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

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  • 1 Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Aurora, Colorado;
  • | 2 Walter and Eliza Hall Institute, Melbourne, Australia;
  • | 3 Department of Mathematics and Statistics, University of Melbourne, Melbourne, Australia;
  • | 4 Department of Statistics, University of California, Berkeley, Berkeley, California

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.

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Author Notes

Address correspondence to Kathryn L. Colborn, Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Building 500, Room C3011, 13001 East 17th Place, Aurora, CO 80045. E-mail: kathryn.colborn@ucdenver.edu

Financial support: This work was funded in part by Australian National Health and Medical Research Council (NHMRC) program grant 490037, Swiss National Science Foundation Grants 310030-134889 and 31003A-112196, and National Institutes of Health Grants AI063135, AI-46919, and TW007872.

Authors’ addresses: Kathryn L. Colborn, Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Aurora, CO, E-mail: kathryn.colborn@ucdenver.edu. Ivo Mueller, Walter and Eliza Hall Institute, Melbourne, Australia, E-mail: ivomueller@fastmail.fm. Terence P. Speed, Walter and Eliza Hall Institute, Melbourne, Australia, Department of Mathematics and Statistics, University of Melbourne, Melbourne, Australia, and Department of Statistics, University of California, Berkeley, Berkeley, CA, E-mail: terry@wehi.edu.au.

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