Five years of successful accomplishment from the University of Pittsburgh MIDAS Center of Excellence's Parameter Estimation project led to an independently funded project in 2015, "Models for Synthesizing Molecular, Clinical and Epidemiological Data, and Translation" (U01GM110721) with Dr. Neil Ferguson as a Principal Investigator.
Work undertaken with the University of Pittsburgh MIDAS Center of Excellence focused on enhancing the power of epidemic models for gaining insight into the transmission dynamics of outbreaks and the likely effectiveness of different control measures. But if models are to be a useful tool for informing policy, it is critical that validation is as rigorous as possible. This demands methods for extracting the maximum possible information from existing epidemiological data to reduce parametric and structural uncertainty in models, and robust tools for real-time parameter estimation and prediction.
Unprecedented amounts of information have been gathered during several recent infectious disease crises, including the global avian H5N1 epidemic and SARS in 2003. As surveillance, diagnostic and communication systems improve, this trend is likely to continue. In parallel, individual-based agent-based simulation models are becoming ever more powerful tools for examining complex transmission dynamics and exploring the impact of multiple different control options. But since model outcomes and conclusions are usually sensitive to input parameters (and are extremely sensitive near key dynamical thresholds or critical points), it is essential that those parameters are robustly estimated from existing data. Thus, the overarching goal of this project is to provide a set of statistical tools for the estimation of key epidemiological features of a pathogen and of its spread in the population, making use of all available data (epidemiological, molecular/genetic, demographic, movement, etc.).
Transmission characteristics of the 2009 H1N1 influenza pandemic: comparison of 8 Southern hemisphere countries
This study estimated the reproduction numbers (R0) of the H1N1 virus in eight Southern hemisphere countries, finding them to be positively associated with the proportion of children in the population. Using mathematical modeling, researchers found a significant decrease in susceptibility to infection with age, confirming that older populations had substantial (if partial) pre-existing immunity to the virus. Opatowski L; Fraser C; Griffin J; de Silva E; Van Kerkhove MD; Lyons EJ; Cauchemez S; Ferguson NM. (Sep 2011). Transmission characteristics of the 2009 H1N1 influenza pandemic: comparison of 8 Southern hemisphere countries. PLoS Pathog. 7:e1002225
Spatial dynamics of the 1918 influenza pandemic in England, Wales and the United States
Using a gravity model for city-to-city contacts, researchers explored the effect of population size and distance on the spread of disease in England, Wales, and the US. For England and Wales, a model with intermediate levels of density dependence in the connectivity between cities gave the best fit to the observed data. In the US, where there are few, large and widely spaced population centers, estimating the degree of density dependence does not improve model fit. Rosalind M Eggo, Simon Cauchemez and Neil M Ferguson. Spatial dynamics of the 1918 influenza pandemic in England, Wales and the United States. Journal of the Royal Society, Interface / the Royal Society 8(55):233–43, February 2011. PMCID: PMC3033019
Serial intervals and the temporal distribution of secondary infections within households of 2009 pandemic influenza A (H1N1): implications for influenza control recommendations
Researchers estimated the distribution of times from onset of influenza-like-illlness (ILI) symptoms in an infector to symptom onset in the household contacts they infect. Only 5% of transmission events were estimated to take place three or more days after the onset of clinical symptoms, supporting the view that prolonged (up to a week) isolation following the onset of clinical symptoms may not be necessary to substantially reduce the risk of transmission from infected individuals to communities and workplaces.
Measurement Errors in Annual Influenza Counts
The number of people infected by an influenza virus each year might be substantially larger than previously thought due to inherent measurement errors and a 70-year old standard. A 4-fold titer increases works well for diagnosing individual cases of influenza, but not as well for estimating community infections. Determined that a key parameter for modeling influenza is the symptomatic rate (the percentage of people who are infected and show symptoms), which is difficult to obtain from current available data, but can greatly impact overall results. Cauchemez S, Horby P, Fox A, Mai LQ, Thanh LT, et al. (2012) Influenza Infection Rates, Measurement Errors and the Interpretation of Paired Serology. PLoS Pathog 8(12): e1003061. doi:10.1371/journal.ppat.1003061
Using Surveillance Data to Estimate Disease Transmission
Simple methods can estimate the epidemic potential of emerging zoonosis, utilizing routine surveillance data and standard case definitions. Deriving transmissibility estimates for emerging zoonoses, such as influenzas, is essential to setting the model parameter of R0. Early surveillance data on emerging zoonotic viruses are often limited and scarce. For effective risk assessment, it is essential that appropriate methods are developed to analyze them. Simon Cauchemez, Scott Epperson, Matthew Biggerstaff, David Swerdlow, Lyn Finelli and Neil M Ferguson. Using Routine Surveillance Data to Estimate the Epidemic Potential of Emerging Zoonoses: Application to the Emergence of US Swine Origin Influenza A H3N2v Virus. PLoS Medicine 10(3):e1001399, 2013. PMCID: PMC3589342
In H1N1 pandemic, case detection was higher in children; R0 was initially 1.2-1.4. Ilaria Dorigatti, Simon Cauchemez, Andrea Pugliese and Neil M. Ferguson. A new approach to characterising infectious disease transmission dynamics from sentinel surveillance: application to the Italian 2009-2010 A/H1N1 influenza pandemic. Epidemics 4(1):9–21, March 2012.
Neil Ferguson, PhD