Maciej F. Boni

Associate Professor of Biology
Maciej Boni
Maciej F.
Boni
Associate Professor of Biology
Department Affiliations
External Affiliations
Address
Phone
814-867-4651
Website
Boni Lab
Biography

Education

Ph.D., Stanford University, 2006

M.S., Stanford University, 2004

A.B., Princeton University, 1999

 

Postdoctoral Training

2008-2011 University of Oxford and Oxford University Clinical Research Unit (Ho Chi Minh City, Vietnam)

2007-2008 Resources for the Future (Washington DC) and Princeton University

 

Awards

Wellcome Trust Sir Henry Dale Fellowship, 2012

British Medical Association H.C. Roscoe Award, 2011

 

Research Interests

Epidemiology of influenza in the tropics.  Tropical disease epidemiology.  Treatment strategies and antimalarial drug resistance.  mHealth and participatory epidemiology.  Theory of infectious disease dynamics.  Pathogen evolution, population genetics, phylogenetics, recombination.

From 2008 to 2016, I ran a research group in Ho Chi Minh City, Vietnam, focused on influenza epidemiology in the tropics and treatment strategies to delay or slow antimalarial drug resistance.  My PhD students and other team members have been mathematical modelers, bioinformaticians, field site coordinators, and laboratory research assistants.  

We have been running a large seroepidemiology study in southern Vietnam, aimed at reconstructing the disease dynamics of influenza virus including both symptomatic and asymptomatic influenza.  This work has expanded to dengue virus, and we are also using the project to look at incidence of a number of other diseases, most recently Tetanus and Hepatitis B and E.

Since 2009, we have been running a community-based mHealth study on influenza-like illness (ILI) in Ho Chi Minh City.  The study has collected over 40,000 data points and is currently used as a real-time epidemiology tool for Ho Chi Minh City.  You can read about the study here.

Our malaria work is focused on designing and calibrating individual-based microsimulations.  The microsimulations are used to compare different long-term drug distribution strategies to see which ones are most effective at delaying drug resistance.  You can read about the most recent modeling results here and here.  Our next goal is to perform health-economic analyses to determine if deploying multiple first-line therapies is an economically feasible strategy.