Joint Modeling of Mixed Plasmodium Species Infections Using a Bivariate Poisson Lognormal Model

Infectious diseases often present as coinfections that may affect each other in positive or negative ways. Understanding the relationship between two coinfecting pathogens is thus important to understand the risk of infection and burden of disease caused by each pathogen. Although coinfections with Plasmodium falciparum and Plasmodium vivax are very common outside Africa, it is yet unclear whether infections by the two parasite species are positively associated or if infection by one parasite suppresses the other. In this study, we use bivariate Poisson lognormal models (BPLM) to estimate covariate-adjusted associations between the incidence of infections (as measured by the force of blood-stage infections, _molFOI) and clinical episodes caused by both P. falciparum and P. vivax in a cohort of Papua New Guinean children. A BPLM permits estimation of either positive or negative correlation, unlike most other multivariate Poisson models. Our results demonstrated a moderately positive association between P. falciparum and P. vivax infection rates, arguing against the hypothesis that P. vivax infections protect against P. falciparum infections. Our findings also suggest that the BPLM is only useful for counts with suitably large means and overdispersion.