Project description
Disease transmission in social groups
The risk of disease outbreaks may be increased in social organisms living in dense groups, in which pathogens can rapidly transmit between hosts. According to theory, disease dynamics should depend on the composition of a social group and on its social network structure. However, there is insufficient empirical data. Experimental epidemiology is obstructed by the lack of study systems that allow research of the causal link between group composition, social network structure, and disease transmission. The EU-funded EPIDEMIC project will use an original system based on the unique biological characteristics of the clonal raider ant. Unlike other ants, this species is deprived of queens and reproduces clonally, allowing to control important aspects of colony composition. The project will use automated techniques for tracking behaviour, aiming to detect those characteristics that protect social organisms against infectious disease.
Objective
The risks and impact of disease are exacerbated in social organisms, which live in dense groups wherein pathogens can rapidly propagate. Theoretical epidemiology predicts that disease dynamics will depend in large part on a group's social interaction network, but empirical data are scarce. Experimental epidemiology is currently hampered by a lack of study systems that would enable a rigorous investigation of the causal link between network structure and disease transmission.
I will tackle this question using a novel system, the clonal raider ant, a social insect whose unique biology affords unparalleled control over the main aspects of colony composition that are thought to modulate social network structure, and therefore, disease transmission. My approach will combine cutting-edge automated techniques for behavioral tracking with molecular tools, and develop new methods to monitor transmission in real time. In a first step, I will create empirical networks that are theoretically predicted to vary in transmission risk and map individual immune function onto these networks, to measure the prophylactic network properties that might reduce disease transmission. Second, I will test if experimental increases in immune activity induce changes in behavior that are relevant for disease transmission, to measure inducible network properties. Finally, I will inoculate colonies with nematodes and quantify infection propagation in real time. This will allow me to compare various types of social networks (healthy, immune-activated, infected), to probe the link between behavior and immunity, and to experimentally test predictions from theoretical epidemiology.
This project takes an integrative approach—from individual immunity to collective behavior—to uncover the properties of social groups that protect them against disease. By linking theoretical epidemiology to real-world disease dynamics, it will push the limits of our ability to predict disease dynamics in social groups.
Fields of science
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Funding Scheme
ERC-STG - Starting GrantHost institution
80539 Munchen
Germany