Applications of Likelihood-based inference with non-mechanistic and mechanistic models in infectious disease modeling

Presented June 21, 2019.

In this talk, Dr. Daihai He presents his recent works on applications of likelihood-based inference with non-mechanistic and mechanistic models in infectious disease modeling. Examples include modeling of the transmission of influenza, measles, yellow-fever virus, Zika virus, and Lassa-fever virus. Combined non-mechanistic and mechanistic models, we gain new insight into the mechanisms under the transmission of infectious diseases. 

June 21, 2019

Quantifying Model Form Uncertainty of Epidemic Forecasting Models from Incidence Data

Uncertainty Quantification (UQ), the ability to quantify the impact of sample-to-sample variations and model misspecification on predictions and forecasts, is a critical aspect of disease surveillance. While quantifying the impact of stochastic uncertainty in the data is well understood, quantifying the impact of model misspecification is significantly harder. For the latter, one needs a "universal model" to which more restrictive parametric models are compared too.


January 25, 2018

Disease Models for Event Prediction

One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information to decision makers, in order to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry.

March 02, 2018

Parametric Uncertainty in Intra-Herd Foot-and-Mouth Disease Epidemiological Models

Epidemiological models that simulate the spread of Foot-and-Mouth Disease within a herd are the foundation of decision support tools used by governments to help advise and inform strategy to combat outbreaks. Contact transmission data used to parameterize these models, contrary to assumption, contain a significant amount of variability and uncertainty. The implications of this finding suggest that the resultant model output might not accurately simulate the spread of an outbreak.

July 09, 2018

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