title: Trying to predict and explain the presence of African trypanosomes in tsetse flies.
abstract: Trypanosome infections identified by polymerase chain reaction on field-caught tsetse flies from various locations were analyzed with respect to factors intrinsic and extrinsic to the trypanosome-tsetse association. These factors were then simultaneously analyzed using artificial neural networks (ANNs) and the important factors were identified to predict and explain the presence of trypanosomes in tsetse. Among 4 trypanosome subgroups (Trypanosoma brucei s.l., T. congolense of the 'savannah' and of the 'riverine-forest' types, and T. simiae), the presence of the 2 types of T. congolense was predictable in more than 80% of cases, suggesting that the model incorporated some of the key variables. These 2 types of T. congolense were significantly associated in tsetse. Among all the examined factors, it was the presence of T. congolense savannah type that best explained the presence of T. congolense riverine forest type. One possible biological mechanism would be 'hitchhiking,' as previously suspected for other parasites. The model could be improved by adding other important variables to the trypanosome tsetse associations.
authors.affiliation: Institut Pierre Richet, Bouaké, Rép. Côte d'Ivoire. solano@ird.ci
authors.forename: P
authors.initials: P
authors.lastname: Solano
authors.forename: J F
authors.initials: JF
authors.lastname: Guégan
authors.forename: J M
authors.initials: JM
authors.lastname: Reifenberg
authors.forename: F
authors.initials: F
authors.lastname: Thomas
mesh_terms: D000354:Africa, Western / Q000453:epidemiology; D000818:Animals; D004777:Environment; D007303:Insect Vectors / Q000469:parasitology*; D008954:Models, Biological; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D014345:Trypanosoma / Q000145:classification* / Q000254:growth & development; D014352:Trypanosomiasis / Q000469:parasitology; D014370:Tsetse Flies / Q000469:parasitology*