Education 

ScD (Epidemiology), Harvard School of Public Health, 2008

MS (Biostatistics), Harvard School of Public Health, 2008

BA (Magna cum laude), Harvard College, 2001

Research

My research focuses on stochastic epidemic models, the analysis of epidemic data, and the links between them.  I am also interested in study design and causal inference in infectious disease epidemiology.

Stochastic SIR epidemic models can be analyzed using a semi-directed random network that I call the "epidemic percolation network" (EPN).  The distribution of out-component sizes of any node i in the EPN is identical to the distribution of outbreak sizes that result from the infection of i in the underlying SIR model.  The epidemic threshold corresponds to the emergence of giant in- and out-components in the EPN.  In SIR models without clustering, the size distribution of in- and out-components and the sizes of the giant components can be calculated analytically using probability generating functions.  When this is not possible, the generation of EPNs is a highly efficient numerical method of analyzing an SIR model.  Since EPNs are defined for any time-homogeneous stochastic SIR model in a closed population, they provide unified methods for the analysis of network-based and fully-mixed epidemic models.

The same specification of a general stochastic SIR model that is used in the formal definition of EPNs also leads naturally to partial likelihoods for epidemic data that resemble those from survival analysis.  The analysis of epidemic data using methods adapted from survival analysis is a promising approach because it allows great flexibility in the use of covariates and the incorporation of an underlying transmission model.

The transmission of disease between individuals may be a source of time-dependent confounding in epidemic data.  One of my future research goals is to understand how this affects standard epidemiologic methods and how advanced methods (such as G-estimation) can be used to obtain more robust estimates of the causal effects of covariates on infectiousness and susceptibility.

Publications

1. E. Kenah, M. Lipsitch, and J.M. Robins (2008). Contraction of serial intervals during epidemics. Mathematical Biosciences 213(1): 71-79.
2. E. Kenah and J.M. Robins (2007). Network-based analysis of stochastic SIR epidemic models with random and proportionate mixing. Journal of Theoretical Biology 249(4): 706-722.
3. E. Kenah and J.M. Robins (2007). Second look at the spread of epidemics on networks.  Physical Review E 76: 036113.
4. S.P. Luby, M. Rahman, M.J. Hossain, L.S. Blum, M.M. Husain, E. Gurley, R. Khan, B.N. Ahmed, S. Rahman, N. Nahar, E. Kenah, J.A. Comer, and T.G. Ksiazek (2006). Foodborne transmission of Nipah virus, Bangladesh. Emerging Infectious Diseases 12(12): 1888-1894.
5. I.B. Ahluwalia, C. Bern, C. Costa, T. Akter, R. Chowdhury, M. Ali, D. Alam, E. Kenah, J. Amann, M. Islam, Y. Wagatsuma, R. Haque, R.F. Breiman, and J.H. Maguire (2003). Visceral leishmaniasis: consequences of a neglected disease in a Bangladeshi community. American Journal of Tropical Medicine and Hygiene 69(6): 624-628.