Összefoglaló Zero altered models were fitted and applied to two datasets one strongyloidosis van Malawi and another helminths kvíz Zambia. Malawi data were collected in a cluster randomized study, in Chikhwawa district helminths kvízwith 18 villages randomized to intervention and control arms with a total of participants.
Zambia data were collected from school children in a cross-sectional study in Helminths kvíz province in with a total of participants. Results from the study showed that Negative Binomial Logit Hurdle NBLH model offered best-fit to data inflated with zeros; with capability to handle over-dispersion, excess zeros and capture true zeros in the data.
Its implementation and interpretation, ease of components, and its direct link with observed data make it a valuable helminths kvíz for analyzing zero inflated count data.
Conclusions drawn from the study indicated that Helminths were highly localized, with small section of people harboring parasites; showing heterogeneous infection risk for both Malawi and Zambia settings. Joint modeling approach allowed identification of risk factors for infection presence and severity hence provide a platform to design combative control efforts.