UK Met Office's NAME III (Numerical Atmospheric dispersion Modelling Environment version 5.2) dispersion model. The results presented extend those from a previous study in which a simple Gaussian plume model was used and confirm the key parameters contributing to imprecision. The potential extent of the sheltering countermeasure resulting from a hypothetical release in real weather conditions occurring in 2007 and 2008 is also presented.