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Modeling Multi-Host Multi-Pathogen Infectious Diseases

Investigators
Zoi Rapti; Carla Caceres
Institutions
University of Illinois - Urbana-Champaign
Start date
2018
End date
2021
Objective
Many pathogens - human and animal alike - act in tandem. The effects of two or more pathogens on the same host might not be easy to predict based on the known effect of each pathogen acting alone. Actually, many disease systems are composed of multiple hosts and multiple pathogens. The behavior and changes of pathogen and host populations in time are of interest to scientists and the public, when population conservation and management or contagion containment are considered. However, mathematical models of such systems become high-dimensional and contain many parameters that need to be determined from existing data. Their mathematical analysis is also not straightforward. As a study system, two Daphnia hosts (waterflea) and four of their pathogens will be considered. The two hosts also act as competitors for food resources and are subject to predation. Hence, they consist a suitable eco-epidemiological system for the development of new models and techniques. Progress made in terms of modeling and mathematical analysis will be applicable to other disease systems, such as those of pollinators, like bees and butterflies. This work will also create opportunities for the interdisciplinary training of students, both graduate and undergraduate. The investigators will partner with local high-schools and a children's museum to disseminate their findings and engage and train younger generations, with emphasis on underrepresented minorities.

Through data-theory coupling, biologically realistic models will be generated. The diversity of hosts and pathogens under consideration will allow the investigation of a wide range of interactions among them. The resulting mathematical models will be highly nonlinear. They will be used to form testable hypotheses and to reveal the mechanisms that shape the epidemics. Besides models based on differential equations and discrete dynamical systems, coarse grained approximations will also be developed. Moreover, information theoretical techniques will be used as measures of the complexity of observed and simulated time-series and to reveal any existing patters, as well as chaos. This integrated approach will be used to reveal insights into the drivers of epidemics and facilitate their prediction.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Funding Source
United States Nat'l. Science Fndn.
Project source
View this project
Project number
1815764
Categories
Bacterial Pathogens