Generating social network data using partially described networks: an example informing avian influenza control in the British poultry industry

<p>Background: Targeted sampling can capture the characteristics of more vulnerable sectors of a population, but may bias the picture of population level disease risk. When sampling network data, an incomplete description of the population may arise leading to biased estimates of between-host connectivity. Avian influenza (AI) control planning in Great Britain (GB) provides one example where network data for the poultry industry (the Poultry Network Database or PND), targeted large premises and is consequently demographically biased. Exposing the effect of such biases on the geographical distribution of network properties could help target future poultry network data collection exercises. These data will be important for informing the control of potential future disease outbreaks.</p> <p>Results: The PND was used to compute between-farm association frequencies, assuming that farms sharing the same slaughterhouse or catching company, or through integration, are potentially epidemiologically linked. The fitted statistical models were extrapolated to the Great Britain Poultry Register (GBPR); this dataset is more representative of the poultry industry but lacks network information. This comparison showed how systematic biases in the demographic characterisation of a network, resulting from targeted sampling procedures, can bias the derived picture of between-host connectivity within the network.</p> <p>Conclusions: With particular reference to the predictive modeling of AI in GB, we find significantly different connectivity patterns across GB when network estimates incorporate the more demographically representative information provided by the GBPR; this has not been accounted for by previous epidemiological analyses. We recommend ranking geographical regions, based on relative confidence in extrapolated estimates, for prioritising further data collection. Evaluating whether and how the between-farm association frequencies impact on the risk of between-farm transmission will be the focus of future work.</p&gt

Identifying genotype specific elevated-risk areas and associated herd risk factors for bovine tuberculosis spread in British cattle

Bovine tuberculosis (bTB) is a chronic zoonosis with major health and economic impact on the cattle industry. Despite extensive control measures in cattle and culling trials in wildlife, the reasons behind the expansion of areas with high incidence of bTB breakdowns in Great Britain remain unexplained. By balancing the importance of cattle movements and local transmission on the observed pattern of cattle outbreaks, we identify areas at elevated risk of infection from specific Mycobacterium bovis genotypes. We show that elevated-risk areas (ERAs) were historically more extensive than previously understood, and that cattle movements alone are insufficient for ERA spread, suggesting the involvement of other factors. For all genotypes, we find that, while the absolute risk of infection is higher in ERAs compared to areas with intermittent risk, the statistically significant risk factors are remarkably similar in both, suggesting that these risk factors can be used to identify incipient ERAs before this is indicated by elevated incidence alone. Our findings identify research priorities for understanding bTB dynamics, improving surveillance and guiding management to prevent further ERA expansion

Optimising passive surveillance of a neglected tropical disease in the era of elimination:A modelling study

BACKGROUND: Surveillance is an essential component of global programs to eliminate infectious diseases and avert epidemics of (re-)emerging diseases. As the numbers of cases decline, costs of treatment and control diminish but those for surveillance remain high even after the 'last' case. Reducing surveillance may risk missing persistent or (re-)emerging foci of disease. Here, we use a simulation-based approach to determine the minimal number of passive surveillance sites required to ensure maximum coverage of a population at-risk (PAR) of an infectious disease. METHODOLOGY AND PRINCIPAL FINDINGS: For this study, we use Gambian human African trypanosomiasis (g-HAT) in north-western Uganda, a neglected tropical disease (NTD) which has been reduced to historically low levels (95% of a total PAR of ~3million individuals living ≤1h from a diagnostic centre, and we demonstrate an approach to best place these facilities, informing a minimal impact scale back. CONCLUSIONS: Our results highlight that surveillance of g-HAT in north-western Uganda can be scaled back without substantially reducing coverage of the PAR. The methodology described can contribute to cost-effective and equable strategies for the surveillance of NTDs and other infectious diseases approaching elimination or (re-)emergence

Using remote sensing and geographic information systems to identify villages at high risk for rhodesiense sleeping sickness in Uganda

Geographic information systems (GIS) and remote sensing were used to identify villages at high risk for sleeping sickness, as defined by reported incidence. Landsat Enhanced Thematic Mapper (ETM) satellite data were classified to obtain a map of land cover, and the Normalised Difference Vegetation Index (NDVI) and Landsat band 5 were derived as unclassified measures of vegetation density and soil moisture, respectively. GIS functions were used to determine the areas of land cover types and mean NDVI and band 5 values within 1.5 km radii of 389 villages where sleeping sickness incidence had been estimated. Analysis using backward binary logistic regression found proximity to swampland and low population density to be predictive of reported sleeping sickness presence, with distance to the sleeping sickness hospital as an important confounding variable. These findings demonstrate the potential of remote sensing and GIS to characterize village-level risk of sleeping sickness in endemic regions

Multi-messenger Observations of a Binary Neutron Star Merger

International audienceOn 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of $\sim 1.7\,{\rm{s}}$ with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg(2) at a luminosity distance of ${40}_{-8}^{+8}$ Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 $\,{M}_{\odot }$. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at $\sim 40\,{\rm{Mpc}}$) less than 11 hours after the merger by the One-Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ∼10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position $\sim 9$ and $\sim 16$ days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC 4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta