A new hepatitis C virus (HCV) transmission model combining genetic and epidemiologic data reveals that the majority of transmission of the virus is by a minority of intravenous drug users.
The authors found that, on average, each intravenous drug user who contracts HCV is likely to infect around 20 others with the virus, with around half of these occurring in the first 2 years of infection.
"For the first time we show that super-spreading in hepatitis C is led by intravenous drug users early in their infection," said author Gkikas Magiorkinis (University of Oxford, UK) in a press statement.
"Using this information we can hopefully soon make a solid argument to support the scaling-up of early diagnosis and antiviral treatment in drug users."
The researchers created a mathematic model that allowed them to estimate the number of transmissions made by so-called "super-spreaders" - infected people who spread the virus to a large number of others.
They tested the model using surveillance information from Greek epidemics of the virus combined with genetic sequencing of 97 samples from infected patients within the epidemic populations.
They estimate that 80% of onward infections for outbreaks of subtypes 1a, 1b, 3a, and 4a were caused by approximately 20%, 5%, 35%, and 15% of infected individuals, respectively.
Furthermore, the number and variance of secondary infections was proportional to the percentage of infections among intravenous drug users for subtypes 1a, 3a and 4a. By comparison, the HCV-1b strain is mainly spread by blood transfusions and resulted in fewer secondary infections and showed lower variance.
Infectious disease researchers have previously found that differences in the behavior of infected individuals lead some to disproportionately contribute toward onward transmission. And Magiokinis and colleagues say that their findings in HCV support the so-called 20:80 rule whereby 20% of infected individuals cause 80% of secondary infections.
Epidemiology and population genetics both partially allow researchers to estimate how many people a carrier of a virus is likely to infect, how this varies, and the time for transmissions to take place, explain the authors. They say their study shows that combining the two gives an even clearer picture that will ultimately inform future public health strategies.
"We anticipate that this integrated approach will form the basis of powerful tools for describing the transmission dynamics of chronic viral diseases, and for evaluating strategies directed against them," they conclude in PLOS Computational Biology.
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