Statistical models for longitudinal analysis of single and mixed species infections
Kathryn Louise Colborn
Doctor of Philosophy in Biostatistics
University of California, Berkeley
Professor Terence P. Speed, Chair
There are numerous examples of infectious diseases that are caused by various species of
the same pathogen. Some examples include Lyme disease, malaria, Leishmaniasis, Dengue
fever, and Ehrlichiosis. The advancement of laboratory methods has facilitated more sensitive
detection of mixed species infections in humans, which has resulted in a surge of research focussing
on the eects of mixed infections on clinical outcomes. Cross-sectional blood samples
compared with clinical outcome measures provide a limited scope of the interactions between
species. It is important to study these infections in humans longitudinally, and within their natural
environments, in order to develop an understanding of the complex relationships between
hosts, pathogens and vectors of transmission.
Papua New Guinea is a country with high prevalence of both Plasmodium falciparum and P.
vivax, two species of parasites that can cause malaria. It is well known that these two parasites
can cause severe morbidity and mortality independently, but there has not been conclusive
evidence of the eect of mixed P. falciparum and P. vivax infections on clinical symptoms.
Children under age five are at highest risk of experiencing adverse outcomes from Plasmodium
infections. In 2006, a cohort study was implemented to conduct an investigation of the eects
of mixed P. falciparum and P. vivax infections on clinical episodes of malaria in children living
in a rural area of Papua New Guinea. The data collected from this study are used throughout
this dissertation to address both the epidemiological questions of the study investigators and to
present statistical models for analyzing longitudinal malaria data and mixed species infections.