Modelling the spread of American foulbrood in honeybees

We investigate the spread of American foulbrood (AFB), a disease caused by the bacterium Paenibacillus larvae, that affects bees and can be extremely damaging to beehives. Our dataset comes from an inspection period carried out during an AFB epidemic of honeybee colonies on the island of Jersey during the summer of 2010. The data include the number of hives of honeybees, location and owner of honeybee apiaries across the island. We use a spatial SIR model with an underlying owner network to simulate the epidemic and characterize the epidemic using a Markov chain Monte Carlo (MCMC) scheme to determine model parameters and infection times (including undetected ‘occult’ infections). Likely methods of infection spread can be inferred from the analysis, with both distance- and owner-based transmissions being found to contribute to the spread of AFB. The results of the MCMC are corroborated by simulating the epidemic using a stochastic SIR model, resulting in aggregate levels of infection that are comparable to the data. We use this stochastic SIR model to simulate the impact of different control strategies on controlling the epidemic. It is found that earlier inspections result in smaller epidemics and a higher likelihood of AFB extinction

Experimental pig-to-pig transmission dynamics for African swine fever virus, Georgia 2007/1 strain

African swine fever virus (ASFV) continues to cause outbreaks in domestic pigs and wild boar in Eastern European countries. To gain insights into its transmission dynamics, we estimated the pig-to-pig basic reproduction number (R 0) for the Georgia 2007/1 ASFV strain using a stochastic susceptible-exposed-infectious-recovered (SEIR) model with parameters estimated from transmission experiments. Models showed that R 0 is 2·8 [95% confidence interval (CI) 1·3–4·8] within a pen and 1·4 (95% CI 0·6–2·4) between pens. The results furthermore suggest that ASFV genome detection in oronasal samples is an effective diagnostic tool for early detection of infection. This study provides quantitative information on transmission parameters for ASFV in domestic pigs, which are required to more effectively assess the potential impact of strategies for the control of between-farm epidemic spread in European countries.ISSN:0950-2688ISSN:1469-440

Climate change as an intergenerational problem

Author Posting. © The Author(s), 2012. This is the author's version of the work. It is posted here by permission of National Academy of Sciences for personal use, not for redistribution. The definitive version was published in Proceedings of the National Academy of Sciences of the United States of America 110 (2013): 4435-4436, doi:10.1073/pnas.1302536110.Predicting climate change is a high priority for society, but such forecasts are notoriously uncertain. Why? Even should climate prove theoretically predictable---by no means certain---the near-absence of adequate observations will preclude its understanding and hence even the hope of useful predictions. Geological and cryospheric records of climate change and our brief recent record of instrumental observations show that the climate system is changeable on all time scales---from a few years out to the age of the earth. Major physical, chemical, and biological processes influence the climate system on decades, centuries, and millennia. Glaciers fluctuate on time scales of years to centuries and beyond. Since the Industrial Revolution, carbon dioxide has been emitted through fossil fuel burning, and it will be absorbed, recycled, and transferred amongst the atmosphere, ocean, and biosphere over decades to thousands of years

A motif-based approach to network epidemics

Networks have become an indispensable tool in modelling infectious diseases, with the structure of epidemiologically relevant contacts known to affect both the dynamics of the infection process and the efficacy of intervention strategies. One of the key reasons for this is the presence of clustering in contact networks, which is typically analysed in terms of prevalence of triangles in the network. We present a more general approach, based on the prevalence of different four-motifs, in the context of ODE approximations to network dynamics. This is shown to outperform existing models for a range of small world networks

ENSO and the Carbon Cycle

This is the author accepted manuscript. The final version is available from the American Geophysical Union via the DOI in this recordObservational studies of atmospheric CO2, land ecosystems, and ocean processes show that variability in the carbon cycle is closely related with ENSO. Years with a warm anomaly in the tropical Pacific show a faster CO2 rise due to weaker land carbon sinks, particularly in the tropics, with a partial offset by stronger net uptake by oceans. The opposite happens in years with cool Pacific SST anomalies. This relationship holds for small ENSO SST anomalies as well as large ones and is robust enough for the annual CO2 growth rate anomaly to be highly predictable on the basis of SST observations and forecasts. Generally, variability in the land‐atmosphere carbon flux is mainly driven by physiological processes (photosynthesis and/or respiration), with a smaller contribution from fire. Fire was important in the 1997–1998 El Niño, making a major contribution to the CO2 rise, which can be viewed as anthropogenic in nature since the ignition was caused by humans. However, in the 2015–2016 El Niño event, the change in land carbon flux was mainly due to physiological processes, particularly reduced productivity. In the oceans, El Niño conditions involve decreased upwelling of carbon in the equatorial Pacific due to a weakening of the trade winds, causing this region to become a weaker sink of CO2, or near neutral if the El Niño event is strong. The year‐to‐year variations in the rate of CO2 rise can be successfully reconstructed and predicted on the basis of sea surface temperatures in the Pacific. ENSO‐CO2 relationships may also provide an emergent constraint on the strength of climate‐carbon cycle feedbacks on future anthropogenic climate change

Epidemics in Networks of Spatially Correlated Three-dimensional Root Branching Structures

Using digitized images of the three-dimensional, branching structures for root systems of bean seedlings, together with analytical and numerical methods that map a common 'SIR' epidemiological model onto the bond percolation problem, we show how the spatially-correlated branching structures of plant roots affect transmission efficiencies, and hence the invasion criterion, for a soil-borne pathogen as it spreads through ensembles of morphologically complex hosts. We conclude that the inherent heterogeneities in transmissibilities arising from correlations in the degrees of overlap between neighbouring plants, render a population of root systems less susceptible to epidemic invasion than a corresponding homogeneous system. Several components of morphological complexity are analysed that contribute to disorder and heterogeneities in transmissibility of infection. Anisotropy in root shape is shown to increase resilience to epidemic invasion, while increasing the degree of branching enhances the spread of epidemics in the population of roots. Some extension of the methods for other epidemiological systems are discussed.Comment: 21 pages, 8 figure