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Teillet C, Devillers R, Tran A, Catry T, Marti R, Dessay N, Rwagitinywa J, Restrepo J, Roux E. Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites. Int J Health Geogr 2024; 23:18. [PMID: 38972982 PMCID: PMC11229250 DOI: 10.1186/s12942-024-00378-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 06/22/2024] [Indexed: 07/09/2024] Open
Abstract
BACKGROUND The spread of mosquito-transmitted diseases such as dengue is a major public health issue worldwide. The Aedes aegypti mosquito, a primary vector for dengue, thrives in urban environments and breeds mainly in artificial or natural water containers. While the relationship between urban landscapes and potential breeding sites remains poorly understood, such a knowledge could help mitigate the risks associated with these diseases. This study aimed to analyze the relationships between urban landscape characteristics and potential breeding site abundance and type in cities of French Guiana (South America), and to evaluate the potential of such variables to be used in predictive models. METHODS We use Multifactorial Analysis to explore the relationship between urban landscape characteristics derived from very high resolution satellite imagery, and potential breeding sites recorded from in-situ surveys. We then applied Random Forest models with different sets of urban variables to predict the number of potential breeding sites where entomological data are not available. RESULTS Landscape analyses applied to satellite images showed that urban types can be clearly identified using texture indices. The Multiple Factor Analysis helped identify variables related to the distribution of potential breeding sites, such as buildings class area, landscape shape index, building number, and the first component of texture indices. Models predicting the number of potential breeding sites using the entire dataset provided an R² of 0.90, possibly influenced by overfitting, but allowing the prediction over all the study sites. Predictions of potential breeding sites varied highly depending on their type, with better results on breeding sites types commonly found in urban landscapes, such as containers of less than 200 L, large volumes and barrels. The study also outlined the limitation offered by the entomological data, whose sampling was not specifically designed for this study. Model outputs could be used as input to a mosquito dynamics model when no accurate field data are available. CONCLUSION This study offers a first use of routinely collected data on potential breeding sites in a research study. It highlights the potential benefits of including satellite-based characterizations of the urban environment to improve vector control strategies.
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Affiliation(s)
- Claire Teillet
- ESPACE-DEV, Univ Montpellier, IRD, Univ Guyane, Univ Reunion, Univ Antilles, Univ Avignon, Maison de la Télédétection, 500 rue Jean‑François Breton, Montpellier Cedex, F‑34093, France.
| | - Rodolphe Devillers
- ESPACE-DEV, Univ Montpellier, IRD, Univ Guyane, Univ Reunion, Univ Antilles, Univ Avignon, Maison de la Télédétection, 500 rue Jean‑François Breton, Montpellier Cedex, F‑34093, France
| | - Annelise Tran
- CIRAD, UMR TETIS, Maison de la Télédétection, 500 rue Jean‑François Breton, Montpellier, Cedex, F‑34093, France
- TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Maison de la Télédétection, 500 rue Jean‑François Breton, Montpellier, Cedex, F‑34093, France
| | - Thibault Catry
- ESPACE-DEV, Univ Montpellier, IRD, Univ Guyane, Univ Reunion, Univ Antilles, Univ Avignon, Maison de la Télédétection, 500 rue Jean‑François Breton, Montpellier Cedex, F‑34093, France
| | - Renaud Marti
- TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Maison de la Télédétection, 500 rue Jean‑François Breton, Montpellier, Cedex, F‑34093, France
| | - Nadine Dessay
- ESPACE-DEV, Univ Montpellier, IRD, Univ Guyane, Univ Reunion, Univ Antilles, Univ Avignon, Maison de la Télédétection, 500 rue Jean‑François Breton, Montpellier Cedex, F‑34093, France
| | - Joseph Rwagitinywa
- Direction de la Démoustication, Collectivité Territoriale de Guyane (CTG), 4179 Route de Montabo, Cayenne, Guyane française, 97300, France
| | - Johana Restrepo
- Direction de la Démoustication, Collectivité Territoriale de Guyane (CTG), 4179 Route de Montabo, Cayenne, Guyane française, 97300, France
| | - Emmanuel Roux
- ESPACE-DEV, Univ Montpellier, IRD, Univ Guyane, Univ Reunion, Univ Antilles, Univ Avignon, Maison de la Télédétection, 500 rue Jean‑François Breton, Montpellier Cedex, F‑34093, France.
- International Joint laboratory Sentinela, FIOCRUZ, UnB, IRD, Maison de la Télédétection, 500 rue Jean‑François Breton, Montpellier, Cedex, F‑34093, France.
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Yin S, Ren C, Shi Y, Hua J, Yuan HY, Tian LW. A Systematic Review on Modeling Methods and Influential Factors for Mapping Dengue-Related Risk in Urban Settings. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192215265. [PMID: 36429980 PMCID: PMC9690886 DOI: 10.3390/ijerph192215265] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/11/2022] [Accepted: 11/17/2022] [Indexed: 05/12/2023]
Abstract
Dengue fever is an acute mosquito-borne disease that mostly spreads within urban or semi-urban areas in warm climate zones. The dengue-related risk map is one of the most practical tools for executing effective control policies, breaking the transmission chain, and preventing disease outbreaks. Mapping risk at a small scale, such as at an urban level, can demonstrate the spatial heterogeneities in complicated built environments. This review aims to summarize state-of-the-art modeling methods and influential factors in mapping dengue fever risk in urban settings. Data were manually extracted from five major academic search databases following a set of querying and selection criteria, and a total of 28 studies were analyzed. Twenty of the selected papers investigated the spatial pattern of dengue risk by epidemic data, whereas the remaining eight papers developed an entomological risk map as a proxy for potential dengue burden in cities or agglomerated urban regions. The key findings included: (1) Big data sources and emerging data-mining techniques are innovatively employed for detecting hot spots of dengue-related burden in the urban context; (2) Bayesian approaches and machine learning algorithms have become more popular as spatial modeling tools for predicting the distribution of dengue incidence and mosquito presence; (3) Climatic and built environmental variables are the most common factors in making predictions, though the effects of these factors vary with the mosquito species; (4) Socio-economic data may be a better representation of the huge heterogeneity of risk or vulnerability spatial distribution on an urban scale. In conclusion, for spatially assessing dengue-related risk in an urban context, data availability and the purpose for mapping determine the analytical approaches and modeling methods used. To enhance the reliabilities of predictive models, sufficient data about dengue serotyping, socio-economic status, and spatial connectivity may be more important for mapping dengue-related risk in urban settings for future studies.
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Affiliation(s)
- Shi Yin
- Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- School of Architecture, South China University of Technology, Guangzhou 510641, China
| | - Chao Ren
- Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Correspondence:
| | - Yuan Shi
- Department of Geography and Planning, University of Liverpool, Liverpool L69 3BX, UK
| | - Junyi Hua
- School of International Affairs and Public Administration, Ocean University of China, Qingdao 266100, China
| | - Hsiang-Yu Yuan
- Department of Biomedical Sciences, City University of Hong Kong, Kowloon, Hong Kong SAR, China
| | - Lin-Wei Tian
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
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Prasetyowati H, Dhewantara PW, Hendri J, Astuti EP, Gelaw YA, Harapan H, Ipa M, Widyastuti W, Handayani DOTL, Salama N, Picasso M. Geographical heterogeneity and socio-ecological risk profiles of dengue in Jakarta, Indonesia. GEOSPATIAL HEALTH 2021; 16. [PMID: 33733650 DOI: 10.4081/gh.2021.948] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 01/26/2021] [Indexed: 06/12/2023]
Abstract
The aim of this study was to assess the role of climate variability on the incidence of dengue fever (DF), an endemic arboviral infection existing in Jakarta, Indonesia. The work carried out included analysis of the spatial distribution of confirmed DF cases from January 2007 to December 2018 characterising the sociodemographical and ecological factors in DF high-risk areas. Spearman's rank correlation was used to examine the relationship between DF incidence and climatic factors. Spatial clustering and hotspots of DF were examined using global Moran's I statistic and the local indicator for spatial association analysis. Classification and regression tree (CART) analysis was performed to compare and identify demographical and socio-ecological characteristics of the identified hotspots and low-risk clusters. The seasonality of DF incidence was correlated with precipitation (r=0.254, P<0.01), humidity (r=0.340, P<0.01), dipole mode index (r= -0.459, P<0.01) and Tmin (r= -0.181, P<0.05). DF incidence was spatially clustered at the village level (I=0.294, P<0.001) and 22 hotspots were identified with a concentration in the central and eastern parts of Jakarta. CART analysis showed that age and occupation were the most important factors explaining DF clustering. Areaspecific and population-targeted interventions are needed to improve the situation among those living in the identified DF high-risk areas in Jakarta.
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Affiliation(s)
- Heni Prasetyowati
- Pangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia, Pangandaran.
| | - Pandji Wibawa Dhewantara
- Center for Research and Development of Public Health Effort, National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia, Jakarta.
| | - Joni Hendri
- Pangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia, Pangandaran.
| | - Endang Puji Astuti
- Pangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia, Pangandaran.
| | - Yalemzewod Assefa Gelaw
- Population Child Health Research Group, School of Women's and Children's Health, UNSW, NSW Australia; Institute of Public Health, College of Medicine and Health Science, University of Gondar, Gondar.
| | - Harapan Harapan
- Medical Research Unit, School of Medicine, Syiah Kuala University, Banda Aceh, Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Syiah Kuala University, Banda Aceh, Aceh, Indonesia; Department of Microbiology, School of Medicine, Syiah Kuala University, Banda Aceh, Aceh.
| | - Mara Ipa
- Pangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia, Pangandaran.
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Li Z, Gurgel H, Dessay N, Hu L, Xu L, Gong P. Semi-Supervised Text Classification Framework: An Overview of Dengue Landscape Factors and Satellite Earth Observation. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E4509. [PMID: 32585932 PMCID: PMC7344967 DOI: 10.3390/ijerph17124509] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 06/17/2020] [Accepted: 06/18/2020] [Indexed: 12/29/2022]
Abstract
In recent years there has been an increasing use of satellite Earth observation (EO) data in dengue research, in particular the identification of landscape factors affecting dengue transmission. Summarizing landscape factors and satellite EO data sources, and making the information public are helpful for guiding future research and improving health decision-making. In this case, a review of the literature would appear to be an appropriate tool. However, this is not an easy-to-use tool. The review process mainly includes defining the topic, searching, screening at both title/abstract and full-text levels and data extraction that needs consistent knowledge from experts and is time-consuming and labor intensive. In this context, this study integrates the review process, text scoring, active learning (AL) mechanism, and bidirectional long short-term memory (BiLSTM) networks, and proposes a semi-supervised text classification framework that enables the efficient and accurate selection of the relevant articles. Specifically, text scoring and BiLSTM-based active learning were used to replace the title/abstract screening and full-text screening, respectively, which greatly reduces the human workload. In this study, 101 relevant articles were selected from 4 bibliographic databases, and a catalogue of essential dengue landscape factors was identified and divided into four categories: land use (LU), land cover (LC), topography and continuous land surface features. Moreover, various satellite EO sensors and products used for identifying landscape factors were tabulated. Finally, possible future directions of applying satellite EO data in dengue research in terms of landscape patterns, satellite sensors and deep learning were proposed. The proposed semi-supervised text classification framework was successfully applied in research evidence synthesis that could be easily applied to other topics, particularly in an interdisciplinary context.
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Affiliation(s)
- Zhichao Li
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System, Science, Tsinghua University, Beijing 100084, China; (Z.L.); (L.X.)
| | - Helen Gurgel
- Department of Geography, University of Brasilia (UnB), Brasilia CEP 70910-900, Brazil;
- International Joint Laboratory Sentinela, FIOCRUZ, UnB, IRD, Rio de Janeiro RJ-21040-900, Brazil;
| | - Nadine Dessay
- International Joint Laboratory Sentinela, FIOCRUZ, UnB, IRD, Rio de Janeiro RJ-21040-900, Brazil;
- IRD, UM, UR, UG, UA, UMR ESPACE-DEV, 34090 Montpellier, France
| | - Luojia Hu
- Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China;
| | - Lei Xu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System, Science, Tsinghua University, Beijing 100084, China; (Z.L.); (L.X.)
| | - Peng Gong
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System, Science, Tsinghua University, Beijing 100084, China; (Z.L.); (L.X.)
- Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University, Beijing 100084, China
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Tran A, Mangeas M, Demarchi M, Roux E, Degenne P, Haramboure M, Le Goff G, Damiens D, Gouagna LC, Herbreteau V, Dehecq JS. Complementarity of empirical and process-based approaches to modelling mosquito population dynamics with Aedes albopictus as an example-Application to the development of an operational mapping tool of vector populations. PLoS One 2020; 15:e0227407. [PMID: 31951601 PMCID: PMC6968851 DOI: 10.1371/journal.pone.0227407] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 12/18/2019] [Indexed: 01/05/2023] Open
Abstract
Mosquitoes are responsible for the transmission of major pathogens worldwide. Modelling their population dynamics and mapping their distribution can contribute effectively to disease surveillance and control systems. Two main approaches are classically used to understand and predict mosquito abundance in space and time, namely empirical (or statistical) and process-based models. In this work, we used both approaches to model the population dynamics in Reunion Island of the 'Tiger mosquito', Aedes albopictus, a vector of dengue and chikungunya viruses, using rainfall and temperature data. We aimed to i) evaluate and compare the two types of models, and ii) develop an operational tool that could be used by public health authorities and vector control services. Our results showed that Ae. albopictus dynamics in Reunion Island are driven by both rainfall and temperature with a non-linear relationship. The predictions of the two approaches were consistent with the observed abundances of Ae. albopictus aquatic stages. An operational tool with a user-friendly interface was developed, allowing the creation of maps of Ae. albopictus densities over the whole territory using meteorological data collected from a network of weather stations. It is now routinely used by the services in charge of vector control in Reunion Island.
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Affiliation(s)
- Annelise Tran
- CIRAD, UMR TETIS, Sainte-Clotilde, Reunion, France
- TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France
- CIRAD, UMR ASTRE, Sainte-Clotilde, Reunion, France
- ASTRE, Univ Montpellier, CIRAD, INRAE, Montpellier, France
- * E-mail:
| | | | | | | | - Pascal Degenne
- CIRAD, UMR TETIS, Sainte-Clotilde, Reunion, France
- TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France
| | - Marion Haramboure
- CIRAD, UMR TETIS, Sainte-Clotilde, Reunion, France
- TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France
- CIRAD, UMR ASTRE, Sainte-Clotilde, Reunion, France
- ASTRE, Univ Montpellier, CIRAD, INRAE, Montpellier, France
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Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review. REMOTE SENSING 2019. [DOI: 10.3390/rs11161862] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Earth Observation (EO) data can be leveraged to estimate environmental variables that influence the transmission cycle of the pathogens that lead to mosquito-borne diseases (MBDs). The aim of this scoping review is to examine the state-of-the-art and identify knowledge gaps on the latest methods that used satellite EO data in their epidemiological models focusing on malaria, dengue and West Nile Virus (WNV). In total, 43 scientific papers met the inclusion criteria and were considered in this review. Researchers have examined a wide variety of methodologies ranging from statistical to machine learning algorithms. A number of studies used models and EO data that seemed promising and claimed to be easily replicated in different geographic contexts, enabling the realization of systems on regional and national scales. The need has emerged to leverage furthermore new powerful modeling approaches, like artificial intelligence and ensemble modeling and explore new and enhanced EO sensors towards the analysis of big satellite data, in order to develop accurate epidemiological models and contribute to the reduction of the burden of MBDs.
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Bartlow AW, Manore C, Xu C, Kaufeld KA, Del Valle S, Ziemann A, Fairchild G, Fair JM. Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment. Vet Sci 2019; 6:E40. [PMID: 31064099 PMCID: PMC6632117 DOI: 10.3390/vetsci6020040] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Revised: 04/12/2019] [Accepted: 04/29/2019] [Indexed: 12/20/2022] Open
Abstract
Infectious diseases are changing due to the environment and altered interactions among hosts, reservoirs, vectors, and pathogens. This is particularly true for zoonotic diseases that infect humans, agricultural animals, and wildlife. Within the subset of zoonoses, vector-borne pathogens are changing more rapidly with climate change, and have a complex epidemiology, which may allow them to take advantage of a changing environment. Most mosquito-borne infectious diseases are transmitted by mosquitoes in three genera: Aedes, Anopheles, and Culex, and the expansion of these genera is well documented. There is an urgent need to study vector-borne diseases in response to climate change and to produce a generalizable approach capable of generating risk maps and forecasting outbreaks. Here, we provide a strategy for coupling climate and epidemiological models for zoonotic infectious diseases. We discuss the complexity and challenges of data and model fusion, baseline requirements for data, and animal and human population movement. Disease forecasting needs significant investment to build the infrastructure necessary to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions. These investments can contribute to building a modeling community around the globe to support public health officials so as to reduce disease burden through forecasts with quantified uncertainty.
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Affiliation(s)
- Andrew W Bartlow
- Los Alamos National Laboratory, Biosecurity and Public Health, Los Alamos, NM 87545, USA.
| | - Carrie Manore
- Los Alamos National Laboratory, Information Systems and Modeling, Los Alamos, NM 87545, USA.
| | - Chonggang Xu
- Los Alamos National Laboratory, Earth Systems Observations, Los Alamos, NM 87545, USA.
| | - Kimberly A Kaufeld
- Los Alamos National Laboratory, Statistical Sciences, Los Alamos, NM 87545, USA.
| | - Sara Del Valle
- Los Alamos National Laboratory, Information Systems and Modeling, Los Alamos, NM 87545, USA.
| | - Amanda Ziemann
- Los Alamos National Laboratory, Space Data Science and Systems, Los Alamos, NM 87545, USA.
| | - Geoffrey Fairchild
- Los Alamos National Laboratory, Information Systems and Modeling, Los Alamos, NM 87545, USA.
| | - Jeanne M Fair
- Los Alamos National Laboratory, Biosecurity and Public Health, Los Alamos, NM 87545, USA.
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Ceccato P, Ramirez B, Manyangadze T, Gwakisa P, Thomson MC. Data and tools to integrate climate and environmental information into public health. Infect Dis Poverty 2018; 7:126. [PMID: 30541601 PMCID: PMC6292116 DOI: 10.1186/s40249-018-0501-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 11/13/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND During the last 30 years, the development of geographical information systems and satellites for Earth observation has made important progress in the monitoring of the weather, climate, environmental and anthropogenic factors that influence the reduction or the reemergence of vector-borne diseases. Analyses resulting from the combination of geographical information systems (GIS) and remote sensing have improved knowledge of climatic, environmental, and biodiversity factors influencing vector-borne diseases (VBDs) such as malaria, visceral leishmaniasis, dengue, Rift Valley fever, schistosomiasis, Chagas disease and leptospirosis. These knowledge and products developed using remotely sensed data helped and continue to help decision makers to better allocate limited resources in the fight against VBDs. MAIN BODY Because VBDs are linked to climate and environment, we present here our experience during the last four years working with the projects under the, World Health Organization (WHO)/ The Special Programme for Research and Training in Tropical Diseases (TDR)-International Development Research Centre (IDRC) Research Initiative on VBDs and Climate Change to integrate climate and environmental information into research and decision-making processes. The following sections present the methodology we have developed, which uses remote sensing to monitor climate variability, environmental conditions, and their impacts on the dynamics of infectious diseases. We then show how remotely sensed data can be accessed and evaluated and how they can be integrated into research and decision-making processes for mapping risks, and creating Early Warning Systems, using two examples from the WHO TDR projects based on schistosomiasis analysis in South Africa and Trypanosomiasis in Tanzania. CONCLUSIONS The tools presented in this article have been successfully used by the projects under the WHO/TDR-IDRC Research Initiative on VBDs and Climate Change. Combined with capacity building, they are an important piece of work which can significantly contribute to the goals of WHO Global Vector Control Response and to the Sustainable Development Goals especially those on health and climate action.
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Affiliation(s)
- Pietro Ceccato
- The International Research Institute for Climate and Society, The Earth Institute, Columbia University, 61 Route 9W, Lamont-Doherty, Palisades, NY 10964 USA
| | - Bernadette Ramirez
- The Special Programme for Research and Training in Tropical Diseases (TDR), World Health Organization, Geneva, Switzerland
| | - Tawanda Manyangadze
- School of Nursing and Public Health, Department of Public Health, College of health Sciences, University of KwaZulu-Natal, P. Bag, 1020 Bindura, Zimbabwe
- South Africa and Geography Department, Faculty of Sciences, Bindura University of Science Education, P. Bag, 1020 Bindura, Zimbabwe
| | - Paul Gwakisa
- Nelson Mandela African Institution of Science and Technology, School of Life Sciences and Bioengineering, P.O. Box 447, Arusha, Tanzania
- Present address: Sokoine University of Agriculture, P.O. Box 3019, Morogoro, Tanzania
| | - Madeleine C. Thomson
- The International Research Institute for Climate and Society, The Earth Institute, Columbia University, 61 Route 9W, Lamont-Doherty, Palisades, NY 10964 USA
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Remote Sensing and Geospatial Technologies in Public Health. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2018. [DOI: 10.3390/ijgi7080303] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Dietrich D, Dekova R, Davy S, Fahrni G, Geissbühler A. Applications of Space Technologies to Global Health: Scoping Review. J Med Internet Res 2018; 20:e230. [PMID: 29950289 PMCID: PMC6041558 DOI: 10.2196/jmir.9458] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 03/21/2018] [Accepted: 04/22/2018] [Indexed: 12/27/2022] Open
Abstract
Background Space technology has an impact on many domains of activity on earth, including in the field of global health. With the recent adoption of the United Nations’ Sustainable Development Goals that highlight the need for strengthening partnerships in different domains, it is useful to better characterize the relationship between space technology and global health. Objective The aim of this study was to identify the applications of space technologies to global health, the key stakeholders in the field, as well as gaps and challenges. Methods We used a scoping review methodology, including a literature review and the involvement of stakeholders, via a brief self-administered, open-response questionnaire. A distinct search on several search engines was conducted for each of the four key technological domains that were previously identified by the UN Office for Outer Space Affairs’ Expert Group on Space and Global Health (Domain A: remote sensing; Domain B: global navigation satellite systems; Domain C: satellite communication; and Domain D: human space flight). Themes in which space technologies are of benefit to global health were extracted. Key stakeholders, as well as gaps, challenges, and perspectives were identified. Results A total of 222 sources were included for Domain A, 82 sources for Domain B, 144 sources for Domain C, and 31 sources for Domain D. A total of 3 questionnaires out of 16 sent were answered. Global navigation satellite systems and geographic information systems are used for the study and forecasting of communicable and noncommunicable diseases; satellite communication and global navigation satellite systems for disaster response; satellite communication for telemedicine and tele-education; and global navigation satellite systems for autonomy improvement, access to health care, as well as for safe and efficient transportation. Various health research and technologies developed for inhabited space flights have been adapted for terrestrial use. Conclusions Although numerous examples of space technology applications to global health exist, improved awareness, training, and collaboration of the research community is needed.
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Affiliation(s)
- Damien Dietrich
- Hopitaux Universitaires de Genève, eHealth and Telemedicine Division, Geneva, Switzerland
| | - Ralitza Dekova
- Hopitaux Universitaires de Genève, eHealth and Telemedicine Division, Geneva, Switzerland
| | - Stephan Davy
- Hopitaux Universitaires de Genève, eHealth and Telemedicine Division, Geneva, Switzerland
| | - Guillaume Fahrni
- Hopitaux Universitaires de Genève, eHealth and Telemedicine Division, Geneva, Switzerland
| | - Antoine Geissbühler
- Hopitaux Universitaires de Genève, eHealth and Telemedicine Division, Geneva, Switzerland
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Misslin R, Daudé É. An environmental suitability index based on the ecological constraints ofAedes aegypti, vector of dengue. ACTA ACUST UNITED AC 2018. [DOI: 10.3166/rig.2017.00044] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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12
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Niche Modeling of Dengue Fever Using Remotely Sensed Environmental Factors and Boosted Regression Trees. REMOTE SENSING 2017. [DOI: 10.3390/rs9040328] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Pitch and Flat Roof Factors’ Association with Spatiotemporal Patterns of Dengue Disease Analysed Using Pan-Sharpened Worldview 2 Imagery. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2015. [DOI: 10.3390/ijgi4042586] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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