Social Connectivity Can Impact Dynamics of Disease Transmission
A new study has shown that infected wild house mice will disengage from their social groups, resulting in a decreased potential for disease transmission; these findings can be applied to improve models used to predict transmission of infectious diseases spread by social contact, such as Ebola and influenza.
According to a new collaborative study conducted by evolutionary biologists from the University of Zurich and the ETH Zurich, when infected, wild house mice will disengage from their social groups, resulting in a decreased potential for disease transmission. Authors of the study feel that these findings can be used to improve current models that predict the transmission of infectious diseases such as influenza or Ebola in humans, according to the press release.
When the host becomes infected, in this case, a wild mouse, it may show behavioral changes (such as becoming less active) that can impact social interactions between the host and its contact network, in this case, the nest of wild mice. According to the study, “Rapid changes in the contact network can subsequently alter the speed and magnitude of the disease spread.”
Patricia Lopes, PhD, professor from the Department of Evolutionary Biology and Environmental Studies at the University of Zurich, as well as the study’s lead author, says that previous research regarding wild animals did not address how the animal’s changes in behavior after becoming infected may impact its social interactions with its contact network, or how these changes can affect disease transmission, according to the press release. Due to the fact that a number of infectious diseases are transmitted through social contact, changes in social connectivity can impact transmission dynamics.
Through experimental manipulation of the wild house mice and through the use of radio frequency to track the animals, Dr. Lopes and her colleagues aimed to address this issue in their study. First, the wild mice were injected with lipopolysaccharides (LPS), a component that is used to elicit an immune response that would result in infection with “generalized disease symptoms,” according to the press release. Where previously, wild mice of this population tended to spend a “considerable amount of time in nests, in contact with other mice,” the study found that once the mice were infected they became less active and disconnected themselves from their contact network.
Authors of the study note that mice possess the ability to know whether other mice are sick; however, mice in the same contact network as the infected mouse did not make any pointed efforts of avoidance, continuing to interact as they would normally.
When speaking of this finding, Dr. Lopes said, “It was the sick mouse that removed itself from the group.” Dr. Lopes suggests that by avoiding the contact network, the infected mouse can prevent transmission of the disease to others, which “could be beneficial from an evolutionary perspective,” according to the press release.
The evolutionary biologists then incorporated the behavioral changes in the infected mice into mathematical models in an effort to predict how infectious disease might spread taking into consideration this new information. When speaking of the results, Dr. Lopes said, “When we account for the behavioural changes and how they affect social contacts, we find that the speed and the extent of disease spread are greatly reduced.” Due to the fact that the infected mice showed a decrease in mobility, the reach of the infection was not as widespread among the contact network.
The findings of this study stress the importance of taking the changes in behavior of infected animals into consideration when predicting how infectious diseases will spread, and also can be extended to humans, who also exhibit changes in behavior when sick. Since diseases such as influenza and Ebola are spread by social contact, these findings can be used to improve models that can more effectively predict the spread of such diseases.