Addressing the global snakebite crisis with geo-spatial analyses - Recent advances and future direction.

Envenomings Medically relevant snakes Neglected tropical diseases Snakebite incidence Spatio-temporal epidemiology Species distribution models

Journal

Toxicon: X
ISSN: 2590-1710
Titre abrégé: Toxicon X
Pays: England
ID NLM: 101741983

Informations de publication

Date de publication:
Sep 2021
Historique:
received: 29 04 2021
revised: 13 07 2021
accepted: 14 07 2021
entrez: 17 8 2021
pubmed: 18 8 2021
medline: 18 8 2021
Statut: epublish

Résumé

Venomous snakebite is a neglected tropical disease that annually leads to hundreds of thousands of deaths or long-term physical and mental ailments across the developing world. Insufficient data on spatial variation in snakebite risk, incidence, human vulnerability, and accessibility of medical treatment contribute substantially to ineffective on-ground management. There is an urgent need to collect data, fill knowledge gaps and address on-ground management problems. The use of novel, and transdisciplinary approaches that take advantage of recent advances in spatio-temporal models, 'big data', high performance computing, and fine-scale spatial information can add value to snakebite management by strategically improving our understanding and mitigation capacity of snakebite. We review the background and recent advances on the topic of snakebite related geospatial analyses and suggest avenues for priority research that will have practical on-ground applications for snakebite management and mitigation. These include streamlined, targeted data collection on snake distributions, snakebites, envenomings, venom composition, health infrastructure, and antivenom accessibility along with fine-scale models of spatio-temporal variation in snakebite risk and incidence, intraspecific venom variation, and environmental change modifying human exposure. These measures could improve and 'future-proof' antivenom production methods, antivenom distribution and stockpiling systems, and human-wildlife conflict management practices, while simultaneously feeding into research on venom evolution, snake taxonomy, ecology, biogeography, and conservation.

Identifiants

pubmed: 34401744
doi: 10.1016/j.toxcx.2021.100076
pii: S2590-1710(21)00012-6
pmc: PMC8350508
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100076

Subventions

Organisme : World Health Organization
ID : 001
Pays : International
Organisme : Medical Research Council
ID : MR/R015600/1
Pays : United Kingdom

Informations de copyright

© 2021 Published by Elsevier Ltd.

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Auteurs

Anna F V Pintor (AFV)

Division of Data, Analytics and Delivery for Impact (DDI), World Health Organization, Geneva, Switzerland.
Australian Institute of Tropical Health and Medicine, Division of Tropical Health and Medicine, James Cook University, Cairns, Australia.

Nicolas Ray (N)

GeoHealth Group, Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland.

Joshua Longbottom (J)

Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
Centre for Health Informatics, Computing and Statistics, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom.

Carlos A Bravo-Vega (CA)

Research Group in Mathematical and Computational Biology (BIOMAC), Department of Biomedical Engineering, University of Los Andes, Bogotá, Colombia.

Masoud Yousefi (M)

School of Biology, College of Science, University of Tehran, Iran.

Kris A Murray (KA)

MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, UK.
MRC Unit the Gambia at London School of Hygiene and Tropical Medicine, Atlantic Blvd, Fajara, Gambia.

Dileepa S Ediriweera (DS)

Health Data Science Unit, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka.

Peter J Diggle (PJ)

Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom.

Classifications MeSH