scholarly journals LINKING SATELLITE REMOTE SENSING BASED ENVIRONMENTAL PREDICTORS TO DISEASE: AN APPLICATION TO THE SPATIOTEMPORAL MODELLING OF SCHISTOSOMIASIS IN GHANA

2016 ◽  
Vol XLI-B8 ◽  
pp. 215-221
Author(s):  
M. Wrable ◽  
A. Liss ◽  
A. Kulinkina ◽  
M. Koch ◽  
N. K. Biritwum ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Disease Transmission ◽  
Land Surface ◽  
Health Surveys ◽  
Risk Model ◽  
Clinical Symptoms ◽  
Sub Saharan Africa ◽  
Small Scale ◽  
Environmental Predictors ◽  
Sub Saharan

90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. Use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. Transmission of schistosomiasis requires specific environmental conditions to sustain freshwater snails, however has unknown seasonality, and is difficult to study due to a long lag between infection and clinical symptoms. To overcome this, we employed a comprehensive 8-year time-series built from remote sensing feeds. The purely environmental predictor variables: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique, were regressed against 8 years of national surveillance data in Ghana. All data were aggregated temporally into monthly observations, and spatially at the level of administrative districts. The result of an initial mixed effects model had 41% explained variance overall. Stratification by climate zone brought the R<sup>2</sup> as high as 50% for major zones and as high as 59% for minor zones. This can lead to a predictive risk model used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys.

scholarly journals LINKING SATELLITE REMOTE SENSING BASED ENVIRONMENTAL PREDICTORS TO DISEASE: AN APPLICATION TO THE SPATIOTEMPORAL MODELLING OF SCHISTOSOMIASIS IN GHANA

2016 ◽  
Vol XLI-B8 ◽  
pp. 215-221
Author(s):  
M. Wrable ◽  
A. Liss ◽  
A. Kulinkina ◽  
M. Koch ◽  
N. K. Biritwum ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Disease Transmission ◽  
Land Surface ◽  
Health Surveys ◽  
Risk Model ◽  
Clinical Symptoms ◽  
Sub Saharan Africa ◽  
Small Scale ◽  
Environmental Predictors ◽  
Sub Saharan

90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. Use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. Transmission of schistosomiasis requires specific environmental conditions to sustain freshwater snails, however has unknown seasonality, and is difficult to study due to a long lag between infection and clinical symptoms. To overcome this, we employed a comprehensive 8-year time-series built from remote sensing feeds. The purely environmental predictor variables: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique, were regressed against 8 years of national surveillance data in Ghana. All data were aggregated temporally into monthly observations, and spatially at the level of administrative districts. The result of an initial mixed effects model had 41% explained variance overall. Stratification by climate zone brought the R&lt;sup&gt;2&lt;/sup&gt; as high as 50% for major zones and as high as 59% for minor zones. This can lead to a predictive risk model used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys.

scholarly journals Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach

2013 ◽  
Vol 17 (3) ◽  
pp. 1079-1091 ◽  
Author(s):  
M. Marshall ◽  
K. Tu ◽  
C. Funk ◽  
J. Michaelsen ◽  
P. Williams ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Time Series ◽  
Hybrid Model ◽  
Land Surface ◽  
Vegetation Indices ◽  
Land Surface Model ◽  
Sub Saharan Africa ◽  
Surface Model ◽  
Sub Saharan

Abstract. Climate change is expected to have the greatest impact on the world's economically poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled evapotranspiration (ET), a key input in continental-scale hydrologic models. In this study, a remote sensing model of transpiration (the primary component of ET), driven by a time series of vegetation indices, was used to substitute transpiration from the Global Land Data Assimilation System realization of the National Centers for Environmental Prediction, Oregon State University, Air Force, and Hydrology Research Laboratory at National Weather Service Land Surface Model (GNOAH) to improve total ET model estimates for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against GNOAH ET and the remote sensing method using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance were at humid sites with dense vegetation, while performance at semi-arid sites was poor, but better than the models before hybridization. The reduction in errors using the hybrid model can be attributed to the integration of a simple canopy scheme that depends primarily on low bias surface climate reanalysis data and is driven primarily by a time series of vegetation indices.

scholarly journals Response of Malaria Hazard and Risk to a Hydroelectric Dam in Ethiopia Using GIS and Remote Sensing

2020 ◽  
Author(s):  
Abigail Smith ◽  
Sunhui Sim
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Environmental Changes ◽  
Breeding Habitat ◽  
Sub Saharan Africa ◽  
Disease Spread ◽  
Increased Risk ◽  
Mosquito Breeding ◽  
Sub Saharan ◽  
Mosquito Breeding Habitat

Malaria is a disease spread by female mosquitos of the Anopheles genus. It is acutely prevalent in Sub-Saharan Africa, where 90% of malaria deaths occur annually. One Sub-Saharan African country historically impacted by malaria is Ethiopia. In the past twenty years, malaria prevalence has decreased throughout Sub-Saharan Africa; yet, anthropogenic environmental changes are changing the landscape of malaria. Scholarly literature has cited a positive relationship between hydroelectric dams and malaria in Sub-Saharan Africa. Ethiopia is currently expanding their hydroelectric infrastructure. The Gilgel Gibe III Dam is located on the Omo River in Southwestern Ethiopia. It began generating electricity in 2015 and its reservoir has a capacity of 14,700 million m3 of water. This research utilized Geographic Information Systems and Remote Sensing to identify populations at an increased risk of malaria due to Gilgel Gibe III Dam. Two different techniques were employed: the proximity approach and the remote sensing approach. The proximity approach was based on distance from the reservoir. It identified all populations living within three kilometers of the reservoir as being at an increased risk. The remote sensing approach evaluated the slope, elevation, water content, and land surface temperature of the study area to create a mosquito breeding habitat risk map. Then, populations living within three kilometers of the two main High-Risk areas were identified. This study suggests that mosquito breeding habitat risk is not equally distributed throughout the Gilgel Gibe III Reservoir. This causes certain populations to be at a heightened risk of malaria.

Interactions between ecological and socio-economic drivers of Buruli ulcer burden in Sub-Saharan Africa: opportunities for an improved control

2018 ◽  
Author(s):  
Andes Garchitorena ◽  
Matthew H. Bonds ◽  
Jean-Francois Guégan ◽  
Benjamin Roche
Keyword(s):  
Socioeconomic Factors ◽  
Disease Transmission ◽  
Access To Health Care ◽  
Buruli Ulcer ◽  
Mycobacterium Ulcerans ◽  
Sub Saharan Africa ◽  
Aquatic Environments ◽  
Human Populations ◽  
Complex Interactions ◽  
Sub Saharan

This chapter provides an overview of the complex interactions between ecological and socioeconomic factors for the development and control of Buruli ulcer in Sub-Saharan Africa. We review key ecological and evolutionary processes driving the environmental persistence and proliferation of Mycobacterium ulcerans, the causative agent, within aquatic environments, as well as transmission processes from these aquatic environments to human populations. We also outline key socioeconomic factors driving the economic and health burden of Buruli ulcer in endemic regions, revealed by reciprocal feedbacks between poverty, disease transmission from exposure aquatic environments and disease progression to severe stages owing to low access to health care. The implications of such insights for disease control, both in terms of limitations of current strategies and directions for the future, are discussed.

Changing sex differences in undernutrition of African children: findings from Demographic and Health Surveys

2021 ◽  
pp. 1-11
Author(s):  
Michel Garenne ◽  
Susan Thurstans ◽  
André Briend ◽  
Carmel Dolan ◽  
Tanya Khara ◽  
...  
Keyword(s):  
Sex Differences ◽  
Health Surveys ◽  
Sex Ratios ◽  
Sub Saharan Africa ◽  
Demographic And Health Surveys ◽  
Growth Charts ◽  
Sub Saharan ◽  
Male To Female ◽  
Height For Age ◽  
Weight For Age

Abstract The study investigates sex differences in the prevalence of undernutrition in sub-Saharan Africa. Undernutrition was defined by Z-scores using the CDC-2000 growth charts. Some 128 Demographic and Health Surveys (DHS) were analysed, totalling 700,114 children under-five. The results revealed a higher susceptibility of boys to undernutrition. Male-to-female ratios of prevalence averaged 1.18 for stunting (height-for-age Z-score <−2.0); 1.01 for wasting (weight-for-height Z-score <−2.0); 1.05 for underweight (weight-for-age Z-score <−2.0); and 1.29 for concurrent wasting and stunting (weight-for-height and height-for-age Z-scores <−2.0). Sex ratios of prevalence varied with age for stunting and concurrent wasting and stunting, with higher values for children age 0–23 months and lower values for children age 24–59 months. Sex ratios of prevalence tended to increase with declining level of mortality for stunting, underweight and concurrent wasting and stunting, but remained stable for wasting. Comparisons were made with other anthropometric reference sets (NCHS-1977 and WHO-2006), and the results were found to differ somewhat from those obtained with CDC-2000. Possible rationales for these patterns are discussed.

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