More than 250 million people worldwide have tested positive for SARS-CoV-2, usually following a clinical nasal swab. Once they give their positive result, those swabs are no longer garbage. For scientists like us, they hold additional valuable information about the coronavirus. The material left over from the swab may help us uncover hidden aspects of the COVID-19 pandemic.
In what are called phylodynamic methods, which can track a pathogen’s journey through changes in its genes, researchers are able to pinpoint factors such as where and when outbreaks begin, the number of undetected infections, and transmission. common routes. Phylodynamics may also aid in understanding and tracking the spread of new pathogen forms, such as those recently discovered. SARS-CoV-2 . ommicron version of,
What’s in a broom?
Pathogens, like people, each have a genome. It is RNA or DNA that contains the genetic code of an organism – its instructions for life and the information necessary for reproduction.
It is now relatively fast and cheap to sequence the genome of a pathogen. In Switzerland, a consortium of government and academic scientists of which we are part, viral genome sequences previously extracted from nearly 80,000 SARS-CoV-2 positive swab tests.
By combining genetic sequences obtained from different patients, scientists can see which positions in the sequence differ. These differences represent mutations, small errors incorporated into the genome when the pathogen copies itself. We can use these mutual differences as clues to reconstruct chains of transmission and learn about epidemic dynamics along the way.
Phylodynamics: Piecing together genetic clues
Phylodynamic methods provide a way to describe how interfacial differences relate to epidemiological dynamics. These approaches allow researchers to derive from raw data about where mutations have occurred in viral or bacterial genomes to understand all the effects. It may sound complicated, but it’s actually very easy to give an intuitive idea of how it works.
Mutations in the pathogen genome are passed from person to person in a transmission chain. Many pathogens acquire lots of mutations during epidemics. Scientists can summarize these mutual similarities and differences by using what is essentially a family tree for the pathogen. Biologists call this the phylogenetic tree. Each branch point represents a transmission event, when the pathogen is passed from person to person.
The branch length is proportional to the number of differences between the sequenced samples. Shorter branches mean less time between branching points – faster transmission from person to person. Studying the length of branches on this tree can tell us about the pathogen spread in the past – perhaps before we knew an epidemic was on the horizon.
Mathematical Models of Disease Dynamics
In general, models are simplifications of reality. They try to describe the basic processes of real life with mathematical equations. In phylodynamics, these equations describe the relationship between epidemiological processes and the phylogenetic tree.
Take tuberculosis, for example. It is the deadliest bacterial infection in the world, and is becoming even more dangerous due to the widespread development of antibiotic resistance. If you catch an antibiotic-resistant version of the tuberculosis bacterium, treatment can take years.
To estimate the future burden of resistant tuberculosis, we want to estimate how rapidly it spreads.
To do this, we need a model that captures two important processes. First, there is a course of infection, and second, the development of antibiotic resistance. In real life, infected people can infect others, receive treatment and, eventually, either recover or, in the worst case, die from the infection. On top of this, the pathogen can develop resistance.
We can translate these epidemiological processes into a mathematical model with two groups of patients – one group infected with common tuberculosis and the other with antibiotic-resistant tuberculosis. Vital processes – transmission, recovery and death – can occur at different rates for each group. Eventually, patients whose infections develop antibiotic resistance are passed from the first group to the second.
This model ignores some aspects of tuberculosis outbreaks, such as asymptomatic infection or relapse after treatment. Nevertheless, when applied to a set of tuberculosis genomes, this model helps us to predict how rapidly tuberculosis spreads.
Capturing the hidden aspects of the pandemic
Uniquely, phylodynamic approaches can help researchers answer questions in situations where diagnosed cases do not give a complete picture. For example, what about an undetermined number of cases or the source of a new pandemic?
A good example of this type of genome-based investigation is our recent work on the highly pathogenic avian influenza (HPAI) H5N8 in Europe. The epidemic spread to poultry farms and wild birds in 30 European countries in 2016. In the end, tens of millions of birds were killed, ravaging the poultry industry.
But were poultry farms or wild birds the real drivers of the spread? Obviously we can’t ask the birds themselves. Instead, phylodynamic modeling based on H5N8 genomes taken from poultry farms and wild birds helped us obtain an answer. It turns out that in some countries the pathogen is mainly transmitted from farm to farm, while in others it is transmitted from wild birds to fields.
In the case of HPAI H5N8, we helped animal health officials focus control efforts. In some countries this meant limiting transmission between poultry farms while in others limiting contact between domestic and wild birds.
Recently, phylodynamic analyzes helped to evaluate the impact of control strategies for SARS-CoV-2, including earlier border closures and stricter initial lockdowns. A major advantage of phylodynamic modeling is that it can account for undetermined cases. The models can also describe the early stages of an outbreak in the absence of samples from that time period.
Phylodynamic models are under intense development, constantly expanding the field for new applications and larger datasets. However, there are still challenges in expanding genome sequencing efforts to undersampled species and regions and maintaining rapid public data sharing. Ultimately, these data and models will help everyone gain new insights about epidemics and how to control them.
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