Jesus Cantu, a Sociology major from Princeton University, worked with Drs. Tobias Brett and Pejman Rohani to model Hepatitis A infection.
Abstract: Hepatitis A is an acute infectious disease caused by the hepatitis A virus (HAV). In the US, an incremental approach to vaccination was initiated after the vaccine became available in 1995. In effect, a continuous decline has been experienced in the overall HAV incidence from 6.0 cases per 100,0000 individuals in 1999 to 0.4 cases per 100,000 individuals in 2011. Recently, an increasing trend in the proportion of HAV cases who were hospitalized was observed, in the US, from 7.3% in 1999 to 24.5% in 2011. Asymptomatic and non-jaundiced HAV-infected persons, especially children, have previously been identified as an important source of HAV transmission. However, the number of asymptomatic HAV-infections, through time, and their role in sustaining transmission have not clearly identified. To answer these questions, we constructed a mechanistic SIR-model with high and low risk classes implemented as a system of ordinary differential equations which were numerically integrated in R. Particular attention was placed on the effect of the implementation of different vaccination strategies on disease burden and transmission. Preliminary results show that infections from low risk individuals contribute negligibly to the number of symptomatic cases.
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Jonathan Waring, a Computer Science major at the University of Georgia, worked with Ana Bento in the lab of Pejman Rohani to examine how the the choice of data used in a disease model affects the results.
Abstract: During an emerging infectious disease outbreak, epidemiological parameters, such as transmission potential and mean infectious period, are estimated for a timely and effective response. The standard procedure for attaining quick estimates of these quantities is fitting transmission models to incidence data. Cumulative incidence (total number of infections to date) is often used rather than raw incidence (number of new cases in a defined reporting period), but there is evidence to suggest that this choice of data can affect our perceptions of the variability in the parameters and hence the uncertainty in our predictions. To further elaborate on this problem, we fit deterministic and stochastic models with both raw and cumulative simulated epidemic data in order to assess the biases and errors associated with data choice. Fitted simulations to the data using deterministic and stochastic methods result in comparable variances, with cumulative models under predicting the true incidence. However, in stochastic parameter estimation and posterior sampling using particle Markov chain Monte Carlo (pMCMC), cumulative data produces much wider confidence intervals, and thus better quantifies uncertainty than models using raw data. When we consider the entire time-series of an epidemic, cumulative and raw data will both be useful in parameter estimation depending on the level of uncertainty we are willing to accept.
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