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Lippi CA, Mundis SJ, Sippy R, Flenniken JM, Chaudhary A, Hecht G, Carlson CJ, Ryan SJ. Trends in mosquito species distribution modeling: insights for vector surveillance and disease control. Parasit Vectors 2023; 16:302. [PMID: 37641089 PMCID: PMC10463544 DOI: 10.1186/s13071-023-05912-z] [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: 03/17/2023] [Accepted: 08/04/2023] [Indexed: 08/31/2023] Open
Abstract
Species distribution modeling (SDM) has become an increasingly common approach to explore questions about ecology, geography, outbreak risk, and global change as they relate to infectious disease vectors. Here, we conducted a systematic review of the scientific literature, screening 563 abstracts and identifying 204 studies that used SDMs to produce distribution estimates for mosquito species. While the number of studies employing SDM methods has increased markedly over the past decade, the overwhelming majority used a single method (maximum entropy modeling; MaxEnt) and focused on human infectious disease vectors or their close relatives. The majority of regional models were developed for areas in Africa and Asia, while more localized modeling efforts were most common for North America and Europe. Findings from this study highlight gaps in taxonomic, geographic, and methodological foci of current SDM literature for mosquitoes that can guide future efforts to study the geography of mosquito-borne disease risk.
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Affiliation(s)
- Catherine A Lippi
- Quantitative Disease Ecology and Conservation (QDEC) Lab, Department of Geography, University of Florida, Gainesville, FL, 32601, USA.
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, 32601, USA.
| | - Stephanie J Mundis
- Quantitative Disease Ecology and Conservation (QDEC) Lab, Department of Geography, University of Florida, Gainesville, FL, 32601, USA
| | - Rachel Sippy
- Quantitative Disease Ecology and Conservation (QDEC) Lab, Department of Geography, University of Florida, Gainesville, FL, 32601, USA
- School of Mathematics and Statistics, University of St Andrews, St Andrews, KY16 9SS, UK
| | - J Matthew Flenniken
- Quantitative Disease Ecology and Conservation (QDEC) Lab, Department of Geography, University of Florida, Gainesville, FL, 32601, USA
| | - Anusha Chaudhary
- Quantitative Disease Ecology and Conservation (QDEC) Lab, Department of Geography, University of Florida, Gainesville, FL, 32601, USA
| | - Gavriella Hecht
- Quantitative Disease Ecology and Conservation (QDEC) Lab, Department of Geography, University of Florida, Gainesville, FL, 32601, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, 32601, USA
| | - Colin J Carlson
- Center for Global Health Science and Security, Georgetown University Medical Center, Georgetown University, Washington, DC, USA
| | - Sadie J Ryan
- Quantitative Disease Ecology and Conservation (QDEC) Lab, Department of Geography, University of Florida, Gainesville, FL, 32601, USA.
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, 32601, USA.
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Glison N, Romero D, Rosso V, Guerrero JC, Speranza PR. Understanding the Geographic Patterns of Closely-Related Species of Paspalum (Poaceae) Using Distribution Modelling and Seed Germination Traits. PLANTS (BASEL, SWITZERLAND) 2023; 12:1342. [PMID: 36987030 PMCID: PMC10052821 DOI: 10.3390/plants12061342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/09/2023] [Accepted: 02/21/2023] [Indexed: 06/19/2023]
Abstract
The sexual species of the Dilatata complex (Paspalum dasypleurum, P. flavescens, P. plurinerve, P. vacarianum, and P. urvillei) are closely related phylogenetically and show allopatric distributions, except P. urvillei. These species show microhabitat similarities and differences in germination traits. We integrated species distribution models (SDMs) and seed germination assays to determine whether germination divergences explain their biogeographic pattern. We trained SDMs in South America using species' presence-absence data and environmental variables. Additionally, populations sampled from highly favourable areas in the SDMs of these species were grown together, and their seeds germinated at different temperatures and dormancy-breaking conditions. Differences among species in seed dormancy and germination niche breadth were tested, and linear regressions between seed dormancy and climatic variables were explored. SDMs correctly classified both the observed presences and absences. Spatial factors and anthropogenic activities were the main factors explaining these distributions. Both SDMs and germination analyses confirmed that the niche of P. urvillei was broader than the other species which showed restricted distributions, narrower germination niches, and high correlations between seed dormancy and precipitation regimes. Both approaches provided evidence about the generalist-specialist status of each species. Divergences in seed dormancy between the specialist species could explain these allopatric distributions.
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Affiliation(s)
- Nicolás Glison
- Departamento de Biología Vegetal, Facultad de Agronomía, Universidad de la República, Avenida Eugenio Garzón 780, Montevideo 12900, Uruguay
| | - David Romero
- Laboratorio de Desarrollo Sustentable y Gestión Ambiental del Territorio, Instituto de Ecología y Ciencias Ambientales, Facultad de Ciencias, Universidad de la República, Iguá 4225, Montevideo 11400, Uruguay
- Grupo Biogeografía, Diversidad & Conservación, Departamento Biología Animal, Facultad de Ciencias, Universidad de Málaga, Campus de Teatinos s/n, 29071 Málaga, Spain
| | - Virginia Rosso
- Cátedra de Botánica Sistemática, Facultad de Agronomía, Universidad de Buenos Aires, Avenida San Martín 4453, Buenos Aires C1417DSE, Argentina
| | - José Carlos Guerrero
- Laboratorio de Desarrollo Sustentable y Gestión Ambiental del Territorio, Instituto de Ecología y Ciencias Ambientales, Facultad de Ciencias, Universidad de la República, Iguá 4225, Montevideo 11400, Uruguay
| | - Pablo Rafael Speranza
- Departamento de Biología Vegetal, Facultad de Agronomía, Universidad de la República, Avenida Eugenio Garzón 780, Montevideo 12900, Uruguay
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Marchetto E, Da Re D, Tordoni E, Bazzichetto M, Zannini P, Celebrin S, Chieffallo L, Malavasi M, Rocchini D. Testing the effect of sample prevalence and sampling methods on probability- and favourability-based SDMs. Ecol Modell 2023. [DOI: 10.1016/j.ecolmodel.2022.110248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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Report of Mosquito Vectors of Arboviruses from a Federal Conservation Unit in the Atlantic Forest, Rio de Janeiro State, Brazil. LIFE (BASEL, SWITZERLAND) 2022; 12:life12101597. [PMID: 36295032 PMCID: PMC9605666 DOI: 10.3390/life12101597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 09/27/2022] [Accepted: 09/30/2022] [Indexed: 11/26/2022]
Abstract
Arbovirus infections, such as dengue, zika, chikungunya, and yellow fever, are a major public health problem worldwide. As the main vectors, mosquitoes have been classified by the Center for Disease Control and Prevention as one of the deadliest animals alive. In this ecological study, we analyzed the population dynamics of important genera and species of mosquito vectors. Mosquito immatures were collected using ovitraps and at natural breeding sites: bamboos and bromeliads. Adult mosquitoes were captured using CDC traps with CO2, Shannon traps, and manual suction tubes. Collections took place during the rainy and dry seasons from 2019 to 2020 in the Serra dos Órgãos National Park, Rio de Janeiro state, Brazil. The highest number of species was recorded in the ovitraps, followed by CDC and bromeliads. The breeding site with the lowest diversity was bamboo, though it showed the highest level of evenness compared to the other breeding sites. The medically important genera reported were Haemagogus spp., Aedes spp., Culex spp., and Wyeomyia spp. Culicid eggs increased in the rainy season, with a peak in November 2019 and January and February 2020, and lower abundance in the dry season, from September to October 2019. Mosquito eggs had a strong positive correlation (ρ = 0.755) with temperature and a moderate positive correlation (ρ = 0.625) with rainfall. This study shows how environmental variables can influence the ecology of disease-vector mosquitoes, which are critical in the maintenance of arbovirus circulation in a threatened biome within the most densely populated region of Brazil.
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Sekarrini CE, Sumarmi, Bachri S, Taryana D, Giofandi EA. The application of geographic information system for dengue epidemic in Southeast Asia: A review on trends and opportunity. J Public Health Res 2022; 11:22799036221104170. [PMID: 35911430 PMCID: PMC9335475 DOI: 10.1177/22799036221104170] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 03/30/2022] [Indexed: 11/17/2022] Open
Abstract
The infectious disease dengue hemorrhagic fever remains an unresolved global problem, with climatic conditions and the location of areas located at the equator more often infected with dengue fever. Various modeling approaches have been employed for the development of a dengue risk map. The geographic information system approach was used as an instrument in applying mathematical algorithms to process field vector data into a preventive objective which is studied, then the application of remote sensing provides spatial-temporal data related to land use/land cover data sources as other variable categories. Map of hotspots for dengue fever cases is used to identify the risk of dengue fever areas by applying various complex methodologies, analysis, and visualization of advanced data are needed for its application in public health. In the last 10 years, the increase in the publication of dengue hemorrhagic fever in Southeast Asia in reputable international journals has increased significantly.
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Affiliation(s)
- Cipta Estri Sekarrini
- Program Doctoral of Geography Education, Faculty of Social Science, State University of Malang, Malang, East Java, Indonesia
| | - Sumarmi
- Department of Geography Education, Faculty of Social Science, State University of Malang, Malang, East Java, Indonesia
| | - Syamsul Bachri
- Department of Geography Education, Faculty of Social Science, State University of Malang, Malang, East Java, Indonesia
| | - Didik Taryana
- Department of Geography Education, Faculty of Social Science, State University of Malang, Malang, East Java, Indonesia
| | - Eggy Arya Giofandi
- Department of Geography, Faculty of Social Science, State University of Padang, Padang, West Sumatera, Indonesia
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Gutiérrez-Molina R, Acevedo P, Sánchez-Montes S, Romero-Salas D, López-Ortiz S, Flores-Primo A, Cruz-Romero A. Spatial epidemiology of Leptospira sp. exposure in bovines from Veracruz, México. Transbound Emerg Dis 2022; 69:e682-e692. [PMID: 34657392 DOI: 10.1111/tbed.14346] [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: 05/24/2021] [Revised: 11/01/2020] [Accepted: 10/03/2021] [Indexed: 11/28/2022]
Abstract
Bovine leptospirosis is a bacterial disease that affects cattle herds, causing economic losses due to reproductive problems which require expensive treatments. The main source of transmission for cattle is still uncertain, but rodents and bats can play an important role in the transmission cycle by being maintenance hosts for the pathogenic species of the bacterium and spreading it through urine. In this study, we characterize possible risk areas for bovine leptospirosis exposure in the state of Veracruz, Mexico, based on the geographical distribution of flying (bats) and terrestrial (rodents and opossums) wild hosts of Leptospira sp. reported in Mexico, in addition to climate, geography, soil characteristics, land use and human activities (environmental variables). We used a generalized linear regression model to understand the association between the frequency of anti-Leptospira sp. antibodies (a proxy of exposure) in cattle herds exposed to Leptospira, the favourability of wild hosts of Leptospira as well as the environmental variables. The parameterized model explained 12.3% of the variance. The frequency of anti-Leptospira sp. antibodies exposure in cattle herds was associated with elevation, geographic longitude, pH of the soil surface and environmental favourability for the presence of rodents, opossums and bats. The variation in exposure was mainly explained by a longitudinal gradient (6.4% of the variance) and the favourability-based indices for wild hosts (9.6% of the variance). Describing the possible risks for exposure to Leptospira in an important and neglected livestock geographical region, we provide valuable information for the selection of areas for diagnosis and prevention of this relevant disease.
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Affiliation(s)
| | - Pelayo Acevedo
- Instituto de Investigación en Recursos Cinegéticos, IREC (UCLM-CSIC-JCCM), Ciudad Real, Spain
| | - Sokani Sánchez-Montes
- Facultad de Ciencias Biológicas y Agropecuarias región Tuxpan, Universidad Veracruzana, Tuxpan, Veracruz, México
- Centro de Medicina Tropical, Unidad de Investigación en Medicina Experimental, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, México
| | - Dora Romero-Salas
- Facultad de Medicina Veterinaria y Zootecnia, Universidad Veracruzana, Veracruz, México
| | | | - Argel Flores-Primo
- Facultad de Medicina Veterinaria y Zootecnia, Universidad Veracruzana, Veracruz, México
| | - Anabel Cruz-Romero
- Facultad de Medicina Veterinaria y Zootecnia, Universidad Veracruzana, Veracruz, México
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Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review. PLoS Negl Trop Dis 2022; 16:e0010056. [PMID: 34995281 PMCID: PMC8740963 DOI: 10.1371/journal.pntd.0010056] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 12/06/2021] [Indexed: 12/23/2022] Open
Abstract
Background Traditionally, dengue surveillance is based on case reporting to a central health agency. However, the delay between a case and its notification can limit the system responsiveness. Machine learning methods have been developed to reduce the reporting delays and to predict outbreaks, based on non-traditional and non-clinical data sources. The aim of this systematic review was to identify studies that used real-world data, Big Data and/or machine learning methods to monitor and predict dengue-related outcomes. Methodology/Principal findings We performed a search in PubMed, Scopus, Web of Science and grey literature between January 1, 2000 and August 31, 2020. The review (ID: CRD42020172472) focused on data-driven studies. Reviews, randomized control trials and descriptive studies were not included. Among the 119 studies included, 67% were published between 2016 and 2020, and 39% used at least one novel data stream. The aim of the included studies was to predict a dengue-related outcome (55%), assess the validity of data sources for dengue surveillance (23%), or both (22%). Most studies (60%) used a machine learning approach. Studies on dengue prediction compared different prediction models, or identified significant predictors among several covariates in a model. The most significant predictors were rainfall (43%), temperature (41%), and humidity (25%). The two models with the highest performances were Neural Networks and Decision Trees (52%), followed by Support Vector Machine (17%). We cannot rule out a selection bias in our study because of our two main limitations: we did not include preprints and could not obtain the opinion of other international experts. Conclusions/Significance Combining real-world data and Big Data with machine learning methods is a promising approach to improve dengue prediction and monitoring. Future studies should focus on how to better integrate all available data sources and methods to improve the response and dengue management by stakeholders. Dengue is one of the most important arbovirus infections in the world and its public health, societal and economic burden is increasing. Although the majority of dengue cases are asymptomatic or mild, severe disease forms can lead to death. For this reason, early diagnosis and monitoring of dengue are crucial to decrease mortality. However, most endemic regions still rely on traditional monitoring methods, despite the growing availability of novel data sources and data-driven methods based on real-world data, Big Data, and machine learning algorithms. In this systematic review, we identified and analyzed studies that used these novel approaches for dengue monitoring and/or prediction. We found that novel data streams, such as Internet search engines and social media platforms, and machine learning methods can be successfully used to improve dengue management, but are still vastly ignored in real life. These approaches should be combined with traditional methods to help stakeholders better prepare for each outbreak and improve early responsiveness.
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Morales-Castilla I, Pappalardo P, Farrell MJ, Aguirre AA, Huang S, Gehman ALM, Dallas T, Gravel D, Davies TJ. Forecasting parasite sharing under climate change. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200360. [PMID: 34538143 PMCID: PMC8450630 DOI: 10.1098/rstb.2020.0360] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/14/2021] [Indexed: 12/12/2022] Open
Abstract
Species are shifting their distributions in response to climate change. This geographic reshuffling may result in novel co-occurrences among species, which could lead to unseen biotic interactions, including the exchange of parasites between previously isolated hosts. Identifying potential new host-parasite interactions would improve forecasting of disease emergence and inform proactive disease surveillance. However, accurate predictions of future cross-species disease transmission have been hampered by the lack of a generalized approach and data availability. Here, we propose a framework to predict novel host-parasite interactions based on a combination of niche modelling of future host distributions and parasite sharing models. Using the North American ungulates as a proof of concept, we show this approach has high cross-validation accuracy in over 85% of modelled parasites and find that more than 34% of the host-parasite associations forecasted by our models have already been recorded in the literature. We discuss potential sources of uncertainty and bias that may affect our results and similar forecasting approaches, and propose pathways to generate increasingly accurate predictions. Our results indicate that forecasting parasite sharing in response to shifts in host geographic distributions allow for the identification of regions and taxa most susceptible to emergent pathogens under climate change. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.
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Affiliation(s)
- Ignacio Morales-Castilla
- Universidad de Alcalá, GloCEE - Global Change Ecology and Evolution Research Group, Departamento de Ciencias de la Vida, 28805, Alcalá de Henares, Madrid, Spain
| | - Paula Pappalardo
- Department of Invertebrate Zoology, Smithsonian National Museum of Natural History, Washington, DC 20560, USA
| | - Maxwell J. Farrell
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada
| | - A. Alonso Aguirre
- Department of Environmental Science and Policy, George Mason University, Fairfax, VA 22030-4400, USA
| | - Shan Huang
- Senckenberg Biodiversity and Climate Centre (SBiK-F), Senckenberganlage 25, Frankfurt (Main) 60325, Germany
| | - Alyssa-Lois M. Gehman
- Department of Zoology, University of British Columbia, Canada
- Hakai Institute, end of Kwakshua Channel, Calvert Island, Canada
| | - Tad Dallas
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70806, USA
- Department of Biological Sciences, University of South Carolina, Columbia, SC 29208, USA
| | - Dominique Gravel
- Département de biologie, Université de Sherbrooke, 2500 Boul. de l'Université, Sherbroke, Canada J1K2R1
| | - T. Jonathan Davies
- Departments of Botany and Forest and Conservation Sciences, University of British Columbia, Canada
- Department of Botany and Plant Biotechnology, African Centre for DNA Barcoding, University of Johannesburg, Johannesburg, South Africa
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Tian Y, Mi X, Cui H, Zhang P, Kang B. Using Z-number to measure the reliability of new information fusion method and its application in pattern recognition. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107658] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Aliaga-Samanez A, Cobos-Mayo M, Real R, Segura M, Romero D, Fa JE, Olivero J. Worldwide dynamic biogeography of zoonotic and anthroponotic dengue. PLoS Negl Trop Dis 2021; 15:e0009496. [PMID: 34097704 PMCID: PMC8211191 DOI: 10.1371/journal.pntd.0009496] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 06/17/2021] [Accepted: 05/22/2021] [Indexed: 11/30/2022] Open
Abstract
Dengue is a viral disease transmitted by mosquitoes. The rapid spread of dengue could lead to a global pandemic, and so the geographical extent of this spread needs to be assessed and predicted. There are also reasons to suggest that transmission of dengue from non-human primates in tropical forest cycles is being underestimated. We investigate the fine-scale geographic changes in transmission risk since the late 20th century, and take into account for the first time the potential role that primate biogeography and sylvatic vectors play in increasing the disease transmission risk. We apply a biogeographic framework to the most recent global dataset of dengue cases. Temporally stratified models describing favorable areas for vector presence and for disease transmission are combined. Our models were validated for predictive capacity, and point to a significant broadening of vector presence in tropical and non-tropical areas globally. We show that dengue transmission is likely to spread to affected areas in China, Papua New Guinea, Australia, USA, Colombia, Venezuela, Madagascar, as well as to cities in Europe and Japan. These models also suggest that dengue transmission is likely to spread to regions where there are presently no or very few reports of occurrence. According to our results, sylvatic dengue cycles account for a small percentage of the global extent of the human case record, but could be increasing in relevance in Asia, Africa, and South America. The spatial distribution of factors favoring transmission risk in different regions of the world allows for distinct management strategies to be prepared. The rate of disease emergence is increasing globally, and many long-existing diseases are extending their distribution ranges. This is the case for dengue, a global pandemic whose mosquito vectors are currently occupying ever-increasing numbers of regions worldwide. We updated the most complete global dataset of dengue cases available, and addressed the fine-scale analysis of the geographic changes experienced in dengue-transmission risk since the late 20th century. Our approach is the first to take into account the potential role of primates and sylvatic vectors in increasing the disease transmission risk in tropical forests. We built models that describe the favorable areas for vector presence and for disease occurrence, and combined them in order to obtain a novel model for predicting transmission risk. We show that dengue transmission is likely to spread to affected areas in Asia, Africa, North and South America, and Oceania, and to regions with presently no or very few cases, including cities in Europe and Japan. The global contribution of sylvatic dengue cycles is small but meaningful. Our methodological approach can differentiate the factors favoring risk in different world regions, thus allowing for management strategies to be prepared specifically for each of these regions.
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Affiliation(s)
- Alisa Aliaga-Samanez
- Grupo de Biogeografía, Diversidad y Conservación, Departamento de Biología Animal, Facultad de Ciencias, Universidad de Málaga, Málaga, Spain
- * E-mail:
| | - Marina Cobos-Mayo
- Grupo de Biogeografía, Diversidad y Conservación, Departamento de Biología Animal, Facultad de Ciencias, Universidad de Málaga, Málaga, Spain
| | - Raimundo Real
- Grupo de Biogeografía, Diversidad y Conservación, Departamento de Biología Animal, Facultad de Ciencias, Universidad de Málaga, Málaga, Spain
- Instituto IBYDA, Centro de Experimentación Grice-Hutchinson, Málaga, Spain
| | - Marina Segura
- Centro de Vacunación Internacional de Málaga, Ministerio de Sanidad, Consumo y Bienestar Social, Málaga, Spain
| | - David Romero
- Grupo de Biogeografía, Diversidad y Conservación, Departamento de Biología Animal, Facultad de Ciencias, Universidad de Málaga, Málaga, Spain
- Laboratorio de Desarrollo Sustentable y Gestión Ambiental del Territorio, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay
| | - Julia E. Fa
- Division of Biology and Conservation Ecology, Manchester Metropolitan University, Manchester, United Kingdom
- Center for International Forestry Research (CIFOR), CIFOR Headquarters, Bogor, Indonesia
| | - Jesús Olivero
- Grupo de Biogeografía, Diversidad y Conservación, Departamento de Biología Animal, Facultad de Ciencias, Universidad de Málaga, Málaga, Spain
- Instituto IBYDA, Centro de Experimentación Grice-Hutchinson, Málaga, Spain
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Tsheten T, Clements ACA, Gray DJ, Wangdi K. Dengue risk assessment using multicriteria decision analysis: A case study of Bhutan. PLoS Negl Trop Dis 2021; 15:e0009021. [PMID: 33566797 PMCID: PMC7875403 DOI: 10.1371/journal.pntd.0009021] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 11/30/2020] [Indexed: 12/20/2022] Open
Abstract
Background Dengue is the most rapidly spreading vector-borne disease globally, with a 30-fold increase in global incidence over the last 50 years. In Bhutan, dengue incidence has been on the rise since 2004, with numerous outbreaks reported across the country. The aim of this study was to identify and map areas that are vulnerable to dengue in Bhutan. Methodology/Principal findings We conducted a multicriteria decision analysis (MCDA) using a weighted linear combination (WLC) to obtain a vulnerability map of dengue. Risk factors (criteria) were identified and assigned with membership values for vulnerability according to the available literature. Sensitivity analysis and validation of the model was conducted to improve the robustness and predictive ability of the map. Our study revealed marked differences in geographical vulnerability to dengue by location and season. Low-lying areas and those located along the southern border were consistently found to be at higher risk of dengue. The vulnerability extended to higher elevation areas including some areas in the Capital city Thimphu during the summer season. The higher risk was mostly associated with relatively high population density, agricultural and built-up landscapes and relatively good road connectivity. Conclusions Using MCDA, our study identified vulnerable areas in Bhutan during specific seasons when and where the transmission of dengue is most likely to occur. This study provides evidence for the National Vector-borne Disease Control programme to optimize the use of limited public health resources for surveillance and vector control, to mitigate the public health threat of dengue. Dengue is an important vector-borne viral disease affecting humans. In Bhutan, dengue incidence is on the rise with increased frequency of outbreaks and spread to new areas. Outbreaks were reported from places as high as above 900m above sea level in recent years. However, dengue control activities in Bhutan are usually initiated at the time of outbreaks. This often leads to a large number of cases and overburden the health system. To address these issues, we developed dengue risk maps at a fine spatial resolution by combining risk factors that mediate the transmission of dengue using a weighted linear combination. Vulnerability to dengue was spatially heterogeneous and varied by season. Dengue is highly vulnerable in low-lying areas throughout the season. However, the vulnerability extended to higher geographical elevations including the nation’s capital during the summer season. The study provides a firm evidence-base to prioritize areas and seasons for dengue control strategies in Bhutan.
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Affiliation(s)
- Tsheten Tsheten
- Australian National University, Canberra, Australia
- Royal Centre for Disease Control, Ministry of Health, Thimphu, Bhutan
- * E-mail:
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12
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García-Carrasco JM, Muñoz AR, Olivero J, Segura M, Real R. Predicting the spatio-temporal spread of West Nile virus in Europe. PLoS Negl Trop Dis 2021; 15:e0009022. [PMID: 33411739 PMCID: PMC7790247 DOI: 10.1371/journal.pntd.0009022] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 12/01/2020] [Indexed: 12/12/2022] Open
Abstract
West Nile virus is a widely spread arthropod-born virus, which has mosquitoes as vectors and birds as reservoirs. Humans, as dead-end hosts of the virus, may suffer West Nile Fever (WNF), which sometimes leads to death. In Europe, the first large-scale epidemic of WNF occurred in 1996 in Romania. Since then, human cases have increased in the continent, where the highest number of cases occurred in 2018. Using the location of WNF cases in 2017 and favorability models, we developed two risk models, one environmental and the other spatio-environmental, and tested their capacity to predict in 2018: 1) the location of WNF; 2) the intensity of the outbreaks (i.e. the number of confirmed human cases); and 3) the imminence of the cases (i.e. the Julian week in which the first case occurred). We found that climatic variables (the maximum temperature of the warmest month and the annual temperature range), human-related variables (rain-fed agriculture, the density of poultry and horses), and topo-hydrographic variables (the presence of rivers and altitude) were the best environmental predictors of WNF outbreaks in Europe. The spatio-environmental model was the most useful in predicting the location of WNF outbreaks, which suggests that a spatial structure, probably related to bird migration routes, has a role in the geographical pattern of WNF in Europe. Both the intensity of cases and their imminence were best predicted using the environmental model, suggesting that these features of the disease are linked to the environmental characteristics of the areas. We highlight the relevance of river basins in the propagation dynamics of the disease, as outbreaks started in the lower parts of the river basins, from where WNF spread towards the upper parts. Therefore, river basins should be considered as operational geographic units for the public health management of the disease.
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Affiliation(s)
- José-María García-Carrasco
- Biogeography, Diversity and Conservation Lab, Department of Animal Biology, Faculty of Sciences, University of Málaga, Málaga, Spain
| | - Antonio-Román Muñoz
- Biogeography, Diversity and Conservation Lab, Department of Animal Biology, Faculty of Sciences, University of Málaga, Málaga, Spain
| | - Jesús Olivero
- Biogeography, Diversity and Conservation Lab, Department of Animal Biology, Faculty of Sciences, University of Málaga, Málaga, Spain
| | - Marina Segura
- International Vaccination Center of Malaga, Maritime Port of Malaga, Ministry of Health, Consumption and Social Welfare, Government of Spain, Málaga, Spain
| | - Raimundo Real
- Biogeography, Diversity and Conservation Lab, Department of Animal Biology, Faculty of Sciences, University of Málaga, Málaga, Spain
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Ávila-Tomás JF, Mayer-Pujadas MA, Quesada-Varela VJ. [Artificial intelligence and its applications in medicine II: Current importance and practical applications]. Aten Primaria 2021; 53:81-88. [PMID: 32571595 PMCID: PMC7752970 DOI: 10.1016/j.aprim.2020.04.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 04/22/2020] [Indexed: 12/16/2022] Open
Abstract
Technology and medicine follow a parallel path during the last decades. Technological advances are changing the concept of health and health needs are influencing the development of technology. Artificial intelligence (AI) is made up of a series of sufficiently trained logical algorithms from which machines are capable of making decisions for specific cases based on general rules. This technology has applications in the diagnosis and follow-up of patients with an individualized prognostic evaluation of them. Furthermore, if we combine this technology with robotics, we can create intelligent machines that make more efficient diagnostic proposals in their work. Therefore, AI is going to be a technology present in our daily work through machines or computer programs, which in a more or less transparent way for the user, will become a daily reality in health processes. Health professionals have to know this technology, its advantages and disadvantages, because it will be an integral part of our work. In these two articles we intend to give a basic vision of this technology adapted to doctors with a review of its history and evolution, its real applications at the present time and a vision of a future in which AI and Big Data will shape the personalized medicine that will characterize the 21st century.
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Affiliation(s)
- Jose Francisco Ávila-Tomás
- Medicina de Familia y Comunitaria, Centro de Salud Santa Isabel, Madrid, España; Medicina Preventiva y Salud Pública, Universidad Rey Juan Carlos, Móstoles, Madrid, España; Estrutura Organizativa de Xestión Integrada (EOXI), Vigo, Pontevedra, España.
| | - Miguel Angel Mayer-Pujadas
- Medicina de Familia y Comunitaria, Research Programme on Biomedical Informatics (GRIB), Instituto Hospital del Mar de Investigaciones Médicas, Barcelona, España; Universitat Pompeu Fabra, Barcelona, España; Miembro del Grupo de Trabajo de Innovación Tecnológica y Sistemas de Información de la semFYC
| | - Victor Julio Quesada-Varela
- Medicina de Familia y Comunitaria, Centro de Salud de A Guarda, A Guarda, Pontevedra, España; Estrutura Organizativa de Xestión Integrada (EOXI), Vigo, Pontevedra, España; Miembro del Grupo de Trabajo de Innovación Tecnológica y Sistemas de Información de la semFYC
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Unravelling the drivers of maned wolf activity along an elevational gradient in the Atlantic Forest, south-eastern Brazil. Mamm Biol 2020. [DOI: 10.1007/s42991-020-00017-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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