- Spread of infectious disease through clustered populations
Joel C Miller
University of British Columbia Centre for Disease Control, Vancouver, British Columbia, V5Z 4R4, Canada
J R Soc Interface 6:1121-34. 2009
..Our most significant contribution is a systematic way to address clustering in infectious disease models, and our results have a number of implications for the design of interventions...
- Percolation and epidemics in random clustered networks
Joel C Miller
Harvard School of Public Health, Boston, Massachusetts 02115, USA
Phys Rev E Stat Nonlin Soft Matter Phys 80:020901. 2009
..Percolation in the clustered networks reduces the component sizes and increases the epidemic threshold compared to the unclustered networks...
- Student behavior during a school closure caused by pandemic influenza A/H1N1
Joel C Miller
Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
PLoS ONE 5:e10425. 2010
..The effectiveness of closing schools to reduce transmission depends largely on student/family behavior during the closure. We sought to improve our understanding of these behaviors...
- Model hierarchies in edge-based compartmental modeling for infectious disease spread
Joel C Miller
Departments of Mathematics and Biology, Penn State University, University Park, USA
J Math Biol 67:869-99. 2013
..Our result about the convergence of models to the mass action model gives clear, rigorous conditions under which the mass action model is accurate. ..
- A note on the derivation of epidemic final sizes
Joel C Miller
Depts of Mathematics and Biology, The Pennsylvania State University, University Park, PA 16802, USA
Bull Math Biol 74:2125-41. 2012
..Thus, the use of integro-differential equations to find a final size relation is unnecessary and a simpler, more general method can be applied...
- Edge-based compartmental modelling for infectious disease spread
Joel C Miller
Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA
J R Soc Interface 9:890-906. 2012
..We introduce a graphical interpretation allowing for easy derivation and communication of the model and focus on applying the technique under different assumptions about how contact rates are distributed and how long partnerships last...
- A note on a paper by Erik Volz: SIR dynamics in random networks
Joel C Miller
Harvard School of Public Health, Boston, MA 02215, USA
J Math Biol 62:349-58. 2011
..Under appropriate assumptions these equations reduce to the standard SIR equations, and we are able to estimate the magnitude of the error introduced by assuming the SIR equations...
- Epidemics with general generation interval distributions
Joel C Miller
Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
J Theor Biol 262:107-15. 2010
..We introduce a "memoryless" ODE system which approximates the true solutions. Finally, we analyze the transition from the stochastic to the deterministic phase...
- Use of cumulative incidence of novel influenza A/H1N1 in foreign travelers to estimate lower bounds on cumulative incidence in Mexico
Marc Lipsitch
Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, United States of America
PLoS ONE 4:e6895. 2009
..Accordingly, the total number of cases will be underestimated and disease severity overestimated. This problem is manifest in the current epidemic of novel influenza A/H1N1...
- Pre-dispensing of antivirals to high-risk individuals in an influenza pandemic
Edward Goldstein
Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, MA, USA
Influenza Other Respir Viruses 4:101-12. 2010
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- Incorporating disease and population structure into models of SIR disease in contact networks
Joel C Miller
Departments of Mathematics and Biology, Penn State University, University Park, Pennsylvania, United States of America
PLoS ONE 8:e69162. 2013
..Our goal is twofold: to provide a number of examples generalizing the EBCM method for various different population or disease structures and to provide insight into how to derive such a model under new sets of assumptions...
- Effects of heterogeneous and clustered contact patterns on infectious disease dynamics
Erik M Volz
Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
PLoS Comput Biol 7:e1002042. 2011
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- Predicting the epidemic sizes of influenza A/H1N1, A/H3N2, and B: a statistical method
Edward Goldstein
Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
PLoS Med 8:e1001051. 2011
..We use publicly available US Centers for Disease Control (CDC) influenza surveillance data between 1997 and 2009 to study the temporal dynamics of influenza over this period...
- Oseltamivir for treatment and prevention of pandemic influenza A/H1N1 virus infection in households, Milwaukee, 2009
Edward Goldstein
Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, MA, USA
BMC Infect Dis 10:211. 2010
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- Cholera modeling: challenges to quantitative analysis and predicting the impact of interventions
Yonatan H Grad
Division of Infectious Diseases, Brigham and Women s Hospital, Boston, MA, USA
Epidemiology 23:523-30. 2012
..We specify sensitivity analyses that would be necessary to improve confidence in model-based quantitative prediction, and suggest types of monitoring in future epidemic settings that would improve analysis and prediction...