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The power function was re-optimized for each fit of the model, hence the shape of the curve relating parasite density to the probability of fever was allowed to vary for different populations.
Furthermore, for any parasite density, the probability of fever was inversely related to CD4 cell count.
The model provides the background level where the probability of fever is considered similar for parasite below the threshold and then probability of fever increases as parasite density increases above the threshold.
In both cases the probability of clinical malaria in individuals with parasite below τ is logit-1, which corresponds to the background (i.e the probability of fever is considered to be similar between parasitaemic and non parasitaemic individuals).
Probability of fever in individuals with parasite less or equal to τ is assumed to be constant, α (which corresponds to background) and that above the τ, the probability increases with the increase in the difference of log parasite density and threshold parameter.
Similar(55)
The probability of positive fever identification on feverish children ranges from ∼40%to77%7% using 1 and 5 sensors respectively, while the detected false positives are a 10%.
Probability of yellow fever cases as predicted by the algorithm.
The probability of "yellow fever never introduced to Asia" and related explanation of geographical barriers are highly unlikely to explain the mystery of YF.
Change in the probability of testing fevers was not well described by the logistic regression; testing rates fluctuated over time and were determined by many factors.
However, if fever occurs later, the probability of an infectious fever is higher and should alert intensivists to an ongoing infection.
Travellers are assigned to detectability classes with probabilities f (presence of fever) and g (awareness of exposure risk).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com