Developing an ionospheric map for South Africa

The development of a map of the ionosphere over South Africa is presented in this paper. The International Reference Ionosphere (IRI) model, South African Bottomside Ionospheric Model (SABIM), and measurements from ionosondes in the South African Ionosonde Network, were combined within their own limitations to develop an accurate representation of the South African ionosphere. The map is essentially in the form of a computer program that shows spatial and temporal representations of the South African ionosphere for a given set of geophysical parameters. A validation of the map is attempted using a comparison of Total Electron Content (TEC) values derived from the map, from the IRI model, and from Global Positioning System (GPS) measurements. It is foreseen that the final South African ionospheric map will be implemented as a Space Weather product of the African Space Weather Regional Warning Centre

GPS TEC and ionosonde TEC over Grahamstown, South Africa: first comparisons

The Grahamstown, South Africa (33.3°S, 26.5°E) ionospheric field station operates a UMass Lowell digital pulse ionospheric sounder (Digisonde) and an Ashtech geodetic grade dual frequency GPS receiver. The GPS receiver is owned by Chief Directorate Surveys and Mapping (CDSM) in Cape Town, forms part of the national TrigNet network and was installed in February 2005. The sampling rates of the GPS receiver and Digisonde were set to 1 s and 15 min, respectively. Data from four continuous months, March–June 2005 inclusive, were considered in this initial investigation. Data available from the Grahamstown GPS receiver was limited, and, therefore, only these 4 months have been considered. Total Electron Content (TEC) values were determined from GPS measurements obtained from satellites passing near vertical (within an 80° elevation) to the station. TEC values were obtained from ionograms recorded at times within 5 min of the near vertical GPS measurement. The GPS derived TEC values are referred to as GTEC and the ionogram derived TEC values as ITEC. Comparisons of GTEC and ITEC values are presented in this paper. The differential clock biases of the GPS satellites and receivers are taken into account. The plasmaspheric contribution to the TEC can be inferred from the results, and confirm findings obtained by other groups. This paper describes the groundwork for a procedure that will allow the validation of GPS derived ionospheric information with ionosonde data. This work will be of interest to the International Reference Ionosphere (IRI) community since GPS receivers are becoming recognised as another source for ionospheric information

Application of neural networks to South African GPS TEC modelling

The propagation of radio signals in the Earth’s atmosphere is dominantly affected by the ionosphere due to its dispersive nature. Global Positioning System (GPS) data provides relevant information that leads to the derivation of total electron content (TEC) which can be considered as the ionosphere’s measure of ionisation. This paper presents part of a feasibility study for the development of a Neural Network (NN) based model for the prediction of South African GPS derived TEC. The South African GPS receiver network is operated and maintained by the Chief Directorate Surveys and Mapping (CDSM) in Cape Town, South Africa. Vertical total electron content (VTEC) was calculated for four GPS receiver stations using the Adjusted Spherical Harmonic (ASHA) model. Factors that influence TEC were then identified and used to derive input parameters for the NN. The well established factors used are seasonal variation, diurnal variation, solar activity and magnetic activity. Comparison of diurnal predicted TEC values from both the NN model and the International Reference Ionosphere (IRI-2001) with GPS TEC revealed that the IRI provides more accurate predictions than the NN model during the spring equinoxes. However, on average the NN model predicts GPS TEC more accurately than the IRI model over the GPS locations considered within South Africa

Development of a regional GPS-based ionospheric TEC model for South Africa

Advances in South African space physics research and related disciplines require better spatial and time resolution ionospheric information than was previously possible with the existing ionosonde network. A GPS-based, variable degree adjusted spherical harmonic (ASHA) model was developed for near real-time regional ionospheric total electron content (TEC) mapping over South Africa. Slant TEC values along oblique GPS signal paths are quantified from a network of GPS receivers and converted to vertical TEC by means of the single layer mapping function. The ASHA model coefficients and GPS differential biases are estimated from vertical TEC at the ionospheric pierce points and used to interpolate TEC at any location within the region of interest. Diurnal TEC variations with one minute time resolution and time-varying 2D regional TEC maps are constructed. In order to validate the ASHA method, simulations with an IRI ionosphere were performed, while the ASHA results from actual data were compared with two independent GPS-based methodologies and measured ionosonde data

The Potential Trajectory of Carbapenem-Resistant Enterobacteriaceae, an Emerging Threat to Health-Care Facilities, and the Impact of the Centers for Disease Control and Prevention Toolkit.

Carbapenem-resistant Enterobacteriaceae (CRE), a group of pathogens resistant to most antibiotics and associated with high mortality, are a rising emerging public health threat. Current approaches to infection control and prevention have not been adequate to prevent spread. An important but unproven approach is to have hospitals in a region coordinate surveillance and infection control measures. Using our Regional Healthcare Ecosystem Analyst (RHEA) simulation model and detailed Orange County, California, patient-level data on adult inpatient hospital and nursing home admissions (2011-2012), we simulated the spread of CRE throughout Orange County health-care facilities under 3 scenarios: no specific control measures, facility-level infection control efforts (uncoordinated control measures), and a coordinated regional effort. Aggressive uncoordinated and coordinated approaches were highly similar, averting 2,976 and 2,789 CRE transmission events, respectively (72.2% and 77.0% of transmission events), by year 5. With moderate control measures, coordinated regional control resulted in 21.3% more averted cases (n = 408) than did uncoordinated control at year 5. Our model suggests that without increased infection control approaches, CRE would become endemic in nearly all Orange County health-care facilities within 10 years. While implementing the interventions in the Centers for Disease Control and Prevention's CRE toolkit would not completely stop the spread of CRE, it would cut its spread substantially, by half

Integrating genetic analysis of mixed populations with a spatially explicit population dynamics model

1. Inferring the dynamics of populations in time and space is a central challenge in ecology. Intra-specific structure (for example genetically distinct sub-populations or meta-populations) may require methods that can jointly infer the dynamics of multiple populations. This is of particular importance for harvested species, for which management must balance utilization of productive populations with protection of weak ones. 2. Here we present a novel method for simultaneous learning about the spatio-temporal dynamics of multiple populations that combines genetic data with prior information about abundance and movement, akin to an integrated population modelling approach. We apply the Bayesian genetic mixed stock analysis to 17 wild and 10 hatchery-reared Baltic salmon (S. salar) stocks, quantifying uncertainty in stock composition in time and space, and in population dynamics parameters such as migration timing and speed. 3. The genetic data were informative about stock-specific movement patterns, updating priors for migration path, timing and speed. Use of a population dynamics model allowed robust interpolation of expected catch composition at areas and times with no genetic observations. Our results indicate that the commonly used "equal prior probabilities" assumption may not be appropriate for all mixed stock analyses: incorporation of prior information about stock abundance and movement resulted in more plausible and precise estimates of mixture compositions in time and space. 4. The model we present here forms the basis for optimizing the spatial and temporal allocation of harvest to support the management of mixed populations of migratory species.Peer reviewe

Variations of foF2 and GPS total electron content over the Antarctic sector

This paper presents a preliminary analysis of the variations of the critical frequency of the F2 region (foF2) and the total electron content (TEC) derived from Global Positioning System (GPS) data. Hourly foF2 values were scaled from ionograms recorded at San Martin (68.1°S, 293.0°E) and the TEC values were derived from GPS observations at O'Higgins (63.3°S, 302.5 °E). The database includes measurements obtained under different seasonal and solar activity conditions. The study shows that the daily peak of foF2 occurs around local noon in winter and fall, and in spring a secondary peak is observed around midnight. In summer (January) foF2 reaches its minimum value around the noon sector while the maximum in the diurnal variation of foF2 is located in a time sector close to midnight. This behaviour is observed at low and high solar activity. The semiannual anomaly appears around noon at high and low solar activity and the winter anomaly is not observed. The effect of the solar activity is generally observed in every season. The analysis of the GPS TEC measurements in the same region indicates that the diurnal, seasonal and solar activity variations are similar to those observed in the foF2 values. An analysis of the performance of the IRI model to predict foF2 is also shown using the two IRI options (URSI and CCIR). The comparisons between the experimental values and the IRI predictions show some discrepancies.Facultad de Ciencias Astronómicas y Geofísica

Not just a matter of size:a hospital-level risk factor analysis of MRSA bacteraemia in Scotland

Background: Worldwide, there is a wealth of literature examining patient-level risk 6 factors for methicillin-resistant Staphylococcus aureus (MRSA) bacteraemia. At the hospital-level it is generally accepted that MRSA bacteraemia is more common in larger hospitals. In Scotland, size does not fully explain all the observed variation among hospitals. The aim of this study was to identify risk factors for the presence and rate of MRSA bacteraemia cases in Scottish mainland hospitals. Specific hypotheses regarding hospital size, type and connectivity were examined. Methods: Data from 198 mainland Scottish hospitals (defined as having at least one inpatient per year) were analysed for financial year 2007-08 using logistic regression (Model 1: presence/absence of MRSA bacteraemia) and Poisson regression (Model 2: rate of MRSA bacteraemia). The significance of risk factors representing various measures of hospital size, type and connectivity were investigated. Results: In Scotland, size was not the only significant risk factor identified for the presence and rate of MRSA bacteraemia. The probability of a hospital having at least one case of MRSA bacteraemia increased with hospital size only if the hospital exceeded a certain level of connectivity. Higher levels of MRSA bacteraemia were associated with the large, highly connected teaching hospitals with high ratios of patients to domestic staff. Conclusions: A hospital’s level of connectedness within a network may be a better measure of a hospital’s risk of MRSA bacteraemia than size. This result could be used to identify high risk hospitals which would benefit from intensified infection control measures