Justin K. Davis
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Justin K. Davis has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Carrie Anna Manore
Theoretical Biology & Biophysics (T-6), Theoretical Division, Los Alamos National Laboratory, Los Alamos, USA
1 shared publication - Christopher N. Mores
Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, Louisiana, USA
1 shared publication - Dawn M. Wesson
Department of Tropical Medicine and Infectious Disease, Celia Scott Weatherhead School of Public Health and Tropical Medicine, Tulane University
1 shared publication - Ivo M. Foppa
Battelle/Epidemiology & Prevention Branch, Influenza Division, CDC, Atlanta, GA, USA
1 shared publication - James M. Hyman
Department of Mathematics, Tulane University, New Orleans, USA
1 shared publication - Kyle S. Hickmann
Center for Computational Science, Department of Mathematics, Tulane University, New Orleans, LA 70118, USA
1 shared publication
Latest Publications
- Open Access
- 1 January 2015
- Published by Taylor & Francis in Journal of Biological Dynamics
- Vol. 9 (1) , 52-72
A network-patch methodology for adapting agent-based models for directly transmitted disease to mosquito-borne disease
Mosquito-borne diseases cause significant public health burden and are widely re-emerging or emerging. Understanding, predicting, and mitigating the spread of mosquito-borne disease in diverse populations and geographies are ongoing modelling challenges. We propose a hybrid network-patch model for the spread of mosquito-borne pathogens that accounts for individual movement through mosquito habitats, extending the capabilities of existing agent-based models (ABMs) to include vector-borne diseases. The ABM are coupled with differential equations representing ‘clouds’ of mosquitoes in patches accounting for mosquito ecology. We adapted an ABM for humans using this method and investigated the importance of heterogeneity in pathogen spread, motivating the utility of models of individual behaviour. We observed that the final epidemic size is greater in patch models with a high risk patch frequently visited than in a homogeneous model. Our hybrid model quantifies the importance of the heterogeneity in the spread of mosquito-borne pathogens, guiding mitigation strategies.
- 1 January 2015
- Published by Taylor & Francis in Journal of Biological Dynamics
- Vol. 9 (1) , 52-72
Open AccessA network-patch methodology for adapting agent-based models for directly transmitted disease to mosquito-borne disease
Mosquito-borne diseases cause significant public health burden and are widely re-emerging or emerging. Understanding, predicting, and mitigating the spread of mosquito-borne disease in diverse populations and geographies are ongoing modelling challenges. We propose a hybrid network-patch model for the spread of mosquito-borne pathogens that accounts for individual movement through mosquito habitats, extending the capabilities of existing agent-based models (ABMs) to include vector-borne diseases. The ABM are coupled with differential equations representing ‘clouds’ of mosquitoes in patches accounting for mosquito ecology. We adapted an ABM for humans using this method and investigated the importance of heterogeneity in pathogen spread, motivating the utility of models of individual behaviour. We observed that the final epidemic size is greater in patch models with a high risk patch frequently visited than in a homogeneous model. Our hybrid model quantifies the importance of the heterogeneity in the spread of mosquito-borne pathogens, guiding mitigation strategies.
- 11 October 2014
- Published by Springer Nature
- p. 251-257
Model Selection for Classification with a Large Number of Classes
- 11 October 2014
- Published by Springer Nature
- p. 251-257
Model Selection for Classification with a Large Number of Classes