12 research outputs found
A Spatio-temporal Model of African Animal Trypanosomosis Risk
[b]Background[/b]African animal trypanosomosis (AAT) is a major constraint to sustainable development of cattle farming in sub-Saharan Africa. The habitat of the tsetse fly vector is increasingly fragmented owing to demographic pressure and shifts in climate, which leads to heterogeneous risk of cyclical transmission both in space and time. In Burkina Faso and Ghana, the most important vectors are riverine species, namely Glossina palpalis gambiensis and G. tachinoides, which are more resilient to human-induced changes than the savannah and forest species. Although many authors studied the distribution of AAT risk both in space and time, spatio-temporal models allowing predictions of it are lacking.[b]Methodology/Principal Findings[/b]We used datasets generated by various projects, including two baseline surveys conducted in Burkina Faso and Ghana within PATTEC (Pan African Tsetse and Trypanosomosis Eradication Campaign) national initiatives. We computed the entomological inoculation rate (EIR) or tsetse challenge using a range of environmental data. The tsetse apparent density and their infection rate were separately estimated and subsequently combined to derive the EIR using a "one layer-one model" approach. The estimated EIR was then projected into suitable habitat. This risk index was finally validated against data on bovine trypanosomosis. It allowed a good prediction of the parasitological status (r(2) = 67%), showed a positive correlation but less predictive power with serological status (r(2) = 22%) aggregated at the village level but was not related to the illness status (r(2) = 2%).[b]Conclusions/Significance[/b]The presented spatio-temporal model provides a fine-scale picture of the dynamics of AAT risk in sub-humid areas of West Africa. The estimated EIR was high in the proximity of rivers during the dry season and more widespread during the rainy season. The present analysis is a first step in a broader framework for an efficient risk management of climate-sensitive vector-borne diseases
Marginal effect of the entomological inoculate rate on seropositivity probability.
<p>The confidence interval is presented as a red dashed line.</p
Mean predicted habitat suitability index for both species.
<p>The index varies between 0 (less suitable, green scale) and 1 (highly suitable, red scale).</p
Logistic regression of disease metrics against EIR (entomological inoculation rate) at the cattle level.
<p>Significant codes: 0 â***â 0.001 â**â 0.01 â*â 0.05 â.â 0.1 â â 1</p><p>The results present the probability of an animal for being ill, having a positive parasitical status and being seropositive. The Age variable is measured in months and the Breed variable represents the breed of the animal (Taurin/Mixed/Zebu). Standard errors in brackets.</p
Predicted risk of bovine trypanosomosis for the dry and rainy season 2005.
<p>The risk indicator is the estimated Entomological Inoculate Rate (EIR). Darker areas in red are more at risk.</p
Optimal spatio-temporal lag for the regression model of serological and parasitological prevalences using AICc.
<p>The bold value presents the best model.</p
Remote sensing data from which environmental data was built.
<p>RFE (precipitation), NDVI (vegetation), MIR (vegetation), DLST (thermal), NLST (thermal) are time series of monthly raster grids from Jan. 2003 to Dec. 2013. DEM (topographic) is a static elevation model.</p
Binomial random effect models for trypanosomose infection rates in tsetse (both species).
<p>Significant codes: 0 â***â 0.001 â**â 0.01 â*â 0.05 â.â 0.1 â â 1</p><p>DLST (Day Land Surface Temperature), Cattle_density (FAO cattle density grid), Seasonality (sinusoidal function of month when infection status was recorded). Standard error for fixed effects in brackets.</p
Environmental data derived from remote sensing used for the analysis.
<p>Environmental data derived from remote sensing used for the analysis.</p