Table 1.
Overview, sources, and resolution of remotely sensed and other geographic information system data used for modeling.
Fig 1.
School survey locations and observed prevalence of M. perstans microfilaremia in Uganda.
Fig 2.
Observed proportional distribution of mono- and co-infections (yellow) with M. perstans (green) and W. bancrofti filariasis (red).
Data from 11,606 pupils aged 5–19 years in 76 schools in Uganda (2000–2003).
Table 2.
Parameter estimates based on bivariate logistic regression models for M. perstans microfilaremia in school-aged children in Uganda (2000–2003).
Table 3.
Factors associated with M. perstans microfilaremia in Ugandan school-aged children based on non-spatial and spatial logistic multivariate regression modeling of national survey data (2000–2003).
Fig 3.
Geostatistical model-based predicted mean prevalence of Mansonella perstans in school-aged children in Uganda.
Smooth map of the predicted mean prevalence of M. perstans (a), and the corresponding map of the standard deviations of the predictions (b), highlighting areas of high/low uncertainty associated with the model predictions.
Fig 4.
Maps of the predicted geographic co-distribution of M. perstans and W. bancroftibased on A) a 10% prevalence threshold, and B) a 5% prevalence threshold.
The predicted areas are based on surveys of Ugandan school-aged children in 2000–2003 and Bayesian geostatistical model predictions of each (single) parasite infection. Predicted areas of high risk malaria (Plasmodium spp. infection prevalence >50%) is shown in hatch as an overlay.
Table 4.
Parasite-parasite associations as assessed by multivariate logistic regression models based on a national survey conducted in Uganda (2000–2003).