The potential distribution of Bacillus anthracis suitability across Uganda using INLA

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dc.contributor.authorNdolo, V A
dc.contributor.authorRedding, D
dc.contributor.authorDeka, M A
dc.contributor.authorSalzer, J S
dc.contributor.authorVieira, A R
dc.contributor.authorOnyuth, H
dc.contributor.authorOcaido, M
dc.contributor.authorTweyongyere, R
dc.contributor.authorAzuba, R
dc.contributor.authorMonje, F
dc.contributor.authorArio, A R
dc.contributor.authorKabwama, S
dc.contributor.authorKisaakye, E
dc.contributor.authorBulage, L
dc.contributor.authorKwesiga, B
dc.contributor.authorNtono, V
dc.contributor.authorHarris, J
dc.contributor.authorWood, J L N
dc.contributor.authorConlan, A J K
dc.date.accessioned2023-08-15T08:17:04Z
dc.date.available2023-08-15T08:17:04Z
dc.date.issued2022-11
dc.description.abstractAbstract To reduce the veterinary, public health, environmental, and economic burden associated with anthrax outbreaks, it is vital to identify the spatial distribution of areas suitable for Bacillus anthracis, the causative agent of the disease. Bayesian approaches have previously been applied to estimate uncertainty around detected areas of B. anthracis suitability. However, conventional simulation-based techniques are often computationally demanding. To solve this computational problem, we use Integrated Nested Laplace Approximation (INLA) which can adjust for spatially structured random effects, to predict the suitability of B. anthracis across Uganda. We apply a Generalized Additive Model (GAM) within the INLA Bayesian framework to quantify the relationships between B. anthracis occurrence and the environment. We consolidate a national database of wildlife, livestock, and human anthrax case records across Uganda built across multiple sectors bridging human and animal partners using a One Health approach. The INLA framework successfully identified known areas of species suitability in Uganda, as well as suggested unknown hotspots across Northern, Eastern, and Central Uganda, which have not been previously identified by other niche models. The major risk factors for B. anthracis suitability were proximity to water bodies (0–0.3 km), increasing soil calcium (between 10 and 25 cmolc/kg), and elevation of 140–190 m. The sensitivity of the final model against the withheld evaluation dataset was 90% (181 out of 202 = 89.6%; rounded up to 90%). The prediction maps generated using this model can guide future anthrax prevention and surveillance plans by the relevant stakeholders in Uganda.en_US
dc.description.sponsorshipThis work was supported, in whole or in part, by the Bill & Melinda Gates Foundation [Grant Number: OPP1144]. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission. Funding was also provided by the Dudley Stamp Memorial Award from the Royal Geographical Society. The Alborada Trust provides support for James Wood.en_US
dc.identifier.citationNdolo, V. A., D. Redding, M. A. Deka, et al. 'The Potential Distribution of Bacillus Anthracis Suitability Across Uganda using INLA', Scientific Reports, vol. 12/no. 1, (2022), pp. 19967-13.en_US
dc.identifier.issn2045-2322 (Online)
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/9140
dc.language.isoenen_US
dc.publisherNature Publishing Groupen_US
dc.titleThe potential distribution of Bacillus anthracis suitability across Uganda using INLAen_US
dc.typeArticleen_US
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