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de Klerk J, Tildesley M, Labuschagne K, Gorsich E. Modelling bluetongue and African horse sickness vector (Culicoides spp.) distribution in the Western Cape in South Africa using random forest machine learning. Parasit Vectors 2024; 17:354. [PMID: 39169433 PMCID: PMC11340078 DOI: 10.1186/s13071-024-06446-8] [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: 06/06/2024] [Accepted: 08/12/2024] [Indexed: 08/23/2024] Open
Abstract
BACKGROUND Culicoides biting midges exhibit a global spatial distribution and are the main vectors of several viruses of veterinary importance, including bluetongue (BT) and African horse sickness (AHS). Many environmental and anthropological factors contribute to their ability to live in a variety of habitats, which have the potential to change over the years as the climate changes. Therefore, as new habitats emerge, the risk for new introductions of these diseases of interest to occur increases. The aim of this study was to model distributions for two primary vectors for BT and AHS (Culicoides imicola and Culicoides bolitinos) using random forest (RF) machine learning and explore the relative importance of environmental and anthropological factors in a region of South Africa with frequent AHS and BT outbreaks. METHODS Culicoides capture data were collected between 1996 and 2022 across 171 different capture locations in the Western Cape. Predictor variables included climate-related variables (temperature, precipitation, humidity), environment-related variables (normalised difference vegetation index-NDVI, soil moisture) and farm-related variables (livestock densities). Random forest (RF) models were developed to explore the spatial distributions of C. imicola, C. bolitinos and a merged species map, where both competent vectors were combined. The maps were then compared to interpolation maps using the same capture data as well as historical locations of BT and AHS outbreaks. RESULTS Overall, the RF models performed well with 75.02%, 61.6% and 74.01% variance explained for C. imicola, C. bolitinos and merged species models respectively. Cattle density was the most important predictor for C. imicola and water vapour pressure the most important for C. bolitinos. Compared to interpolation maps, the RF models had higher predictive power throughout most of the year when species were modelled individually; however, when merged, the interpolation maps performed better in all seasons except winter. Finally, midge densities did not show any conclusive correlation with BT or AHS outbreaks. CONCLUSION This study yielded novel insight into the spatial abundance and drivers of abundance of competent vectors of BT and AHS. It also provided valuable data to inform mathematical models exploring disease outbreaks so that Culicoides-transmitted diseases in South Africa can be further analysed.
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Affiliation(s)
- Joanna de Klerk
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK.
| | - Michael Tildesley
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
| | - Karien Labuschagne
- Epidemiology, Parasites and Vectors, Agricultural Research Council, Onderstepoort Veterinary Research, Onderstepoort, 0110, South Africa
| | - Erin Gorsich
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
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Penhaskashi J, Sekimoto O, Chiappelli F. Permafrost viremia and immune tweening. Bioinformation 2023; 19:685-691. [PMID: 37885785 PMCID: PMC10598357 DOI: 10.6026/97320630019685] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 06/30/2023] [Accepted: 06/30/2023] [Indexed: 10/28/2023] Open
Abstract
The immune system, an exquisitely regulated physiological system, utilizes a wide spectrum of soluble factors and multiple cell populations and subpopulations at diverse states of maturation to monitor and protect the organism against foreign organisms. Immune surveillance is ensured by distinguishing self-antigens from self-associated with non-self (e.g., viral) peptides presented by major histocompatibility complexes (MHC). Pathology is often identified as unregulated inflammatory responses (e.g., cytokine storm), or recognizing self as a non-self entity (i.e., auto-immunity). Artificial intelligence (AI), and in particular specific machine learning (ML) paradigms (e.g., Deep Learning [DL]) proffer powerful algorithms to better understand and more accurately predict immune responses, immune regulation and homeostasis, and immune reactivity to challenges (i.e., immune allostasis) by their intrinsic ability to interpret immune parameters, pathways and events by analyzing large amounts of complex data and drawing predictive inferences (i.e., immune tweening). We propose here that DL models play an increasingly significant role in better defining and characterizing immunological surveillance to ancient and novel virus species released by thawing permafrost.
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Affiliation(s)
- Jaden Penhaskashi
- />Division of West Valley Dental Implant Center, Encino, CA 91316, USA
| | | | - Francesco Chiappelli
- />Dental Group of Sherman Oaks, CA 91403 , USA
- />Center for the Health Sciences, UCLA, Los Angeles, CA, USA
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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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Arif S, MacNeil A. Predictive models aren't for causal inference. Ecol Lett 2022; 25:1741-1745. [DOI: 10.1111/ele.14033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/21/2022] [Accepted: 05/08/2022] [Indexed: 12/14/2022]
Affiliation(s)
- Suchinta Arif
- Ocean Frontier Institute Dalhousie University, Department of Biology Halifax Nova Scotia Canada
| | - Aaron MacNeil
- Ocean Frontier Institute Dalhousie University, Department of Biology Halifax Nova Scotia Canada
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