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Neves JMM, Belo VS, Catita CMS, de Oliveira BFA, Horta MAP. Modeling of Human Rabies Cases in Brazil in Different Future Global Warming Scenarios. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:212. [PMID: 38397701 PMCID: PMC10888213 DOI: 10.3390/ijerph21020212] [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: 12/27/2023] [Revised: 01/22/2024] [Accepted: 01/29/2024] [Indexed: 02/25/2024]
Abstract
Bat species have been observed to have the potential to expand their distribution in response to climate change, thereby influencing shifts in the spatial distribution and population dynamics of human rabies cases. In this study, we applied an ensemble niche modeling approach to project climatic suitability under different future global warming scenarios for human rabies cases in Brazil, and assessed the impact on the probability of emergence of new cases. We obtained notification records of human rabies cases in all Brazilian cities from January 2001 to August 2023, as reported by the State and Municipal Health Departments. The current and future climate data were sourced from a digital repository on the WorldClim website. The future bioclimatic variables provided were downscaled climate projections from CMIP6 (a global model ensemble) and extracted from the regionalized climate model HadGEM3-GC31-LL for three future socioeconomic scenarios over four periods (2021-2100). Seven statistical algorithms (MAXENT, MARS, RF, FDA, CTA, GAM, and GLM) were selected for modeling human rabies. Temperature seasonality was the bioclimatic variable with the highest relative contribution to both current and future consensus models. Future scenario modeling for human rabies indicated a trend of changes in the areas of occurrence, maintaining the current pace of global warming, population growth, socioeconomic instability, and the loss of natural areas. In Brazil, there are areas with a higher likelihood of climatic factors contributing to the emergence of cases. When assessing future scenarios, a change in the local climatic suitability is observed that may lead to a reduction or increase in cases, depending on the region.
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Affiliation(s)
| | - Vinicius Silva Belo
- Laboratory of Parasitology, Federal University of São João del-Rei, Divinopolis 36307-352, Brazil;
| | - Cristina Maria Souza Catita
- Department of Geographic Engineering, Geophysics and Energy, University of Lisbon, Lisbon 1649-004, Portugal;
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Kumar G, Pandurengan RK, Parra ER, Kannan K, Haymaker C. Spatial modelling of the tumor microenvironment from multiplex immunofluorescence images: methods and applications. Front Immunol 2023; 14:1288802. [PMID: 38179056 PMCID: PMC10765501 DOI: 10.3389/fimmu.2023.1288802] [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: 09/04/2023] [Accepted: 12/07/2023] [Indexed: 01/06/2024] Open
Abstract
Spatial modelling methods have gained prominence with developments in high throughput imaging platforms. Multiplex immunofluorescence (mIF) provides the scope to examine interactions between tumor and immune compartment at single cell resolution using a panel of antibodies that can be chosen based on the cancer type or the clinical interest of the study. The markers can be used to identify the phenotypes and to examine cellular interactions at global and local scales. Several translational studies rely on key understanding of the tumor microenvironment (TME) to identify drivers of immune response in immunotherapy based clinical trials. To improve the success of ongoing trials, a number of retrospective approaches can be adopted to understand differences in response, recurrence and progression by examining the patient's TME from tissue samples obtained at baseline and at various time points along the treatment. The multiplex immunofluorescence (mIF) technique provides insight on patient specific cell populations and their relative spatial distribution as qualitative measures of a favorable treatment outcome. Spatial analysis of these images provides an understanding of the intratumoral heterogeneity and clustering among cell populations in the TME. A number of mathematical models, which establish clustering as a measure of deviation from complete spatial randomness, can be applied to the mIF images represented as spatial point patterns. These mathematical models, developed for landscape ecology and geographic information studies, can be applied to the TME after careful consideration of the tumor type (cold vs. hot) and the tumor immune landscape. The spatial modelling of mIF images can show observable engagement of T cells expressing immune checkpoint molecules and this can then be correlated with single-cell RNA sequencing data.
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Affiliation(s)
| | | | | | - Kasthuri Kannan
- Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, United States
| | - Cara Haymaker
- Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, United States
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Dakhil MA, El-Keblawy A, El-Sheikh MA, Halmy MWA, Ksiksi T, Hassan WA. Global Invasion Risk Assessment of Prosopis juliflora at Biome Level: Does Soil Matter? BIOLOGY 2021; 10:biology10030203. [PMID: 33803081 PMCID: PMC7999975 DOI: 10.3390/biology10030203] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 02/23/2021] [Accepted: 02/25/2021] [Indexed: 11/16/2022]
Abstract
Prosopis juliflora is one of the most problematic invasive trees in tropical and subtropical regions. Understanding driving forces affecting the potential global distribution would help in managing its current and future spread. The role of climate on the global spatial distribution of P. juliflora has been well studied, but little is known about the role of soil and human impacts as potential drivers. Here, we used maximum entropy (MaxEnt) for species distribution modelling to understand the role of climate (C), soil (S) and human impacts (H), C+S, and C+S+H in controlling the potential invasion range of P. juliflora, and to project its global potential invasive risk. We defined the top threatened global biomes, as predicted by the best-selected model. The incorporation of the edaphic factors improved the model performance and enhanced the accuracy of the outcome. Our findings revealed that the potential invasion risk increases with increases in mean temperature of the driest quarter (Bio9), soil alkalinity and clay fractions. Arid and semi-arid lands are at the highest risk of invasion than other moist biomes.
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Affiliation(s)
- Mohammed A. Dakhil
- Botany and Microbiology Department, Faculty of Science, Helwan University, Cairo 11790, Egypt;
| | - Ali El-Keblawy
- Department of Applied Biology, Faculty of Science, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
- Correspondence:
| | - Mohamed A. El-Sheikh
- Botany & Microbiology Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia;
| | - Marwa Waseem A. Halmy
- Department of Environmental Sciences, Faculty of Science, Alexandria University, Alexandria 21511, Egypt;
| | - Taoufik Ksiksi
- Biology Department, United Arab Emirates University, Al Ain 15258, United Arab Emirates;
| | - Walaa A. Hassan
- Botany and Microbiology Department, Faculty of Science, Beni-Suef University, Beni-Suef 62511, Egypt;
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Escobar LE. Ecological Niche Modeling: An Introduction for Veterinarians and Epidemiologists. Front Vet Sci 2020; 7:519059. [PMID: 33195507 PMCID: PMC7641643 DOI: 10.3389/fvets.2020.519059] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 08/25/2020] [Indexed: 01/08/2023] Open
Abstract
Most infectious diseases in animals are not distributed randomly. Instead, diseases in livestock and wildlife are predictable in terms of the geography, time, and species affected. Ecological niche modeling approaches have been crucial to the advancement of our understanding of diversity and diseases distributions. This contribution is an introductory overview to the field of distributional ecology, with emphasis on its application for spatial epidemiology. A new, revised modeling framework is proposed for more detailed and replicable models that account for both the biology of the disease to be modeled and the uncertainty of the data available. Considering that most disease systems need at least two organisms interacting (i.e., host and pathogen), biotic interactions lie at the core of the pathogen's ecological niche. As a result, neglecting interacting organisms in pathogen dynamics (e.g., maintenance, reproduction, and transmission) may limit efforts to forecast disease distributions in veterinary epidemiology. Although limitations of ecological niche modeling are noted, it is clear that the application and value of ecological niche modeling to epidemiology will increase in the future. Potential research lines include the examination of the effects of biotic variables on model performance, assessments of protocols for model calibration in disease systems, and new tools and metrics for robust model evaluation. Epidemiologists aiming to employ ecological niche modeling theory and methods to reconstruct and forecast epidemics should familiarize themselves with ecological literature and must consider multidisciplinary collaborations including veterinarians to develop biologically sound, statistically robust analyses. This review attempts to increase the use of tools from ecology in disease mapping.
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Affiliation(s)
- Luis E Escobar
- Department of Fish and Wildlife Conservation, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
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Wang X, Qi C, Zhang DD, Li CY, Zheng ZL, Wang PZ, Xu QQ, Ding SJ, Li XJ. Epidemic character and environmental factors in epidemic areas of severe fever with thrombocytopenia syndrome in Shandong Province. Ticks Tick Borne Dis 2020; 12:101593. [PMID: 33096512 DOI: 10.1016/j.ttbdis.2020.101593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 09/20/2020] [Accepted: 10/05/2020] [Indexed: 01/28/2023]
Abstract
Severe fever with thrombocytopenia syndrome (SFTS) is an emerging lethal tick-borne disease that has been widely prevalent in East Asia in recent years, and raised an important public health problem in China. However, a comprehensive and thorough understanding of the current SFTS epidemic areas in Shandong Province is not available. Accordingly, a descriptive analysis was applied to explore the demographic and spatio-temporal features of SFTS cases in Shandong Province from 2010 to 2015. The division between epidemic areas and non-epidemic areas was given by maximum entropy niche model (MaxEnt) based on environmental factors such as temperature and precipitation. There were 1,786 SFTS cases between 2010 and 2015 in Shandong, mainly involving middle-aged and elderly individuals (age:40-80) and farmers (84.6 %). May-October was the high-incidence period and the SFTS cases were mostly clustered in the central and eastern regions of Shandong Province. In light of MaxEnt, 3 specific environmental features between dichotomous areas were identified, including 1) most epidemic areas are covered by acidic soils (Constituent ratio: 63.8 %) while 29.1 % coverage appears in non-epidemic areas, 2) compared with non-epidemic areas, the identical kinds of agricultural areas accounted for a higher constituent ratio (64.9 % vs. 42.7 %), and 3) lower level of annual temperature in epidemic areas compared to non-epidemic areas [Median: 13.2℃ vs. 14.2℃; (25th IQR, 75th IQR): (12.5, 13.7) vs. (13.6, 14.9)]. Our study suggests middle-aged and elderly farmers are high-risk population to be focused on in future prevention and acidic soils, agricultural activities as well lower temperature that may be related to increased SFTS incidence.
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Affiliation(s)
- Xu Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China
| | - Chang Qi
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China
| | - Dan-Dan Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China
| | - Chun-Yu Li
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China
| | - Zhao-Lei Zheng
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China
| | - Pei-Zhu Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China
| | - Qin-Qin Xu
- Zibo Center for Disease Control and Prevention, Shandong Province, China
| | - Shu-Jun Ding
- Department of Viral Infectious Diseases Control and Prevention, Shandong Center for Disease Control and Prevention, Shandong Provincial Key Laboratory of Communicable Disease Control and Prevention, Jinan, Shandong Province, China.
| | - Xiu-Jun Li
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China.
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Escobar LE, Moen R, Craft ME, VanderWaal KL. Mapping parasite transmission risk from white-tailed deer to a declining moose population. EUR J WILDLIFE RES 2019. [DOI: 10.1007/s10344-019-1297-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Fountain-Jones NM, Pearse WD, Escobar LE, Alba-Casals A, Carver S, Davies TJ, Kraberger S, Papeş M, Vandegrift K, Worsley-Tonks K, Craft ME. Towards an eco-phylogenetic framework for infectious disease ecology. Biol Rev Camb Philos Soc 2017; 93:950-970. [PMID: 29114986 DOI: 10.1111/brv.12380] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Revised: 09/22/2017] [Accepted: 09/28/2017] [Indexed: 12/12/2022]
Abstract
Identifying patterns and drivers of infectious disease dynamics across multiple scales is a fundamental challenge for modern science. There is growing awareness that it is necessary to incorporate multi-host and/or multi-parasite interactions to understand and predict current and future disease threats better, and new tools are needed to help address this task. Eco-phylogenetics (phylogenetic community ecology) provides one avenue for exploring multi-host multi-parasite systems, yet the incorporation of eco-phylogenetic concepts and methods into studies of host pathogen dynamics has lagged behind. Eco-phylogenetics is a transformative approach that uses evolutionary history to infer present-day dynamics. Here, we present an eco-phylogenetic framework to reveal insights into parasite communities and infectious disease dynamics across spatial and temporal scales. We illustrate how eco-phylogenetic methods can help untangle the mechanisms of host-parasite dynamics from individual (e.g. co-infection) to landscape scales (e.g. parasite/host community structure). An improved ecological understanding of multi-host and multi-pathogen dynamics across scales will increase our ability to predict disease threats.
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Affiliation(s)
| | - William D Pearse
- Ecology Center and Department of Biology, Utah State University, Logan, UT, 84321, U.S.A
| | - Luis E Escobar
- Department of Veterinary Population Medicine, University of Minnesota, St Paul, MN, 55108, U.S.A.,Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA 24061, U.S.A
| | - Ana Alba-Casals
- Department of Veterinary Population Medicine, University of Minnesota, St Paul, MN, 55108, U.S.A
| | - Scott Carver
- School of Biological Sciences, University of Tasmania, Hobart, 7001, Australia
| | | | - Simona Kraberger
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, 80523, U.S.A
| | - Monica Papeş
- Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, 37996, U.S.A
| | - Kurt Vandegrift
- Department of Biology, The Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, 16802, U.S.A
| | - Katherine Worsley-Tonks
- Department of Veterinary Population Medicine, University of Minnesota, St Paul, MN, 55108, U.S.A
| | - Meggan E Craft
- Department of Veterinary Population Medicine, University of Minnesota, St Paul, MN, 55108, U.S.A
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Escobar LE, Qiao H, Lee C, Phelps NBD. Novel Methods in Disease Biogeography: A Case Study with Heterosporosis. Front Vet Sci 2017; 4:105. [PMID: 28770215 PMCID: PMC5511963 DOI: 10.3389/fvets.2017.00105] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 06/19/2017] [Indexed: 11/16/2022] Open
Abstract
Disease biogeography is currently a promising field to complement epidemiology, and ecological niche modeling theory and methods are a key component. Therefore, applying the concepts and tools from ecological niche modeling to disease biogeography and epidemiology will provide biologically sound and analytically robust descriptive and predictive analyses of disease distributions. As a case study, we explored the ecologically important fish disease Heterosporosis, a relatively poorly understood disease caused by the intracellular microsporidian parasite Heterosporis sutherlandae. We explored two novel ecological niche modeling methods, the minimum-volume ellipsoid (MVE) and the Marble algorithm, which were used to reconstruct the fundamental and the realized ecological niche of H. sutherlandae, respectively. Additionally, we assessed how the management of occurrence reports can impact the output of the models. Ecological niche models were able to reconstruct a proxy of the fundamental and realized niche for this aquatic parasite, identifying specific areas suitable for Heterosporosis. We found that the conceptual and methodological advances in ecological niche modeling provide accessible tools to update the current practices of spatial epidemiology. However, careful data curation and a detailed understanding of the algorithm employed are critical for a clear definition of the assumptions implicit in the modeling process and to ensure biologically sound forecasts. In this paper, we show how sensitive MVE is to the input data, while Marble algorithm may provide detailed forecasts with a minimum of parameters. We showed that exploring algorithms of different natures such as environmental clusters, climatic envelopes, and logistic regressions (e.g., Marble, MVE, and Maxent) provide different scenarios of potential distribution. Thus, no single algorithm should be used for disease mapping. Instead, different algorithms should be employed for a more informed and complete understanding of the pathogen or parasite in question.
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Affiliation(s)
- Luis E Escobar
- Minnesota Aquatic Invasive Species Research Center, University of Minnesota, St. Paul, MN, United States.,Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, St. Paul, MN, United States.,Escuela de Estudios de Postgrado, Facultad de Medicina Veterinaria y Zootecnia, Universidad de San Carlos de Guatemala, Guatemala, Guatemala
| | - Huijie Qiao
- Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Christine Lee
- Minnesota Aquatic Invasive Species Research Center, University of Minnesota, St. Paul, MN, United States
| | - Nicholas B D Phelps
- Minnesota Aquatic Invasive Species Research Center, University of Minnesota, St. Paul, MN, United States.,Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, St. Paul, MN, United States
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