The Library
Spatio-temporal modelling of Leishmania infantum infection among domestic dogs : a simulation study and sensitivity analysis applied to rural Brazil
Tools
Tools
Tools
Buckingham-Jeffery, Elizabeth, Hill, Edward M., Datta, Samik, Dilger, Erin and Courtenay, Orin (2019) Spatio-temporal modelling of Leishmania infantum infection among domestic dogs : a simulation study and sensitivity analysis applied to rural Brazil. Parasites & Vectors, 12 (1). 215. doi:10.1186/s13071-019-3430-y
|
PDF
Spatio-temporal-modelling-Leishmania-infantum-dogs-simulation-Brazil-Hill-2019.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1613Kb) | Preview |
Official URL: https://doi.org/10.1186/s13071-019-3430-y
Abstract
The parasite Leishmania infantum causes zoonotic visceral leishmaniasis (VL), a potentially fatal vector-borne disease of canids and humans. Zoonotic VL poses a significant risk to public health, with regions of Latin America being particularly afflicted by the disease. Leishmania infantum parasites are transmitted between hosts during blood-feeding by infected female phlebotomine sand flies. With a principal reservoir host of L. infantum being domestic dogs, limiting prevalence in this reservoir may result in a reduced risk of infection for the human population. To this end, a primary focus of research efforts has been to understand disease transmission dynamics among dogs. One way this can be achieved is through the use of mathematical models. We have developed a stochastic, spatial, individual-based mechanistic model of L. infantum transmission in domestic dogs. The model framework was applied to a rural Brazilian village setting with parameter values informed by fieldwork and laboratory data. To ensure household and sand fly populations were realistic, we statistically fitted distributions for these entities to existing survey data. To identify the model parameters of highest importance, we performed a stochastic parameter sensitivity analysis of the prevalence of infection among dogs to the model parameters. We computed parametric distributions for the number of humans and animals per household and a non-parametric temporal profile for sand fly abundance. The stochastic parameter sensitivity analysis determined prevalence of L. infantum infection in dogs to be most strongly affected by the sand fly associated parameters and the proportion of immigrant dogs already infected with L. infantum parasites. Establishing the model parameters with the highest sensitivity of average L. infantum infection prevalence in dogs to their variation helps motivate future data collection efforts focusing on these elements. Moreover, the proposed mechanistic modelling framework provides a foundation that can be expanded to explore spatial patterns of zoonotic VL in humans and to assess spatially targeted interventions.
| Item Type: | Journal Article | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Subjects: | R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine R Medicine > RC Internal medicine |
|||||||||
| Divisions: | Faculty of Science > Life Sciences (2010- ) Faculty of Science > Mathematics |
|||||||||
| SWORD Depositor: | Library Publications Router | |||||||||
| Library of Congress Subject Headings (LCSH): | Kala-azar -- Brazil, Sand flies -- Brazil, Dogs as carriers of disease -- Brazil | |||||||||
| Journal or Publication Title: | Parasites & Vectors | |||||||||
| Publisher: | BioMed Central Ltd. | |||||||||
| ISSN: | 1756-3305 | |||||||||
| Official Date: | 7 May 2019 | |||||||||
| Dates: |
|
|||||||||
| Volume: | 12 | |||||||||
| Number: | 1 | |||||||||
| Article Number: | 215 | |||||||||
| DOI: | 10.1186/s13071-019-3430-y | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access | |||||||||
| RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
