Search PubMed⌕ Search

Biomedical subjects

S I Hay

Publications and source records attributed to S I Hay.

33 records · Page 2Linked to original sources
PubMed2001

Predicting the distribution of urinary schistosomiasis in Tanzania using satellite sensor data.

In this paper, remotely sensed (RS) satellite sensor environmental data, using logistic regression, are used to develop prediction maps of the probability of having infection prevalence exceeding 50%, and warranting mass treatment according to World Health Organization (WHO) guidelines. The model was developed using data from one area of coastal Tanzania and validated with independent data from different areas of the country. Receiver operating characteristic (ROC) analysis was used to evaluate the model's predictive performance. The model allows reasonable discrimination between high and low prevalence schools, at least within those geographical areas in which they were originally developed, and performs reasonably well in other coastal areas, but performs poorly by comparison in the Great Lakes area of Tanzania. These results may be explained by reference to an ecological zone map based on RS-derived environmental data. This map suggests that areas where the model reliably predicts a high prevalence of schistosomiasis fall within the same ecological zone, which has common intermediate-host snail species responsible for transmission. By contrast, the model's performance is poor near Lake Victoria, which is in a different ecological zone with different snail species. The ecological map can potentially define a template for those areas where existing models can be applied, and highlight areas where further data and models are required. The developed model was then used to provide estimates of the number of schoolchildren at risk of high prevalence and associated programme costs.

Adolescent↗
PubMed2000

Etiology of interepidemic periods of mosquito-borne disease.

Dengue viruses and malaria protozoa are of increasing global concern in public health. The diseases caused by these pathogens often show regular seasonal patterns in incidence because of the sensitivity of their mosquito vectors to climate. Between years in endemic areas, however, there can be further significant variation in case numbers for which public health systems are generally unprepared. There is an acute need for reliable predictions of within-year and between-year epidemic events. The prerequisite for developing any system of early warning is a detailed understanding of the factors involved in epidemic genesis. In this report we discuss the potential causes of the interepidemic periods in dengue hemorrhagic fever in Bangkok and of Plasmodium falciparum malaria in a highland area of western Kenya. The alternative causes are distinguished by a retrospective analysis of two unique and contemporaneous 33-year time series of epidemiological and associated meteorological data recorded at these two sites. We conclude that intrinsic population dynamics offer the most parsimonious explanation for the observed interepidemic periods of disease in these locations.

Animals↗
PubMed2000

An overview of remote sensing and geodesy for epidemiology and public health application.

The techniques of remote sensing (RS) and geodesy have the potential to revolutionize the discipline of epidemiology and its application in human health. As a new departure from conventional epidemiological methods, these techniques require some detailed explanation. This review provides the theoretical background to RS including (i) its physical basis, (ii) an explanation of the orbital characteristics and specifications of common satellite sensor systems, (iii) details of image acquisition and procedures adopted to overcome inherent sources of data degradation, and (iv) a background to geophysical data preparation. This information allows RS applications in epidemiology to be readily interpreted. Some of the techniques used in geodesy, to locate features precisely on Earth so that they can be registered to satellite sensor-derived images, are also included. While the basic principles relevant to public health are presented here, inevitably many of the details must be left to specialist texts.

Epidemiologic Methods↗
PubMed2000

Earth observation, geographic information systems and Plasmodium falciparum malaria in sub-Saharan Africa.

This review highlights the progress and current status of remote sensing (RS) and geographical information systems (GIS) as currently applied to the problem of Plasmodium falciparum malaria in sub-Saharan Africa (SSA). The burden of P. falciparum malaria in SSA is first summarized and then contrasted with the paucity of accurate and recent information on the nature and extent of the disease. This provides perspective on both the global importance of the pathogen and the potential for contribution of RS and GIS techniques. The ecology of P. falciparum malaria and its major anopheline vectors in SSA in then outlined, to provide the epidemiological background for considering disease transmission processes and their environmental correlates. Because RS and GIS are recent techniques in epidemiology, all mosquito-borne diseases are considered in this review in order to convey the range of ideas, insights and innovation provided. To conclude, the impact of these initial studies is assessed and suggestions provided on how these advances could be best used for malaria control in an appropriate and sustainable manner, with key areas for future research highlighted.

Africa South of the Sahara↗
PubMed2000

Forecasting disease risk for increased epidemic preparedness in public health.

Emerging infectious diseases pose a growing threat to human populations. Many of the world's epidemic diseases (particularly those transmitted by intermediate hosts) are known to be highly sensitive to long-term changes in climate and short-term fluctuations in the weather. The application of environmental data to the study of disease offers the capability to demonstrate vector-environment relationships and potentially forecast the risk of disease outbreaks or epidemics. Accurate disease forecasting models would markedly improve epidemic prevention and control capabilities. This chapter examines the potential for epidemic forecasting and discusses the issues associated with the development of global networks for surveillance and prediction. Existing global systems for epidemic preparedness focus on disease surveillance using either expert knowledge or statistical modelling of disease activity and thresholds to identify times and areas of risk. Predictive health information systems would use monitored environmental variables, linked to a disease system, to be observed and provide prior information of outbreaks. The components and varieties of forecasting systems are discussed with selected examples, along with issues relating to further development.

Communicable Diseases↗
PubMed1999

The rise and fall of the Aldabran giant tortoise population.

At the end of the 19th century, after prolonged and extensive harvesting, indigenous giant tortoises had been eliminated from all islands in the Indian Ocean, except Aldabra atoll, where only a few survived. With greatly reduced levels of exploitation during the 20th century, the population recovered to a revised estimated total of 129,000 in 1973-1974, when the first sample census was conducted. A repeat census in 1997 revealed a highly significant reduction in numbers over the past 24 years to an estimated total of 100,000. The great majority of tortoises are still found at relatively high density in south-eastern Grande Terre, where the number of animals has declined by more than one-third. In contrast, low-density subpopulations on Malabar and Picard have almost doubled in size, but they represent less than 5% of the total population. Corroborative evidence for the crash in the Grande Terre subpopulation comes from two independent observations: a significant increase in tortoise mortality; and a significant decline in tortoise counts on long-term population monitoring transects. These population changes are attributed to natural population regulatory mechanisms, exacerbated by low rainfall years in the period 1980-1997, including two consecutive years of below average rainfall in 1995-1996 and 1996-1997.

Animals↗
PubMed1999

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) on-board 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 and 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.

Africa↗
PubMed1998

From predicting mosquito habitat to malaria seasons using remotely sensed data: practice, problems and perspectives.

Remote sensing techniques are becoming increasingly important for identifying mosquito habitats, investigating malaria epidemiology and assisting malaria control. Here, Simon Hay, Bob Snow and David Rogers review the development of these techniques, from aerial photographic identification of mosquito larval habitats on the local scale through to the space-based survey of malaria risk over continental areas using increasingly sophisticated airborne and satellite-sensor technology. They indicate that previous constraints to uptake are becoming less relevant and suggest how future delays in the use of remotely sensed data in malaria control might be avoided.

Journal Article↗
PubMed1998

Predicting key malaria transmission factors, biting and entomological inoculation rates, using modelled soil moisture in Kenya.

While malaria transmission varies seasonally, large inter-annual heterogeneity of malaria incidence occurs. Variability in entomological parameters, biting rates and entomological inoculation rates (EIR) have been strongly associated with attack rates in children. The goal of this study was to assess the weather's impact on weekly biting and EIR in the endemic area of Kisian, Kenya. Entomological data collected by the U.S. Army from March 1986 through June 1988 at Kisian, Kenya was analysed with concurrent weather data from nearby Kisumu airport. A soil moisture model of surface-water availability was used to combine multiple weather parameters with landcover and soil features to improve disease prediction. Modelling soil moisture substantially improved prediction of biting rates compared to rainfall; soil moisture lagged two weeks explained up to 45% of An. gambiae biting variability, compared to 8% for raw precipitation. For An. funestus, soil moisture explained 32% variability, peaking after a 4-week lag. The interspecies difference in response to soil moisture was significant (P < 0.00001). A satellite normalized differential vegetation index (NDVI) of the study site yielded a similar correlation (r = 0.42 An. gambiae). Modelled soil moisture accounted for up to 56% variability of An. gambiae EIR, peaking at a lag of six weeks. The relationship between temperature and An. gambiae biting rates was less robust; maximum temperature r2 = -0.20, and minimum temperature r2 = 0.12 after lagging one week. Benefits of hydrological modelling are compared to raw weather parameters and to satellite NDVI. These findings can improve both current malaria risk assessments and those based on El Niño forecasts or global climate change model projections.

Animals↗
PubMed1996

Remotely sensed surrogates of meteorological data for the study of the distribution and abundance of arthropod vectors of disease.

This paper gives an overview of how certain meteorological data used in studies of the population dynamics of arthropod vectors of disease may be predicted using remotely sensed, satellite data. Details are given of the stages of processing necessary to convert digital data arising from satellite sensors into ecologically meaningful information. Potential sources of error in these processing steps are also highlighted. Relationships between ground-measured meteorological variables (saturation deficit, ground temperature and rainfall) and data from both the National Oceanic and Atmospheric Administration's, polar-orbiting, meteorological satellites and the geostationary, Meteosat satellite are defined and examples detailed for Africa. Finally, the current status of existing satellite platforms and future satellite missions are reviewed and potential data availability discussed. How such satellite-based predictions have proved valuable in understanding the distribution of tsetse fly species in Côte d'Ivoire and Burkina Faso will be the subject of a future review.

Africa↗
PubMed1996

Predicting the distribution of tsetse flies in West Africa using temporal Fourier processed meteorological satellite data.

An example is given of the application of remotely-sensed, satellite data to the problems of predicting the distribution and abundance of tsetse flies in West Africa. The distributions of eight species of tsetse, Glossina morsitans, G. longipalpis, G. palpalis, G. tachinoides, G. pallicera, G. fusca, G. nigrofusca and G. medicorum in Côte d'Ivoire and Burkina Faso, were analysed using discriminant analysis applied to temporal Fourier-processed surrogates for vegetation, temperature and rainfall derived from meteorological satellites. The vegetation and temperature surrogates were the normalized difference vegetation index and channel-4-brightness temperature, respectively, from the advanced, very-high-resolution radiometers on board the National Oceanic and Atmospheric Administration's polar-orbiting, meteorological satellites. For rainfall the surrogate was the Cold-Cloud-Duration (CCD) index derived from the geostationary, Meteosat satellite series. The presence or absence of tsetse was predicted with accuracies ranging from 67%-100% (mean = 82.3%). A further data-set, for the abundance of five tsetse species across the northern part of Côte d'Ivoire (an area of about 140,000 km2), was analysed in the same way, and fly-abundance categories predicted with accuracies of 30%-100% (mean = 73.0%). The thermal data appeared to be the most useful of the predictor variables, followed by vegetation and rainfall indices. Refinements of the analytical technique and the problems of extending the predictions through space and time are discussed.

Africa, Western↗
PubMedDate not supplied

Annual Plasmodium falciparum entomological inoculation rates (EIR) across Africa: literature survey, Internet access and review.

This paper presents the results of an extensive search of the formal and informal literature on annual Plasmodium falciparum entomological inoculation rates (EIR) across Africa from 1980 onwards. It first describes how the annual EIR data were collated, summarized, geo-referenced and staged for public access on the internet. Problems of data standardization, reporting accuracy and the subsequent publishing of information on the internet follow. The review was conducted primarily to investigate the spatial heterogeneity of malaria exposure in Africa and supports the idea of highly heterogeneous risk at the continental, regional and country levels. The implications for malaria control of the significant spatial (and seasonal) variation in exposure to infected mosquito bites are discussed.

Africa↗
PubMedDate not supplied

Changing patterns of clinical malaria since 1965 among a tea estate population located in the Kenyan highlands.

The changing epidemiology of clinical malaria since 1965 among hospitalized patients was studied at a group of tea estates in the western highlands of Kenya. These data indicate recent dramatic increases in the numbers of malaria admissions (6.5 to 32.5% of all admissions), case fatality (1.3 to 6%) and patients originating from low-risk, highland areas (34 to 59%). Climate change, environmental management, population migration, and breakdown in health service provision seem unlikely explanations for this changing disease pattern. The coincident arrival of chloroquine resistance during the late 1980s in the subregion suggests that drug resistance is a key factor in the current pattern and burden of malaria among this highland population.

Altitude↗
PubMedDate not supplied

Predicting malaria seasons in Kenya using multitemporal meteorological satellite sensor data.

This article describes research that predicts the seasonality of malaria in Kenya using remotely sensed images from satellite sensors. The predictions were made using relationships established between long-term data on paediatric severe malaria admissions and simultaneously collected data from the Advanced Very High Resolution Radiometer (AVHRR) on the National Oceanic and Atmospheric Administrations (NOAA) polar-orbiting meteorological satellites and the High Resolution Radiometer (HRR) on the European Organization for the Exploitation of Meteorological Satellites' (EUMETSAT) geostationary Meteosat satellites. The remotely sensed data were processed to provide surrogate information on land surface temperature, reflectance in the middle infra-red, rainfall, and the normalized difference vegetation index (NDVI). These variables were then subjected to temporal Fourier processing and the fitted Fourier data were compared with the mean percentage of total annual malaria admissions recorded in each month. The NDVI in the preceding month correlated most significantly and consistently with malaria presentations across the 3 sites (mean adjusted r2 = 0.71, range 0.61-0.79). Regression analyses showed that an NDVI threshold of 0.35-0.40 was required for more than 5% of the annual malaria cases to be presented in a given month. These thresholds were then extrapolated spatially with the temporal Fourier-processed NDVI data to define the number of months, in which malaria admissions could be expected across Kenya in an average year, at an 8 x 8 km resolution. The resulting maps were compared with the only existing map (Butler's) of malaria transmission periods for Kenya, compiled from expert opinion. Conclusions are drawn on the appropriateness of remote sensing techniques for compiling national strategies for malaria intervention.

Data Collection↗