Dear members, if you wish to submit and post your upcoming talks on the InForMID page, please send your title of the talk, date, venue, and abstract to Manisha Pandita (informid@tufts.edu). Thank you very much.

Upcoming Talks from Members

November 7-11, 2009, APHA Annual Meeting

Past Talks

Naumova, E.N. 2009. Climate Change, Extreme Weather, and Infections: New Venues for Statistical Applications, Boston Chapter of the American Statistical Association, Waltham, MA

Naumova, E.N. 2009. Challenges in assessing seasonality in disease surveillance, Boston University School of Public Health, Boston, MA
Abstract: Many diseases manifest themselves through a low endemic level and well-pronounced seasonal outbursts indicating effects of various time-dependent factors. For example, studies of waterborne infections conducted in tropical climates note an increase in incidence during warm, rainy season and studies conducted in temperate climates demonstrate such increases in the spring and fall. In Massachusetts, waterborne infections peaks about 2-6 weeks after ambient temperature reached its annual maxima. It has been shown that long-term climate change can affect seasonal patterns of infectious diseases by lengthening the transmission cycle and changing thresholds, which determine seasonal peaks. Therefore, understanding the seasonality of a disease and its variation from year to year is the first step in understanding the impact of long-term climate change on disease patterns. Methodology for systematic investigations of seasonal patterns in disease dynamics is poorly developed. In this talk I will outline the main challenges of the seasonality assessment and practical solutions for improving statistical inferences.

Fefferman, N.H. 2009. Mathematical Insights into Behavioral Epidemiology, Univ. of Texas Health Science Center, Houston, TX

Fefferman, N.H. 2009. Mathematical and Computational Methods in Epidemiology and BioSurveillance, Jackson State University, MS

Fefferman, N.H. 2009. Mathematics, Optimization, and the Evolution and Behavior of Social Insects, UNC, Chapel Hill, Applied Math, NC

Fefferman, N.H. 2009. Network models in Epidemiology and Sociobiology: Introduction, Overview, and Recent Advances, Mathematical Sciences, RPI, NY

Fefferman, N.H. 2008. Social Behavior and the Dynamics of Corrupted Blood. Rice University/Games for Health, Houston, TX

Fefferman, N.H. 2008. Possible Selective Mechanisms for the Evolution of Disease-defensive Social Organizations. Ecology and Evolution Seminar, Boston Univ., MA

Naumova E.N. 2006. Spatial Dynamics of Influenza hospitalizations in the US Elderly
Date & time: Nov. 20th, 12:00pm
Venue: DIMACS, 4th Floor, CORE Building, Busch Campus, Rutgers Univ
Abstract: I suggest that the spatial variations in influenza hospitalization in the frail elderly subpopulations stem from differences between individual "immunological" and "chronological" age influenced by the presence of environmental stressors and preexisting medical conditions. I will illustrate such variability by using dynamic maps of weekly county-specific rates of P&I hospitalizations for four influenza seasons based on actual 6mln individual records of hospitalization. To describe the traveling waves of seasonal flu I used a set of characteristics (peak timing, duration, and amplitude). To model spatial dynamics I consider a model in which peak timing followed two scenarios of weak and strong disorder.

Fefferman, N.H. 2006. Determining Optimal Vaccination Strategies in Dynamic Social Networks. DIMACS Computational and Mathematical Epidemiology Seminar, Rutgers Univ.
Abstract: Epidemiological models have begun examining social contact networks to create more realistic transmission scenarios. However, very few have examined the effect of constantly changing social dynamics on those networks and on the resulting disease spread. We'll examine the dynamics of a population of individuals who shift their social affiliations based on the social status of others. We will explore how different mechanisms of evaulating status can lead to different network structures and, therefore, different patterns of disease spread. Further, we will examine whether vaccinating those individuals with the highest network centrality (the evaluative mechanism employed by the members of the population) is actually most effective at combatting disease.

Fefferman, N.H. 2006. Preparing Societal Infrastructure Against Disease-Related Workforce Depletion. DIMACS Workshop on Facing the Challenge of Infectious Diseases in Africa, University of the Witswatersrand, South Africa
Abstract: Disease related work-force depletion can cause the breakdown of necessary societal infrastructure and threaten the safety of a population over and above the direct effects of serious illness. We will discuss a few preliminary models examining how to plan ahead: where redundancies in the training of a workforce and in workforce deployment in critical positions can increase the robustness of the society to be able to better withstand these threats. Time permitting, we will discuss how such planning should change to reflect the threat posed by the disease itself (e.g. seasonal depletion from Malaria, or growing constantly from AIDS).

Lofgren E. 2006. Association of influenza seasonality with temperature and temperature-related indicators. APHA 134th Annual Meeting, Boston (View abstract)

Jagai J. 2006. Environmental indicators for clostridium difficile in the US elderly.
APHA 134th Annual Meeting, Boston (View abstract)

Chui K.H.K. 2006. Preexisting rate of gastroenteric infections among elderly residing in areas affected by Hurricane Katrina. APHA 134th Annual Meeting, Boston (View abstract)