Spatio-temporal modelling of Leishmania infantum infection among domestic dogs: a simulation study and sensitivity analysis applied to rural Brazil
Abstract Background 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. Leishmani...
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| Format: | Article |
| Language: | English |
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BMC
2019-05-01
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| Series: | Parasites & Vectors |
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| Online Access: | http://link.springer.com/article/10.1186/s13071-019-3430-y |
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| author | Elizabeth Buckingham-Jeffery Edward M. Hill Samik Datta Erin Dilger Orin Courtenay |
| author_facet | Elizabeth Buckingham-Jeffery Edward M. Hill Samik Datta Erin Dilger Orin Courtenay |
| author_sort | Elizabeth Buckingham-Jeffery |
| collection | DOAJ |
| description | Abstract Background 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. Methods 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. Results 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. Conclusions 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. |
| first_indexed | 2024-12-10T09:40:43Z |
| format | Article |
| id | doaj.art-dc424928edda4ba29841b3904ca3a5aa |
| institution | Directory Open Access Journal |
| issn | 1756-3305 |
| language | English |
| last_indexed | 2024-12-10T09:40:43Z |
| publishDate | 2019-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Parasites & Vectors |
| spelling | doaj.art-dc424928edda4ba29841b3904ca3a5aa2022-12-22T01:54:01ZengBMCParasites & Vectors1756-33052019-05-0112111310.1186/s13071-019-3430-ySpatio-temporal modelling of Leishmania infantum infection among domestic dogs: a simulation study and sensitivity analysis applied to rural BrazilElizabeth Buckingham-Jeffery0Edward M. Hill1Samik Datta2Erin Dilger3Orin Courtenay4School of Mathematics, University of ManchesterZeeman Institute: SBIDER (Systems Biology & Infectious Disease Epidemiology Research), University of WarwickPopulation Modelling Group, National Institute of Water and Atmospheric ResearchZeeman Institute: SBIDER (Systems Biology & Infectious Disease Epidemiology Research), University of WarwickZeeman Institute: SBIDER (Systems Biology & Infectious Disease Epidemiology Research), University of WarwickAbstract Background 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. Methods 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. Results 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. Conclusions 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.http://link.springer.com/article/10.1186/s13071-019-3430-yLeishmania infantumVisceral leishmaniasisMathematical modellingSpatio-temporal modellingTransmission dynamicsVector-borne transmission |
| spellingShingle | Elizabeth Buckingham-Jeffery Edward M. Hill Samik Datta Erin Dilger Orin Courtenay Spatio-temporal modelling of Leishmania infantum infection among domestic dogs: a simulation study and sensitivity analysis applied to rural Brazil Parasites & Vectors Leishmania infantum Visceral leishmaniasis Mathematical modelling Spatio-temporal modelling Transmission dynamics Vector-borne transmission |
| title | Spatio-temporal modelling of Leishmania infantum infection among domestic dogs: a simulation study and sensitivity analysis applied to rural Brazil |
| title_full | Spatio-temporal modelling of Leishmania infantum infection among domestic dogs: a simulation study and sensitivity analysis applied to rural Brazil |
| title_fullStr | Spatio-temporal modelling of Leishmania infantum infection among domestic dogs: a simulation study and sensitivity analysis applied to rural Brazil |
| title_full_unstemmed | Spatio-temporal modelling of Leishmania infantum infection among domestic dogs: a simulation study and sensitivity analysis applied to rural Brazil |
| title_short | Spatio-temporal modelling of Leishmania infantum infection among domestic dogs: a simulation study and sensitivity analysis applied to rural Brazil |
| title_sort | spatio temporal modelling of leishmania infantum infection among domestic dogs a simulation study and sensitivity analysis applied to rural brazil |
| topic | Leishmania infantum Visceral leishmaniasis Mathematical modelling Spatio-temporal modelling Transmission dynamics Vector-borne transmission |
| url | http://link.springer.com/article/10.1186/s13071-019-3430-y |
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