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Contagious 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. 


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. If this is true, the potential impact due to uncertainty inherent to the decision support tools used by governments might be significant.


The objective of this project is to understand how parametric un- certainty within intra-herd Foot-and-Mouth disease epidemiological models affects the outbreak simulations and what implications this has on surveillance and control strategy and policy.

Submitted by dbedford on

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.

Background: A rich and diverse field of infectious disease modeling has emerged over the past 60 years and has advanced our understanding of population- and individual-level disease transmission dynamics, including risk factors, virulence and spatio-temporal patterns of disease spread. Recent modeling advances include biostatistical methods, and massive agent-based population, biophysical, ordinary differential equation, and ecological-niche models. Diverse data sources are being integrated into these models as well, such as demographics, remotely-sensed measurements and imaging, environmental measurements, and surrogate data such as news alerts and social media. Yet, there remains a gap in the sensitivity and specificity of these models not only in tracking infectious disease events but also predicting their occurrence.


The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event.

Submitted by teresa.hamby@d… on

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.


We present a mathematical framework for non-parametric estimation of the force of infection, together with statistical upper and lower confidence bands. The resulting estimates allow to assess how well simpler models, such as SEIR, fit the observed time series of incidence data.

Submitted by elamb on