Scientific Program 5.1 Research Project 1: Modeling, Spatial, Statistics The overall objective of this research project is to develop, validate, and implement mathematical and statistical models for the transmission of naturally occurring infectious diseases and bioterrorism agents. These models will be used to assess the effectiveness and efficacy of various interventions to aid the distribution and allocation of resources in response to infectious disease outbreaks.
The specific aims are as follows: 1. To develop or further develop mathematical and statistical models for important infectious disease threats including influenza, cholera, dengue fever, TB, novel coronaviruses, and new emerging infectious diseases. This will involve the development of general models to understand the transsmission of infectious diseases, as well as the development of specific models for each infectious disease studied. 2. To use the epidemic mathematical and statistical models to evaluate the effectiveness of interventions involving surveillance and containment, vaccination, antimicrobials, social distancing, and other control strategies for the infectious diseases in specific aim 1. 3. To use and develop statistical methods to estimate the important parameters and variables from data available for the infectious diseases. These statistical methods will include both novel and innovative likelihood and empirical, hierarchical Bayesian models, as well as MCMC methods
| Pavía-Ruz, Norma; Diana Patricia Rojas; Salha Villanueva et al. (2018) Seroprevalence of Dengue Antibodies in Three Urban Settings in Yucatan, Mexico. Am J Trop Med Hyg 98:1202-1208 |
| Massaro, Emanuele; Ganin, Alexander; Perra, Nicola et al. (2018) Resilience management during large-scale epidemic outbreaks. Sci Rep 8:1859 |
| Yang, Yang; Meng, Ya; Halloran, M Elizabeth et al. (2018) Dependency of Vaccine Efficacy on Preexposure and Age: A Closer Look at a Tetravalent Dengue Vaccine. Clin Infect Dis 66:178-184 |
| Ho, Lam Si Tung; Xu, Jason; Crawford, Forrest W et al. (2018) Birth/birth-death processes and their computable transition probabilities with biological applications. J Math Biol 76:911-944 |
| Liu, Quan-Hui; Ajelli, Marco; Aleta, Alberto et al. (2018) Measurability of the epidemic reproduction number in data-driven contact networks. Proc Natl Acad Sci U S A 115:12680-12685 |
| Gallagher, Molly E; Brooke, Christopher B; Ke, Ruian et al. (2018) Causes and Consequences of Spatial Within-Host Viral Spread. Viruses 10: |
| Dean, Natalie E; Halloran, M Elizabeth; Longini, Ira M (2018) DESIGN OF VACCINE TRIALS DURING OUTBREAKS WITH AND WITHOUT A DELAYED VACCINATION COMPARATOR. Ann Appl Stat 12:330-347 |
| Bento, Ana I; Riolo, Maria A; Choi, Yoon H et al. (2018) Core pertussis transmission groups in England and Wales: A tale of two eras. Vaccine 36:1160-1166 |
| Moore, James; Ahmed, Hasan; Jia, Jonathan et al. (2018) What Controls the Acute Viral Infection Following Yellow Fever Vaccination? Bull Math Biol 80:46-63 |
| Ajelli, Marco; Zhang, Qian; Sun, Kaiyuan et al. (2018) The RAPIDD Ebola forecasting challenge: Model description and synthetic data generation. Epidemics 22:3-12 |
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