Spatial epidemiology of Foot and Mouth Disease in Great Britain

During 2007 the UK experienced outbreaks of three notifiable exotic livestock diseases; Foot and Mouth Disease (FMD), Highly Pathogenic Avian Influenza (HPAI) and bluetongue. Large epidemics of any of these diseases would have a serious impact on animal welfare, farming, food production and the economy. In light of this, understanding holdings which are most likely to acquire and spread infection and being able to identify areas at higher risk of an epidemic is valuable when preparing for and managing an epidemic. This thesis uses a spatial epidemiological framework and the detailed disease and demographic data from the 2001 Great Britain (GB) FMD epidemic to develop static models of the risk of FMD susceptibility and transmission. These models are used to develop maps of FMD risk. These methods are then applied to the outbreak of FMD in 2007. The inputs for this analysis comprised a set of data relating to the farms diagnosed with FMD and farms culled as part of the disease control measures. The cleaning of these data is described and data which were estimated relating to dates of infection and putative sources of infection are evaluated. The distribution of farm holdings and animals is taken from the June 2000 GB agricultural census, off-fields of farms in the agricultural census are recorded in other datasets and these have been identified and linked to census holdings. A model of holding level susceptibility is developed using both farm level variables and measures of animal numbers in the locality of the holding as well as the distance to the nearest farm infected before the ban on animal movements (seeds). The overall fit of the model was very good with an area under the Receiver Operator Characteristic (ROC) curve of 0.91. A further model was developed to describe the risk of FMD transmission. However, due to incompleteness of transmission data, this was a model of the risk of finding a subsequent Infected Premises (IP) within 3km of an IP. Risk factors were a combination of holding level variables and locality measures as well as data relevant to the infection, such as infectious period and the species initially infected. The area under the ROC curve for this model was 0.71, which is regarded as an acceptable fit. Geographical barriers to FMD transmission were investigated using a case-control methodology, linear barriers comprising rivers and railways had a significant protective effect with respect to disease transmission (odds ratio = 0.54, 95% CIs = 0.30,0.96, p=0.038). Modelled values for the transmission and susceptibility models were transformed to a raster surface in ESRI ArcMap for both the disease as it was seeded in the 2001 epidemic and a non-specific background risk surface independent of the distribution of seeds. A risk map generated for the outbreak of FMD in Surrey in August 2007 suggested that there was little risk of a large outbreak in Surrey. Potential disease introductions through livestock movements from Surrey into Scotland were identified and these suggested that if the disease were introduced into Scotland there was great danger of substantial local spread. These methods described in this thesis have been used to map risk of FMD and subsequently applied to inform the risk presented by a different outbreak of FMD. The study underlines the value of detailed data both disease and demographic, for epidemic management. Similar methods could and should be applied to other infectious diseases threats of livestock such as HPAI and bluetongue

Assessing the importance of demographic risk factors across two waves of SARS-CoV-2 using fine-scale case data

For the long term control of an infectious disease such as COVID-19, it is crucial to identify the most likely individuals to become infected and the role that differences in demographic characteristics play in the observed patterns of infection. As high-volume surveillance winds down, testing data from earlier periods are invaluable for studying risk factors for infection in detail. Observed changes in time during these periods may then inform how stable thepattern will be in the long term.To this end we analyse the distribution of cases of COVID-19 across Scotland in 2021, where the location (census areas of order 500–1,000 residents) and reporting date of cases are known. We consider over 450,000 individually recorded cases, in two infection waves triggered by different lineages: B.1.1.529 (“Omicron”) and B.1.617.2 (“Delta”). We use random forests, informed by measures of geography, demography, testing and vaccination. We show that the distributions are only adequately explained when considering multiple explanatory variables, implying that case heterogeneity arose from a combination of individual behaviour, immunity, and testing frequency.Despite differences in virus lineage, time of year, and interventions in place, we find the risk factors remained broadly consistent between the two waves. Many of the observed smaller differences could be reasonably explained by changes in control measures

Risk factors for bovine Tuberculosis at the national level in Great Britain

<p><b>Background:</b> The continuing expansion of high incidence areas of bovine Tuberculosis (bTB) in Great Britain (GB) raises a number of questions concerning the determinants of infection at the herd level that are driving spread of the disease. Here, we develop risk factor models to quantify the importance of herd sizes, cattle imports from Ireland, history of bTB, badgers and cattle restocking in determining bTB incidence. We compare the significance of these different risk factors in high and low incidence areas (as determined by parish testing intervals).</p> <p><b>Results:</b> Large herds and fattening herds are more likely to breakdown in all areas. In areas with lower perceived risk (longer testing intervals), the risk of breaking down is largely determined by the number of animals that a herd buys in from high incidence areas. In contrast, in higher perceived risk areas (shorter testing intervals), the risk of breakdown is defined by the history of disease and the probability of badger occurrence. Despite differences in the management of bTB across different countries of GB (England, Wales and Scotland), we found no significant differences in bTB risk at the national level after these other factors had been taken into account.</p> <p><b>Conclusions:</b> This paper demonstrates that different types of farm are at risk of breakdown and that the most important risk factors vary according to bTB incidence in an area. The results suggest that significant gains in bTB control could be made by targeting herds in low incidence areas that import the greatest number of cattle from high incidence areas.</p&gt

A Survey of Local Group Galaxies Currently Forming Stars. I. UBVRI Photometry of Stars in M31 and M33

We present UBVRI photometry obtained from Mosaic images of M31 and M33 using the KPNO 4-m telescope. The survey covers 2.2 sq degrees of M31, and 0.8 sq degrees of M33, chosen so as to include all of the regions currently active in forming massive stars. The catalog contains 371,781 and 146,622 stars in M31 and M33, respectively, where every star has a counterpart (at least) in B, V, and R. We compare our photometry to previous studies. We provide cross references to the stars confirmed as members by spectroscopy, and compare the location of these to the complete set in color-magnitude diagrams. While follow-up spectroscopy is needed for many projects, we demonstrate the success of our photometry in being able to distinguish M31/M33 members from foreground Galactic stars. We also present the results of newly obtained spectroscopy, which identifies 34 newly confirmed members, including B-A supergiants, the earliest O star known in M31, and two new Luminous Blue Variable candidates whose spectra are similar to that of P Cygni.Comment: Accepted by the Astronomical Journal. A version with higher resolution figures can be found at: http://www.lowell.edu/users/massey/M3133.pdf.g

Risk‐based strategies for surveillance of bovine Tuberculosis infection in cattle for low risk areas in England and Scotland

Disease surveillance can be made more effective by either improving disease detection, providing cost savings, or doing both. Currently, cattle herds in low-risk areas for bovine tuberculosis (bTB) in England (LRAs) are tested once every four years. In Scotland, the default herd testing frequency is also four years, but a risk-based system exempts some herds from testing altogether. To extend this approach to other areas, a bespoke understanding of at-risk herds and how risk-based surveillance can affect bTB detection is required. Here, we use a generalized linear mixed model (GLMM) to inform a Bayesian probabilistic model of freedom from infection and explore risk-based surveillance strategies in LRAs and Scotland. Our analyses show that in both areas the primary herd-level risk factors for bTB infection are the size of the herd and purchasing cattle from high-risk areas of Great Britain and/or Ireland. A risk-based approach can improve the current surveillance system by both increasing detection (9% and 7% fewer latent infections), and reducing testing burden (6 % and 26% fewer animal tests) in LRAs and Scotland, respectively. Testing at-risk herds more frequently can also improve the level of detection by identifying more infected cases and reducing the hidden burden of the disease, and reduce surveillance effort by exempting low-risk herds from testing