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Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (30 September 2016) | Viewed by 74005

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Prof. Dr. Jamal Jokar Arsanjani

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Geoinformatics and Earth Observation Research Group, Department of Planning, Aalborg University Copenhagen, A.C. Meyers Vænge 15, DK-2450 Copenhagen, Denmark
Interests: citizen observations; earth observation; geocomputation; GEO-artificial intelligence; data quality; environmental monitoring and assessment
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Special Issue Information

Dear Colleagues,

Remote sensing, as well as the recent advancements of crowd sensing, along with novel and recent geospatial technologies, have great potential to explore and understand the relationships between our surroundings in particular our urban and rural environments and natural spaces with public health through environmental factors.

Phenomena including climate change, extreme weather conditions, dynamic and mega cities, air pollution, and dust storms, among others, have significant impacts on human and environmental health. On the one hand, the rising volume of Earth observatories and citizen observatories have provided research scholars with a tremendous amount of data streams in space and time, which are novel, unique, and even freely available so that new research agenda are to be defined to explore the power of these data. On the other hand, the recent geospatial technologies, such as novel geocomputational techniques, clustering algorithms, visual analytics, data/information mining approaches, Web 2.0, and collaborative sensing techniques, among others, have presented a wide variety of techniques for exploring these data and discovering latent information about public health.

In this Special Issue, we aim to present novel sensing and computational techniques for better understanding of public health, developing diverse public health applications, and explore their underlying implications towards securing healthier urban/rural environments and natural spaces.

This Special Issue calls for original papers on application of remote sensing, crowd sensing and geospatial technologies in the areas of:

Air pollution and noise pollution monitoring, analysis, and modeling
Big Data in Public Health studies
Environmental Public Health Surveillance
Climate change and its impacts on Health
Urban epidemiology
Environmental health factors
Health Informatics
Social media geographic information

Dr. Jamal Jokar Arsanjani
Guest Editor

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Keywords

Big data
climate change and health
Earth observations
citizen observations
remote sensing
exposure to air pollution and noise
geospatial technology
health GIS
landscape epidemiology
public health
public health tracking
spatiotemporal epidemiology

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Published Papers (12 papers)

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3442 KiB

Open AccessArticle

Ground Level PM_2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO₂ and Enhanced Vegetation Index (EVI)

by Tianhao Zhang, Wei Gong, Wei Wang, Yuxi Ji, Zhongmin Zhu and Yusi Huang

Int. J. Environ. Res. Public Health 2016, 13(12), 1215; https://doi.org/10.3390/ijerph13121215 - 7 Dec 2016

Cited by 45 | Viewed by 6605

Abstract

Highly accurate data on the spatial distribution of ambient fine particulate matter (<2.5 μm: PM_2.5) is currently quite limited in China. By introducing NO₂ and Enhanced Vegetation Index (EVI) into the Geographically Weighted Regression (GWR) model, a newly developed GWR model combined with a fused Aerosol Optical Depth (AOD) product and meteorological parameters could explain approximately 87% of the variability in the corresponding PM_2.5 mass concentrations. There existed obvious increase in the estimation accuracy against the original GWR model without NO₂ and EVI, where cross-validation R² increased from 0.77 to 0.87. Both models tended to overestimate when measurement is low and underestimate when high, where the exact boundary value depended greatly on the dependent variable. There was still severe PM_2.5 pollution in many residential areas until 2015; however, policy-driven energy conservation and emission reduction not only reduced the severity of PM_2.5 pollution but also its spatial range, to a certain extent, from 2014 to 2015. The accuracy of satellite-derived PM_2.5 still has limitations for regions with insufficient ground monitoring stations and desert areas. Generally, the use of NO₂ and EVI in GWR models could more effectively estimate PM_2.5 at the national scale than previous GWR models. The results in this study could provide a reasonable reference for assessing health impacts, and could be used to examine the effectiveness of emission control strategies under implementation in China. Full article

(This article belongs to the Special Issue Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health)

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5857 KiB

Open AccessArticle

Spatial Analysis of Severe Fever with Thrombocytopenia Syndrome Virus in China Using a Geographically Weighted Logistic Regression Model

by Liang Wu, Fei Deng, Zhong Xie, Sheng Hu, Shu Shen, Junming Shi and Dan Liu

Int. J. Environ. Res. Public Health 2016, 13(11), 1125; https://doi.org/10.3390/ijerph13111125 - 11 Nov 2016

Cited by 29 | Viewed by 6712

Abstract

Severe fever with thrombocytopenia syndrome (SFTS) is caused by severe fever with thrombocytopenia syndrome virus (SFTSV), which has had a serious impact on public health in parts of Asia. There is no specific antiviral drug or vaccine for SFTSV and, therefore, it is important to determine the factors that influence the occurrence of SFTSV infections. This study aimed to explore the spatial associations between SFTSV infections and several potential determinants, and to predict the high-risk areas in mainland China. The analysis was carried out at the level of provinces in mainland China. The potential explanatory variables that were investigated consisted of meteorological factors (average temperature, average monthly precipitation and average relative humidity), the average proportion of rural population and the average proportion of primary industries over three years (2010–2012). We constructed a geographically weighted logistic regression (GWLR) model in order to explore the associations between the selected variables and confirmed cases of SFTSV. The study showed that: (1) meteorological factors have a strong influence on the SFTSV cover; (2) a GWLR model is suitable for exploring SFTSV cover in mainland China; (3) our findings can be used for predicting high-risk areas and highlighting when meteorological factors pose a risk in order to aid in the implementation of public health strategies. Full article

(This article belongs to the Special Issue Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health)

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1787 KiB

Open AccessArticle

Evaluating the Governing Factors of Variability in Nocturnal Boundary Layer Height Based on Elastic Lidar in Wuhan

by Wei Wang, Feiyue Mao, Wei Gong, Zengxin Pan and Lin Du

Int. J. Environ. Res. Public Health 2016, 13(11), 1071; https://doi.org/10.3390/ijerph13111071 - 1 Nov 2016

Cited by 23 | Viewed by 5077

Abstract

The atmospheric boundary layer (ABL), an atmospheric region near the Earth’s surface, is affected by surface forcing and is important for studying air quality, climate, and weather forecasts. In this study, long-term urban nocturnal boundary layers (NBLs) were estimated by an elastic backscatter light detection and ranging (LiDAR) with various methods in Wuhan (30.5° N, 114.4° E), a city in Central China. This study aims to explore two ABL research topics: (1) the relationship between NBL height (NBLH) and near-surface parameters (e.g., sensible heat flux, temperature, wind speed, and relative humidity) to elucidate meteorological processes governing NBL variability; and (2) the influence of NBLH variations in surface particulate matter (PM) in Wuhan. We analyzed the nocturnal ABL-dilution/ABL-accumulation effect on surface particle concentration by using a typical case. A long-term analysis was then performed from 5 December 2012–17 June 2016. Results reveal that the seasonal averages of nocturnal (from 20:00 to 05:00 next day, Chinese standard time) NBLHs are 386 ± 161 m in spring, 473 ± 154 m in summer, 383 ± 137 m in autumn, and 309 ± 94 m in winter. The seasonal variations in NBLH, AOD, and PM_2.5 display a deep (shallow) seasonal mean NBL, consistent with a small (larger) seasonal mean PM_2.5 near the surface. Seasonal variability of NBLH is partly linearly correlated with sensible heat flux at the surface (R = 0.72). Linear regression analyses between NBLH and other parameters show the following: (1) the positive correlation (R = 0.68) between NBLH and surface temperature indicates high (low) NBLH corresponding to warm (cool) conditions; (2) the slight positive correlation (R = 0.52) between NBLH and surface relative humidity in Wuhan; and (3) the weak positive correlation (R = 0.38) between NBLH and wind speed inside the NBL may imply that the latter is not an important direct driver that governs the seasonal variability of NBLH. Full article

(This article belongs to the Special Issue Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health)

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5508 KiB

Open AccessArticle

Integrated Application of Multivariate Statistical Methods to Source Apportionment of Watercourses in the Liao River Basin, Northeast China

by Jiabo Chen, Fayun Li, Zhiping Fan and Yanjie Wang

Int. J. Environ. Res. Public Health 2016, 13(10), 1035; https://doi.org/10.3390/ijerph13101035 - 21 Oct 2016

Cited by 18 | Viewed by 5236

Abstract

Source apportionment of river water pollution is critical in water resource management and aquatic conservation. Comprehensive application of various GIS-based multivariate statistical methods was performed to analyze datasets (2009–2011) on water quality in the Liao River system (China). Cluster analysis (CA) classified the 12 months of the year into three groups (May–October, February–April and November–January) and the 66 sampling sites into three groups (groups A, B and C) based on similarities in water quality characteristics. Discriminant analysis (DA) determined that temperature, dissolved oxygen (DO), pH, chemical oxygen demand (COD_Mn)_, 5-day biochemical oxygen demand (BOD₅), NH₄⁺–N, total phosphorus (TP) and volatile phenols were significant variables affecting temporal variations, with 81.2% correct assignments. Principal component analysis (PCA) and positive matrix factorization (PMF) identified eight potential pollution factors for each part of the data structure, explaining more than 61% of the total variance. Oxygen-consuming organics from cropland and woodland runoff were the main latent pollution factor for group A. For group B, the main pollutants were oxygen-consuming organics, oil, nutrients and fecal matter. For group C, the evaluated pollutants primarily included oxygen-consuming organics, oil and toxic organics. Full article

(This article belongs to the Special Issue Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health)

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1839 KiB

Open AccessArticle

Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations

by Junxiao Wang, Xiaorui Wang, Shenglu Zhou, Shaohua Wu, Yan Zhu and Chunfeng Lu

Int. J. Environ. Res. Public Health 2016, 13(10), 980; https://doi.org/10.3390/ijerph13100980 - 30 Sep 2016

Cited by 12 | Viewed by 4413

Abstract

With China’s rapid economic development, the reduction in arable land has emerged as one of the most prominent problems in the nation. The long-term dynamic monitoring of arable land quality is important for protecting arable land resources. An efficient practice is to select optimal sample points while obtaining accurate predictions. To this end, the selection of effective points from a dense set of soil sample points is an urgent problem. In this study, data were collected from Donghai County, Jiangsu Province, China. The number and layout of soil sample points are optimized by considering the spatial variations in soil properties and by using an improved simulated annealing (SA) algorithm. The conclusions are as follows: (1) Optimization results in the retention of more sample points in the moderate- and high-variation partitions of the study area; (2) The number of optimal sample points obtained with the improved SA algorithm is markedly reduced, while the accuracy of the predicted soil properties is improved by approximately 5% compared with the raw data; (3) With regard to the monitoring of arable land quality, a dense distribution of sample points is needed to monitor the granularity. Full article

(This article belongs to the Special Issue Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health)

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3336 KiB

Open AccessArticle

Real-Time Estimation of Satellite-Derived PM_2.5 Based on a Semi-Physical Geographically Weighted Regression Model

by Tianhao Zhang, Gang Liu, Zhongmin Zhu, Wei Gong, Yuxi Ji and Yusi Huang

Int. J. Environ. Res. Public Health 2016, 13(10), 974; https://doi.org/10.3390/ijerph13100974 - 30 Sep 2016

Cited by 31 | Viewed by 5565

Abstract

The real-time estimation of ambient particulate matter with diameter no greater than 2.5 μm (PM_2.5) is currently quite limited in China. A semi-physical geographically weighted regression (GWR) model was adopted to estimate PM_2.5 mass concentrations at national scale using the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth product fused by the Dark Target (DT) and Deep Blue (DB) algorithms, combined with meteorological parameters. The fitting results could explain over 80% of the variability in the corresponding PM_2.5 mass concentrations, and the estimation tends to overestimate when measurement is low and tends to underestimate when measurement is high. Based on World Health Organization standards, results indicate that most regions in China suffered severe PM_2.5 pollution during winter. Seasonal average mass concentrations of PM_2.5 predicted by the model indicate that residential regions, namely Jing-Jin-Ji Region and Central China, were faced with challenge from fine particles. Moreover, estimation deviation caused primarily by the spatially uneven distribution of monitoring sites and the changes of elevation in a relatively small region has been discussed. In summary, real-time PM_2.5 was estimated effectively by the satellite-based semi-physical GWR model, and the results could provide reasonable references for assessing health impacts and offer guidance on air quality management in China. Full article

(This article belongs to the Special Issue Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health)

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8808 KiB

Open AccessArticle

Identifying the Uncertainty in Physician Practice Location through Spatial Analytics and Text Mining

by Xuan Shi, Bowei Xue and Imam M. Xierali

Int. J. Environ. Res. Public Health 2016, 13(9), 930; https://doi.org/10.3390/ijerph13090930 - 21 Sep 2016

Cited by 7 | Viewed by 4683

Abstract

In response to the widespread concern about the adequacy, distribution, and disparity of access to a health care workforce, the correct identification of physicians’ practice locations is critical to access public health services. In prior literature, little effort has been made to detect and resolve the uncertainty about whether the address provided by a physician in the survey is a practice address or a home address. This paper introduces how to identify the uncertainty in a physician’s practice location through spatial analytics, text mining, and visual examination. While land use and zoning code, embedded within the parcel datasets, help to differentiate resident areas from other types, spatial analytics may have certain limitations in matching and comparing physician and parcel datasets with different uncertainty issues, which may lead to unforeseen results. Handling and matching the string components between physicians’ addresses and the addresses of the parcels could identify the spatial uncertainty and instability to derive a more reasonable relationship between different datasets. Visual analytics and examination further help to clarify the undetectable patterns. This research will have a broader impact over federal and state initiatives and policies to address both insufficiency and maldistribution of a health care workforce to improve the accessibility to public health services. Full article

(This article belongs to the Special Issue Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health)

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8833 KiB

Open AccessArticle

Predicting Avian Influenza Co-Infection with H5N1 and H9N2 in Northern Egypt

by Sean G. Young, Margaret Carrel, George P. Malanson, Mohamed A. Ali and Ghazi Kayali

Int. J. Environ. Res. Public Health 2016, 13(9), 886; https://doi.org/10.3390/ijerph13090886 - 6 Sep 2016

Cited by 17 | Viewed by 5754

Abstract

Human outbreaks with avian influenza have been, so far, constrained by poor viral adaptation to non-avian hosts. This could be overcome via co-infection, whereby two strains share genetic material, allowing new hybrid strains to emerge. Identifying areas where co-infection is most likely can help target spaces for increased surveillance. Ecological niche modeling using remotely-sensed data can be used for this purpose. H5N1 and H9N2 influenza subtypes are endemic in Egyptian poultry. From 2006 to 2015, over 20,000 poultry and wild birds were tested at farms and live bird markets. Using ecological niche modeling we identified environmental, behavioral, and population characteristics of H5N1 and H9N2 niches within Egypt. Niches differed markedly by subtype. The subtype niches were combined to model co-infection potential with known occurrences used for validation. The distance to live bird markets was a strong predictor of co-infection. Using only single-subtype influenza outbreaks and publicly available ecological data, we identified areas of co-infection potential with high accuracy (area under the receiver operating characteristic (ROC) curve (AUC) 0.991). Full article

(This article belongs to the Special Issue Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health)

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10874 KiB

Open AccessArticle

Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis

by Jianhua Ni, Tianlu Qian, Changbai Xi, Yikang Rui and Jiechen Wang

Int. J. Environ. Res. Public Health 2016, 13(8), 833; https://doi.org/10.3390/ijerph13080833 - 18 Aug 2016

Cited by 33 | Viewed by 7492

Abstract

The spatial distribution of urban service facilities is largely constrained by the road network. In this study, network point pattern analysis and correlation analysis were used to analyze the relationship between road network and healthcare facility distribution. The weighted network kernel density estimation method proposed in this study identifies significant differences between the outside and inside areas of the Ming city wall. The results of network K-function analysis show that private hospitals are more evenly distributed than public hospitals, and pharmacy stores tend to cluster around hospitals along the road network. After computing the correlation analysis between different categorized hospitals and street centrality, we find that the distribution of these hospitals correlates highly with the street centralities, and that the correlations are higher with private and small hospitals than with public and large hospitals. The comprehensive analysis results could help examine the reasonability of existing urban healthcare facility distribution and optimize the location of new healthcare facilities. Full article

(This article belongs to the Special Issue Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health)

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2361 KiB

Open AccessArticle

Spatial Distribution, Sources Apportionment and Health Risk of Metals in Topsoil in Beijing, China

by Chunyuan Sun, Wenji Zhao, Qianzhong Zhang, Xue Yu, Xiaoxia Zheng, Jiayin Zhao and Ming Lv

Int. J. Environ. Res. Public Health 2016, 13(7), 727; https://doi.org/10.3390/ijerph13070727 - 20 Jul 2016

Cited by 23 | Viewed by 5721

Abstract

In order to acquire the pollution feature and regularities of distribution of metals in the topsoil within the sixth ring road in Beijing, a total of 46 soil samples were collected, and the concentrations of twelve elements (Nickel, Ni, Lithium, Li, Vanadium, V, Cobalt, Co, Barium, Ba, Strontium, Sr, Chrome, Cr, Molybdenum, Mo, Copper, Cu, Cadmium, Cd, Zinc, Zn, Lead, Pb) were analyzed. Geostatistics and multivariate statistics were conducted to identify spatial distribution characteristics and sources. In addition, the health risk of the analyzed heavy metals to humans (adult) was evaluated by an U.S. Environmental Protection Agency health risk assessment model. The results indicate that these metals have notable variation in spatial scale. The concentration of Cr was high in the west and low in the east, while that of Mo was high in the north and low in the south. High concentrations of Cu, Cd, Zn, and Pb were found in the central part of the city. The average enrichment degree of Cd is 5.94, reaching the standard of significant enrichment. The accumulation of Cr, Mo, Cu, Cd, Zn, and Pb is influenced by anthropogenic activity, including vehicle exhaustion, coal burning, and industrial processes. Health risk assessment shows that both non-carcinogenic and carcinogenic risks of selected heavy metals are within the safety standard and the rank of the carcinogenic risk of the four heavy metals is Cr > Co > Ni > Cd. Full article

(This article belongs to the Special Issue Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health)

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2296 KiB

Open AccessArticle

Measurement and Study of Lidar Ratio by Using a Raman Lidar in Central China

by Wei Wang, Wei Gong, Feiyue Mao, Zengxin Pan and Boming Liu

Int. J. Environ. Res. Public Health 2016, 13(5), 508; https://doi.org/10.3390/ijerph13050508 - 18 May 2016

Cited by 24 | Viewed by 5949

Abstract

We comprehensively evaluated particle lidar ratios (i.e., particle extinction to backscatter ratio) at 532 nm over Wuhan in Central China by using a Raman lidar from July 2013 to May 2015. We utilized the Raman lidar data to obtain homogeneous aerosol lidar ratios near the surface through the Raman method during no-rain nights. The lidar ratios were approximately 57 ± 7 sr, 50 ± 5 sr, and 22 ± 4 sr under the three cases with obviously different pollution levels. The haze layer below 1.8 km has a large particle extinction coefficient (from 5.4e-4 m⁻¹ to 1.6e-4 m⁻¹) and particle backscatter coefficient (between 1.1e-05 m⁻¹sr⁻¹ and 1.7e-06 m⁻¹sr⁻¹) in the heavily polluted case. Furthermore, the particle lidar ratios varied according to season, especially between winter (57 ± 13 sr) and summer (33 ± 10 sr). The seasonal variation in lidar ratios at Wuhan suggests that the East Asian monsoon significantly affects the primary aerosol types and aerosol optical properties in this region. The relationships between particle lidar ratios and wind indicate that large lidar ratio values correspond well with weak winds and strong northerly winds, whereas significantly low lidar ratio values are associated with prevailing southwesterly and southerly wind. Full article

(This article belongs to the Special Issue Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health)

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Review

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1069 KiB

Open AccessReview

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa

by Osadolor Ebhuoma and Michael Gebreslasie

Int. J. Environ. Res. Public Health 2016, 13(6), 584; https://doi.org/10.3390/ijerph13060584 - 14 Jun 2016

Cited by 17 | Viewed by 9233

Abstract

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of Knowledge^SM databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably. Full article

(This article belongs to the Special Issue Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health)

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