The effects of climate change on ovine parasitic gastroenteritis determined using veterinary surveillance and meteorological data for Northern Ireland over the period 1999-2009

While the influence of temperature and moisture on the free-living stages of gastrointestinal nematodes have been described in detail, and evidence for global climate change is mounting, there have been only a few attempts to relate altered incidence or seasonal patterns of disease to climate change. Studies of this type have been completed for England Scotland and Wales, but not for Northern Ireland (NI). Here we present an analysis of veterinary diagnostic data that relates three categories of gastrointestinal nematode infection in sheep to historical meteorological data for NI. The infections are: trichostrongylosis/teladorsagiosis (Teladorsagia/Trichostrongylus), strongyloidosis and nematodirosis. This study aims to provide a baseline for future climate change analyses and to provide basic information for the development of nematode control programmes. After identifying and evaluating possible sources of bias, climate change was found to be the most likely explanation for the observed patterns of change in parasite epidemiology, although other hypotheses could not be refuted. Seasonal rates of diagnosis showed a uniform year-round distribution for Teladorsagia and Trichostrongylus infections, suggesting consistent levels of larval survival throughout the year and extension of the traditionally expected seasonal transmission windows. Nematodirosis showed a higher level of autumn than Spring infection, suggesting that suitable conditions for egg and larval development occurred after the Spring infection period. Differences between regions within the Province were shown for strongyloidosis, with peaks of infection falling in the period September-November. For all three-infection categories (trichostrongylosis/teladorsagiosis, strongyloidosis and nematodirosis), significant differences in the rates of diagnosis, and in the seasonality of disease, were identified between regions. (C) 2012 Elsevier B.V. All rights reserved.

Seasonal variation in month of diagnosis in children with type 1 diabetes registered in 23 EURODIAB centres during 1989-2008

Objectives: To investigate seasonal variation in month of diagnosis in children with type 1 diabetes registered in EURODIAB centres during 1989-2008.
Methods: 23 population-based registers recorded date of diagnosis in new cases of clinically diagnosed type 1 diabetes in children aged under 15 years. Completeness of ascertainment was assessed through capture-recapture methodology and was high in most centres. A general test for seasonal variation (11df) and Edward's test for sinusoidal (sine wave) variation (2df) were employed. Time series methods were also used to investigate if meteorological data were predictive of monthly counts after taking account of seasonality and long term trends.
Results: Significant seasonal variation was apparent in all but two small centres, with an excess of cases apparent in the winter quarter. Significant sinusoidal pattern was also evident in all but two small centres with peaks in December (14 centres), January (5 centres) or February (2 centres). Relative amplitude varied from ±11% to ±39% (median ±18%). There was no relationship across the centres between relative amplitude and incidence level. However there was evidence of significant deviation from the sinusoidal pattern in the majority of centres. Pooling results over centres, there was significant seasonal variation in each age-group at diagnosis, but with significantly less variation in those aged under 5 years. Boys showed marginally greater seasonal variation than girls. There were no differences in seasonal pattern between four sub-periods of the 20 year period. In most centres monthly counts of cases were not associated with deviations from normal monthly average temperature or sunshine hours; short term meteorological variations do not explain numbers of cases diagnosed.
Conclusions: Seasonality with a winter excess is apparent in all age-groups and both sexes, but girls and the under 5s show less marked variation. The seasonal pattern changed little in the 20 year period.

Deriving meteorological variables across Africa for the study and control of vector-borne disease:a comparison of remote sensing and spatial interpolation of climate

This paper presents the results of an investigation into the utility of remote sensing (RS) using meteorological satellites sensors and spatial interpolation (SI) of data from meteorological stations, for the prediction of spatial variation in monthly climate across continental Africa in 1990. Information from the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration's (NOAA) polar-orbiting meteorological satellites was used to estimate land surface temperature (LST) and atmospheric moisture. Cold cloud duration (CCD) data derived from the High Resolution Radiometer (HRR) onboard the European Meteorological Satellite programme's (EUMETSAT) Meteosat satellite series were also used as a RS proxy measurement of rainfall. Temperature, atmospheric moisture and rainfall surfaces were independently derived from SI of measurements from the World Meteorological Organization (WMO) member stations of Africa. These meteorological station data were then used to test the accuracy of each methodology, so that the appropriateness of the two techniques for epidemiological research could be compared. SI was a more accurate predictor of temperature, whereas RS provided a better surrogate for rainfall; both were equally accurate at predicting atmospheric moisture. The implications of these results for mapping short and long-term climate change and hence their potential for the study anti control of disease vectors are considered. Taking into account logistic and analytical problems, there were no clear conclusions regarding the optimality of either technique, but there was considerable potential for synergy.

Offshore wind resource estimation using wave buoy data

In the coming decade installed offshore wind capacity is expected to expand rapidly. This will be both technically and economically challenging. Precise wind resource assessment is one of the more imminent challenges. It is more difficult to assess wind power offshore than onshore due to the paucity of representative wind speed data. Offshore site-specific data is less accessible and is far more costly to collect. However, offshore wind speed data collected from sources such as wave buoys, remote sensing from satellites, national weather ships, and coastal meteorological stations and met masts on barges and platforms may be extrapolated to assess offshore wind power. This study attempts to determine the usefulness of pre-existing offshore wind speed measurements in resource assessment, and presents the results of wind resource estimation in the Atlantic Ocean and in the Irish Sea using data from two offshore meteorological buoys. © 2012 IEEE.