Spatial heterogeneities and spatial separation of hosts are often seen as key factors when developing accurate predictive models of the spread of pathogens. The question we address in this paper is how coarse the resolution of the spatial data can be for a model to be a useful tool for informing control policies. We examine this problem using the specific case of foot-and-mouth disease spreading between farms using the formulation developed during the 2001 epidemic in the United Kingdom. We show that, if our model is carefully parameterized to match epidemic behavior, then using aggregate county-scale data from the United States is sufficient to closely determine optimal control measures (specifically ring culling). This result also holds when the approach is extended to theoretical distributions of farms where the spatial clustering can be manipulated to extremes. We have therefore shown that, although spatial structure can be critically important in allowing us to predict the emergent population-scale behavior from a knowledge of the individual-level dynamics, for this specific applied question, such structure is mostly subsumed in the parameterization allowing us to make policy predictions in the absence of high-quality spatial information. We believe that this approach will be of considerable benefit across a range of disciplines where data are only available at intermediate spatial scales.foot-and-mouth | modeling T he spatial distribution of organisms is viewed as critically important for determining population dynamics. Numerous examples from the epidemiological and ecological literature have shown that spatial structure has a profound impact on how population-level dynamics emerge from individual-level behavior (123-4). For infectious diseases in particular, where transmission generally occurs over relatively short distances, spatial structure (and in particular the spatial distribution of sessile hosts) plays three roles: hosts that are far from sources of infection are at very little risk, local transmission and depletion of susceptible hosts can dramatically reduce the speed of epidemic growth, and local control measures can be applied using spatial proximity as a method of targeting at-risk hosts. These three elements are present for any spatial distribution of hosts, but are generally amplified by clustering. The impact of spatial structure on the spread of infectious disease has been examined for humans (5), wildlife (6, 7), and livestock (8, 9), but the ability to make useful quantitative predictions relies on the availability of good quality spatial and epidemic data. In recent years, considerable research has focused on the spread of livestock infections due to the extreme vulnerability of the livestock industry, the potential economic costs, the variety of strategies that can be used as control measures, and the costs associated with such measures.The UK 2001 epidemic of foot-and-mouth disease (FMD) provides a prime example of what can be achieved when comprehensive spatial models, detailed hos...

No abstract

In June 2013, at the launch of its National Neglected Tropical Disease (NTD) Master Plan, the Ethiopian government pledged to achieve WHO NTD elimination and control targets by 2020. With an estimated 80 million people living in areas where one or more NTDs are endemic, this goal presented an enormous challenge for the Federal Ministry of Health. However, as of September 2015, the Federal Ministry of Health has managed to mobilize support to implement mass drug administration in 84% of the trachoma endemic districts and 100% of the endemic districts for onchocerciasis, lymphatic filariasis, soil-transmitted helminthes and schistosomiasis. The national program still is facing large gaps in its podoconiosis and leishmaniasis programs, and it faces significant other challenges to stay on track for 2020 targets. However, this unprecedented scale-up in support was achieved through significant government investment in NTD interventions and creative coordination between donors and implementing partners, which may provide valuable lessons for other national NTD programs trying to achieve nationwide coverage.

Accurate projections of seasonal agricultural output are essential for improving food security. However, the collection of agricultural information through seasonal agricultural surveys is often not timely enough to inform public and private stakeholders about crop status during the growing season. Acquiring timely and accurate crop estimates can be particularly challenging in countries with predominately smallholder farms because of the large number of small plots, intense intercropping, and high diversity of crop types. In this study, we used RGB images collected from unmanned aerial vehicles (UAVs) flown in Rwanda to develop a deep learning algorithm for identifying crop types, specifically bananas, maize, and legumes, which are key strategic food crops in Rwandan agriculture. The model leverages advances in deep convolutional neural networks and transfer learning, employing the VGG16 architecture and the publicly accessible ImageNet dataset for pretraining. The developed model performs with an overall test set F1 of 0.86, with individual classes ranging from 0.49 (legumes) to 0.96 (bananas). Our findings suggest that although certain staple crops such as bananas and maize can be classified at this scale with high accuracy, crops involved in intercropping (legumes) can be difficult to identify consistently. We discuss the potential use cases for the developed model and recommend directions for future research in this area.