Public Health Surveillance Working Group

This working group will focus on statistical issues related to public health surveillance. There will be special emphasis placed on biosurveillance and monitoring for bioterrorist attacks. Talks will be accessible to students, as well as researchers and other officials outside of the biostatistics department.

Caroline Jeffrey
Doctoral Student, Department of Biostatistics, Harvard University

"Disease Mapping and Statistical Issues in Public Health Surveillance"

ABSTRACT: An important component of public health surveillance is monitoring disease incidence. When cases are reported with a geographic location, for example an address, it is advantageous to study the observed spatial distribution to determine any unusual behavior throughout the study region.
When the spatial data are in point form, that is, a precise point location is available for each case, disease mapping methods estimate a surface of risk of disease throughout the study region. These exploratory methods allow visualization of variations in risk across the study region, for example highlighting any subareas with high or low incidence. Current methods are based on kernel density estimates, which suffer from edge effects in two dimensions and the curse of dimensionality in higher dimensions.
We develop disease mapping methods for point data within the framework of comparing a multidimensional distribution of observations F to a pre-specified null distribution F0. We propose a non-parametric approach based on distances and inspired by the dimension reduction concept of tomographic imaging. We evaluate the method by measuring its accuracy to identify simulated spatial clusters superimposed on a uniform distribution in the unit disk. Results are similar to those obtained with a ratio of kernel density estimates, provided both methods are implemented with an appropriate choice of parameters. In contrast to the kernel methods, our proposed method can generalize to arbitrary metric spaces and/or high-dimensional data. In particular the reduction of dimension may bypass the curse of dimensionality.
Our distance-based approach can be adapted to different types of spatial data, for example with less resolution (aggregated by subareas) or more complexity (multiple location per case, multiple diseases, covariates). The effects of both aggregation and residential history have been studied for spatial detection methods. We present extensions of our method to other types of data and show how the quality of the mapping is affected.

Till Baernighausen, M.D., Ph.D.
Assistant Professor of Global Health, Department of Global Health and Population, Harvard School of Public Health

"HIV Incidence and its Determinants in Rural South Africa: Evidence from a Population-based Surveillance"

ABSTRACT: None Given.

Laura White, Ph.D.
Assistant Professor, Department of Biostatistics, Boston University

"Estimation of the Reproductive Number and the Serial Interval in Early Phase of the 2009 Influenza the Current Influenza A/H1N1 Pandemic in the USA"

ABSTRACT:
Background: The United States was the second country to have a major outbreak of novel influenza A/H1N1 in what has become a new pandemic. Appropriate public health responses to this pandemic depend in part on early estimates of key epidemiological parameters of the virus in defined populations.

Methods: We use a likelihood-based method to estimate the basic reproductive number (R0) and serial interval using individual level US data from the Centers for Disease Control and Prevention (CDC). We adjust for missing dates of illness and changes in case ascertainment. Using prior estimates for the serial interval we also estimate the reproductive number only. Results Using the raw CDC data, we estimate the reproductive number to be between 2.2 and 2.3 and the mean of the serial interval (µ) between 2.5 and 2.6 days. After adjustment for increased case ascertainment our estimates change to 1.7 to 1.8 for R0 and 2.2 to 2.3 days for µ. In a sensitivity analysis making use of previous estimates of the mean of the serial interval, both for this epidemic (µ =1.91 days) and for seasonal influenza (µ =3.6 days), we estimate the reproductive number at 1.5 to 3.1.

Conclusions: With adjustments for data imperfections we obtain useful estimates of key epidemiological parameters for the current Influenza H1N1 outbreak in the United States. Estimates that adjust for suspected increases in reporting suggest that substantial reductions in the spread of this epidemic may be achievable with aggressive control measures, while sensitivity analyses suggest the possibility that even such measures would have limited effect in reducing total attack rates.

Stéphane Helleringer, Ph.D.
Assistant Professor of Population and Family Health, Mailman School of Public Health, Columbia University

"Sexual Networks and HIV Infection: The Neglected Role of Contact Tracing in Controlling Generalized HIV Epidemics"

ABSTRACT: The roll-out of antiretroviral treatment (ART) in sub-Saharan settings has generated significant improvement in health outcomes, and has showed promise in reducing the onward transmission of HIV infection. However, the full potential of HAART has not been reached, and its population-level impact remains limited in sub-Saharan settings because (i) the uptake of HIV testing and counseling (HTC) is low, and (ii) affected individuals present for testing at already advanced stages of the disease. Increasing the case finding capacity of sub-Saharan health systems is thus a crucial HIV control priority. Current approaches to increasing HIV case finding all rely on screening mechanisms (e.g., routine testing in clinical settings, door-to-door HIV testing). In this presentation, I use unique data on the sexual networks connecting members of a small island population of Northern Malawi to suggest that contact tracing may be an important, but so far neglected, tool for HIV control in generalized epidemics. I describe, in details, the sociocentric cohort data on sexual networks collected between 2005 and 2008 on Likoma Island. I then describe partner tracing outcomes obtained during the study, and provide initial estimates of the prevalence of HIV infection among both marital and concurrent casual partners of HIV index cases (a previously unknown parameter). I end by discussing the possible benefits and incremental costs attached to contact tracing, as well as its operational feasibility.