PR Predict
Malaria prevalence, traditionally called the parasite rate (PR), varies by location, age, sex, and time of the year. There has been a long standing tradition in malaria to report the prevalence in older children, in part, because it has good properties as a metric [1]. An economical way to store information about malaria prevalence is to have well-calibrated predictive models.
Cohort Dynamics
Using the notation developed in History of Exposure, we can translate \(E(t)\) into a model for exposure for a human cohort born on day \(d\) as it ages – using the transformation \(t=a+d:\)
\[E_d(a) = \omega(a) \; E(t-d)\]
Using functions from ramp.work and ramp.xds, we can then predict the PfPR using the xde_cohort functions to compute the PfPR at any age for a cohort born on day \(d\):
\[\frac{d {\cal X}}{da} = F_{\cal X}\left({\cal X} \; | \; E_d\left(a\right) (1-\xi) + \xi \delta\left(a\right) \right),\]
The algorithms depend on a model, \(\cal X,\) that includes information about care seeking and drug taking. Each model must supply The predictive algorithm can also be modified to consider effects of various study designs.