scholarly journals Advances in mapping malaria for elimination: fine resolution modelling of Plasmodium falciparum incidence

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Victor A. Alegana ◽  
Peter M. Atkinson ◽  
Christopher Lourenço ◽  
Nick W. Ruktanonchai ◽  
Claudio Bosco ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Population Distribution ◽  
Malaria Incidence ◽  
World Health Organisation ◽  
World Health ◽  
Fine Spatial Resolution ◽  
Multi Temporal ◽  
Spatio Temporal ◽  
Fine Resolution ◽  
And Control

Abstract The long-term goal of the global effort to tackle malaria is national and regional elimination and eventually eradication. Fine scale multi-temporal mapping in low malaria transmission settings remains a challenge and the World Health Organisation propose use of surveillance in elimination settings. Here, we show how malaria incidence can be modelled at a fine spatial and temporal resolution from health facility data to help focus surveillance and control to population not attending health facilities. Using Namibia as a case study, we predicted the incidence of malaria, via a Bayesian spatio-temporal model, at a fine spatial resolution from parasitologically confirmed malaria cases and incorporated metrics on healthcare use as well as measures of uncertainty associated with incidence predictions. We then combined the incidence estimates with population maps to estimate clinical burdens and show the benefits of such mapping to identifying areas and seasons that can be targeted for improved surveillance and interventions. Fine spatial resolution maps produced using this approach were then used to target resources to specific local populations, and to specific months of the season. This remote targeting can be especially effective where the population distribution is sparse and further surveillance can be limited to specific local areas.

scholarly journals A Spatio-temporal disaggregation method to derive time series of Normalized Difference Vegetation Index and Land Surface Temperature at fine spatial resolution

2014 ◽  
Vol XL-8 ◽  
pp. 1397-1401
Author(s):  
V. M. Bindhu ◽  
B. Narasimhan
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Energy Balance ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Fine Spatial Resolution ◽  
Temporal Disaggregation ◽  
Energy Balance Models ◽  
Spatio Temporal ◽  
Fine Resolution

Estimation of evapotranspiration (ET) from remote sensing based energy balance models have evolved as a promising tool in the field of water resources management. Performance of energy balance models and reliability of ET estimates is decided by the availability of remote sensing data at high spatial and temporal resolutions. However huge tradeoff in the spatial and temporal resolution of satellite images act as major constraints in deriving ET at fine spatial and temporal resolution using remote sensing based energy balance models. Hence a need exists to derive finer resolution data from the available coarse resolution imagery, which could be applied to deliver ET estimates at scales to the range of individual fields. The current study employed a spatio-temporal disaggregation method to derive fine spatial resolution (60 m) images of NDVI by integrating the information in terms of crop phenology derived from time series of MODIS NDVI composites with fine resolution NDVI derived from a single AWiFS data acquired during the season. The disaggregated images of NDVI at fine resolution were used to disaggregate MODIS LST data at 960 m resolution to the scale of Landsat LST data at 60 m resolution. The robustness of the algorithm was verified by comparison of the disaggregated NDVI and LST with concurrent NDVI and LST images derived from Landsat ETM+. The results showed that disaggregated NDVI and LST images compared well with the concurrent NDVI and LST derived from ETM+ at fine resolution with a high Nash Sutcliffe Efficiency and low Root Mean Square Error. The proposed disaggregation method proves promising in generating time series of ET at fine resolution for effective water management.

A Collaborative Study to Establish an International Standard for Alpha-Thrombin

1992 ◽  
Vol 67 (04) ◽  
pp. 424-427 ◽  
Author(s):  
P J Gaffney ◽  
A B Heath ◽  
J W Fenton II
Keyword(s):  
Elevated Temperatures ◽  
Collaborative Study ◽  
World Health Organisation ◽  
Significant Loss ◽  
Thrombin Inhibitors ◽  
World Health ◽  
Expert Committee ◽  
International Standard ◽  
Assay Procedure ◽  
And Control

SummarySince 1975 an International Standard for Thrombin of low purity has been used. While this standard was stable and of value for calibrating thrombins of unknown potency the need for a pure a-thrombin standard arose both for accurate calibration and for precise measurement of thrombin inhibitors, notably hirudin. An international collaborative study was undertaken to establish the potency and stability of an ampouled pure a-thrombin preparation. A potency of 97.5 international units (95% confidence limits 86.5-98.5) was established for the new a-thrombin standard (89/ 588) using a clotting-assay procedure. Stability data at various elevated temperatures indicated that the standard could be transported and stored with no significant loss of potency.Ampoules of lyophilised a-thrombin (coded 89/588) have been recommended as an International Standard for a-thrombin with an assigned potency of 100 international units per ampoule by the International Society for Thrombosis and Haemostasis (Thrombin and its Inhibitors Sub-Committee) in Barcelona, Spain in July 1990 while the Expert Committee on Biological Standardisation and Control of the World Health Organisation will consider its status at its next meeting in Geneva in 1991.

scholarly journals Spatio-Temporal Super-Resolution Land Cover Mapping Based on Fuzzy C-Means Clustering

2018 ◽  
Vol 10 (8) ◽  
pp. 1212 ◽  
Author(s):  
Xiaohong Yang ◽  
Zhong Xie ◽  
Feng Ling ◽  
Xiaodong Li ◽  
Yihang Zhang ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Pattern ◽  
Spatial Resolution ◽  
Super Resolution ◽  
Land Cover Mapping ◽  
Fuzzy C Means Clustering ◽  
Land Cover Map ◽  
Spatio Temporal ◽  
Fine Resolution ◽  
Land Cover Maps

Super-resolution land cover mapping (SRM) is a method that aims to generate land cover maps with fine spatial resolutions from the original coarse spatial resolution remotely sensed image. The accuracy of the resultant land cover map produced by existing SRM methods is often limited by the errors of fraction images and the uncertainty of spatial pattern models. To address these limitations in this study, we proposed a fuzzy c-means clustering (FCM)-based spatio-temporal SRM (FCM_STSRM) model that combines the spectral, spatial, and temporal information into a single objective function. The spectral term is constructed with the FCM criterion, the spatial term is constructed with the maximal spatial dependence principle, and the temporal term is characterized by the land cover transition probabilities in the bitemporal land cover maps. The performance of the proposed FCM_STSRM method is assessed using data simulated from the National Land Cover Database dataset and real Landsat images. Results of the two experiments show that the proposed FCM_STSRM method can decrease the influence of fraction errors by directly using the original images as the input and the spatial pattern uncertainty by inheriting land cover information from the existing fine resolution land cover map. Compared with the hard classification and FCM_SRM method applied to mono-temporal images, the proposed FCM_STSRM method produced fine resolution land cover maps with high accuracy, thus showing the efficiency and potential of the novel approach for producing fine spatial resolution maps from coarse resolution remotely sensed images.

scholarly journals DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

2020 ◽  
Author(s):  
Mahdi Rezaei ◽  
Mohsen Azarmi
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
World Health Organisation ◽  
Superior Performance ◽  
World Health ◽  
People Detection ◽  
Social Distancing ◽  
Online Risk ◽  
Risk Zones ◽  
Spatio Temporal

Abstract Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-meter physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a generic Deep Neural Network-Based model for automated people detection, tracking, and inter-people distances estimation in the crowd, using common CCTV security cameras. The proposed model includes a YOLOv4-based framework and inverse perspective mapping for accurate people detection and social distancing monitoring in challenging conditions, including people occlusion, partial visibility, and lighting variations. We also provide an online risk assessment scheme by statistical analysis of the Spatio-temporal data from the moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infection. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The efficiency of the proposed methodology is evaluated on the Oxford Town Centre dataset, with superior performance in terms of accuracy and speed compared to three state-of-the-art methods.

scholarly journals A Simple Spatio–Temporal Data Fusion Method Based on Linear Regression Coefficient Compensation

2020 ◽  
Vol 12 (23) ◽  
pp. 3900
Author(s):  
Bingxin Bai ◽  
Yumin Tan ◽  
Gennadii Donchyts ◽  
Arjen Haag ◽  
Albrecht Weerts
Keyword(s):  
Linear Regression ◽  
Data Fusion ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Fusion Method ◽  
Temporal Data ◽  
Resolution Image ◽  
Coarse Resolution ◽  
Fine Spatial Resolution ◽  
Spatio Temporal

High spatio–temporal resolution remote sensing images are of great significance in the dynamic monitoring of the Earth’s surface. However, due to cloud contamination and the hardware limitations of sensors, it is difficult to obtain image sequences with both high spatial and temporal resolution. Combining coarse resolution images, such as the moderate resolution imaging spectroradiometer (MODIS), with fine spatial resolution images, such as Landsat or Sentinel-2, has become a popular means to solve this problem. In this paper, we propose a simple and efficient enhanced linear regression spatio–temporal fusion method (ELRFM), which uses fine spatial resolution images acquired at two reference dates to establish a linear regression model for each pixel and each band between the image reflectance and the acquisition date. The obtained regression coefficients are used to help allocate the residual error between the real coarse resolution image and the simulated coarse resolution image upscaled by the high spatial resolution result of the linear prediction. The developed method consists of four steps: (1) linear regression (LR), (2) residual calculation, (3) distribution of the residual and (4) singular value correction. The proposed method was tested in different areas and using different sensors. The results show that, compared to the spatial and temporal adaptive reflectance fusion model (STARFM) and the flexible spatio–temporal data fusion (FSDAF) method, the ELRFM performs better in capturing small feature changes at the fine image scale and has high prediction accuracy. For example, in the red band, the proposed method has the lowest root mean square error (RMSE) (ELRFM: 0.0123 vs. STARFM: 0.0217 vs. FSDAF: 0.0224 vs. LR: 0.0221). Furthermore, the lightweight algorithm design and calculations based on the Google Earth Engine make the proposed method computationally less expensive than the STARFM and FSDAF.

scholarly journals Patients with more comorbidities have better detection of chronic conditions, but poorer management and control: findings from six middle-income countries

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Grace Sum ◽  
Gerald Choon-Huat Koh ◽  
Stewart W. Mercer ◽  
Lim Yee Wei ◽  
Azeem Majeed ◽  
...  
Keyword(s):  
Chronic Conditions ◽  
World Health Organisation ◽  
World Health ◽  
Uncontrolled Hypertension ◽  
Adult Health ◽  
Cross Sectional ◽  
Middle Income ◽  
Middle Income Countries ◽  
Increased Risk ◽  
And Control

Abstract Background The burden of non-communicable diseases (NCDs) is rising rapidly in middle-income countries (MICs), where NCDs are often undiagnosed, untreated and uncontrolled. How comorbidity impacts diagnosis, treatment, and control of NCDs is an emerging area of research inquiry and have important clinical implications as highlighted in the recent National Institute for Health and Care Excellence guidelines for treating patients suffering from multiple NCDs. This is the first study to examine the association between increasing numbers of comorbidities with being undiagnosed, untreated, and uncontrolled for NCDs, in 6 large MICs. Methods Cross-sectional analysis of the World Health Organisation Study of Global Ageing and Adult Health (WHO SAGE) Wave 1 (2007–10), which consisted of adults aged ≥18 years from 6 populous MICs, including China, Ghana, India, Mexico, Russia and South Africa (overall n = 41, 557). Results A higher number of comorbidities was associated with better odds of diagnosis for hypertension, angina, and arthritis, and higher odds of having treatment for hypertension and angina. However, more comorbidities were associated with increased odds of uncontrolled hypertension, angina, arthritis, and asthma. Comorbidity with concordant conditions was associated with improved diagnosis and treatment of hypertension and angina. Conclusion Patients with more comorbidities have better diagnosis of chronic conditions, but this does not translate into better management and control of these conditions. Patients with multiple NCDs are high users of health services and are at an increased risk of adverse health outcomes. Hence, improving their access to care is a priority for healthcare systems.

The Novel Coronavirus SARS Cov-2 and its evolving impact on Ophthalmic Practice

2020 ◽  
Vol 5 (Special) ◽  
Keyword(s):  
World Health Organisation ◽  
World Health ◽  
The Novel ◽  
Movement Restriction ◽  
Mortality And Morbidity ◽  
Risk Of Mortality ◽  
Difficulty Breathing ◽  
Containment Measures ◽  
Novel Coronavirus ◽  
And Control

SARS-Cov-2 is a novel coronavirus that is believed to have emerged from the wet markets in Wuhan, Hubei Province in China late in December 2019. The spread of this virus was soon declared to be a pandemic by the World Health Organisation, with nearly 1 million cases reported worldwide by 31st March 2020 [1]. Those who contract the virus can go on to develop coronavirus disease 2019 (COVID-19) – with symptoms commonly presenting as fever, dry cough and associated fatigue [2]. These symptoms can progress to difficulty breathing or shortness of breath, chest pain or pressure, loss of speech or movement [3]. The high risk of mortality and morbidity of this illness has resulted in worldwide awareness and control campaigns, resulting in varying levels of movement restriction and containment measures implemented to reduce the rate of transmission of SARS-Cov-2.

OPeNDAP-based access for PCR-GLOBWB input files

2020 ◽  
Author(s):  
Edwin Sutanudjaja ◽  
Egbert Gramsbergen ◽  
Paula Martinez Lavanchy ◽  
Annemiek van der Kuil ◽  
Jan van der Heul ◽  
...  
Keyword(s):  
Physical Geography ◽  
Total Size ◽  
Remote Server ◽  
Time Period ◽  
Fine Spatial Resolution ◽  
Data Files ◽  
Complete Set ◽  
Spatio Temporal ◽  
Fine Resolution ◽  
Hydrology And Water Resources

<p>PCR-GLOBWB (Sutanudjaja et al., 2018, https://doi.org/10.5194/gmd-11-2429-2018, https://github.com/UU-Hydro/PCR-GLOBWB_model) is an open source global hydrology and water resources model being developed over the past two decades at the Department of Physical Geography, Utrecht University, The Netherlands. The latest version of the model has a fine spatial resolution of 5 arcmin (less than 10 km at the equator) and runs on daily resolution covering several decade simulation time period, i.e. > 50 years. Due to its fine resolution and extensive spatio-temporal extent, the total size of a complete set of PCR-GLOBWB input files is huge (about 250 GB if they are uncompressed; 45 GB if compressed, see e.g. https://doi.org/10.5281/zenodo.1045338). Consequently, sharing and downloading them are difficult, even for a user that wants to run the model for a limited and specific catchment area only. </p><p>In this presentation we aim to share our recent successful effort to prepare and upload PCR-GLOBWB input files to the 4TU.ResearchData server, https://opendap.4tu.nl, that supports OPeNDAP protocol (https://www.opendap.org) allowing users to access files from a remote server without the need to download the data files. This includes inspection of the metadata enabling subsampling specific ranges of the data (over space and time). OPeNDAP is especially suited to netCDF files, and, therefore, we have ensured compatibility of PCR-GLOBWB input files in the correct netCDF format, i.e. following CF conventions, before uploading the files to the remote server. </p><p>The PCR-GLOBWB input files are now available on https://opendap.4tu.nl/thredds/catalog/data2/pcrglobwb/catalog.html. PCR-GLOBWB users can run the model by simply adjusting the input directory location to this address (and, therefore, without having to download the entire input files). In this presentation, we aim to demonstrate on how to make such runs, not only for global extent, but also for specific or limited regions only (river basin extent).</p>

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