Novel network analysis confirms: #stayathome helps limit virus mutations

 
16/04/2020 – Both the virus diseases of the 2013 Ebola regional epidemic and the current COVID-19 global pandemic have seen virus mutations between hosts – a normal phenomenon with the potential to turn viruses even more harmful. A team of scientists including researchers from Humboldt University and Potsdam Institute for Climate Impact Research has now employed advanced mathematical models to explore these dynamics. Their findings confirm public health responses like suspending long-haul travel, but also the call to stay at home. Further, they underline the importance of closely tracking genetic mutations during virus outbreaks to facilitate crisis response.
Novel network analysis confirms: #stayathome helps limit virus mutations
Mathematical network analyses can shed useful new lights on virus dynamics. Photo by Alina Grubnyak on Unsplash

Within an animal or a human being, viruses can randomly mutate, i.e. modify their genetic composition. These accidental modifications may lead to less or more harmful viruses. Virus mutations which e.g. improve their attacking power on their host’s cells can under certain circumstances consequently also improve their chance of spreading. But not only within a host: Observations of both the Ebola epidemic in West Africa in 2013-2016 and the current COVID-19 pandemic have shown that viruses even mutate as they spread from host to host – a key difference for disease mitigation, yet hardly accounted for so far by epidemiological models.

Researchers have now added the missing bit: Employing the mathematical methods of network analysis and evolutionary dynamics, they look beyond the individual host at whole networks of hosts, i.e. of people living in a family, a city, or a country. They find that long-range connections – long-haul air travel, for instance – significantly increase the likelihood of mutations between hosts: As one host carries the virus into an area where the number of infections is still very low, virus mutations from that “patient zero” to further patients can more easily form and potentially turn the virus ever more harmful. That’s because there is not yet a dominant virus form around that new mutations would have to compete with.

This has two consequences for public health responses: First, the analysis confirms that largely suspending long-haul travel is key. But what’s true for large networks also applies on a smaller scale: When we #stayathome, we limit our contacts in range, too. It makes a difference for virus mitigation if all contacts are in a small area like at home, or if it’s expanded to a market, an office space, or even a city. Second, it shows that a close observation of genetic changes during an outbreak is needed in order to facilitate crisis response.

Article: Rüdiger, S., Plietzsch, A., Sagués, F. et al. Epidemics with mutating infectivity on small-world networks. Sci Rep 10, 5919 (2020). DOI: 10.1038/s41598-020-62597-5

Weblink to the article: https://www.nature.com/articles/s41598-020-62597-5

More information:

  • The open-source project Nextstrain is tracking mutations of pathogenes, including for COVID-19. They continually update their publicly available data.