CNES strategy: satellite data and modelling for public health: towards a cooperation with NASA

For ages, links between environment, climate and their impacts on human health have been observed and studied. Research to improve our understanding of environmental key determinants of infectious diseases can provide innovative information for adaptation strategies and lead to new tools optimizing surveillance, vector control measures, and disease prevention. As earth observation satellites can measure meteorological and environmental parameters, NASA and CNES have separately engaged in an innovative use of their earth observation infrastructure development programs: space tools addressing public health. As NASA and CNES have fruitful cooperation for satellite development missions for years, both health programs have proposed to explore a new area of collaboration: satellites addressing health issues. As members of international organizations, NASA and CNES could promote their common views towards the Group on Earth Observations (GEO) Community of Practice for Health & Environment and the Committee on Earth Observation Satellites (CEOS) Societal Benefit Area on Health.     

移动GIS在传染病防控方面的应用——以COVID-19为例

新型冠状病毒肺炎（COVID-19）疫情的出现和暴发流行,给社会、经济及人群健康提出巨大的挑战,已经成为重大公共卫生事件和社会问题。作为一种新发传染病,提早发现、迅速采用有效应对举措,是防止病毒蔓延扩散的重要环节。地理信息系统（GIS）在传染病的控制、预防、预警中有举足轻重的地位,移动GIS（Mobile GIS）作为GIS技术的发展,进一步提高了我国卫生部门应对突发传染病的能力。本文以COVID-19防控为例,重点介绍了移动GIS技术在传染病防控中的应用。     

Use of earth observation data for applications in public health

The Earth Observation (EO) data with their advantages in spectral, spatial and temporal resolutions have demonstrated their great value in providing information about many of the components that comprise environmental systems and ecosystems for decades that are crucial to the understating of public health issues. This literature review shows that in conjunction with in situ data collection, EO data have been used to observe, monitor, measure and model many environmental variables that are associated with disease vectors. Furthermore, satellite derived aerosol optical depth has been increasingly employed to estimate ground-level PM_2.5 concentrations, which have been found to associate with various health outcomes such as cardiovascular and respiratory diseases. It is suggested that Landsat-like imagery data may provide important data sources to analyse and understand contagious and infectious diseases at the local and regional scales, which are tied to urbanisation and associated impacts on the environment. There is also a great need of data products from coarse resolution imagery, such as those from moderate resolution imaging spectrometer, multiangle imaging spectroradiometer and geostationary operational environmental satellite , to model and characterise infectious diseases at the continental and global scales. The infectious diseases at greater geographical scales have become unprecedentedly significant as global climate change and the process of globalisation intensify. The relationship between infectious diseases and environmental characteristic have been explored by using statistical, geostatistical and physical models, with recent emphasis on the use of machine-learning techniques such as artificial neural networks. Lastly, we suggest that the planned HyspIRI mission is crucial for observing, measuring and modelling environmental variables impacting various diseases as it will improve both spectral resolution and revisit time, thus contributing to better prediction of occurrence of infectious diseases, target intervention and tracking of epidemic events.     

Using GIS to improve public health emergency response in rural areas during the COVID-19 crisis: A case study of South Carolina,US

Geographic information systems (GIS) have become essential tools in the public health domain, especially when it comes to monitoring and surveillance of disease. The purpose of this article is to describe and explore the benefits of using GIS to improve public health emergency response during a global pandemic and, in particular, how to effectively optimize the allocation of public health resources in a rural setting using a data-driven approach that considers the multifactorial demand for new COVID-19 testing sites. Herein, the authors present their interprofessional project as an example of such efforts to inform applications for practice. The team developed a GIS-based multicriteria decision analysis model for use by decision-makers and public health experts in similar future planning and response scenarios. Focus is placed on rural characteristics (e.g., accessibility), vulnerable populations, and daily changing conditions (e.g., COVID-19 daily case fluctuations) that create additional challenges for public health agencies and policymakers.     

Water resource applications with RADARSAT-2 – a preview

Abstract

Fresh water is arguably the most vital resource for many aspects of a healthy and stable environment. Monitoring the extent of surface water enables resource managers to detect perturbations and long term trends in water availability, and set consumption guidelines accordingly. Potential end-users of water-related observations are numerous and reflect society as a whole. They encompass scientists and managers at all levels of government, aboriginal groups, water/power utility managers, farmers, planners, engineers, hydrologists, medical researchers, climate scientists, recreation enthusiasts, public school to post-graduate students, many special interest groups and the general public. Water data and analyses generate information products that benefit water resources planning and management, engineering design, plant operations, navigation activities, health research, water quality assessments and ecosystem management. As well, they serve as inputs for flood and drought warnings and weather and climate prediction models. Radar data in general, and RADARSAT in particular, are very good for detecting open surface water and have been used operationally for flood monitoring in many countries. Significant radar data archives now exist to analyse seasonal, annual and decadal trends, in order to attain a better understanding of the freshwater cycle. Radar data are also useful for wetland classification and soil moisture estimation. With the increasing pressure on water resources, both from a quality as well as a quantity perspective, the need will continue to increase for reliable information.

RADARSAT-2 has several innovations that will enhance the ability to provide useful information about water resources. This paper provides an overview of the use of radar in general, and RADARSAT-2 in particular, for the generation of information products useful to water resource managers.     

Environmental public health applications using remotely sensed data

We describe a remote sensing and geographic information system (GIS)-based study that has three objectives: (1) characterize fine particulate matter (PM_2.5), insolation and land surface temperature (LST) using NASA satellite observations, Environmental Protection Agency (EPA) ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making.     

A GIS Approach to Estimation of Building Population for Micro-spatial Analysis

Population data used in GIS analyses is generally assumed to be homogeneous and planar (i.e. census tracts, townships or prefectures) due to the public unavailability of building population data. However, information on building population is required for micro-spatial analysis for improved disaster management and emergency preparedness, public facility management for urban planning, consumer and retail market analysis, environment and public health programs and other demographic studies. This article discusses a GIS approach using the Areametric and Volumetric methods for estimating building population based on census tracts and building footprint datasets. The estimated results were evaluated using actual building population data by visual, statistical and spatial means, and validated for use in micro-spatial analysis. We have also implemented a standalone GIS tool (known as 'PopShape GIS') for generating new building footprint with population attribute information based on user-defined criteria.     

THE USE OF GEOGRAPHIC INFORMATION SYSTEMS IN ANALYSIS OF URBAN AND RURAL LANDSCAPES

This paper reviews major concerns in the development of geographic information systems in Czechoslovakia (underlying theory, design and system requirements, data acquisition and classification, and mathematical-cartographic modeling), and describes the structure of two systems developed for the city of Brno and the surrounding semi-rural countryside. These systems are designed to permit the widest possible range of spatial information to be incorporated into their data bases, to allow maximum flexibility of response to changing user requirements, to produce a wide variety of graphic products, and to provide a capacity for automated forecasting and output of forecast maps. Presented at the Euro-Carto III Seminar sponsored by the International Cartographic Association at Graz, Austria, October 23-25, 1984 and submitted to the Editor-in-Chief for publication.     

Assessing factors impacting the spatial discrepancy of remote sensing based cropland products: A case study in Africa

Many African countries are facing increasing risks of food insecurity due to rising populations. Accurate and timely information on the spatial distribution of cropland is critical for the effective management of crop production and yield forecast. Most recent cropland products (2015 and 2016) derived from multi-source remote sensing data are available for public use. However, discrepancies exist among these cropland products, and the level of discrepancy is particularly high in several Africa regions. The overall goal of this study was to identify and assess the driving factors contributing to the spatial discrepancies among four cropland products derived from remotely sensed data. A novel approach was proposed to evaluate the spatial agreement of these cropland products and assess the impact of environmental factors such as elevation dispersion, field size, land-cover richness and frequency of cloud cover on these spatial differences. Results from this study show that the overall accuracies of the four cropland products are below 65%. In particular, large disagreements are seen on datasets covering Sahel zone and along the West African coasts. This study has identified land-cover richness as the driving factor with the largest contribution to the spatial disagreement among cropland products over Africa, followed by the high frequency of cloud cover, small and fragmented field size, and elevation complexity. To improve the accuracy of future cropland products for African regions, the data producers are encouraged to take a multi-classification approach and incorporate multi-sensors into their cropland mapping processes.     

The international reference ionosphere today and in the future

The international reference ionosphere (IRI) is the internationally recognized and recommended standard for the specification of plasma parameters in Earth’s ionosphere. It describes monthly averages of electron density, electron temperature, ion temperature, ion composition, and several additional parameters in the altitude range from 60 to 1,500 km. A joint working group of the Committee on Space Research (COSPAR) and the International Union of Radio Science (URSI) is in charge of developing and improving the IRI model. As requested by COSPAR and URSI, IRI is an empirical model being based on most of the available and reliable data sources for the ionospheric plasma. The paper describes the latest version of the model and reviews efforts towards future improvements, including the development of new global models for the F2 peak density and height, and a new approach to describe the electron density in the topside and plasmasphere. Our emphasis will be on the electron density because it is the IRI parameter most relevant to geodetic techniques and studies. Annual IRI meetings are the main venue for the discussion of IRI activities, future improvements, and additions to the model. A new special IRI task force activity is focusing on the development of a real-time IRI (RT-IRI) by combining data assimilation techniques with the IRI model. A first RT-IRI task force meeting was held in 2009 in Colorado Springs. We will review the outcome of this meeting and the plans for the future. The IRI homepage is at .     

The Role of Ground-Based GPS Meteorological Observations in Numerical Weather Prediction

For lack of sufficient observations, the definition of atmospheric moisture fields (including water vapor and clouds) remains a difficult problem whose solution is essential for improved weather forecasts. Moisture fields are under-observed in time and space, primarily because the distribution of water in the atmosphere is highly variable. Because water is important in weather and climate processes, a significant effort has been expended to develop new or improved remote sensing systems to mitigate this problem. One such system uses ground-based Global Positoning System (GPS) receivers to make accurate all-weather estimates of atmospheric refractivity at very low cost. This largely unanticipated application of GPS had led to a new and potentially significant upper-air observing system for meteorological agencies and researchers around the world (Wolfe & Gutman, 2000). The first and most mature use of GPS for this purpose is in the estimation of integrated (total column) precipitable water vapor above a fixed site (Duan et al., 1996, with improvements by Niell, 1996, and Fang et al., 1998). The techniques currently used by the National Oceanic and Atmospheric Administration's Forecast Systems Laboratory (NOAA/FSL) to collect, process, and distribute GPS water vapor observations are mature and almost ready for transition to operational use. NOAA/FSL has shown that GPS integrated water vapor data can be used effectively in objective (i. e., numerical weather prediction) and subjective weather forecasting. To understand the strengths and limitations of GPS for weather forecasting, it is essential to understant what types of information are currently available to forecasters and modelers, and how models use the data to describe the current and probable future state of the atmosphere. It is also important to understand the current trends in modern weather prediction to ensure that GPS observing system play a significant role in the future. ? 2001 John Wiley & Sons, Inc.     

Cartographic Forecasting of Special Medical-Ecological Problems

A group of Siberian researchers addresses a range of issues surrounding efforts to apply public health information derived from medical-ecological monitoring and mapping to the task of forecasting/prevention of hazardous medical-ecological situations. Some specific examples are provided from the authors' work in Altay Kray. Translated by Edward Torrey, Alexandria, Virginia from: Geografiya i prirodnyye resursy, 2002, No. 1, pp. 54-58.     

SARS空间传播模式及其在疫情监控信息系统设计中的应用

在中国控制SARS(Severe Acute Respiratory Syndrome)疫情扩散与蔓延的大量科学实践活动基础上，从人地系统科学的理论和观点出发，提出了各种致病因子在人地互动过程中转移路径的逻辑模型，进而又提出了控制SARS疫情发展的人地互动逻辑模型。从后一个模型出发，把中国SARS疫情的发展规律归纳为5种传播模式，即异类病毒转移传播模式、家庭和社区接触传播模式、医院和门诊感染传播模式、市内随机扩散传播模式和飞点跳跃跨区传播模式。其中，跨区传播模式的影响最广泛、危害最巨大、控制起来最艰巨、复杂，是建立中国突发公共卫生事件应急机制、增强应急能力需要解决的主要问题和关键内容。为此，作者对中国的疫情控制应急机制及其实现提出了一些具体的看法和建议，包括对现有疫情系统进行技术改造和创新的建议，在全新运作机制和高技术基础上建立应急疫情监测控制信息系统的概念结构等内容。     

An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data

The Midwestern United States is one of the world’s most important corn-producing regions. Monitoring and forecasting of corn yields in this intensive agricultural region are important activities to support food security, commodity markets, bioenergy industries, and formation of national policies. This study aims to develop forecasting models that have the capability to provide mid-season prediction of county-level corn yields for the entire Midwestern United States. We used multi-temporal MODIS NDVI (normalized difference vegetation index) 16-day composite data as the primary input, with digital elevation model (DEM) and parameter-elevation relationships on independent slopes model (PRISM) climate data as additional inputs. The DEM and PRISM data, along with three types of cropland masks were tested and compared to evaluate their impacts on model predictive accuracy. Our results suggested that the use of general cropland masks (e.g., summer crop or cultivated crops) generated similar results compared with use of an annual corn-specific mask. Leave-one-year-out cross-validation resulted in an average R² of 0.75 and RMSE value of 1.10 t/ha. Using a DEM as an additional model input slightly improved performance, while inclusion of PRISM climate data appeared not to be important for our regional corn-yield model. Furthermore, our model has potential for real-time/early prediction. Our corn yield esitmates are available as early as late July, which is an improvement upon previous corn-yield prediction models. In addition to annual corn yield forecasting, we examined model uncertainties through spatial and temporal analysis of the model's predictive error distribution. The magnitude of predictive error (by county) appears to be associated with the spatial patterns of corn fields in the study area.     

Malaria incidence in Nairobi,Kenya and dekadal trends in NDVI and climatic variables

The primary objective of this research was to determine if the remotely-sensed metric, Normalised Difference Vegetation Index (NDVI) and ground-collected dekadal climatological variables were useful predictors of future malaria outbreaks in an epidemic-prone area of Nairobi, Kenya. Data collected consisted of 36 dekadal (10-day) periods for the variables rainfall, temperature and NDVI along with yearly documented malaria admissions in 2003 for Nairobi, Kenya. Linear regression models were built for malaria cases reported in Nairobi, Kenya, as the dependent variable and various time-based groupings of temperature, rainfall and NDVI data from the dekads in both the current and the previous month as the independent variables. Data from 2003 show that malaria incidence in any given month is best predicted (R²  = 0.881, p < 0.001) by the average NDVI for the 30 days including the final two dekads of the previous month and first dekad of the current month, and by the average rainfall for the 30 days including the three dekads of rainfall data from the prior month. Forecasting an outbreak in an epidemic zone would allow public health entities to plan for and disseminate resources to the general public such as antimalarials and insecticide impregnated bed nets. In addition, vector control measures could be implemented to slow the rate of transmission in the impacted population.     

Adaptive Multi-Scale Population Spatialization Model Constrained by Multiple Factors: A Case Study of Russia

Population spatialization is the foundation for the visualization and analysis of population integrated with other information, such as environmental resources, economy, and public health. The existing population spatialization models have solved many problems for population distribution, but most of these studies have focused on a specific, single-scale approach and ignored the scale transformation for population spatialization. However, multi-scale visualization and the analysis of spatial information need multi-scale information. Meanwhile, the population distribution map as one kind of thematic map is always overlaid with the digital vector map or remote sensing map and visualized in the Web Geographic Information Systems (Web GIS), so it should adapt to the map scale showed in browser, when the user zoom in and zoom out. Hence adaptive multi-scale is necessary for population spatialization. Therefore, in this study, an adaptive multi-scale population spatialization model (APSM) is proposed with comprehensive factors constrained. These factors are residential area, land cover, public transport, hydrology, terrain, and climate. All of them are closely associated with population distribution. The overall methodology of APSM and the process for APSM are expounded in this paper. Meanwhile, with a case study of Russia, the processes of APSM for Russia are stated, and a population spatialization tool is implemented for expanding the application of APSM. The experimental analysis showed that APSM satisfied the requirement of geospatial analysis well and obtained a reliable accuracy.     

基于GIS的农村公共卫生管理信息系统框架设计

在信息化高度发展的今天,对传统的公共卫生数据传输机制和管理方式进行信息化改革,成为当前农村地区公共卫生建设的当务之急。本文通过调查分析,设计了基于GIS的农村公共卫生管理信息系统的总体结构框架和网络结构框架,将系统人员分成不同的层次,系统功能分设不同的模块,从而有效地进行管理,同时以虚拟专网与上级机构进行通讯,以普通Internet网与农村基层机构建立联系,从而构建有效的传输网络,使公共卫生信息畅通无阻。     

地理信息公共服务平台模式探讨

目前地理信息公共服务平台发展的瓶颈是信息维护和更新。地理信息公共服务平台有3种模式,即信息集中存放和集中维护、集中存放和分布维护、分布存放和分布维护,其中第二种模式能在现有的条件下较好地解决信息维护问题。应当建立一系列基于分布维护的兼顾公众、企业和政府的地理信息服务平台,使我国的地理信息公共服务进入一个新的时代。     

Using Spatially Explicit Simulated Data to Analyze Animal Interactions: A Case Study with Brown Hyenas in Northern Botswana

New developments in global positioning systems (GPS) and related satellite tracking technologies have facilitated the collection of highly accurate data on moving objects, far surpassing the ability to analyze them. Within geographic information science, ‘movement pattern analysis’ (MPA) has developed as a subfield that addresses concepts and theories used to explore the spatio‐temporal structure in data, although the methodological and analytical framework associated with MPA is new and still evolving. Interactions between individuals can be considered a second order property of movement and have been far less studied. The nature of interactions between individuals in a population is a fundamental aspect of a species' behavioral ecology and information on the frequency and duration of these interactions is vital to understanding mating and territorial behavior, resource use, and infectious disease epidemiology. The focus of this work was to explore how spatially explicit simulated data can be used to analyse dynamic interactions between individuals. Five different techniques that have been used to quantify dynamic interactions based on GPS data of pairs of individuals were utilised, and all were compared in the context of spatially explicit simulated data intended to represent biologically realistic null models for individual movement, and subsequently paired interactions.     

Emerging and vector-borne diseases: Role of high spatial resolution and hyperspectral images in analyses and forecasts

 Many infectious diseases that are emerging or transmitted by arthropod vectors have a strong link to landscape features. Depending on the source of infection or ecology of the transmitting vector, micro-habitat characteristics at the spatial scale of square meters or less may be important. Recently, satellite images have been used to classify habitats in an attempt to understand associations with infectious diseases. Whether high spatial resolution and hyperspectral (HSRH) images can be useful in studies of such infectious diseases is addressed. The nature of questions that such studies address and the desired accuracy and precision of answers will determine the utility of HSRH data. Need for such data should be based on the goals of the effort. Examples of kinds of questions and applications are discussed. The research implications and public health applications may depend on available analytic tools as well as epidemiological observations. Received: 30 July 2001 / Accepted: 14 October 2001