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Longo-Pendy NM, Sevidzem SL, Makanga BK, Ndotit-Manguiengha S, Boussougou-Sambe ST, Obame Ondo Kutomy P, Obame-Nkoghe J, Nkoghe-Nkoghe LC, Ngossanga B, Mvoubou FK, Koumba CRZ, Adegnika AA, Razack AS, Mavoungou JF, Mintsa-Nguema R. Assessment of environmental and spatial factors influencing the establishment of Anopheles gambiae larval habitats in the malaria endemic province of Woleu-Ntem, northern Gabon. Malar J 2024; 23:158. [PMID: 38773512 PMCID: PMC11106858 DOI: 10.1186/s12936-024-04980-5] [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: 01/02/2024] [Accepted: 05/10/2024] [Indexed: 05/23/2024] Open
Abstract
BACKGROUND This study aimed to assess the spatial distribution of Anopheles mosquito larval habitats and the environmental factors associated with them, as a prerequisite for the implementation of larviciding. METHODS The study was conducted in December 2021, during the transition period between the end of the short rainy season (September-November) and the short dry season (December-February). Physical, biological, and land cover data were integrated with entomological observations to collect Anopheles larvae in three major towns: Mitzic, Oyem, and Bitam, using the "dipping" method during the transition from rainy to dry season. The collected larvae were then reared in a field laboratory established for the study period. After the Anopheles mosquitoes had emerged, their species were identified using appropriate morphological taxonomic keys. To determine the influence of environmental factors on the breeding of Anopheles mosquitoes, multiple-factor analysis (MFA) and a binomial generalized linear model were used. RESULTS According to the study, only 33.1% out of the 284 larval habitats examined were found to be positive for Anopheles larvae, which were primarily identified as belonging to the Anopheles gambiae complex. The findings of the research suggested that the presence of An. gambiae complex larvae in larval habitats was associated with various significant factors such as higher urbanization, the size and type of the larval habitats (pools and puddles), co-occurrence with Culex and Aedes larvae, hot spots in ambient temperature, moderate rainfall, and land use patterns. CONCLUSIONS The results of this research mark the initiation of a focused vector control plan that aims to eradicate or lessen the larval habitats of An. gambiae mosquitoes in Gabon's Woleu Ntem province. This approach deals with the root causes of malaria transmission through larvae and is consistent with the World Health Organization's (WHO) worldwide objective to decrease malaria prevalence in regions where it is endemic.
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Affiliation(s)
- Neil-Michel Longo-Pendy
- Unité de Recherche en Ecologie de la Santé (URES), Centre Interdisciplinaire de Recherches Médicales de Franceville (CIRMF), Franceville, Gabon.
| | - Silas Lendzele Sevidzem
- Laboratoire d'Ecologie des Maladies Transmissibles (LEMAT), Université Libreville Nord (ULN), Libreville, Gabon
| | | | - Saturnin Ndotit-Manguiengha
- Institut de Recherche en Écologie Tropicale (IRET), Libreville, Gabon
- Agence Gabonaise d'Etudes et d'Observations Spatiales (AGEOS), Libreville, Gabon
| | | | - Piazzy Obame Ondo Kutomy
- Programme National de Lutte Contre Le Paludisme (PNLP), Libreville, Gabon
- Universite Cheikh Anta Diop de Dakar (UCAD), Dakar, Sénégal
| | - Judicaël Obame-Nkoghe
- Unité de Recherche en Ecologie de la Santé (URES), Centre Interdisciplinaire de Recherches Médicales de Franceville (CIRMF), Franceville, Gabon
- Université des Sciences et Techniques de Masuku (USTM), Franceville, Gabon
- Department of Zoology and Entomology, Faculty of Natural and Agricultural Sciences, University of the Free State, Phuthaditjhaba, Republic of South Africa
| | - Lynda-Chancelya Nkoghe-Nkoghe
- Unité de Recherche en Ecologie de la Santé (URES), Centre Interdisciplinaire de Recherches Médicales de Franceville (CIRMF), Franceville, Gabon
| | | | | | | | - Ayôla Akim Adegnika
- Centre de Recherches Médicales de Lambaréné, Lambaréné, Gabon
- Institut Für Tropenmedizin, Eberhard Karls Universität, Tübingen, Germany
- Fondation Pour la Recherche Scientifique (FORS), P.O. Box 88, Cotonou, Benin
- German Center for Infection Research (DZIF), Partner site Tübingen, Tübingen, Germany
| | | | | | - Rodrigue Mintsa-Nguema
- Laboratoire d'Ecologie des Maladies Transmissibles (LEMAT), Université Libreville Nord (ULN), Libreville, Gabon
- Institut de Recherche en Écologie Tropicale (IRET), Libreville, Gabon
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Trujillano F, Jimenez G, Manrique E, Kahamba NF, Okumu F, Apollinaire N, Carrasco-Escobar G, Barrett B, Fornace K. Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments. Int J Health Geogr 2024; 23:13. [PMID: 38764024 PMCID: PMC11102859 DOI: 10.1186/s12942-024-00371-w] [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: 02/29/2024] [Accepted: 04/29/2024] [Indexed: 05/21/2024] Open
Abstract
BACKGROUND In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies. RESULTS We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance. CONCLUSIONS Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.
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Affiliation(s)
- Fedra Trujillano
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow, Scotland, UK.
- School of Geographical & Earth Sciences, University of Glasgow, Glasgow, Scotland, UK.
| | - Gabriel Jimenez
- Sorbonne Université, Institute du Cerveau - ICM, CNRS, Inria, AP-HP, Paris, Inserm, France
| | - Edgar Manrique
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow, Scotland, UK
| | - Najat F Kahamba
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow, Scotland, UK
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, P. O. Box 53, Ifakara, Tanzania
| | - Fredros Okumu
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow, Scotland, UK
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, P. O. Box 53, Ifakara, Tanzania
| | - Nombre Apollinaire
- Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso
| | - Gabriel Carrasco-Escobar
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Brian Barrett
- School of Geographical & Earth Sciences, University of Glasgow, Glasgow, Scotland, UK
| | - Kimberly Fornace
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow, Scotland, UK
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
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Sa-Ngamuang C, Lawpoolsri S, Su Yin M, Barkowsky T, Cui L, Prachumsri J, Haddawy P. Assessment of malaria risk in Southeast Asia: a systematic review. Malar J 2023; 22:339. [PMID: 37940923 PMCID: PMC10631000 DOI: 10.1186/s12936-023-04772-3] [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: 02/27/2023] [Accepted: 10/26/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND Several countries in Southeast Asia are nearing malaria elimination, yet eradication remains elusive. This is largely due to the challenge of focusing elimination efforts, an area where risk prediction can play an essential supporting role. Despite its importance, there is no standard numerical method to quantify the risk of malaria infection. Thus, there is a need for a consolidated view of existing definitions of risk and factors considered in assessing risk to analyse the merits of risk prediction models. This systematic review examines studies of the risk of malaria in Southeast Asia with regard to their suitability in addressing the challenges of malaria elimination in low transmission areas. METHODS A search of four electronic databases over 2010-2020 retrieved 1297 articles, of which 25 met the inclusion and exclusion criteria. In each study, examined factors included the definition of the risk and indicators of malaria transmission used, the environmental and climatic factors associated with the risk, the statistical models used, the spatial and temporal granularity, and how the relationship between environment, climate, and risk is quantified. RESULTS This review found variation in the definition of risk used, as well as the environmental and climatic factors in the reviewed articles. GLM was widely adopted as the analysis technique relating environmental and climatic factors to malaria risk. Most of the studies were carried out in either a cross-sectional design or case-control studies, and most utilized the odds ratio to report the relationship between exposure to risk and malaria prevalence. CONCLUSIONS Adopting a standardized definition of malaria risk would help in comparing and sharing results, as would a clear description of the definition and method of collection of the environmental and climatic variables used. Further issues that need to be more fully addressed include detection of asymptomatic cases and considerations of human mobility. Many of the findings of this study are applicable to other low-transmission settings and could serve as a guideline for further studies of malaria in other regions.
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Affiliation(s)
- Chaitawat Sa-Ngamuang
- Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand
| | - Saranath Lawpoolsri
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Myat Su Yin
- Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand
| | - Thomas Barkowsky
- Bremen Spatial Cognition Center (BSCC), University of Bremen, Bremen, Germany
| | - Liwang Cui
- Division of Infectious Diseases and International Medicine, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, USA
| | - Jetsumon Prachumsri
- Mahidol Vivax Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Peter Haddawy
- Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand.
- Bremen Spatial Cognition Center (BSCC), University of Bremen, Bremen, Germany.
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Trujillano F, Garay GJ, Alatrista-Salas H, Byrne I, Nunez-del-Prado M, Chan K, Manrique E, Johnson E, Apollinaire N, Kouame Kouakou P, Oumbouke WA, Tiono AB, Guelbeogo MW, Lines J, Carrasco-Escobar G, Fornace K. Mapping Malaria Vector Habitats in West Africa: Drone Imagery and Deep Learning Analysis for Targeted Vector Surveillance. REMOTE SENSING 2023; 15:2775. [PMID: 37324796 PMCID: PMC7614662 DOI: 10.3390/rs15112775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Disease control programs are needed to identify the breeding sites of mosquitoes, which transmit malaria and other diseases, in order to target interventions and identify environmental risk factors. The increasing availability of very-high-resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, drone images from two malaria-endemic regions in Burkina Faso and Côte d'Ivoire were assembled and labeled using open-source tools. We developed and applied a workflow using region-of-interest-based and deep learning methods to identify land cover types associated with vector breeding sites from very-high-resolution natural color imagery. Analysis methods were assessed using cross-validation and achieved maximum Dice coefficients of 0.68 and 0.75 for vegetated and non-vegetated water bodies, respectively. This classifier consistently identified the presence of other land cover types associated with the breeding sites, obtaining Dice coefficients of 0.88 for tillage and crops, 0.87 for buildings and 0.71 for roads. This study establishes a framework for developing deep learning approaches to identify vector breeding sites and highlights the need to evaluate how results will be used by control programs.
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Affiliation(s)
- Fedra Trujillano
- Health Innovation Laboratory, Institute of Tropical Medicine “Alexander von Humboldt”, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow G12 8QQ, UK
| | - Gabriel Jimenez Garay
- Health Innovation Laboratory, Institute of Tropical Medicine “Alexander von Humboldt”, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
- Department of Engineering and Computer Science, Faculty of Science and Engineering, Sorbonne University, 75005 Paris, France
| | - Hugo Alatrista-Salas
- Escuela de Posgrado Newman, Tacna 23001, Peru
- Science and Engineering School, Pontificia Universidad Católica del Perú (PUCP), Lima 15088, Peru
| | - Isabel Byrne
- Department of Infection Biology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Miguel Nunez-del-Prado
- Peru Research, Development and Innovation Center (Peru IDI), Lima 15076, Peru
- The World Bank, Washington, DC 20433, USA
| | - Kallista Chan
- Department of Infection Biology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Edgar Manrique
- Health Innovation Laboratory, Institute of Tropical Medicine “Alexander von Humboldt”, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
| | - Emilia Johnson
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow G12 8QQ, UK
| | - Nombre Apollinaire
- Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou 01 BP 2208, Burkina Faso
| | | | - Welbeck A. Oumbouke
- Department of Infection Biology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
- Innovative Vector Control Consortium, Liverpool School of Tropical Medicine, London L3 5QA, UK
| | - Alfred B. Tiono
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Moussa W. Guelbeogo
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Jo Lines
- Department of Infection Biology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Gabriel Carrasco-Escobar
- Health Innovation Laboratory, Institute of Tropical Medicine “Alexander von Humboldt”, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093, USA
| | - Kimberly Fornace
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow G12 8QQ, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 119077, Singapore
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da Silva CFA, Dos Santos AM, do Bonfim CV, da Silva Melo JL, Sato SS, Barreto EP. Deforestation impacts on dengue incidence in the Brazilian Amazon. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:593. [PMID: 37079116 DOI: 10.1007/s10661-023-11174-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 03/28/2023] [Indexed: 05/03/2023]
Abstract
The objective of the study is to perform the spatial analysis of the conditioning factors for the increase in the incidence rate of dengue cases in municipalities located in the Amazon biome, in the period from 2016 to 2021. Three statistical approaches were applied: Moran's index, ordinary least squares regression, and geographically weighted regression. The results revealed that the incidence rates of dengue cases cluster in two areas, both located in the south of the Amazon biome, which is associated with the Arc of Deforestation. The variable deforestation influences the increase in dengue incidence rates revealed by the OLS and GWR model. The adjusted R2 of the GWR model was 0.70, that is, the model explains about 70% of the total case variation of dengue incidence rates in the Amazon biome. The results of the study evidence the need for public policies aimed at the prevention and combat of deforestation in the Amazon region.
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Affiliation(s)
- Carlos Fabricio Assunção da Silva
- Department of Civil and Environmental Engineering, Center of Technologies and Geosciences, Federal University of Pernambuco, UFPE, Avenida da Engenharia, S/N - Cidade Universitária, 50670-420, Recife, Pernambuco, Brazil.
| | - Alex Mota Dos Santos
- Center of Agroforestry Sciences and Technologies, Federal University of Southern Bahia, Rodovia Ilhéus/Itabuna, Km 22, 45604-811, Itabuna, Brazil
| | | | - José Lucas da Silva Melo
- Department of Statistics, Center of Nature and Exact Sciences, Federal University of Pernambuco, UFPE, Avenida Professor Moraes Rego, Cidade Universitária, Recife, 123550670-901, Pernambuco, Brazil
| | - Simone Sayuri Sato
- Department of Cartographic Engineering, Center of Technologies and Geosciences, Federal University of Pernambuco, UFPE, Acadêmico Hélio Ramos, Cidade Universitária, S/N, 50740-530, Recife, Avenida, Brazil
| | - Eduardo Paes Barreto
- Master in Environmental Technology, Pernambuco Institute of Technology, ITEP, Avenida Professor Luís Freire, 700 - Cidade Universitária, Recife - PE, 50740-540, Recife, Pernambuco, Brazil
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Vanhuysse S, Diédhiou SM, Grippa T, Georganos S, Konaté L, Niang EHA, Wolff E. Fine-scale mapping of urban malaria exposure under data scarcity: an approach centred on vector ecology. Malar J 2023; 22:113. [PMID: 37009873 PMCID: PMC10069057 DOI: 10.1186/s12936-023-04527-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 03/08/2023] [Indexed: 04/04/2023] Open
Abstract
BACKGROUND Although malaria transmission has experienced an overall decline in sub-Saharan Africa, urban malaria is now considered an emerging health issue due to rapid and uncontrolled urbanization and the adaptation of vectors to urban environments. Fine-scale hazard and exposure maps are required to support evidence-based policies and targeted interventions, but data-driven predictive spatial modelling is hindered by gaps in epidemiological and entomological data. A knowledge-based geospatial framework is proposed for mapping the heterogeneity of urban malaria hazard and exposure under data scarcity. It builds on proven geospatial methods, implements open-source algorithms, and relies heavily on vector ecology knowledge and the involvement of local experts. METHODS A workflow for producing fine-scale maps was systematized, and most processing steps were automated. The method was evaluated through its application to the metropolitan area of Dakar, Senegal, where urban transmission has long been confirmed. Urban malaria exposure was defined as the contact risk between adult Anopheles vectors (the hazard) and urban population and accounted for socioeconomic vulnerability by including the dimension of urban deprivation that is reflected in the morphology of the built-up fabric. Larval habitat suitability was mapped through a deductive geospatial approach involving the participation of experts with a strong background in vector ecology and validated with existing geolocated entomological data. Adult vector habitat suitability was derived through a similar process, based on dispersal from suitable breeding site locations. The resulting hazard map was combined with a population density map to generate a gridded urban malaria exposure map at a spatial resolution of 100 m. RESULTS The identification of key criteria influencing vector habitat suitability, their translation into geospatial layers, and the assessment of their relative importance are major outcomes of the study that can serve as a basis for replication in other sub-Saharan African cities. Quantitative validation of the larval habitat suitability map demonstrates the reliable performance of the deductive approach, and the added value of including local vector ecology experts in the process. The patterns displayed in the hazard and exposure maps reflect the high degree of heterogeneity that exists throughout the city of Dakar and its suburbs, due not only to the influence of environmental factors, but also to urban deprivation. CONCLUSIONS This study is an effort to bring geospatial research output closer to effective support tools for local stakeholders and decision makers. Its major contributions are the identification of a broad set of criteria related to vector ecology and the systematization of the workflow for producing fine-scale maps. In a context of epidemiological and entomological data scarcity, vector ecology knowledge is key for mapping urban malaria exposure. An application of the framework to Dakar showed its potential in this regard. Fine-grained heterogeneity was revealed by the output maps, and besides the influence of environmental factors, the strong links between urban malaria and deprivation were also highlighted.
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Affiliation(s)
- Sabine Vanhuysse
- Department of Geosciences, Environment and Society, Université Libre de Bruxelles (ULB), 1050, Brussels, Belgium.
| | - Seynabou Mocote Diédhiou
- Laboratoire d'Ecologie Vectorielle et Parasitaire, Université Cheikh-Anta-Diop de Dakar, Dakar, Sénégal
| | - Taïs Grippa
- Department of Geosciences, Environment and Society, Université Libre de Bruxelles (ULB), 1050, Brussels, Belgium
| | - Stefanos Georganos
- Geomatics, Department of Environmental and Life Sciences, Faculty of Health, Science and Technology, Karlstad University, Karlstad, Sweden
| | - Lassana Konaté
- Laboratoire d'Ecologie Vectorielle et Parasitaire, Université Cheikh-Anta-Diop de Dakar, Dakar, Sénégal
| | - El Hadji Amadou Niang
- Laboratoire d'Ecologie Vectorielle et Parasitaire, Université Cheikh-Anta-Diop de Dakar, Dakar, Sénégal
| | - Eléonore Wolff
- Department of Geosciences, Environment and Society, Université Libre de Bruxelles (ULB), 1050, Brussels, Belgium
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Hartinger SM, Yglesias-González M, Blanco-Villafuerte L, Palmeiro-Silva YK, Lescano AG, Stewart-Ibarra A, Rojas-Rueda D, Melo O, Takahashi B, Buss D, Callaghan M, Chesini F, Flores EC, Gil Posse C, Gouveia N, Jankin S, Miranda-Chacon Z, Mohajeri N, Helo J, Ortiz L, Pantoja C, Salas MF, Santiago R, Sergeeva M, Souza de Camargo T, Valdés-Velásquez A, Walawender M, Romanello M. The 2022 South America report of The Lancet Countdown on health and climate change: trust the science. Now that we know, we must act. LANCET REGIONAL HEALTH. AMERICAS 2023; 20:100470. [PMID: 37125022 PMCID: PMC10122119 DOI: 10.1016/j.lana.2023.100470] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/13/2023] [Accepted: 02/28/2023] [Indexed: 05/02/2023]
Affiliation(s)
- Stella M. Hartinger
- Centro Latino Americano de Excelencia en Cambio Climático y Salud, Universidad Peruana Cayetano Heredia, Lima, Peru
- Corresponding author. Av. Honorio Delgado 430, San Martín de Porres, 15102, Lima, Peru.
| | - Marisol Yglesias-González
- Centro Latino Americano de Excelencia en Cambio Climático y Salud, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Luciana Blanco-Villafuerte
- Centro Latino Americano de Excelencia en Cambio Climático y Salud, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Yasna K. Palmeiro-Silva
- Pontificia Universidad Católica de Chile, Santiago, Chile
- University College London, London, UK
| | - Andres G. Lescano
- Centro Latino Americano de Excelencia en Cambio Climático y Salud, Universidad Peruana Cayetano Heredia, Lima, Peru
| | | | | | - Oscar Melo
- Centro Interdisciplinario de Cambio Global, Pontificia Universidad Católica de Chile, Santiago, Chile
| | | | - Daniel Buss
- Pan American Health Organization, Washington, DC, USA
| | - Max Callaghan
- Mercator Research Institute on Global Commons and Climate Change, Berlin, Germany
| | | | - Elaine C. Flores
- Centro Latino Americano de Excelencia en Cambio Climático y Salud, Universidad Peruana Cayetano Heredia, Lima, Peru
- Centre on Climate Change and Planetary Health, LSHTM, London, UK
| | | | | | | | | | | | | | | | - Chrissie Pantoja
- Duke University, Durham, NC, USA
- Universidad del Pacífico, Lima, Peru
| | | | - Raquel Santiago
- Universidade de São Paulo, São Paulo, Brazil
- Universidade Federal de Goiás, Goiás, Brazil
| | | | | | - Armando Valdés-Velásquez
- Centro Latino Americano de Excelencia en Cambio Climático y Salud, Universidad Peruana Cayetano Heredia, Lima, Peru
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McMahon A, França CMB, Wimberly MC. Comparing Satellite and Ground-Based Measurements of Environmental Suitability for Vector Mosquitoes in an Urban Landscape. JOURNAL OF MEDICAL ENTOMOLOGY 2022; 59:1936-1946. [PMID: 36189969 PMCID: PMC9667728 DOI: 10.1093/jme/tjac145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Indexed: 06/16/2023]
Abstract
Exposure to mosquito-borne diseases is influenced by landscape patterns and microclimates associated with land cover. These influences can be particularly strong in heterogeneous urban landscapes where human populations are concentrated. We investigated how land cover and climate influenced abundances of Ae. albopictus (Skuse) (Diptera: Culicidae) and Cx. quinquefasciatus (Say) (Diptera: Culicidae) in Norman, Oklahoma (United States). From June-October 2019 and May-October 2020 we sampled mosquitoes along an urban-rural gradient using CO2 baited BG Sentinel traps. Microclimate sensors at these sites measured temperature and humidity. We mapped environmental variables using satellite images from Landsat, Sentinel-2, and VIIRS, and the CHIRPS rainfall dataset. We also obtained meteorological data from the closest weather station. We compared statistical models of mosquito abundance based on microclimate, satellite, weather station, and land cover data. Mosquitoes were more abundant on trap days with higher temperature and relative humidity. Rainfall 2 wk prior to the trap day negatively affected mosquito abundances. Impervious surface cover was positively associated with Cx. quinquefasciatus and tree cover was negatively associated with Ae. albopictus. Among the data sources, models based on satellite variables and land cover data had the best fits for Ae. albopictus (R2 = 0.7) and Cx. quinquefasciatus (R2 = 0.51). Models based on weather station or microclimate data had weaker fits (R2 between 0.09 and 0.17) but were improved by adding land cover variables (R2 between 0.44 and 0.61). These results demonstrate the potential for using satellite remote sensing for mosquito habitat analyses in urban areas.
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Affiliation(s)
- Andrea McMahon
- Department of Geography and Environmental Sustainability, University of Oklahoma, Norman OK, USA
| | - Caio M B França
- Department of Biology, Southern Nazarene University, Bethany, OK, USA
- Quetzal Education and Research Center, Southern Nazarene University, San Gerardo de Dota, Costa Rica
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Bell GJ, Goel V, Essone P, Dosoo D, Adu B, Mensah BA, Gyaase S, Wiru K, Mougeni F, Osei M, Minsoko P, Sinai C, Niaré K, Juliano JJ, Hudgens M, Ghansah A, Kamthunzi P, Mvalo T, Agnandji ST, Bailey JA, Asante KP, Emch M. Malaria Transmission Intensity Likely Modifies RTS, S/AS01 Efficacy Due to a Rebound Effect in Ghana, Malawi, and Gabon. J Infect Dis 2022; 226:1646-1656. [PMID: 35899811 PMCID: PMC10205900 DOI: 10.1093/infdis/jiac322] [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: 05/20/2022] [Accepted: 07/26/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND RTS,S/AS01 is the first malaria vaccine to be approved and recommended for widespread implementation by the World Health Organization (WHO). Trials reported lower vaccine efficacies in higher-incidence sites, potentially due to a "rebound" in malaria cases in vaccinated children. When naturally acquired protection in the control group rises and vaccine protection in the vaccinated wanes concurrently, malaria incidence can become greater in the vaccinated than in the control group, resulting in negative vaccine efficacies. METHODS Using data from the 2009-2014 phase III trial (NCT00866619) in Lilongwe, Malawi; Kintampo, Ghana; and Lambaréné, Gabon, we evaluate this hypothesis by estimating malaria incidence in each vaccine group over time and in varying transmission settings. After estimating transmission intensities using ecological variables, we fit models with 3-way interactions between vaccination, time, and transmission intensity. RESULTS Over time, incidence decreased in the control group and increased in the vaccine group. Three-dose efficacy in the lowest-transmission-intensity group (0.25 cases per person-year [CPPY]) decreased from 88.2% to 15.0% over 4.5 years, compared with 81.6% to -27.7% in the highest-transmission-intensity group (3 CPPY). CONCLUSIONS These findings suggest that interventions, including the fourth RTS,S dose, that protect vaccinated individuals during the potential rebound period should be implemented for high-transmission settings.
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Affiliation(s)
- Griffin J Bell
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Varun Goel
- Carolina Population Center, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Paulin Essone
- Centre de Recherches Médicales de Lambaréné, Lambaréné, Gabon
| | - David Dosoo
- Kintampo Health Research Centre, Kintampo, Ghana
| | - Bright Adu
- Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Ghana
| | | | | | - Kenneth Wiru
- Kintampo Health Research Centre, Kintampo, Ghana
| | - Fabrice Mougeni
- Centre de Recherches Médicales de Lambaréné, Lambaréné, Gabon
| | - Musah Osei
- Kintampo Health Research Centre, Kintampo, Ghana
| | - Pamela Minsoko
- Centre de Recherches Médicales de Lambaréné, Lambaréné, Gabon
| | - Cyrus Sinai
- Carolina Population Center, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Karamoko Niaré
- Department of Pathology and Laboratory Medicine, Brown University, Providence, Rhode Island, USA
| | - Jonathan J Juliano
- Division of Infectious Diseases, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Michael Hudgens
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Anita Ghansah
- Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Ghana
| | | | | | - Selidji Todagbe Agnandji
- Centre de Recherches Médicales de Lambaréné, Lambaréné, Gabon
- Institute of Tropical Medicine, University of Tübingen, Tübingen, Germany
| | - Jeffrey A Bailey
- Department of Pathology and Laboratory Medicine, Brown University, Providence, Rhode Island, USA
| | | | - Michael Emch
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
- Carolina Population Center, University of North Carolina, Chapel Hill, North Carolina, USA
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10
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Ifejika Speranza C, Akinyemi FO, Baratoux D, Benveniste J, Ceperley N, Driouech F, Helmschrot J. Enhancing the Uptake of Earth Observation Products and Services in Africa Through a Multi-level Transdisciplinary Approach. SURVEYS IN GEOPHYSICS 2022; 44:7-41. [PMID: 36032547 PMCID: PMC9398042 DOI: 10.1007/s10712-022-09724-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
UNLABELLED Africa stands to gain from Earth Observation (EO) science, products and applications. However, its use and application remain below potential on the continent. This article examines how EO can better serve the needs of African users. First, we argue that a successful uptake of EO services is conditional on understanding the African context and matching EO development and deployment to it. Using reference cases, we find that actors outside Africa drive most EO initiatives, whereas country-level expenditures on EO remain low. Recent developments, such as the African space policy and strategy, and initiatives in partnerships with Africa-based organisations to develop a community of practice on EO hold the potential to fill the identified gaps. The analysis indicates that most EO users are either government organisations or researchers, with very few cases involving other types of users. It is generally assumed that users at the local levels are educated and digitally literate, or that the transmission of EO-based knowledge is achieved by government officers and researchers. Although still very few, potentials are emerging for the private sector to deploy EO products and services such as crop or index-based insurance directly to farmers. These private initiatives have prospects for further developing indigenous EO capacity as envisioned in the African space policy and strategy. We then formulate recommendations for a transdisciplinary approach that integrates user contexts, attributes and needs to enhance the uptake of EO products and services in Africa. We conclude by proposing actions to close some of the identified gaps and seize emerging opportunities. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s10712-022-09724-1.
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Affiliation(s)
| | | | - David Baratoux
- Géosciences Environnement Toulouse, University of Toulouse, CNRS & IRD, 14 Av Édouard Belin, 31400 Toulouse, France
- UFR Sciences de la Terre et des Ressources Minières, Université Félix Houphouët-Boigny, Abidjan-Cocody, Côte d’Ivoire
| | - Jérôme Benveniste
- Directorate of Earth Observation Programmes, EO Science, Applications and Climate Department, European Space Agency (ESA-ESRIN), Largo Galileo Galilei, 1, 00044 Frascati, RM Italy
| | - Natalie Ceperley
- Institute of Geography, University of Bern, Hallerstrasse 12, 3012 Bern, Switzerland
| | - Fatima Driouech
- Mohammed VI Polytechnic University, IWRI, Ben Guerir, Morocco
| | - Jörg Helmschrot
- Stellenbosch University Water Institute, Stellenbosch University, Private Bag X1, Matieland, 7602 South Africa
- Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Wolfgang-Gaede-Str. 1, 76131 Karlsruhe, Germany
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11
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Cloud-based applications for accessing satellite Earth observations to support malaria early warning. Sci Data 2022; 9:208. [PMID: 35577816 PMCID: PMC9110363 DOI: 10.1038/s41597-022-01337-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 04/11/2022] [Indexed: 11/25/2022] Open
Abstract
Malaria epidemics can be triggered by fluctuations in temperature and precipitation that influence vector mosquitoes and the malaria parasite. Identifying and monitoring environmental risk factors can thus provide early warning of future outbreaks. Satellite Earth observations provide relevant measurements, but obtaining these data requires substantial expertise, computational resources, and internet bandwidth. To support malaria forecasting in Ethiopia, we developed software for Retrieving Environmental Analytics for Climate and Health (REACH). REACH is a cloud-based application for accessing data on land surface temperature, spectral indices, and precipitation using the Google Earth Engine (GEE) platform. REACH can be implemented using the GEE code editor and JavaScript API, as a standalone web app, or as package with the Python API. Users provide a date range and data for 852 districts in Ethiopia are automatically summarized and downloaded as tables. REACH was successfully used in Ethiopia to support a pilot malaria early warning project in the Amhara region. The software can be extended to new locations and modified to access other environmental datasets through GEE.
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12
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Needs Assessment of Southeastern United States Vector Control Agencies: Capacity Improvement Is Greatly Needed to Prevent the Next Vector-Borne Disease Outbreak. Trop Med Infect Dis 2022; 7:tropicalmed7050073. [PMID: 35622700 PMCID: PMC9143300 DOI: 10.3390/tropicalmed7050073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/06/2022] [Accepted: 05/10/2022] [Indexed: 02/01/2023] Open
Abstract
A national 2017 vector control capacity survey was conducted to assess the United States’ (U.S.’s) ability to prevent emerging vector-borne disease. Since that survey, the southeastern U.S. has experienced continued autochthonous exotic vector-borne disease transmission and establishment of invasive vector species. To understand the current gaps in control programs and establish a baseline to evaluate future vector control efforts for this vulnerable region, a focused needs assessment survey was conducted in early 2020. The southeastern U.S. region was targeted, as this region has a high probability of novel vector-borne disease introduction. Paper copies delivered in handwritten envelopes and electronic copies of the survey were delivered to 386 unique contacts, and 150 returned surveys were received, corresponding to a 39% response rate. Overall, the survey found vector control programs serving areas with over 100,000 residents and those affiliated with public health departments had more core capabilities compared to smaller programs and those not affiliated with public health departments. Furthermore, the majority of vector control programs in this region do not routinely monitor for pesticide resistance. Taken as a whole, these results suggest that the majority of the southeastern U.S. is vulnerable to vector-borne disease outbreaks. Results from this survey raise attention to the critical need of providing increased resources to bring all vector control programs to a competent level, ensuring that public health is protected from the threat of vector-borne disease.
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13
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van Rees CB, Hand BK, Carter SC, Bargeron C, Cline TJ, Daniel W, Ferrante JA, Gaddis K, Hunter ME, Jarnevich CS, McGeoch MA, Morisette JT, Neilson ME, Roy HE, Rozance MA, Sepulveda A, Wallace RD, Whited D, Wilcox T, Kimball JS, Luikart G. A framework to integrate innovations in invasion science for proactive management. Biol Rev Camb Philos Soc 2022; 97:1712-1735. [PMID: 35451197 DOI: 10.1111/brv.12859] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 11/26/2022]
Abstract
Invasive alien species (IAS) are a rising threat to biodiversity, national security, and regional economies, with impacts in the hundreds of billions of U.S. dollars annually. Proactive or predictive approaches guided by scientific knowledge are essential to keeping pace with growing impacts of invasions under climate change. Although the rapid development of diverse technologies and approaches has produced tools with the potential to greatly accelerate invasion research and management, innovation has far outpaced implementation and coordination. Technological and methodological syntheses are urgently needed to close the growing implementation gap and facilitate interdisciplinary collaboration and synergy among evolving disciplines. A broad review is necessary to demonstrate the utility and relevance of work in diverse fields to generate actionable science for the ongoing invasion crisis. Here, we review such advances in relevant fields including remote sensing, epidemiology, big data analytics, environmental DNA (eDNA) sampling, genomics, and others, and present a generalized framework for distilling existing and emerging data into products for proactive IAS research and management. This integrated workflow provides a pathway for scientists and practitioners in diverse disciplines to contribute to applied invasion biology in a coordinated, synergistic, and scalable manner.
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Affiliation(s)
- Charles B van Rees
- Flathead Lake Biological Station, University of Montana, 32125 Bio Station Lane, Polson, MT, 59860, U.S.A
| | - Brian K Hand
- Flathead Lake Biological Station, University of Montana, 32125 Bio Station Lane, Polson, MT, 59860, U.S.A
| | - Sean C Carter
- Numerical Terradynamic Simulation Group, University of Montana, ISB 415, Missoula, MT, 59812, U.S.A
| | - Chuck Bargeron
- Center for Invasive Species and Ecosystem Health, University of Georgia, 4601 Research Way, Tifton, GA, 31793, U.S.A
| | - Timothy J Cline
- U.S. Geological Survey, Northern Rocky Mountain Science Center, 2327 University Way STE 2, Bozeman MT 59717 & 320 Grinnel Drive, West Glacier, MT, 59936, U.S.A
| | - Wesley Daniel
- U.S. Geological Survey, Wetland and Aquatic Research Center, 7920 NW 71st Street, Gainesville, FL, 32653, U.S.A
| | - Jason A Ferrante
- U.S. Geological Survey, Wetland and Aquatic Research Center, 7920 NW 71st Street, Gainesville, FL, 32653, U.S.A
| | - Keith Gaddis
- NASA Biological Diversity and Ecological Forecasting Programs, 300 E St. SW, Washington, DC, 20546, U.S.A
| | - Margaret E Hunter
- U.S. Geological Survey, Wetland and Aquatic Research Center, 7920 NW 71st Street, Gainesville, FL, 32653, U.S.A
| | - Catherine S Jarnevich
- U.S. Geological Survey, Fort Collins Science Center, 2150 Centre Avenue Bldg C, Fort Collins, CO, 80526, U.S.A
| | - Melodie A McGeoch
- Department of Environment and Genetics, La Trobe University, Plenty Road & Kingsbury Drive, Bundoora, Victoria, 3086, Australia
| | - Jeffrey T Morisette
- U.S. Forest Service Rocky Mountain Research Station, 26 Fort Missoula Road, Missoula, 59804, MT, U.S.A
| | - Matthew E Neilson
- U.S. Geological Survey, Wetland and Aquatic Research Center, 7920 NW 71st Street, Gainesville, FL, 32653, U.S.A
| | - Helen E Roy
- UK Centre for Ecology & Hydrology, MacLean Building, Benson Lane, Crowmarsh Gifford, OX10 8BB, U.K
| | - Mary Ann Rozance
- Northwest Climate Adaptation Science Center, University of Washington, Box 355674, Seattle, WA, 98195, U.S.A
| | - Adam Sepulveda
- U.S. Forest Service Rocky Mountain Research Station, 26 Fort Missoula Road, Missoula, 59804, MT, U.S.A
| | - Rebekah D Wallace
- Center for Invasive Species and Ecosystem Health, University of Georgia, 4601 Research Way, Tifton, GA, 31793, U.S.A
| | - Diane Whited
- Flathead Lake Biological Station, University of Montana, 32125 Bio Station Lane, Polson, MT, 59860, U.S.A
| | - Taylor Wilcox
- U.S. Forest Service Rocky Mountain Research Station, 26 Fort Missoula Road, Missoula, 59804, MT, U.S.A
| | - John S Kimball
- Numerical Terradynamic Simulation Group, University of Montana, ISB 415, Missoula, MT, 59812, U.S.A
| | - Gordon Luikart
- Flathead Lake Biological Station, University of Montana, 32125 Bio Station Lane, Polson, MT, 59860, U.S.A
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14
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Youssefi F, Zoej MJV, Hanafi-Bojd AA, Dariane AB, Khaki M, Safdarinezhad A, Ghaderpour E. Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data through Google Earth Engine. SENSORS 2022; 22:s22051942. [PMID: 35271089 PMCID: PMC8915056 DOI: 10.3390/s22051942] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/27/2022] [Accepted: 02/28/2022] [Indexed: 01/06/2023]
Abstract
In many studies regarding the field of malaria, environmental factors have been acquired in single-time, multi-time or a short-time series using remote sensing and meteorological data. Selecting the best periods of the year to monitor the habitats of Anopheles larvae can be effective in better and faster control of malaria outbreaks. In this article, high-risk times for three regions in Iran, including Qaleh-Ganj, Sarbaz and Bashagard counties with a history of malaria prevalence were estimated. For this purpose, a series of environmental factors affecting the growth and survival of Anopheles were used over a seven-year period through the Google Earth Engine. The results of this study indicated two high-risk times for Qaleh-Ganj and Bashagard counties and three high-risk times for Sarbaz county over the course of a year observing an increase in the abundance of Anopheles mosquitoes. Further evaluation of the results against the entomological data available in previous studies showed that the high-risk times predicted in this study were consistent with an increase in the abundance of Anopheles mosquitoes in the study areas. The proposed method is extremely useful for temporal prediction of the increase in abundance of Anopheles mosquitoes in addition to the use of optimal data aimed at monitoring the exact location of Anopheles habitats.
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Affiliation(s)
- Fahimeh Youssefi
- Department of Photogrammetry and Remote Sensing, K. N. Toosi University of Technology, Tehran 19967-15433, Iran;
- Correspondence:
| | - Mohammad Javad Valadan Zoej
- Department of Photogrammetry and Remote Sensing, K. N. Toosi University of Technology, Tehran 19967-15433, Iran;
| | - Ahmad Ali Hanafi-Bojd
- Department of Medical Entomology & Vector Control, School of Public Health, Tehran University of Medical Sciences, Tehran 6446-14155, Iran;
| | - Alireza Borhani Dariane
- Department of Civil Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran;
| | - Mehdi Khaki
- School of Engineering, University of Newcastle, Callaghan, NSW 2308, Australia;
| | - Alireza Safdarinezhad
- Department of Geodesy and Surveying Engineering, Tafresh University, Tafresh 79611-39518, Iran;
| | - Ebrahim Ghaderpour
- Department of Geomatics Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada;
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15
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Byrne I, Chan K, Manrique E, Lines J, Wolie RZ, Trujillano F, Garay GJ, Del Prado Cortez MN, Alatrista-Salas H, Sternberg E, Cook J, N'Guessan R, Koffi A, Ahoua Alou LP, Apollinaire N, Messenger LA, Kristan M, Carrasco-Escobar G, Fornace K. Technical Workflow Development for Integrating Drone Surveys and Entomological Sampling to Characterise Aquatic Larval Habitats of Anopheles funestus in Agricultural Landscapes in Côte d'Ivoire. JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH 2021; 2021:3220244. [PMID: 34759971 PMCID: PMC8575637 DOI: 10.1155/2021/3220244] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 10/07/2021] [Indexed: 11/17/2022]
Abstract
Land-use practices such as agriculture can impact mosquito vector breeding ecology, resulting in changes in disease transmission. The typical breeding habitats of Africa's second most important malaria vector Anopheles funestus are large, semipermanent water bodies, which make them potential candidates for targeted larval source management. This is a technical workflow for the integration of drone surveys and mosquito larval sampling, designed for a case study aiming to characterise An. funestus breeding sites near two villages in an agricultural setting in Côte d'Ivoire. Using satellite remote sensing data, we developed an environmentally and spatially representative sampling frame and conducted paired mosquito larvae and drone mapping surveys from June to August 2021. To categorise the drone imagery, we also developed a land cover classification scheme with classes relative to An. funestus breeding ecology. We sampled 189 potential breeding habitats, of which 119 (63%) were positive for the Anopheles genus and nine (4.8%) were positive for An. funestus. We mapped 30.42 km2 of the region of interest including all water bodies which were sampled for larvae. These data can be used to inform targeted vector control efforts, although its generalisability over a large region is limited by the fine-scale nature of this study area. This paper develops protocols for integrating drone surveys and statistically rigorous entomological sampling, which can be adjusted to collect data on vector breeding habitats in other ecological contexts. Further research using data collected in this study can enable the development of deep-learning algorithms for identifying An. funestus breeding habitats across rural agricultural landscapes in Côte d'Ivoire and the analysis of risk factors for these sites.
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Affiliation(s)
- Isabel Byrne
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Kallista Chan
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Edgar Manrique
- Health Innovation Laboratory, Institute of Tropical Medicine “Alexander von Humboldt”, Universidad Peruana Cayetano Heredia, Lima, Peru
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Jo Lines
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Rosine Z. Wolie
- Institut Pierre Richet, Bouaké, Côte d'Ivoire
- Laboratoire de génétique, Unité de Formation et de Recherche en Biosciences, Université Félix Houphouët Boigny, Abidjan, Côte d'Ivoire
| | | | | | | | | | - Eleanore Sternberg
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Jackie Cook
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Raphael N'Guessan
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
- Institut Pierre Richet, Bouaké, Côte d'Ivoire
| | | | | | | | - Louisa A. Messenger
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Mojca Kristan
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Gabriel Carrasco-Escobar
- Health Innovation Laboratory, Institute of Tropical Medicine “Alexander von Humboldt”, Universidad Peruana Cayetano Heredia, Lima, Peru
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Kimberly Fornace
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
- Institute of Biodiversity, Animal Health & Comparative Medicine, University of Glasgow, Glasgow, UK
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16
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Cuenca PR, Key S, Jumail A, Surendra H, Ferguson HM, Drakeley CJ, Fornace K. Epidemiology of the zoonotic malaria Plasmodium knowlesi in changing landscapes. ADVANCES IN PARASITOLOGY 2021; 113:225-286. [PMID: 34620384 DOI: 10.1016/bs.apar.2021.08.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Within the past two decades, incidence of human cases of the zoonotic malaria Plasmodium knowlesi has increased markedly. P. knowlesi is now the most common cause of human malaria in Malaysia and threatens to undermine malaria control programmes across Southeast Asia. The emergence of zoonotic malaria corresponds to a period of rapid deforestation within this region. These environmental changes impact the distribution and behaviour of the simian hosts, mosquito vector species and human populations, creating new opportunities for P. knowlesi transmission. Here, we review how landscape changes can drive zoonotic disease emergence, examine the extent and causes of these changes across Southeast and identify how these mechanisms may be impacting P. knowlesi dynamics. We review the current spatial epidemiology of reported P. knowlesi infections in people and assess how these demographic and environmental changes may lead to changes in transmission patterns. Finally, we identify opportunities to improve P. knowlesi surveillance and develop targeted ecological interventions within these landscapes.
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Affiliation(s)
- Pablo Ruiz Cuenca
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Stephanie Key
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | - Henry Surendra
- Eijkman-Oxford Clinical Research Unit, Jakarta, Indonesia; Centre for Tropical Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Heather M Ferguson
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, Scotland, United Kingdom
| | - Chris J Drakeley
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Kimberly Fornace
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom; Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, Scotland, United Kingdom.
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17
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McMahon A, Mihretie A, Ahmed AA, Lake M, Awoke W, Wimberly MC. Remote sensing of environmental risk factors for malaria in different geographic contexts. Int J Health Geogr 2021; 20:28. [PMID: 34120599 PMCID: PMC8201719 DOI: 10.1186/s12942-021-00282-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 06/03/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Despite global intervention efforts, malaria remains a major public health concern in many parts of the world. Understanding geographic variation in malaria patterns and their environmental determinants can support targeting of malaria control and development of elimination strategies. METHODS We used remotely sensed environmental data to analyze the influences of environmental risk factors on malaria cases caused by Plasmodium falciparum and Plasmodium vivax from 2014 to 2017 in two geographic settings in Ethiopia. Geospatial datasets were derived from multiple sources and characterized climate, vegetation, land use, topography, and surface water. All data were summarized annually at the sub-district (kebele) level for each of the two study areas. We analyzed the associations between environmental data and malaria cases with Boosted Regression Tree (BRT) models. RESULTS We found considerable spatial variation in malaria occurrence. Spectral indices related to land cover greenness (NDVI) and moisture (NDWI) showed negative associations with malaria, as the highest malaria rates were found in landscapes with low vegetation cover and moisture during the months that follow the rainy season. Climatic factors, including precipitation and land surface temperature, had positive associations with malaria. Settlement structure also played an important role, with different effects in the two study areas. Variables related to surface water, such as irrigated agriculture, wetlands, seasonally flooded waterbodies, and height above nearest drainage did not have strong influences on malaria. CONCLUSION We found different relationships between malaria and environmental conditions in two geographically distinctive areas. These results emphasize that studies of malaria-environmental relationships and predictive models of malaria occurrence should be context specific to account for such differences.
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Affiliation(s)
- Andrea McMahon
- Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, OK USA
| | - Abere Mihretie
- Health, Development, and Anti-Malaria Association, Addis Ababa, Ethiopia
| | - Adem Agmas Ahmed
- Malaria Control and Elimination Partnership in Africa, Bahir Dar, Ethiopia
| | | | - Worku Awoke
- School of Public Health, Bahir Dar University, Bahir Dar, Ethiopia
| | - Michael Charles Wimberly
- Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, OK USA
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18
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Ha TV, Kim W, Nguyen-Tien T, Lindahl J, Nguyen-Viet H, Thi NQ, Nguyen HV, Unger F, Lee HS. Spatial distribution of Culex mosquito abundance and associated risk factors in Hanoi, Vietnam. PLoS Negl Trop Dis 2021; 15:e0009497. [PMID: 34153065 PMCID: PMC8248591 DOI: 10.1371/journal.pntd.0009497] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 07/01/2021] [Accepted: 05/21/2021] [Indexed: 12/19/2022] Open
Abstract
Japanese encephalitis (JE) is the major cause of viral encephalitis (VE) in most Asian-Pacific countries. In Vietnam, there is no nationwide surveillance system for JE due to lack of medical facilities and diagnoses. Culex tritaeniorhynchus, Culex vishnui, and Culex quinquefasciatus have been identified as the major JE vectors in Vietnam. The main objective of this study was to forecast a risk map of Culex mosquitoes in Hanoi, which is one of the most densely populated cities in Vietnam. A total of 10,775 female adult Culex mosquitoes were collected from 513 trapping locations. We collected temperature and precipitation information during the study period and its preceding month. In addition, the other predictor variables (e.g., normalized difference vegetation index [NDVI], land use/land cover and human population density), were collected for our analysis. The final model selected for estimating the Culex mosquito abundance included centered rainfall, quadratic term rainfall, rice cover ratio, forest cover ratio, and human population density variables. The estimated spatial distribution of Culex mosquito abundance ranged from 0 to more than 150 mosquitoes per 900m2. Our model estimated that 87% of the Hanoi area had an abundance of mosquitoes from 0 to 50, whereas approximately 1.2% of the area showed more than 100 mosquitoes, which was mostly in the rural/peri-urban districts. Our findings provide better insight into understanding the spatial distribution of Culex mosquitoes and its associated environmental risk factors. Such information can assist local clinicians and public health policymakers to identify potential areas of risk for JE virus. Risk maps can be an efficient way of raising public awareness about the virus and further preventive measures need to be considered in order to prevent outbreaks and onwards transmission of JE virus.
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Affiliation(s)
- Tuyen V. Ha
- Faculty of Resources Management, Thai Nguyen University of Agriculture and Forestry (TUAF), Thai Nguyen, Vietnam
| | - Wonkook Kim
- Pusan National University, Busan, South Korea
| | | | - Johanna Lindahl
- International Livestock Research Institute (ILRI), Hanoi, Vietnam
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
- Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Hung Nguyen-Viet
- International Livestock Research Institute (ILRI), Hanoi, Vietnam
| | - Nguyen Quang Thi
- Faculty of Resources Management, Thai Nguyen University of Agriculture and Forestry (TUAF), Thai Nguyen, Vietnam
| | - Huy Van Nguyen
- Faculty of Resources Management, Thai Nguyen University of Agriculture and Forestry (TUAF), Thai Nguyen, Vietnam
| | - Fred Unger
- International Livestock Research Institute (ILRI), Hanoi, Vietnam
| | - Hu Suk Lee
- International Livestock Research Institute (ILRI), Hanoi, Vietnam
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