Simplified phylogenetic tree for sars viruses in different host species. Dashed lines show estimated times when viruses diverged from one another, finding their way into a new group of hosts. (Data: Hon et al., 2008)
Schmidt’s trial was the first time that phylogenetic analysis had been used in a US criminal case. Since then, the methods have appeared in other cases around the world. Following a surge in cases of hepatitis C in Valencia, Spain, police investigators linked many of the patients to an anaesthetist named Juan Maeso. Phylogenetic analysis confirmed he was the likely source of the outbreak, and in 2007 he was convicted of infecting hundreds of patients by reusing syringes.[5] Genetic data has also helped prove innocence. Shortly after the Maeso case, a group of medics were released from a prison in Libya. They’d been held for eight years after accusations that they’d deliberately infected children with hiv. The group were freed in part because of phylogenetic analysis, which showed that many of the infections had occurred years before the team had arrived in the country.[6]
As well as pointing to the likely source of an outbreak, phylogenetic methods can reveal when a disease arrived in a particular location. Suppose we are investigating a virus like hiv, which evolves relatively quickly. If the hiv viruses circulating in an area are relatively similar, it suggests they haven’t had long to evolve, so the outbreak is probably quite recent. In contrast, if there is a lot of diversity among current viruses, it means that there has been a lot of time for evolution, which suggests the original virus was introduced a while ago. These methods are now commonly used in public health. Recall how in earlier chapters, we looked at the arrival of Zika into Latin America and hiv into North America. In both cases, teams used genetic data to estimate the timing of the virus’s introduction. Researchers have also applied these same ideas to other infections, from pandemic influenza to hospital superbugs like MRSA.[7]
With access to genetic data, we can also work out whether an outbreak started with a single case or multiple introductions. When our team analysed Zika viruses isolated in Fiji during 2015 and 2016, we found two distinct groups of viruses in the phylogenetic tree. Based on the rate of evolution, one group of viruses had arrived into the capital Suva in 2013–14, spreading at low levels for the subsequent year or two, while a separate outbreak had later started in the west of the country.[8] I didn’t realise it at the time, but some of the mosquitoes I swatted away during my 2015 visit had probably been infected with Zika.
Another benefit of phylogenetic analysis is that we can track transmission in the final stages of an outbreak. In March 2016, a new cluster of Ebola cases appeared in Guinea, three months after who had declared the West Africa epidemic over. Perhaps the virus had been spreading undetected in humans all along? When epidemiologist Boubacar Diallo and his collaborators sequenced viruses from the new cluster of cases, they hit upon an alternative explanation. The new viruses were closely related to an Ebola virus found in the semen of a local man who’d recovered from the disease back in 2014. The virus had persisted in his body for almost a year-and-a-half, before spreading to a sexual partner and sparking a new outbreak.[9]
Sequence data is becoming an important part of outbreak analysis, but the idea of evolving viruses can sometimes lead to alarmist coverage. During the Ebola and Zika epidemics, several media reports played up the fact that the viruses were evolving.[10] But this isn’t necessarily as bad as it sounds: all viruses evolve, in the sense that their genetic sequence gradually changes over time. Occasionally this evolution will lead to a difference we care about – like the flu virus changing its appearance – but often it will just happen in the background without having a noticeable effect on an outbreak.
The rate of evolution can affect our ability to analyse outbreaks, though. Phylogenetic analysis is more effective when looking at pathogens that evolve fairly quickly, like hiv and flu. This is because the genetic sequence will change as pathogens spread from one person to another, making it possible to estimate the likely path of infection. In contrast, viruses like measles evolve slowly, which means there won’t be much variation from one person to another.[11] As a result, working out how the cases are related is a bit like trying to piece together a human family tree in a country where everyone has the same surname.
As well as biological limitations to phylogenetic methods, there are also practical ones. In the early stages of the West Africa Ebola epidemic, Pardis Sabeti, a geneticist with the Broad Institute in Boston, analysed sequence data from ninety-nine viruses from Sierra Leone. Phylogenetic trees showed that the infection had spread from Guinea to Sierra Leone in May 2014, possibly after a funeral. Given the seriousness of the outbreak, Sabeti and her colleagues quickly added the new genetic sequences to a public database. This initial burst of research was then followed by a period of relative silence. Although several other teams had been collecting virus samples, nobody else released any new genetic sequences between 2 August and 9 November 2014. During this same period, there were over 10,000 Ebola cases reported in West Africa, with the epidemic reaching its peak in October.[12]
There are a couple of possible reasons for the delay in releasing
