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Singleton AL, Glidden CK, Chamberlin AJ, Tuan R, Palasio RGS, Pinter A, Caldeira RL, Mendonça CLF, Carvalho OS, Monteiro MV, Athni TS, Sokolow SH, Mordecai EA, De Leo GA. Species distribution modeling for disease ecology: A multi-scale case study for schistosomiasis host snails in Brazil. PLOS GLOBAL PUBLIC HEALTH 2024; 4:e0002224. [PMID: 39093879 PMCID: PMC11296653 DOI: 10.1371/journal.pgph.0002224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 07/17/2024] [Indexed: 08/04/2024]
Abstract
Species distribution models (SDMs) are increasingly popular tools for profiling disease risk in ecology, particularly for infectious diseases of public health importance that include an obligate non-human host in their transmission cycle. SDMs can create high-resolution maps of host distribution across geographical scales, reflecting baseline risk of disease. However, as SDM computational methods have rapidly expanded, there are many outstanding methodological questions. Here we address key questions about SDM application, using schistosomiasis risk in Brazil as a case study. Schistosomiasis is transmitted to humans through contact with the free-living infectious stage of Schistosoma spp. parasites released from freshwater snails, the parasite's obligate intermediate hosts. In this study, we compared snail SDM performance across machine learning (ML) approaches (MaxEnt, Random Forest, and Boosted Regression Trees), geographic extents (national, regional, and state), types of presence data (expert-collected and publicly-available), and snail species (Biomphalaria glabrata, B. straminea, and B. tenagophila). We used high-resolution (1km) climate, hydrology, land-use/land-cover (LULC), and soil property data to describe the snails' ecological niche and evaluated models on multiple criteria. Although all ML approaches produced comparable spatially cross-validated performance metrics, their suitability maps showed major qualitative differences that required validation based on local expert knowledge. Additionally, our findings revealed varying importance of LULC and bioclimatic variables for different snail species at different spatial scales. Finally, we found that models using publicly-available data predicted snail distribution with comparable AUC values to models using expert-collected data. This work serves as an instructional guide to SDM methods that can be applied to a range of vector-borne and zoonotic diseases. In addition, it advances our understanding of the relevant environment and bioclimatic determinants of schistosomiasis risk in Brazil.
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Affiliation(s)
- Alyson L. Singleton
- Emmett Interdisciplinary Program in Environment and Resources, Stanford University, Stanford, California, United States of America
| | - Caroline K. Glidden
- Department of Biology, Stanford University, Stanford, California, United States of America
- Institute for Human-centered Artificial Intelligence, Stanford University, Stanford, California, United States of America
| | - Andrew J. Chamberlin
- Department of Oceans, Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America
| | | | | | | | | | | | - Omar S. Carvalho
- Fiocruz Minas/Belo Horizonte-Minas Gerais, Belo Horizonte, Brazil
| | - Miguel V. Monteiro
- Geoinformation & Earth Observation Division, National Institute for Space Research (INPE), São Paulo, Brazil
| | - Tejas S. Athni
- Department of Biology, Stanford University, Stanford, California, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Susanne H. Sokolow
- Department of Oceans, Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America
- Marine Science Institute, University of California Santa Barbara, Santa Barbara, California, United States of America
| | - Erin A. Mordecai
- Department of Biology, Stanford University, Stanford, California, United States of America
- Woods Institute for the Environment, Stanford University, Stanford, California, United States of America
| | - Giulio A. De Leo
- Department of Oceans, Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America
- Woods Institute for the Environment, Stanford University, Stanford, California, United States of America
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Sarı F, Kavallieratos NG, Eleftheriadou N. Determination of forest fire risk with respect to Marchalina hellenica potential distribution to protect pine honey production sites in Turkey. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:53348-53368. [PMID: 39186202 DOI: 10.1007/s11356-024-34664-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 08/05/2024] [Indexed: 08/27/2024]
Abstract
Turkey is the leading producer of pine honey worldwide, accounting for 90% of global production, largely due to the presence of Marchalina hellenica populations. However, in recent years, devastating forest fires have caused substantial damage to Pinus brutia forests and M. hellenica populations, leading to a dramatic decline in pine honey production areas. The urgency for early intervention procedures against forest fires and relocation of M. hellenica populations to other P. brutia forests has become apparent. A comprehensive assessment of 25 criteria was conducted to investigate the thresholds and behaviors of each criterion, which play a vital role in the distribution of M. hellenica, using the maximum entropy model (MaxEnt). To evaluate the impact of forest fires on the distribution of M. hellenica, the potential locations of pine honey production areas were determined for 2022. Furthermore, the susceptibility of forest fires was modeled for all pine honey production months. The findings revealed that forest fires have destroyed 384.9 km2 (12.8% of the total pine honey production area), predominantly in August and September, with the most severe damage in Marmaris (156 km2) and significant impacts in Ula, Köyceğiz, and Milas. The analysis facilitates the estimation of new areas suitable for M. hellenica and pine honey production while providing valuable insights into strategies for mitigating forest fires and formulating proactive protection protocols.
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Affiliation(s)
- Fatih Sarı
- Faculty of Engineering and Natural Sciences, Geomatic Engineering Department, Konya Technical University, Ardıçlı Neighborhood, Rauf Orbay Road 42250, Selçuklu, Konya, Turkey
| | - Nickolas G Kavallieratos
- Laboratory of Agricultural Zoology and Entomology, Department of Crop Science, Agricultural University of Athens, 75 Iera Odos str, 11855, Athens, Greece
| | - Nikoleta Eleftheriadou
- Laboratory of Agricultural Zoology and Entomology, Department of Crop Science, Agricultural University of Athens, 75 Iera Odos str, 11855, Athens, Greece.
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Tuñon A, García J, Carrera LC, Chaves LF, Lenhart AE, Loaiza JR. Chemical control of medically important arthropods in Panama: A systematic literature review of historical efforts. Acta Trop 2024; 255:107217. [PMID: 38677361 DOI: 10.1016/j.actatropica.2024.107217] [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: 01/29/2024] [Revised: 03/22/2024] [Accepted: 04/09/2024] [Indexed: 04/29/2024]
Abstract
Vector-borne diseases are a major source of morbidity in Panama. Herein, we describe historical usage patterns of synthetic insecticides to control arthropod disease vectors in this country. We examine the influence of interventions by vector control programs on the emergence of insecticide resistance. Chemical control has traditionally focused on two mosquito species: Anopheles albimanus, a major regional malaria vector, and Aedes aegypti, a historical vector of yellow fever, and current vector of dengue, chikungunya, and Zika. Countrywide populations of An. albimanus depict hyperirritability to organochlorine insecticides administered by indoor residual spraying, although they appear susceptible to these insecticides in bioassays settings, as well as to organophosphate and carbamate insecticides in field tests. Populations of Ae. aegypti show resistance to pyrethroids, particularly in areas near Panama City, but the spread of resistance remains unknown in Ae. aegypti and Aedes albopictus. A One Health approach is needed in Panama to pinpoint the insecticide resistance mechanisms including the frequency of knockdown mutations and behavioral plasticity in populations of Anopheles and Aedes mosquitoes. This information is necessary to guide the sustainable implementation of chemical control strategies and the use of modern vector control technologies such as genetically modified mosquitoes, and endosymbiont Wolbachia-based biological control.
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Affiliation(s)
- Anyi Tuñon
- Programa Centroamericano de Maestría en Entomología, Vicerrectoría de Investigación y Postgrado, Universidad de Panamá, República de Panamá; Instituto Conmemorativo Gorgas de Estudios de la Salud, Ciudad de Panamá, Apartado 0816-02593, Panama
| | - Joel García
- Programa Centroamericano de Maestría en Entomología, Vicerrectoría de Investigación y Postgrado, Universidad de Panamá, República de Panamá; Instituto de Investigaciones Científicas & Servicios de Alta Tecnología, Edificio 219, Clayton, PO 0843-01103, Ciudad del Saber, República de Panamá
| | - Lorenzo Cáceres Carrera
- Instituto Conmemorativo Gorgas de Estudios de la Salud, Ciudad de Panamá, Apartado 0816-02593, Panama
| | - Luis Fernando Chaves
- Department of Environmental and Occupational Health, School of Public Health and Department of Geography, Indiana University, Bloomington, IN, USA
| | - Audrey E Lenhart
- Entomology Branch, Centers for Disease Control and Prevention (CDC), 1600 Clifton Rd, Atlanta, GA, 30329, USA
| | - Jose R Loaiza
- Programa Centroamericano de Maestría en Entomología, Vicerrectoría de Investigación y Postgrado, Universidad de Panamá, República de Panamá; Smithsonian Tropical Research Institute, Panama City, Panama; Instituto de Investigaciones Científicas & Servicios de Alta Tecnología, Edificio 219, Clayton, PO 0843-01103, Ciudad del Saber, República de Panamá.
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Chaves LF, Friberg MD, Pascual M, Calzada JE, Luckhart S, Bergmann LR. Community-serving research addressing climate change impacts on vector-borne diseases. Lancet Planet Health 2024; 8:e334-e341. [PMID: 38729673 PMCID: PMC11323095 DOI: 10.1016/s2542-5196(24)00049-4] [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: 07/31/2023] [Revised: 01/29/2024] [Accepted: 03/25/2024] [Indexed: 05/12/2024]
Abstract
The impacts of climate change on vector-borne diseases are uneven across human populations. This pattern reflects the effect of changing environments on the biology of transmission, which is also modulated by social and other inequities. These disparities are also linked to research outcomes that could be translated into tools for transmission reduction, but are not necessarily actionable in the communities where transmission occurs. The transmission of vector-borne diseases could be averted by developing research that is both hypothesis-driven and community-serving for populations affected by climate change, where local communities interact as equal partners with scientists, developing and implementing research projects with the aim of improving community health. In this Personal View, we share five principles that have guided our research practice to serve the needs of communities affected by vector-borne diseases.
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Affiliation(s)
- Luis Fernando Chaves
- Department of Environmental and Occupational Health, School of Public Health and Department of Geography, Indiana University, Bloomington, IN, USA; Instituto Conmemorativo Gorgas de Estudios de la Salud, Ciudad de Panamá, Panama.
| | - Mariel D Friberg
- Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA; Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Mercedes Pascual
- Department of Biology and Department of Environmental Studies, New York University, New York, NY, USA
| | - Jose E Calzada
- Instituto Conmemorativo Gorgas de Estudios de la Salud, Ciudad de Panamá, Panama
| | - Shirley Luckhart
- Department of Entomology, Plant Pathology and Nematology and Department of Biological Sciences, University of Idaho, Moscow, ID, USA
| | - Luke R Bergmann
- Department of Geography, University of British Columbia, Vancouver, BC, Canada
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Zhou G, Lee MC, Wang X, Zhong D, Githeko AK, Yan G. Mapping Potential Malaria Vector Larval Habitats for Larval Source Management in Western Kenya: Introduction to Multimodel Ensembling Approaches. Am J Trop Med Hyg 2024; 110:421-430. [PMID: 38350135 PMCID: PMC10919169 DOI: 10.4269/ajtmh.23-0108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 11/03/2023] [Indexed: 02/15/2024] Open
Abstract
Identification and mapping of larval sources are a prerequisite for effective planning and implementing mosquito larval source management (LSM). Ensemble modeling is increasingly used for prediction modeling, but it lacks standard procedures. We proposed a detailed framework to predict potential malaria vector larval habitats by using multimodel ensemble modeling, which includes selection of models, ensembling method, and predictors, evaluation of variable importance, prediction of potential larval habitats, and assessment of prediction uncertainty. The models were built and validated based on multisite, multiyear field observations and climatic/environmental variables. Model performance was tested using independent field observations. Overall, we found that the ensembled model predicted larval habitats with about 20% more accuracy than the average of the individual models ensembled. Key larval habitat predictors in western Kenya were elevation, geomorphon class, and precipitation for the 2 months prior. Additional predictors may be required to increase the predictive accuracy of the larva-positive habitats. This is the first study to provide a detailed framework for the process of multimodel ensemble modeling for malaria vector habitats. Mapping of potential habitats will be helpful in LSM planning.
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Affiliation(s)
- Guofa Zhou
- Program in Public Health, University of California, Irvine, California
| | - Ming-Chieh Lee
- Program in Public Health, University of California, Irvine, California
| | - Xiaoming Wang
- Program in Public Health, University of California, Irvine, California
| | - Daibin Zhong
- Program in Public Health, University of California, Irvine, California
| | - Andrew K. Githeko
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Guiyun Yan
- Program in Public Health, University of California, Irvine, California
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Newman EA, Feng X, Onland JD, Walker KR, Young S, Smith K, Townsend J, Damian D, Ernst K. Defining the roles of local precipitation and anthropogenic water sources in driving the abundance of Aedes aegypti, an emerging disease vector in urban, arid landscapes. Sci Rep 2024; 14:2058. [PMID: 38267474 PMCID: PMC10808563 DOI: 10.1038/s41598-023-50346-3] [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: 06/11/2023] [Accepted: 12/19/2023] [Indexed: 01/26/2024] Open
Abstract
Understanding drivers of disease vectors' population dynamics is a pressing challenge. For short-lived organisms like mosquitoes, landscape-scale models must account for their highly local and rapid life cycles. Aedes aegypti, a vector of multiple emerging diseases, has become abundant in desert population centers where water from precipitation could be a limiting factor. To explain this apparent paradox, we examined Ae. aegypti abundances at > 660 trapping locations per year for 3 years in the urbanized Maricopa County (metropolitan Phoenix), Arizona, USA. We created daily precipitation layers from weather station data using a kriging algorithm, and connected localized daily precipitation to numbers of mosquitoes trapped at each location on subsequent days. Precipitation events occurring in either of two critical developmental periods for mosquitoes were correlated to suppressed subsequent adult female presence and abundance. LASSO models supported these analyses for female presence but not abundance. Precipitation may explain 72% of Ae. aegypti presence and 90% of abundance, with anthropogenic water sources supporting mosquitoes during long, precipitation-free periods. The method of using kriging and weather station data may be generally applicable to the study of various ecological processes and patterns, and lead to insights into microclimates associated with a variety of organisms' life cycles.
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Affiliation(s)
- Erica A Newman
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA.
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, 78712, USA.
| | - Xiao Feng
- Department of Biology, University of North Carolina, Chapel Hill, NC, 27599, USA
| | | | - Kathleen R Walker
- Department of Entomology, University of Arizona, 1140 E South Campus Drive, Forbes 410, Tucson, AZ, 85721, USA
| | - Steven Young
- Maricopa County Environmental Services Vector Control Division, 3220 W Gibson Ln, Phoenix, AZ, 85009, USA
| | - Kirk Smith
- Maricopa County Environmental Services Vector Control Division, 3220 W Gibson Ln, Phoenix, AZ, 85009, USA
| | - John Townsend
- Maricopa County Environmental Services Vector Control Division, 3220 W Gibson Ln, Phoenix, AZ, 85009, USA
| | - Dan Damian
- Maricopa County Office of Enterprise Technology, 301 S 4Th Ave #200, Phoenix, AZ, 85003, USA
| | - Kacey Ernst
- Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, 85721, USA
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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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Nduwayezu G, Zhao P, Kagoyire C, Eklund L, Bizimana JP, Pilesjo P, Mansourian A. Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest: A case study in Rwanda. GEOSPATIAL HEALTH 2023; 18. [PMID: 37246535 DOI: 10.4081/gh.2023.1184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/28/2023] [Indexed: 05/30/2023]
Abstract
As found in the health studies literature, the levels of climate association between epidemiological diseases have been found to vary across regions. Therefore, it seems reasonable to allow for the possibility that relationships might vary spatially within regions. We implemented the geographically weighted random forest (GWRF) machine learning method to analyze ecological disease patterns caused by spatially non-stationary processes using a malaria incidence dataset for Rwanda. We first compared the geographically weighted regression (WGR), the global random forest (GRF), and the geographically weighted random forest (GWRF) to examine the spatial non-stationarity in the non-linear relationships between malaria incidence and their risk factors. We used the Gaussian areal kriging model to disaggregate the malaria incidence at the local administrative cell level to understand the relationships at a fine scale since the model goodness of fit was not satisfactory to explain malaria incidence due to the limited number of sample values. Our results show that in terms of the coefficients of determination and prediction accuracy, the geographical random forest model performs better than the GWR and the global random forest model. The coefficients of determination of the geographically weighted regression (R2), the global RF (R2), and the GWRF (R2) were 4.74, 0.76, and 0.79, respectively. The GWRF algorithm achieves the best result and reveals that risk factors (rainfall, land surface temperature, elevation, and air temperature) have a strong non-linear relationship with the spatial distribution of malaria incidence rates, which could have implications for supporting local initiatives for malaria elimination in Rwanda.
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Affiliation(s)
- Gilbert Nduwayezu
- Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden; Department of Civil, Environmental and Geomatics Engineering, University of Rwanda.
| | - Pengxiang Zhao
- Department of Physical Geography and Ecosystem Science, Lund University, Lund.
| | - Clarisse Kagoyire
- Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden; Centre for Geographic Information Systems and Remote Sensing, University of Rwanda, Kigali.
| | - Lina Eklund
- Department of Physical Geography and Ecosystem Science, Lund University, Lund.
| | | | - Petter Pilesjo
- Department of Physical Geography and Ecosystem Science, Lund University, Lund.
| | - Ali Mansourian
- Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden; Lund University's Profile Area: Nature-based Future Solutions.
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Cuervo PF, Artigas P, Lorenzo-Morales J, Bargues MD, Mas-Coma S. Ecological Niche Modelling Approaches: Challenges and Applications in Vector-Borne Diseases. Trop Med Infect Dis 2023; 8:tropicalmed8040187. [PMID: 37104313 PMCID: PMC10141209 DOI: 10.3390/tropicalmed8040187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/17/2023] [Accepted: 03/21/2023] [Indexed: 03/29/2023] Open
Abstract
Vector-borne diseases (VBDs) pose a major threat to human and animal health, with more than 80% of the global population being at risk of acquiring at least one major VBD. Being profoundly affected by the ongoing climate change and anthropogenic disturbances, modelling approaches become an essential tool to assess and compare multiple scenarios (past, present and future), and further the geographic risk of transmission of VBDs. Ecological niche modelling (ENM) is rapidly becoming the gold-standard method for this task. The purpose of this overview is to provide an insight of the use of ENM to assess the geographic risk of transmission of VBDs. We have summarised some fundamental concepts and common approaches to ENM of VBDS, and then focused with a critical view on a number of crucial issues which are often disregarded when modelling the niches of VBDs. Furthermore, we have briefly presented what we consider the most relevant uses of ENM when dealing with VBDs. Niche modelling of VBDs is far from being simple, and there is still a long way to improve. Therefore, this overview is expected to be a useful benchmark for niche modelling of VBDs in future research.
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Affiliation(s)
- Pablo Fernando Cuervo
- Departamento de Parasitologia, Facultad de Farmacia, Universidad de Valencia, Av. Vicent Andres Estelles s/n, 46100 Burjassot, Valencia, Spain
- CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos IIII, C/Monforte de Lemos 3-5. Pabellón 11, Planta 0, 28029 Madrid, Madrid, Spain
- Correspondence:
| | - Patricio Artigas
- Departamento de Parasitologia, Facultad de Farmacia, Universidad de Valencia, Av. Vicent Andres Estelles s/n, 46100 Burjassot, Valencia, Spain
- CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos IIII, C/Monforte de Lemos 3-5. Pabellón 11, Planta 0, 28029 Madrid, Madrid, Spain
| | - Jacob Lorenzo-Morales
- CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos IIII, C/Monforte de Lemos 3-5. Pabellón 11, Planta 0, 28029 Madrid, Madrid, Spain
- Instituto Universitario de Enfermedades Tropicales y Salud Pública de Canarias, Universidad de La Laguna, Av. Astrofísico Fco. Sánchez s/n, 38203 La Laguna, Canary Islands, Spain
| | - María Dolores Bargues
- Departamento de Parasitologia, Facultad de Farmacia, Universidad de Valencia, Av. Vicent Andres Estelles s/n, 46100 Burjassot, Valencia, Spain
- CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos IIII, C/Monforte de Lemos 3-5. Pabellón 11, Planta 0, 28029 Madrid, Madrid, Spain
| | - Santiago Mas-Coma
- Departamento de Parasitologia, Facultad de Farmacia, Universidad de Valencia, Av. Vicent Andres Estelles s/n, 46100 Burjassot, Valencia, Spain
- CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos IIII, C/Monforte de Lemos 3-5. Pabellón 11, Planta 0, 28029 Madrid, Madrid, Spain
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Romero-Vega LM, Piche-Ovares M, Soto-Garita C, Barantes Murillo DF, Chaverri LG, Alfaro-Alarcón A, Corrales-Aguilar E, Troyo A. Seasonal changes in the diversity, host preferences and infectivity of mosquitoes in two arbovirus-endemic regions of Costa Rica. Parasit Vectors 2023; 16:34. [PMID: 36703148 PMCID: PMC9881273 DOI: 10.1186/s13071-022-05579-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 11/04/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Mosquitoes are vectors of various arboviruses belonging to the genera Alphavirus and Flavivirus, and Costa Rica is endemic to several of them. The aim of this study was to describe and analyze the community structure of such vectors in Costa Rica. METHODS Sampling was performed in two different coastal locations of Costa Rica with evidence of arboviral activity during rainy and dry seasons. Encephalitis vector surveillance traps, CDC female gravid traps and ovitraps were used. Detection of several arboviruses by Pan-Alpha and Pan-Flavi PCR was attempted. Blood meals were also identified. The Normalized Difference Vegetation Index (NDVI) was estimated for each area during the rainy and dry seasons. The Chao2 values for abundance and Shannon index for species diversity were also estimated. RESULTS A total of 1802 adult mosquitoes belonging to 55 species were captured, among which Culex quinquefasciatus was the most caught species. The differences in NDVI were higher between seasons and between regions, yielding lower Chao-Sørensen similarity index values. Venezuelan equine encephalitis virus, West Nile virus and Madariaga virus were not detected at all, and dengue virus and Zika virus were detected in two separate Cx. quinquefasciatus specimens. The primary blood-meal sources were chickens (60%) and humans (27.5%). Both sampled areas were found to have different seasonal dynamics and population turnover, as reflected in the Chao2 species richness estimation values and Shannon diversity index. CONCLUSION Seasonal patterns in mosquito community dynamics in coastal areas of Costa Rica have strong differences despite a geographical proximity. The NDVI influences mosquito diversity at the regional scale more than at the local scale. However, year-long continuous sampling is required to better understand local dynamics.
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Affiliation(s)
- Luis M. Romero-Vega
- Universidad de Costa Rica, San José, Costa Rica
- Universidad Nacional, Heredia, Costa Rica
| | - Marta Piche-Ovares
- Universidad de Costa Rica, San José, Costa Rica
- Universidad Nacional, Heredia, Costa Rica
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