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Knoblauch S, Su Yin M, Chatrinan K, de Aragão Rocha AA, Haddawy P, Biljecki F, Lautenbach S, Resch B, Arifi D, Jänisch T, Morales I, Zipf A. High-resolution mapping of urban Aedes aegypti immature abundance through breeding site detection based on satellite and street view imagery. Sci Rep 2024; 14:18227. [PMID: 39107395 PMCID: PMC11303731 DOI: 10.1038/s41598-024-67914-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 07/17/2024] [Indexed: 08/10/2024] Open
Abstract
Identification of Aedes aegypti breeding hotspots is essential for the implementation of targeted vector control strategies and thus the prevention of several mosquito-borne diseases worldwide. Training computer vision models on satellite and street view imagery in the municipality of Rio de Janeiro, we analyzed the correlation between the density of common breeding grounds and Aedes aegypti infestation measured by ovitraps on a monthly basis between 2019 and 2022. Our findings emphasized the significance (p ≤ 0.05) of micro-habitat proxies generated through object detection, allowing to explain high spatial variance in urban abundance of Aedes aegypti immatures. Water tanks, non-mounted car tires, plastic bags, potted plants, and storm drains positively correlated with Aedes aegypti egg and larva counts considering a 1000 m mosquito flight range buffer around 2700 ovitrap locations, while dumpsters, small trash bins, and large trash bins exhibited a negative association. This complementary application of satellite and street view imagery opens the pathway for high-resolution interpolation of entomological surveillance data and has the potential to optimize vector control strategies. Consequently it supports the mitigation of emerging infectious diseases transmitted by Aedes aegypti, such as dengue, chikungunya, and Zika, which cause thousands of deaths each year.
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Affiliation(s)
- Steffen Knoblauch
- GIScience Chair, Heidelberg University, 69120, Heidelberg, Germany.
- Interdisciplinary Center of Scientific Computing, Heidelberg University, 69120, Heidelberg, Germany.
- Heidelberg Institute for Geoinformation Technology, 69118, Heidelberg, Germany.
| | - Myat Su Yin
- Faculty of ICT, Mahidol University, 73170, Nakhon Pathom, Thailand
| | | | | | - Peter Haddawy
- Faculty of ICT, Mahidol University, 73170, Nakhon Pathom, Thailand
- Bremen Spatial Cognition Center, University of Bremen, 28359, Bremen, Germany
| | - Filip Biljecki
- Department of Architecture, National University of Singapore, 117566, Singapore, Singapore
- Department of Real Estate, National University of Singapore, 119245, Singapore, Singapore
| | - Sven Lautenbach
- Heidelberg Institute for Geoinformation Technology, 69118, Heidelberg, Germany
| | - Bernd Resch
- Geo-social Analytics Lab, Paris Lodron University of Salzburg, 5020, Salzburg, Austria
- Center for Geographic Analysis, Harvard University, 02138, Cambridge, USA
| | - Dorian Arifi
- Geo-social Analytics Lab, Paris Lodron University of Salzburg, 5020, Salzburg, Austria
| | - Thomas Jänisch
- Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 80045, Aurora, USA
- Heidelberg Institute of Global Health, Heidelberg University Hospital, 69120, Heidelberg, Germany
| | - Ivonne Morales
- Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 80045, Aurora, USA
| | - Alexander Zipf
- GIScience Chair, Heidelberg University, 69120, Heidelberg, Germany
- Interdisciplinary Center of Scientific Computing, Heidelberg University, 69120, Heidelberg, Germany
- Heidelberg Institute for Geoinformation Technology, 69118, Heidelberg, Germany
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Yu K, Wu J, Wang M, Cai Y, Zhu M, Yao S, Zhou Y. Using UAV images and deep learning in investigating potential breeding sites of Aedes albopictus. Acta Trop 2024; 255:107234. [PMID: 38688444 DOI: 10.1016/j.actatropica.2024.107234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 04/27/2024] [Accepted: 04/27/2024] [Indexed: 05/02/2024]
Abstract
Aedes albopictus (Diptera: Culicidae) plays a crucial role as a vector for mosquito-borne diseases like dengue and zika. Given the limited availability of effective vaccines, the prevention of Aedes-borne diseases mainly relies on extensive efforts in vector surveillance and control. In multiple mosquito control methods, the identification and elimination of potential breeding sites (PBS) for Aedes are recognized as effective methods for population control. Previous studies utilizing unmanned aerial vehicles (UAVs) and deep learning to identify PBS have primarily focused on large, regularly-shaped containers. However, there has been a small amount of empirical research into their practical application in the field. We have thus constructed a PBS dataset specifically tailored for Ae. albopictus, including items such as buckets, bowls, bins, aquatic plants, jars, lids, pots, boxes, and sinks that were common in the Yangtze River Basin in China. Then, a YOLO v7 model for identifying these PBS was developed. Finally, we recognized and labeled the area with the highest PBS density, as well as the subarea with the most urgent need for source reduction in the empirical region, by calculating the kernel density value. Based on the above research, we proposed a UAV-AI-based methodological framework to locate the spatial distribution of PBS, and conducted empirical research on Jinhulu New Village, a typical model community. The results revealed that the YOLO v7 model achieved an excellent result on the F1 score and mAP(both above 0.99), with 97% of PBS correctly located. The predicted distribution of different PBS categories in each subarea was completely consistent with true distribution; the five houses with the most PBS were correctly located. The results of the kernel density map indicate the subarea 4 with the highest density of PBS, where PBS needs to be removed or destroyed with immediate effect. These results demonstrate the reliability of the prediction results and the feasibility of the UAV-AI-based methodological framework. It can minimize repetitive labor, enhance efficiency, and provide guidance for the removal and destruction of PBS. The research can shed light on the investigation of mosquito PBS investigation both methodologically and practically.
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Affiliation(s)
- Keyi Yu
- Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, 200241, China; School of Geographic Sciences, East China Normal University, Shanghai, 200241, China; Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai, 200241, China
| | - Jianping Wu
- Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, 200241, China; School of Geographic Sciences, East China Normal University, Shanghai, 200241, China; Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai, 200241, China
| | - Minghao Wang
- Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, 200241, China; School of Geographic Sciences, East China Normal University, Shanghai, 200241, China; Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai, 200241, China
| | - Yizhou Cai
- Minhang District Centre for Disease Control and Prevention, Shanghai, 201011, China
| | - Minhui Zhu
- Minhang District Centre for Disease Control and Prevention, Shanghai, 201011, China
| | - Shenjun Yao
- Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, 200241, China; School of Geographic Sciences, East China Normal University, Shanghai, 200241, China; Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai, 200241, China.
| | - Yibin Zhou
- Minhang District Centre for Disease Control and Prevention, Shanghai, 201011, China.
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Rosser JI, Tarpenning MS, Bramante JT, Tamhane A, Chamberlin AJ, Mutuku PS, De Leo GA, Ndenga B, Mutuku F, LaBeaud AD. Development of a trash classification system to map potential Aedes aegypti breeding grounds using unmanned aerial vehicle imaging. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:41107-41117. [PMID: 38842780 PMCID: PMC11189966 DOI: 10.1007/s11356-024-33801-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 05/20/2024] [Indexed: 06/07/2024]
Abstract
Aedes aegypti mosquitos are the primary vector for dengue, chikungunya, and Zika viruses and tend to breed in small containers of water, with a propensity to breed in small piles of trash and abandoned tires. This study piloted the use of aerial imaging to map and classify potential Ae. aegypti breeding sites with a specific focus on trash, including discarded tires. Aerial images of coastal and inland sites in Kenya were obtained using an unmanned aerial vehicle. Aerial images were reviewed for identification of trash and suspected trash mimics, followed by extensive community walk-throughs to identify trash types and mimics by description and ground photography. An expert panel reviewed aerial images and ground photos to develop a classification scheme and evaluate the advantages and disadvantages of aerial imaging versus walk-through trash mapping. A trash classification scheme was created based on trash density, surface area, potential for frequent disturbance, and overall likelihood of being a productive Ae. aegypti breeding site. Aerial imaging offers a novel strategy to characterize, map, and quantify trash at risk of promoting Ae. aegypti proliferation, generating opportunities for further research on trash associations with disease and trash interventions.
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Affiliation(s)
- Joelle I Rosser
- School of Medicine, Division of Infectious Diseases, Stanford University, Stanford, CA, USA.
| | | | | | | | - Andrew J Chamberlin
- Hopkins Marine Institute, Department of Earth System Sciences and Department of Oceans, Stanford University, Stanford, CA, USA
| | - Paul S Mutuku
- Division of Vector Borne Disease Control Unit, Msambweni County Referral Hospital, Msambweni, Kenya
| | - Giulio A De Leo
- Hopkins Marine Institute, Department of Earth System Sciences and Department of Oceans, Stanford University, Stanford, CA, USA
| | - Bryson Ndenga
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Francis Mutuku
- Department of Environment and Health Sciences, Technical University of Mombasa, Mombasa, Kenya
| | - Angelle Desiree LaBeaud
- School of Medicine, Division of Pediatric Infectious Diseases, Stanford University, Stanford, CA, USA
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Laranjeira C, Pereira M, Oliveira R, Barbosa G, Fernandes C, Bermudi P, Resende E, Fernandes E, Nogueira K, Andrade V, Quintanilha JA, dos Santos JA, Chiaravalloti-Neto F. Automatic mapping of high-risk urban areas for Aedes aegypti infestation based on building facade image analysis. PLoS Negl Trop Dis 2024; 18:e0011811. [PMID: 38829905 PMCID: PMC11192312 DOI: 10.1371/journal.pntd.0011811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 06/21/2024] [Accepted: 05/17/2024] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND Dengue, Zika, and chikungunya, whose viruses are transmitted mainly by Aedes aegypti, significantly impact human health worldwide. Despite the recent development of promising vaccines against the dengue virus, controlling these arbovirus diseases still depends on mosquito surveillance and control. Nonetheless, several studies have shown that these measures are not sufficiently effective or ineffective. Identifying higher-risk areas in a municipality and directing control efforts towards them could improve it. One tool for this is the premise condition index (PCI); however, its measure requires visiting all buildings. We propose a novel approach capable of predicting the PCI based on facade street-level images, which we call PCINet. METHODOLOGY Our study was conducted in Campinas, a one million-inhabitant city in São Paulo, Brazil. We surveyed 200 blocks, visited their buildings, and measured the three traditional PCI components (building and backyard conditions and shading), the facade conditions (taking pictures of them), and other characteristics. We trained a deep neural network with the pictures taken, creating a computational model that can predict buildings' conditions based on the view of their facades. We evaluated PCINet in a scenario emulating a real large-scale situation, where the model could be deployed to automatically monitor four regions of Campinas to identify risk areas. PRINCIPAL FINDINGS PCINet produced reasonable results in differentiating the facade condition into three levels, and it is a scalable strategy to triage large areas. The entire process can be automated through data collection from facade data sources and inferences through PCINet. The facade conditions correlated highly with the building and backyard conditions and reasonably well with shading and backyard conditions. The use of street-level images and PCINet could help to optimize Ae. aegypti surveillance and control, reducing the number of in-person visits necessary to identify buildings, blocks, and neighborhoods at higher risk from mosquito and arbovirus diseases.
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Affiliation(s)
- Camila Laranjeira
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Matheus Pereira
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Raul Oliveira
- Department of Epidemiology, School of Public Health of University of São Paulo, São Paulo, Brazil
| | - Gerson Barbosa
- Pasteur Institute, Secretary of Health of the State of São Paulo, São Paulo, Brazil
| | - Camila Fernandes
- Department of Epidemiology, School of Public Health of University of São Paulo, São Paulo, Brazil
| | - Patricia Bermudi
- Department of Epidemiology, School of Public Health of University of São Paulo, São Paulo, Brazil
| | - Ester Resende
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Eduardo Fernandes
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Keiller Nogueira
- Computer Science and Mathematics, University of Stirling, Stirling, United Kingdom
| | - Valmir Andrade
- Epidemiologic Surveillance Center, Secretary of Health of the State of São Paulo, São Paulo, Brazil
| | | | - Jefersson A. dos Santos
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
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Sahavechaphan N, Chatrattikorn A, Rattananen M, Sadakorn P, Areechokchai D, Iamsirithaworn S. Identifying villages and breeding habitats for dengue transmission in Thailand: insights from long-term larval surveys. BMC Infect Dis 2024; 24:523. [PMID: 38789932 PMCID: PMC11119398 DOI: 10.1186/s12879-024-09398-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND In Thailand, the Department of Disease Control (DDC) regularly performs visual larval surveys throughout the country to monitor dengue fever outbreaks. Since 2016, the DDC switched from a paper-based to a digital-based larval survey process. The significant amount of larval survey data collected digitally presents a valuable opportunity to precisely identify the villages and breeding habitats that are vulnerable to dengue transmission. METHODS The study used digitally collected larval survey data from 2017 to 2019. It employed larval indices to evaluate the risk of dengue transmission in villages based on seasonal, regional, and categorical perspectives. Furthermore, the study comprehensively scrutinized each container category by employing different measures to determine its breeding preference ratio. RESULTS The result showed that villages with a very high-risk of dengue transmission were present year-round in all regions, with the highest proportion during the rainy season. The Southern region had more high-risk villages during the winter season due to rainfall. Slums and residential communities were more vulnerable to dengue than commercial areas. All container categories could potentially serve as breeding habitats for dengue-carrying mosquitoes, with abandoned containers being the most significant breeding sites. CONCLUSIONS The risk of dengue transmission was present year-round throughout Thailand. This underscores the importance of community and government initiatives, along with sustained public awareness campaigns and active community engagement, to efficiently and permanently eradicate mosquito breeding habitats. It should be noted that larval indices may not strongly correlate with dengue cases, as indicated by the preliminary analysis. However, they offer valuable insights into potential breeding sites for targeted preventive measures.
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Affiliation(s)
- Naiyana Sahavechaphan
- National Electronics and Computer Technology Center, National Science and Technology Development Agency, 112 Thailand Science Park, Phahonyothin Road, 12120, Pathum Thani, Thailand.
| | - Asamaporn Chatrattikorn
- National Electronics and Computer Technology Center, National Science and Technology Development Agency, 112 Thailand Science Park, Phahonyothin Road, 12120, Pathum Thani, Thailand
| | - Manot Rattananen
- National Electronics and Computer Technology Center, National Science and Technology Development Agency, 112 Thailand Science Park, Phahonyothin Road, 12120, Pathum Thani, Thailand
| | - Pongsakorn Sadakorn
- Department of Disease Control, Ministry of Public Health, 88/21 Tawanon Road, 11000, Nonthaburi, Thailand
| | - Darin Areechokchai
- Department of Disease Control, Ministry of Public Health, 88/21 Tawanon Road, 11000, Nonthaburi, Thailand
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Morueta-Holme N, Iversen LL, Corcoran D, Rahbek C, Normand S. Unlocking ground-based imagery for habitat mapping. Trends Ecol Evol 2024; 39:349-358. [PMID: 38087707 DOI: 10.1016/j.tree.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 11/06/2023] [Accepted: 11/14/2023] [Indexed: 04/05/2024]
Abstract
Fine-grained environmental data across large extents are needed to resolve the processes that impact species communities from local to global scales. Ground-based images (GBIs) have the potential to capture habitat complexity at biologically relevant spatial and temporal resolutions. Moving beyond existing applications of GBIs for species identification and monitoring ecological change from repeat photography, we describe promising approaches to habitat mapping, leveraging multimodal data and computer vision. We illustrate empirically how GBIs can be applied to predict distributions of species at fine scales along Street View routes, or to automatically classify and quantify habitat features. Further, we outline future research avenues using GBIs that can bring a leap forward in analyses for ecology and conservation with this underused resource.
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Affiliation(s)
- N Morueta-Holme
- Center for Macroecology, Evolution and Climate, Globe Institute, University of Copenhagen, Copenhagen, Denmark.
| | - L L Iversen
- Department of Biology, McGill University, Montréal, Québec, H3A 1B1, Canada
| | - D Corcoran
- Section for Ecoinformatics & Biodiversity, Department of Biology, Aarhus University, Aarhus, Denmark; Center for Sustainable Landscapes under Global Change, Department of Biology, Aarhus University, Aarhus, Denmark
| | - C Rahbek
- Center for Macroecology, Evolution and Climate, Globe Institute, University of Copenhagen, Copenhagen, Denmark; Center for Global Mountain Biodiversity, Globe Institute, University of Copenhagen, Copenhagen, Denmark; Institute of Ecology, Peking University, Beijing, China; Danish Institute for Advanced Study, University of Southern Denmark, Odense, Denmark
| | - S Normand
- Section for Ecoinformatics & Biodiversity, Department of Biology, Aarhus University, Aarhus, Denmark; Center for Sustainable Landscapes under Global Change, Department of Biology, Aarhus University, Aarhus, Denmark; Center for Landscape Research in Sustainable Agricultural Futures, Department of Biology, Aarhus University, Aarhus, Denmark
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Vasconcelos D, Nunes NJ, Förster A, Gomes JP. Optimal 2D audio features estimation for a lightweight application in mosquitoes species: Ecoacoustics detection and classification purposes. Comput Biol Med 2024; 168:107787. [PMID: 38070201 DOI: 10.1016/j.compbiomed.2023.107787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 11/20/2023] [Accepted: 11/28/2023] [Indexed: 01/10/2024]
Abstract
Mosquitoes are the vector of diseases that kill more than one million people per year worldwide. Surveillance systems are essential for understanding their complex ecology and behaviour. This is fundamental for predicting disease risk caused by mosquitoes and formulating effective control strategies against mosquito-borne diseases such as malaria, dengue, and Zika. Mosquito populations vary heterogeneously in urban and rural landscapes, fluctuating with seasonal and climatic trends and human activity. Several approaches provide environmental data for mosquito mapping and risk prediction. However, they rely traditionally upon labour-intensive techniques such as manual traps. This paper presents the optimal audio features for mosquito identification using ecoacoustics signals to automatically identify different mosquito species from their wingbeat sounds based on popular audio features. The audio selection method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Silhouette coefficient to evaluate the clusters in the data through the optimal-combined audio features. To classify the mosquito species and distinguish them from environmental-urban noise, the method comprises the Gaussian Mixture Model (GMM) and Gibbs approach for Aedes aegypti, and Culex quinquefasciatus, using the acoustic recordings of their wingbeat signals. Finally, comparing GMM and Gibbs, the two have very similar accuracy, but the classification time is much faster for Gibbs sampling, making it a good candidate for a lightweight solution. These are essential when deploying the described models to monitor mosquito vectors in the wild with Internet of Things (IoT) technologies.
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Affiliation(s)
- Dinarte Vasconcelos
- ITI/LARSYS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisbon, 1049-001, Portugal.
| | - Nuno Jardim Nunes
- ITI/LARSYS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisbon, 1049-001, Portugal.
| | - Anna Förster
- Sustainable Communication Networks, University of Bremen, Otto-Hahn-Allee 1, Bremen, 28359, Germany.
| | - João Pedro Gomes
- ISR/LARSYS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisbon, 1049-001, Portugal.
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Abdulsalam FI, Antúnez P, Jawjit W. Spatio-temporal dengue risk modelling in the south of Thailand: a Bayesian approach to dengue vulnerability. PeerJ 2023; 11:e15619. [PMID: 37465156 PMCID: PMC10351518 DOI: 10.7717/peerj.15619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 06/01/2023] [Indexed: 07/20/2023] Open
Abstract
Background More than half of the global population is predicted to be living in areas susceptible to dengue transmission with the vast majority in Asia. Dengue fever is of public health concern, particularly in the southern region of Thailand due to favourable environmental factors for its spread. The risk of dengue infection at the population level varies in time and space among sub-populations thus, it is important to study the risk of infection considering spatio-temporal variation. Methods This study presents a joint spatio-temporal epidemiological model in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation with the CARBayesST package of R software. For this purpose, monthly dengue records by district from 2002 to 2018 from the southern region of Thailand provided by the Ministry of Public Health of Thailand and eight environmental variables were used. Results Results show that an increasing level of temperature, number of rainy days and sea level pressure are associated with a higher occurrence of dengue fever and consequently higher incidence risk, while an increasing level of wind speed seems to suggest a protective factor. Likewise, we found that the elevated risks of dengue in the immediate future are in the districts of Phipun, Phrom Kili, Lan Saka, Phra Phrom and Chaloem Phakiat. The resulting estimates provide insights into the effects of covariate risk factors, spatio-temporal trends and dengue-related health inequalities at the district level in southern Thailand. Conclusion Possible implications are discussed considering some anthropogenic factors that could inhibit or enhance dengue occurrence. Risk maps indicated which districts are above and below baseline risk, allowing for the identification of local anomalies and high-risk boundaries. In the event of near future, the threat of elevated disease risk needs to be prevented and controlled considering the factors underlying the spread of mosquitoes in the Southeast Asian region.
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Affiliation(s)
| | - Pablo Antúnez
- División de Estudios de Postgrado, Universidad de la Sierra Juárez, Ixtlán de Juárez, Oaxaca, México
| | - Warit Jawjit
- School of Public Health, Walailak University, Thasala, Nakhon Si Thammarat, Thailand
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Chen YX, Pan CY, Chen BY, Jeng SW, Chen CH, Huang JJ, Chen CD, Liu WL. Use of unmanned ground vehicle systems in urbanized zones: A study of vector Mosquito surveillance in Kaohsiung. PLoS Negl Trop Dis 2023; 17:e0011346. [PMID: 37289665 DOI: 10.1371/journal.pntd.0011346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 04/29/2023] [Indexed: 06/10/2023] Open
Abstract
Dengue fever is a vector-borne disease that has become a serious global public health problem over the past decade. An essential aspect of controlling and preventing mosquito-borne diseases is reduction of mosquito density. Through the process of urbanization, sewers (ditches) have become easy breeding sources of vector mosquitoes. In this study, we, for the first time, used unmanned ground vehicle systems (UGVs) to enter ditches in urban areas to observe vector mosquito ecology. We found traces of vector mosquitoes in ~20.7% of inspected ditches, suggesting that these constitute viable breeding sources of vector mosquitoes in urban areas. We also analyzed the average gravitrap catch of five administrative districts in Kaohsiung city from May to August 2018. The gravitrap indices of Nanzi and Fengshan districts were above the expected average (3.26), indicating that the vector mosquitoes density in these areas is high. Using the UGVs to detect positive ditches within the five districts followed by insecticide application generally yielded good control results. Further improving the high-resolution digital camera and spraying system of the UGVs may be able to effectively and instantly monitor vector mosquitoes and implement spraying controls. This approach may be suitable to solve the complex and difficult task of detecting mosquito breeding sources in urban ditches.
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Affiliation(s)
- Yu-Xuan Chen
- National Mosquito-Borne Diseases Control Research Center, National Health Research Institutes, Miaoli, Taiwan
- Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, Tainan, Taiwan
| | - Chao-Ying Pan
- Department of Health, Kaohsiung City Government, Kaohsiung, Taiwan
- Graduate Institute of Science Education & Environmental Education, National Kaohsiung Normal University, Kaohsiung, Taiwan
| | - Bo-Yu Chen
- National Mosquito-Borne Diseases Control Research Center, National Health Research Institutes, Miaoli, Taiwan
| | - Shu-Wen Jeng
- National Mosquito-Borne Diseases Control Research Center, National Health Research Institutes, Miaoli, Taiwan
| | - Chun-Hong Chen
- National Mosquito-Borne Diseases Control Research Center, National Health Research Institutes, Miaoli, Taiwan
- National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Miaoli, Taiwan
- Institute of Molecular Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Joh-Jong Huang
- Department of Health, Kaohsiung City Government, Kaohsiung, Taiwan
- Department of Medical Humanity and Education, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chaur-Dong Chen
- Department of Health, Kaohsiung City Government, Kaohsiung, Taiwan
- Sanmin District Public Health Center, Department of Health, Kaohsiung City Government, Kaohsiung, Taiwan
| | - Wei-Liang Liu
- National Mosquito-Borne Diseases Control Research Center, National Health Research Institutes, Miaoli, Taiwan
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10
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Sharif M, Khan MAS, Hasan MJ, Naher T, Rudra S, Fardous J, Gozal D, Rahman MK, Amin MR. Spatial association of Aedes aegypti with dengue fever hotspots in an endemic region. Heliyon 2022; 8:e11640. [PMID: 36439726 PMCID: PMC9694391 DOI: 10.1016/j.heliyon.2022.e11640] [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: 02/13/2022] [Revised: 07/25/2022] [Accepted: 11/09/2022] [Indexed: 11/22/2022] Open
Abstract
Background and objective Dengue is a vector-borne viral disease usually transmitted by Aedes mosquitoes. Around the world, the relationship between local vector density and frequency of dengue cases is being explored and needs further evidence. This study aimed to analyze the potential spatial relationships between the dengue vector (Aedes aegypti) and dengue cases in the megacity of Bangladesh during the 2019 dengue outbreak. Methods Vector density measures were used to estimate spatial associations with dengue case distribution. Location was determined for 364 dengue cases who were admitted to Dhaka Medical College Hospital over a period of 4 months. Data were collected using a semi-structured questionnaire, and prior consent was ensured before participation. The Moran global index, Getis-Ord Gi∗, ordinary least squares regression, geographically weighted regression and count data regression methods were used for spatial analysis. Results We found that dengue case distribution was not associated with immature Aedes aegypti mosquito (larvae) density across the city. The relationship between larval density measured by the Breteau Index (BI) and House Index (HI) with dengue cases was nonstationary and not statistically significant. Conclusion The location of dengue cases appears to be unrelated to vector distribution and vector density. These findings should prompt the search for other transmission risk factors.
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Affiliation(s)
- Mohiuddin Sharif
- Department of Medicine, Dhaka Medical College Hospital, Dhaka, Bangladesh
| | | | | | - Tanzin Naher
- Department of Clinical Pathology, Dhaka Medical College Hospital, Dhaka, Bangladesh
| | - Sujan Rudra
- Department of Statistics, University of Chittagong, Chottogram, Bangladesh
| | | | - David Gozal
- Department of Child Health and the Child Health Research Institute, University of Missouri School of Medicine, Columbia, MO 65201, USA
| | - Md Khalilur Rahman
- National Malaria Elimination and Aedes Transmitted Diseases Control Programme, Disease Control Division, Directorate General of Health Services, Bangladesh
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11
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Wang Y, Liu D, Luo J. Identification and Improvement of Hazard Scenarios in Non-Motorized Transportation Using Multiple Deep Learning and Street View Images. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:14054. [PMID: 36360941 PMCID: PMC9658839 DOI: 10.3390/ijerph192114054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/20/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
In the prioritized vehicle traffic environment, motorized transportation has been obtaining more spatial and economic resources, posing potential threats to the travel quality and life safety of non-motorized transportation participants. It is becoming urgent to improve the safety situation of non-motorized transportation participants. Most previous studies have focused on the psychological aspects of pedestrians and cyclists exposed to the actual road environment rather than quantifying the objective safety hazards, which has led to a non-rigorous evaluation of their basic safety situation. An integrated processing approach is proposed to comprehensively and objectively evaluate the overall safety level of non-motorized transportation participants on each road segment. Our main contributions include (1) the universal approach is established to automatically identify hazard scenarios related to non-motorized transportation and their direct causing factors from street view images based on multiple deep learning models; (2) a seed points spreading algorithm is designed to convert semantic images into target detection results with detail contour, which breaks the functional limitation of these two types of methods to a certain extent; (3) The safety situation of non-motorized transportation on various road sections in Gulou District, Nanjing, China has been evaluated and based on this, a series of suggestions have been put forward to guide the better adaptation among multiple transportation participants.
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Affiliation(s)
- Yiwen Wang
- School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China
| | - Di Liu
- School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518107, China
| | - Jiameng Luo
- School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China
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12
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Street View Imagery (SVI) in the Built Environment: A Theoretical and Systematic Review. BUILDINGS 2022. [DOI: 10.3390/buildings12081167] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Street view imagery (SVI) provides efficient access to data that can be used to research spatial quality at the human scale. The previous reviews have mainly focused on specific health findings and neighbourhood environments. There has not been a comprehensive review of this topic. In this paper, we systematically review the literature on the application of SVI in the built environment, following a formal innovation–decision framework. The main findings are as follows: (I) SVI remains an effective tool for automated research assessments. This offers a new research avenue to expand the built environment-measurement methods to include perceptions in addition to physical features. (II) Currently, SVI is functional and valuable for quantifying the built environment, spatial sentiment perception, and spatial semantic speculation. (III) The significant dilemmas concerning the adoption of this technology are related to image acquisition, the image quality, spatial and temporal distribution, and accuracy. (IV) This research provides a rapid assessment and provides researchers with guidance for the adoption and implementation of SVI. Data integration and management, proper image service provider selection, and spatial metrics measurements are the critical success factors. A notable trend is the application of SVI towards a focus on the perceptions of the built environment, which provides a more refined and effective way to depict urban forms in terms of physical and social spaces.
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13
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de Lima CL, da Silva ACG, Moreno GMM, Cordeiro da Silva C, Musah A, Aldosery A, Dutra L, Ambrizzi T, Borges IVG, Tunali M, Basibuyuk S, Yenigün O, Massoni TL, Browning E, Jones K, Campos L, Kostkova P, da Silva Filho AG, dos Santos WP. Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review. Front Public Health 2022; 10:900077. [PMID: 35719644 PMCID: PMC9204152 DOI: 10.3389/fpubh.2022.900077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 05/03/2022] [Indexed: 11/13/2022] Open
Abstract
Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.
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Affiliation(s)
- Clarisse Lins de Lima
- Nucleus for Computer Engineering, Polytechnique School of the University of Pernambuco, Poli-UPE, Recife, Brazil
| | - Ana Clara Gomes da Silva
- Nucleus for Computer Engineering, Polytechnique School of the University of Pernambuco, Poli-UPE, Recife, Brazil
| | | | | | - Anwar Musah
- Centre for Digital Public Health and Emergencies, Institute for Risk and Disaster Reduction, University College London, London, United Kingdom
| | - Aisha Aldosery
- Centre for Digital Public Health and Emergencies, Institute for Risk and Disaster Reduction, University College London, London, United Kingdom
| | - Livia Dutra
- Department of Atmospheric Sciences, IAG-USP, University of São Paulo, São Paulo, Brazil
| | - Tercio Ambrizzi
- Department of Atmospheric Sciences, IAG-USP, University of São Paulo, São Paulo, Brazil
| | - Iuri V. G. Borges
- Department of Atmospheric Sciences, IAG-USP, University of São Paulo, São Paulo, Brazil
| | - Merve Tunali
- Boǧaziçi University, Institute of Environmental Sciences, Istanbul, Turkey
| | - Selma Basibuyuk
- Boǧaziçi University, Institute of Environmental Sciences, Istanbul, Turkey
| | - Orhan Yenigün
- Boǧaziçi University, Institute of Environmental Sciences, Istanbul, Turkey
| | - Tiago Lima Massoni
- Department of Systems and Computing, Federal University of Campina Grande, Campina Grande, Brazil
| | - Ella Browning
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Kate Jones
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Luiza Campos
- Department of Civil Environmental and Geomatic Engineering, University College London, London, United Kingdom
| | - Patty Kostkova
- Centre for Digital Public Health and Emergencies, Institute for Risk and Disaster Reduction, University College London, London, United Kingdom
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14
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Annan E, Guo J, Angulo-Molina A, Yaacob WFW, Aghamohammadi N, C Guetterman T, Yavaşoglu Sİ, Bardosh K, Dom NC, Zhao B, Lopez-Lemus UA, Khan L, Nguyen USDT, Haque U. Community acceptability of dengue fever surveillance using unmanned aerial vehicles: A cross-sectional study in Malaysia, Mexico, and Turkey. Travel Med Infect Dis 2022; 49:102360. [PMID: 35644475 DOI: 10.1016/j.tmaid.2022.102360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/01/2022] [Accepted: 05/19/2022] [Indexed: 11/29/2022]
Abstract
Surveillance is a critical component of any dengue prevention and control program. There is an increasing effort to use drones in mosquito control surveillance. Due to the novelty of drones, data are scarce on the impact and acceptance of their use in the communities to collect health-related data. The use of drones raises concerns about the protection of human privacy. Here, we show how willingness to be trained and acceptance of drone use in tech-savvy communities can help further discussions in mosquito surveillance. A cross-sectional study was conducted in Malaysia, Mexico, and Turkey to assess knowledge of diseases caused by Aedes mosquitoes, perceptions about drone use for data collection, and acceptance of drones for Aedes mosquito surveillance around homes. Compared with people living in Turkey, Mexicans had 14.3 (p < 0.0001) times higher odds and Malaysians had 4.0 (p = 0.7030) times the odds of being willing to download a mosquito surveillance app. Compared to urban dwellers, rural dwellers had 1.56 times the odds of being willing to be trained. There is widespread community support for drone use in mosquito surveillance and this community buy-in suggests a potential for success in mosquito surveillance using drones. A successful surveillance and community engagement system may be used to monitor a variety of mosquito spp. Future research should include qualitative interview data to add context to these findings.
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Affiliation(s)
- Esther Annan
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA.
| | - Jinghui Guo
- Department of Computer Science, The University of Texas at Dallas, Richardson, TX, 75080, USA
| | - Aracely Angulo-Molina
- Department of Chemical and Biological Sciences, University of Sonora, Hermosillo, 83000, Sonora, Mexico
| | - Wan Fairos Wan Yaacob
- Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Kelantan, Kampus Kota Bharu, Lembah Sireh, 15050, Kota Bharu, Kelantan, Malaysia; Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Kompleks Al-Khawarizmi, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia
| | - Nasrin Aghamohammadi
- Centre for Epidemiology and Evidence-Based Practice, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, 50603, Malaysia
| | | | - Sare İlknur Yavaşoglu
- Department of Biology, Faculty of Science and Arts, Aydın Adnan Menderes University, Aydın, 09010, Turkey
| | - Kevin Bardosh
- Center for One Health Research, School of Public Health, University of Washington, USA
| | - Nazri Che Dom
- Faculty of Health Sciences, Universiti Teknologi MARA Cawangan Selangor, Selangor, Malaysia
| | - Bingxin Zhao
- Department of Statistics, Purdue University, 250 N. University St, West Lafayette, IN, 47907, USA
| | - Uriel A Lopez-Lemus
- Department of Health Sciences, Center for Biodefense and Global Infectious Diseases, Colima, 28078, Mexico
| | - Latifur Khan
- Department of Computer Science, The University of Texas at Dallas, Richardson, TX, 75080, USA
| | - Uyen-Sa D T Nguyen
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA
| | - Ubydul Haque
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA
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15
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Gao P, Pilot E, Rehbock C, Gontariuk M, Doreleijers S, Wang L, Krafft T, Martens P, Liu Q. Land use and land cover change and its impacts on dengue dynamics in China: A systematic review. PLoS Negl Trop Dis 2021; 15:e0009879. [PMID: 34669704 PMCID: PMC8559955 DOI: 10.1371/journal.pntd.0009879] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 11/01/2021] [Accepted: 10/05/2021] [Indexed: 12/29/2022] Open
Abstract
Background Dengue is a prioritized public health concern in China. Because of the larger scale, more frequent and wider spatial distribution, the challenge for dengue prevention and control has increased in recent years. While land use and land cover (LULC) change was suggested to be associated with dengue, relevant research has been quite limited. The “Open Door” policy introduced in 1978 led to significant LULC change in China. This systematic review is the first to review the studies on the impacts of LULC change on dengue dynamics in China. This review aims at identifying the research evidence, research gaps and provide insights for future research. Methods A systematic literature review was conducted following the PRISMA protocol. The combinations of search terms on LULC, dengue and its vectors were searched in the databases PubMed, Web of Science, and Baidu Scholar. Research conducted on China published from 1978 to December 2019 and written in English or Chinese was selected for further screening. References listed in articles meeting the inclusion criteria were also reviewed and included if again inclusion criteria were met to minimize the probability of missing relevant research. Results 28 studies published between 1978 and 2017 were included for the full review. Guangdong Province and southern Taiwan were the major regional foci in the literature. The majority of the reviewed studies observed associations between LULC change factors and dengue incidence and distribution. Conflictive evidence was shown in the studies about the impacts of green space and blue space on dengue in China. Transportation infrastructure and urbanization were repeatedly suggested to be positively associated with dengue incidence and spread. The majority of the studies reviewed considered meteorological and sociodemographic factors when they analyzed the effects of LULC change on dengue. Primary and secondary remote sensing (RS) data were the primary source for LULC variables. In 21 of 28 studies, a geographic information system (GIS) was used to process data of environmental variables and dengue cases and to perform spatial analysis of dengue. Conclusions The effects of LULC change on the dynamics of dengue in China varied in different periods and regions. The application of RS and GIS enriches the means and dimensions to explore the relations between LULC change and dengue. Further comprehensive regional research is necessary to assess the influence of LULC change on local dengue transmission to provide practical advice for dengue prevention and control. Dengue is a major public health concern in China. The rapid development of urbanization along with climate change increases the challenge for dengue prevention and control. Previous research has mainly focused on the meteorological variables whereas land use and land cover (LULC) change received comparatively less attention. Our review identified that the regional research hotspots of dengue epidemics in China were Guangdong Province and southern Taiwan. Though inconsistent, most included studies somehow observed associations between at least one of the LULC change factors and dengue. A geographical information system (GIS) was widely used to perform spatial analysis in the selected literature. Its application provided a novel view to describe the relationships between environmental factors and the situation of dengue, which enabled scholars to explore more characteristics of dengue transmission. Meanwhile, the use of remote sensing (RS) enriched the means of environmental monitoring. However, there are research gaps in the area of dengue and LULC change, such as the less consideration of dengue vector study, the lack of interplays between factors, and the lack of considering interventions and policies. Furthermore, because of different research settings, results from these studies were difficult to compare. Thus, further comprehensive and comparable investigations are necessary to better understand the effects of LULC change on dengue in China. This review is the first to expound the studies on the associations between LULC change and dengue dynamics in China. It demonstrates the findings and methodologies and provided insights for future research.
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Affiliation(s)
- Panjun Gao
- Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Eva Pilot
- Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Cassandra Rehbock
- Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Marie Gontariuk
- Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Simone Doreleijers
- Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Li Wang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Thomas Krafft
- Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Pim Martens
- Maastricht Sustainability Institute (MSI), Maastricht University, Maastricht, The Netherlands
| | - Qiyong Liu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
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16
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Lee GO, Vasco L, Márquez S, Zuniga-Moya JC, Van Engen A, Uruchima J, Ponce P, Cevallos W, Trueba G, Trostle J, Berrocal VJ, Morrison AC, Cevallos V, Mena C, Coloma J, Eisenberg JNS. A dengue outbreak in a rural community in Northern Coastal Ecuador: An analysis using unmanned aerial vehicle mapping. PLoS Negl Trop Dis 2021; 15:e0009679. [PMID: 34570788 PMCID: PMC8475985 DOI: 10.1371/journal.pntd.0009679] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 07/23/2021] [Indexed: 11/19/2022] Open
Abstract
Dengue is recognized as a major health issue in large urban tropical cities but is also observed in rural areas. In these environments, physical characteristics of the landscape and sociodemographic factors may influence vector populations at small geographic scales, while prior immunity to the four dengue virus serotypes affects incidence. In 2019, a rural northwestern Ecuadorian community, only accessible by river, experienced a dengue outbreak. The village is 2-3 hours by boat away from the nearest population center and comprises both Afro-Ecuadorian and Indigenous Chachi households. We used multiple data streams to examine spatial risk factors associated with this outbreak, combining maps collected with an unmanned aerial vehicle (UAV), an entomological survey, a community census, and active surveillance of febrile cases. We mapped visible water containers seen in UAV images and calculated both the green-red vegetation index (GRVI) and household proximity to public spaces like schools and meeting areas. To identify risk factors for symptomatic dengue infection, we used mixed-effect logistic regression models to account for the clustering of symptomatic cases within households. We identified 55 dengue cases (9.5% of the population) from 37 households. Cases peaked in June and continued through October. Rural spatial organization helped to explain disease risk. Afro-Ecuadorian (versus Indigenous) households experience more symptomatic dengue (OR = 3.0, 95%CI: 1.3, 6.9). This association was explained by differences in vegetation (measured by GRVI) near the household (OR: 11.3 95% 0.38, 38.0) and proximity to the football field (OR: 13.9, 95% 4.0, 48.4). The integration of UAV mapping with other data streams adds to our understanding of these dynamics.
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Affiliation(s)
- Gwenyth O. Lee
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Luis Vasco
- Instituto de Geografía, Universidad San Francisco de Quito, Quito, Ecuador
| | - Sully Márquez
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito, Ecuador
| | - Julio C. Zuniga-Moya
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Amanda Van Engen
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jessica Uruchima
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Patricio Ponce
- Gestión de Investigación, desarrollo e Innovación, Instituto Nacional de Investigación en Salud Pública (INSPI), Quito, Ecuador
| | - William Cevallos
- Instituto de Biomedicina, Universidad Central del Ecuador, Quito, Ecuador
| | - Gabriel Trueba
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito, Ecuador
| | - James Trostle
- Department of Anthropology, Trinity College, Hartford, Connecticut, United States of America
| | - Veronica J. Berrocal
- Department of Statistics, University of California, Irvine, California, United States of America
| | - Amy C. Morrison
- Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicince, University of California, San Diego, California, United States
| | - Varsovia Cevallos
- Gestión de Investigación, desarrollo e Innovación, Instituto Nacional de Investigación en Salud Pública (INSPI), Quito, Ecuador
| | - Carlos Mena
- Instituto de Geografía, Universidad San Francisco de Quito, Quito, Ecuador
| | - Josefina Coloma
- Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, California, United States of America
| | - Joseph N. S. Eisenberg
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
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17
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Su Yin M, Bicout DJ, Haddawy P, Schöning J, Laosiritaworn Y, Sa-angchai P. Added-value of mosquito vector breeding sites from street view images in the risk mapping of dengue incidence in Thailand. PLoS Negl Trop Dis 2021; 15:e0009122. [PMID: 33684130 PMCID: PMC7971869 DOI: 10.1371/journal.pntd.0009122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 03/18/2021] [Accepted: 01/11/2021] [Indexed: 11/19/2022] Open
Abstract
Dengue is an emerging vector-borne viral disease across the world. The primary dengue mosquito vectors breed in containers with sufficient water and nutrition. Outdoor containers can be detected from geotagged images using state-of-the-art deep learning methods. In this study, we utilize such container information from street view images in developing a risk mapping model and determine the added value of including container information in predicting dengue risk. We developed seasonal-spatial models in which the target variable dengue incidence was explained using weather and container variable predictors. Linear mixed models with fixed and random effects are employed in our models to account for different characteristics of containers and weather variables. Using data from three provinces of Thailand between 2015 and 2018, the models are developed at the sub-district level resolution to facilitate the development of effective targeted intervention strategies. The performance of the models is evaluated with two baseline models: a classic linear model and a linear mixed model without container information. The performance evaluated with the correlation coefficients, R-squared, and AIC shows the proposed model with the container information outperforms both baseline models in all three provinces. Through sensitivity analysis, we investigate the containers that have a high impact on dengue risk. Our findings indicate that outdoor containers identified from street view images can be a useful data source in building effective dengue risk models and that the resulting models have potential in helping to target container elimination interventions.
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Affiliation(s)
- Myat Su Yin
- Faculty of ICT, Mahidol University, Nakhon Pathom, Thailand
| | - Dominique J. Bicout
- Biomathematics and Epidemiology, EPSP-TIMC, UMR CNRS 5525, Grenoble-Alpes University, VetAgro Sup, Grenoble, France
- Laue–Langevin Institute, Theory group, Grenoble, France
| | - Peter Haddawy
- Faculty of ICT, Mahidol University, Nakhon Pathom, Thailand
- Bremen Spatial Cognition Center, University of Bremen, Bremen, Germany
| | - Johannes Schöning
- Bremen Spatial Cognition Center, University of Bremen, Bremen, Germany
| | - Yongjua Laosiritaworn
- Information Technology Center, Department of Disease Control, Ministry of Public Health, Bangkok, Thailand
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18
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Joshi A, Miller C. Review of machine learning techniques for mosquito control in urban environments. ECOL INFORM 2021. [DOI: 10.1016/j.ecoinf.2021.101241] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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19
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van Heerden A, Leppanen J, Rotheram-Borus MJ, Worthman CM, Kohrt BA, Skeen S, Giese S, Hughes R, Bohmer L, Tomlinson M. Emerging Opportunities Provided by Technology to Advance Research in Child Health Globally. Glob Pediatr Health 2020; 7:2333794X20917570. [PMID: 32523976 PMCID: PMC7235657 DOI: 10.1177/2333794x20917570] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 02/04/2020] [Accepted: 03/06/2020] [Indexed: 11/15/2022] Open
Abstract
Current approaches to longitudinal assessment of children's developmental and psychological well-being, as mandated in the United Nations Sustainable Development Goals, are expensive and time consuming. Substantive understanding of global progress toward these goals will require a suite of new robust, cost-effective research tools designed to assess key developmental processes in diverse settings. While first steps have been taken toward this end through efforts such as the National Institutes of Health's Toolbox, experience-near approaches including naturalistic observation have remained too costly and time consuming to scale to the population level. This perspective presents 4 emerging technologies with high potential for advancing the field of child health and development research, namely (1) affective computing, (2) ubiquitous computing, (3) eye tracking, and (4) machine learning. By drawing attention of scientists, policy makers, investors/funders, and the media to the applications and potential risks of these emerging opportunities, we hope to inspire a fresh wave of innovation and new solutions to the global challenges faced by children and their families.
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Affiliation(s)
- Alastair van Heerden
- Human Sciences Research Council, Pietermaritzburg, South Africa.,University of the Witwatersrand, Johannesburg, South Africa
| | | | | | | | | | - Sarah Skeen
- Stellenbosch University, Stellenbosch, Western Cape, South Africa
| | | | - Rob Hughes
- The Children's Investment Fund Foundation, London, UK
| | - Lisa Bohmer
- Conrad N. Hilton Foundation, Westlake Village, CA, USA
| | - Mark Tomlinson
- Stellenbosch University, Stellenbosch, Western Cape, South Africa
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