Mathematical and Statistical Methods for the Control of Global Infectious Disease Threats ABSTRACT Outbreaks of emerging and re-emerging infectious diseases have become more frequent over time and pose a critical threat to human health. Pandemic and seasonal influenza, dengue and other arboviruses continue to spread on a global scale. Other specific infectious disease problems include Ebola, Lassa fever, plague, and Middle East Respiratory Syndrome (MERS-CoV). The consistent and rapid deployment of control measures, especially vaccines and antimicrobials, is crucial for reducing transmission and preventing or mitigating outbreaks caused by these infectious diseases. The goal of this research is to develop, validate, and implement novel mathematical and statistical techniques for modeling the transmission of major infectious disease threats. The resultant models will be applied to assess the impact of various layered control interventions and to guide the optimal allocation of resources for disease mitigation and control.
Our specific aims correspond to this research challenge.
(Aim 1) Develop innovative methods for mathematical modeling of important infectious disease threats.
(Aim 2) To derive a portfolio of innovative statistical methods to improve estimation of key model parameters from surveillance data.
(Aim 3) Optimize the use of layered interventions using the mathematical models. Overall, we will develop mathematical models with realistic transmission dynamics that achieve superior computational tractability. We will also derive statistical methods and apply these approaches to specific infectious disease threats. The output of our research will include comprehensive modeling results useful for understanding the transmission and control of the targeted infectious diseases. We hypothesize that the output of our research will provide a comprehensive analytic framework for understanding the transmission and control of the infectious diseases modeled, and to deal with future threats. The contribution of this research is significant because we will provide methods for modeling and analyzing the transmission and control of the significant infectious disease threats. The mathematical models with allow us to understand and predict the infectious disease transmission, and to devise optimal control strategies using vaccines, anti-microbial agents and non-pharmaceutical interventions. The statistical modeling will provide parameter estimation and fitting methods for the mathematical models, while the optimal control strategies devised will provide the decision method for the effective control of the infectious disease threats. This work be integrated into the infectious disease control efforts of the WHO Research and Development Blueprint for Action to Prevent Epidemics and the Emerging Pathogens Institute at the University of Florida. Our team has over 30 years of experience in this work, and it is uniquely positioned to conduct this research. The proposed work is innovative because it challenges existing paradigms on the integration of methods for the mathematical modeling and statistical analysis of infectious disease transmission into a comprehensive framework to determine the most effective combination of control measures for epidemic containment and mitigation.

Public Health Relevance

We propose an innovative, complex network of mathematical models and statistical methods for the analysis and control of the leading emerging and re-emerging infectious disease threats on a local and global scale. These methods will allow us to elucidate and predict infectious disease transmission. This will lead to the development of optimal layered control strategies for these infectious disease threats using vaccines, anti- microbial agents, and non-pharmaceutical interventions.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
High Priority, Short Term Project Award (R56)
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Biostatistical Methods and Research Design Study Section (BMRD)
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Gezmu, Misrak
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University of Florida
Biostatistics & Other Math Sci
Schools of Medicine
United States
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