IDUs - HCV 'Super-Spreaders' Are Key to Prevention: Early Diagnosis & Treatment, new study
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The work, led by researchers from Oxford University, suggests that early diagnosis and treatment of Hepatitis C in intravenous drug users could prevent many transmissions by limiting the impact of these 'super-spreaders' (a highly infectious person who spreads a disease to many other people).
from various online news sources:
IDUs: Hepatitis C 'Super-Spreaders' Are Key to Prevention - published study below "describing the transmission dynamics of chronic viral diseases, and for evaluating control strategies directed against them"
Researchers have identified intravenous drug users as 'super-spreaders' of hepatitis C, but early diagnosis and treatment may help keep them from giving the disease to others......Researchers think that early diagnosis and treatment of hepatitis C among these super-spreaders could help prevent further transmission of the disease.
"Intravenous drug users with hepatitis C are likely to infect around 20 other people with the virus, and about half of those new infections occur within the first two years after a person contracts hepatitis C, according to an Oxford University study published in PLOS Computational Biology."
The World Health Organisation has identified Hepatitis C as a major public health problem: up to 180 million people worldwide live with the virus, most are unaware that they have been infected and remain undiagnosed for more than 10 years. 20% of those infected will develop cancer or liver scarring (cirrhosis) after 20 years of infection, at which point the only treatment is liver transplantation, which costs around 100,000 ($160,000) for each patient.
This isn't the first study to focus on the link between the hepatitis C epidemic and drug use. A June 2012 study published in the journal Public Health Reports reported that 27 percent of homeless adults in Los Angeles may harbor the virus, with the rate of infection higher among those who injected drugs. In that study researchers concluded that hepatitis c education and testing services were crucial to the prevention of disease spread.
More Than 27% of LA Homeless Adults Have Hepatitis C, and ...
"26.7% of the sample tested HCV-positive and 4.0% tested HIV-positive......We sampled a community-based probability sample of 534 homeless adults
"This study is potentially very important.......'For the first time we show that super-spreading in Hepatitis C is led by intravenous drug users early in their infection,' said Dr Gkikas Magiorkinis of Oxford University's Department of Zoology, lead author of the study. '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. Helping these people and stopping the spread of Hepatitis C is our ultimate target.'....."Our research has resolved this issue and paves the way for a modelling study to show what kind of public health interventions could really make a difference"....."If we are better able to identify where the majority of transmission is happening in many Western countries, we will be able to improve and more cost effectively target interventions.
These so-called super-spreaders of hepatitis C were the subject of a study at Oxford in which researchers collected data from studies on four hepatitis C epidemics in Greece, as well as 100 genetic sequences from frozen plasma samples, and then used a mathematical model to estimate when and how the people in the studies were infected with hepatitis C.
The virus mainly transmits through contaminated blood and before 1990 the major transmission route was blood transfusions and blood products. Since screening for blood transfusions was introduced, after the discovery of the virus in 1989, the only significant transmission route for Hepatitis C is now intravenous drug use -- users are at risk through the sharing and re-use of syringes.
"Scientists say they have, for the first time, worked out the pattern of spread of hepatitis C, showing early diagnosis is key to preventing epidemics. A study in injecting drug users in Greece indicated that each infected person spread the disease to 20 others - 10 of these in the first two years. Study leader Dr Gkikas Magiorkinis, from Oxford University, said when people were infected with something such as flu it was very easy to work out where it had come from, because people knew they were infected within days. But with hepatitis C, no-one has been able to pin down how the virus spreads, because cases occur months or years apart. To overcome this problem, the researchers looked at four hepatitis C epidemics in Greece, using data from 943 patients collected between 1995 and 2000."
"But to provide more detail on how it spreads, they also included genetic information on the virus taken from 100 samples.
Plugging the details into a computer model, they calculated that injecting drug users were "super-spreaders", each transmitting the virus to 20 other people.
Most importantly they discovered that most of the transmissions occurred in the first couple of years, they report in PLoS Computational Biology.
The researchers said that people were more infectious at in the early days of catching hepatitis C because they had higher levels of virus.
The evidence they have produced suggests programmes targeting the diagnosis and treatment of hepatitis C in high-risk groups as early as possible would prevent many new infections and associated health care costs many years down the line."
"Working out how many people are likely to be infected by each 'super-spreader' of hepatitis C, as well as how soon they will be infected, has been a puzzle for over 20 years. Our research has resolved this issue and paves the way for a modeling study to show what kind of public health interventions could really make a difference," said Gkikas Magiorkinis, MD, of Oxford University's Department of Zoology and lead author of the study in a press release.
The research draws on data from four Hepatitis C epidemics in Greece, using information on 943 patients in treatment studies between 1995 and 2000, and around 100 genetic sequences representative of the epidemic taken from frozen plasma samples collected between 1996 and 2006. The team then used a mathematical model to estimate the variance of secondary infection and how long it takes for such infection to occur.
A report of the research, entitled 'Integrating Phylodynamics and Epidemiology to Estimate Transmission Diversity in Viral Epidemics', is to be published in PLoS Computational Biology. The work has been supported by the Wellcome Trust, the European Commission, the Royal Society, the European Social Fund and Greek National Resources (EPEAK-II) and the Hellenic Scientific Society for the Study of AIDS and Sexually Transmitted Diseases.
Integrating Phylodynamics and Epidemiology to Estimate Transmission Diversity in Viral Epidemics
The epidemiology of chronic viral infections, such as those caused by Hepatitis C Virus (HCV) and Human Immunodeficiency Virus (HIV), is affected by the risk group structure of the infected population. Risk groups are defined by each of their members having acquired infection through a specific behavior. However, risk group definitions say little about the transmission potential of each infected individual. Variation in the number of secondary infections is extremely difficult to estimate for HCV and HIV but crucial in the design of efficient control interventions. Here we describe a novel method that combines epidemiological and population genetic approaches to estimate the variation in transmissibility of rapidly-evolving viral epidemics. We evaluate this method using a nationwide HCV epidemic and for the first time co-estimate viral generation times and superspreading events from a combination of molecular and epidemiological data. 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 control strategies directed against them.
To design strategies that efficiently mitigate an epidemic requires estimates of how many people each carrier is likely to infect, what is the variation of this number among infections, and what is the time needed for these transmissions to take place. The disciplines of epidemiology and population genetics independently provide partial answers to these questions by analysing surveillance data and molecular sequences, respectively. Here we propose a novel integration of the two fields that can reveal the underlying transmission dynamics of rapidly-evolving viruses such as HIV or HCV. We explore a well-described nationwide HCV epidemic and show that our method provides new insights into the nature and variation of HCV transmission among infected individuals. We suggest that this approach could form the basis of new tools that can help in the design of effective public health interventions targeting the spread of viral pathogens.
Mathematical epidemiology describes the spread of infectious diseases and aims to aid in the design of effective public health interventions -. Central to this endeavour is the basic reproductive number (R0) of an infectious disease, the mean number of secondary infections per primary infection in a completely susceptible population  (for notations see Table 1). Under simple epidemiological scenarios, in which all infected individuals behave identically, R0 depends on the transmission probability per contact with a susceptible individual, the duration of infectiousness and the rate at which new contacts are made , , . However, studies on sexually transmitted and vector-borne infections indicate that infected individuals behave far from identically and that variation in the number of secondary infections per infected individual can play a major role in epidemic dynamics. For example, some researchers have invoked the so-called 20-80 rule to describe the finding that approximately 20% of infected individuals are responsible for 80% of onward transmission , , . The term 'superspreaders' has been coined to describe hosts that contribute disproportionately to onward infection.
In previous work, variation in the number of secondary infections per infected individual, Z, has been represented by a negative binomial distribution that is described by two parameters, (i) mean R0 among infections and (ii) the dispersion parameter k , . A small k (<0.1) indicates that a small proportion of infected individuals actively transmit the pathogen, whilst a large k (>4) means that all infected individuals contribute approximately equally to onwards transmission , . Lloyd-Smith et al. introduced a definition of superspreaders as the top 1% of hosts when ranked by the number of secondary infections they create . Although superspreading events (SSE) (i.e. the minimum number of secondary infections generated by a superspreader) have been estimated for directly-transmitted acute infections , they have never been described for chronic viral infections. The indolent and subclinical nature of chronic infections makes it difficult to track primary and secondary infections of the multiple strains that concurrently transmit in a given population. The problem is further compounded for HIV and the hepatitis C virus (HCV) that circulate in socially-marginalised groups such as injecting drug users (IDUs) and commercial sex workers.
In addition to R0 and the variation in onward transmission, another epidemiologically-important parameter is the average time between the primary and secondary infections, typically termed the infection generation time (T; several other definitions are used in the literature). A short T indicates rapid transmission, whilst a longer T suggests slower spread but also longer carriage. The duration of carriage of pathogens, which is usually known, represents an upper-limit on T and thus it is reasonable to conclude that directly transmitted acute infections have T<1 month whilst chronic infections have T values on the order of months or years.
Here we show how transmission variability and infection generation time can be estimated by combining viral genomic data with surveillance data and mathematical epidemiology.
Conceptual modelling framework
The concept of effective population size (Ne) has been used in population genetics for at least 50 years (for a brief review see Text S1) , . Ne(t) is generally defined as the size of an idealised population (one without selection or population structure) that experiences the same level of genetic drift as the studied population at time t. Ne(t) is typically lower than N(t), the population's actual size at time t. The ratio N(t)/Ne(t) thus indicates how similarly the real population's reproduction matches the assumptions of the idealised model , . Under a wide range of scenarios this ratio represents the variation in offspring numbers among individuals , .
If the population in question is a viral epidemic, then N(t) is the number of infections at time t (or number of prevalent cases) and Ne(t) represents the effective number of infections (i.e. the number of infections of an idealised epidemic that experiences the same level of genetic drift as the studied population). Crucially, if genetic variation among strains has little or no effect on their ability to infect hosts, as appears to be the case for HIV and HCV  then the ratio N(t)/Ne(t), is formally equal to var(Z), the variance in the number of secondary infections , :
N(t) can be directly observed or estimated from surveillance data using classical epidemiological methods . Ne(t) can be estimated by analysing the pattern of genetic diversity in a sample of the viral population. Specifically, methods based on coalescent theory, such as the skyline plot , , estimate the product of the coalescent Ne(t) multiplied by T, the generation time. The value var(Z)/T is inferable from empirical data and we here call it the phylodynamic transmission parameter, PTP. With all these estimates in hand it is therefore possible to estimate var(Z) from equation 1 as follows:
PTP reflects two important features of the intensity of transmission within a population, (i) the variance of secondary infections among infections, and (ii) time between infections. Equation 2 suggests that an epidemic with a specific PTP is equally well described either by slow and highly variable onward transmission or by fast and more homogeneous onward transmission. This means that by comparing prevalent cases and genetic diversity (as measured by the skyline plot) alone, we cannot directly infer var(Z) and T; more information is required to separate these parameters. In the next two sections we consider practical aspects of inferring these two variables.
Infection generation time
Volz and Frost ,  incorporated mathematical epidemiology in coalescent models assuming that pathogens spread in the population according to compartmental models of epidemic spread. As theory predicts they showed that there is no constant transformation from NeT to N because as susceptible hosts decline in the population, T expands; a constant transformation from NeT to N is observed when the epidemic is on the exponential phase (i.e. T remains constant). Koelle and Rasmussen  showed similarly that a linear constant transformation of NeT to N is also observed when the epidemic is within a steady endemic state. Thus, if we compare NeT with N at the exponential phase or the endemic state we can assume that T remains constant.
Distributions of numbers of secondary infections for epidemics with active and inactive transmitters
To describe the variability in onward transmission we require a probability density function of the random variable Z, the number of secondary infections per infected individual. Previous work has modeled variation in this number with a negative binomial distribution described by two parameters, mean R0 and a dispersion parameter k , . Chronic viral infections, such as those caused by HIV and HCV, are unlikely to be well described by a single distribution. For these epidemics a significant proportion of transmissions result in inactive infections that transmit the virus no further and thus a mixed distribution is a more realistic representation.
In our study we define a sub-population of "inactive" infections whose expected number of secondary infections is equal to 0. The rest of the population is defined as "active". Active infections comprise a proportion u of all infections and their expected number of secondary infections are assumed to be Poisson distributed with mean R0,a. The distribution of the number of secondary infections Z in the whole population (active and inactive combined) is therefore a zero-inflated Poisson distribution, such that:
Equations 3 and 4 can be used to estimate the number of secondary infections of active infections (R0,a) provided that estimates of E(Z), u and var(Z) are available.
Proof of concept: Concurrent nationwide epidemics of HCV
Well-described cohorts of HCV infections (of subtypes 1a, 1b, 3a and 4a) have been described in Greek populations , . Crucially, for these epidemics we have both surveillance information and concurrent samples of viral genome sequences from the same population. First, we used inferred HCV incidence and prevalence by subtype from previous studies . Next, we used the skyline plot method to estimate the value Ne(t)T for each subtype from the viral genome sequences sampled concurrently from the same populations (see Table S1) -.
For both methods we assume that the population corresponds to the set of individuals chronically infected with HCV. The majority of patients with HCV infection develop persistent or chronic infection (60-92%) whilst a minority clears HCV-RNA (8-40%); viral clearance is much faster within the first 2 years of infection and slower thereafter (<1% per year), while increased rates of viral clearance are associated with younger age, female gender, lack of HIV co-infection, chronic HBV infection and genetic variation in IL28B -.
HCV phylodynamic analysis
In total, 24, 27, 24 and 22 samples from Greek patients were amplified and sequenced for subtypes 1a, 1b, 3a and 4a, respectively (Table S1). The majority of subtype 1a and 3a infections were associated with injecting drug use, while for subtype 1b and 4a infections the source of infection was usually unknown. These distributions are consistent with previous epidemiological findings .
Phylogenetic trees (Figure S1) were estimated using a part of the NS5B region (nt 8297-8597) for which more reference sequences from other locations are available. These revealed the epidemics of different subtypes in Greece are not monophyletic and thus they arose through multiple introductions.
Since the outbreaks were not monophyletic we can only provide upper limits of the date of introduction of each subtype (i.e. the date of the oldest possible introduction). Analysis using molecular clock coalescent methods (Figure 1, Figure S2) indicates that the 1a, 1b, 3a and 4a epidemics first entered the Greek population around 1965, 1958, 1975 and 1967, respectively (Table S2). It is important to note that the methods developed here depend on the exponential growth phase of each subtype, and not on the date of its most recent common ancestor, as the latter is more sensitive to sampling biases. The most striking difference in epidemic history among the subtypes is the rapid exponential growth of subtype 3a during 1978-1990, whereas the other subtypes appeared to expand more slowly during 1960-1990 (Figure 1).
Epidemic and phylodynamic estimates are correlated
For each HCV subtype, the estimated plots of Ne(t)T and N(t) for each subtype correspond with each other in relative size (Figure 1a), indicating that larger N corresponds to larger NeT. The plots of Ne(t)T and N(t) for each subtype are also remarkably similar in shape (Figure 1b), indicating that PTP = (N(t)/Ne(t)T) is relatively constant through time. Subsequently, to estimate the ratio N/NeT for each subtype, we assessed the correlation of NeT and N during the period of exponential growth using linear regression (suppressing the constant term, since theory proposes that N is directly proportional to Ne). The correlation of N(t) and Ne(t)T is thus given by N(t) = a Ne(t)T, such that a is an estimate of the phylodynamic transmission parameter PTP = (N/NeT). Since all these metrics are time-series data we corrected the cross-correlations between NeT and N for auto-correlation by means of the Newey-West method . Specifically, we assessed the auto-correlation structure for each parameter and each subtype and then used the maximum lag between the cross-correlated data to correct statistical significance. Linear regressions of N(t) against Ne(t)T for each HCV subtype are strong and significant (p<0.01; R2 = 0.70-0.95). The regression gradients (a) provide estimates of PTP = (N/NeT), which vary from 15.6 to 43.4 for the different HCV subtypes (Table 2, S3).
Subtype-specific R0 estimates
The subtype-specific estimates of mean R0 during the exponential growth phase of Ne or N were 2.4-11.5 (Table 2, Table S3) assuming that infectivity period is 40 years and life expectancy is 70 years. These estimates are similar to those reported previously for subtypes 1a and 1b (both global samples) and 4a (sampled from Egypt) . The expansion of subtype 3a is characterised by faster epidemic growth over a shorter timeframe compared to the other subtypes (Figure 1) and this is reflected in the large R0 value for that subtype, which suggests an average of >10 secondary infections per primary infection.
Model of secondary infections in the Greek HCV epidemics
Historically, HCV epidemics have taken two distinct forms: older transfusion and iatrogenic-related transmission, and more recent intravenous drug use-related (IDU-related) outbreaks. The earlier transmission was characterised by slower spread; individuals infected by transfusion or nosocomial transmission are less likely to practice high-risk behaviors and thus often represent transmission chain dead-ends. The more recent IDU-related epidemics are characterised by rapid spread. HCV is hyperendemic in IDUs worldwide with anti-HCV prevalence of 15-90% ; IDUs may share syringes, needles and other contaminated equipment and are likely to cause long transmission chains , . As explained above, the Z-values of HCV epidemics are thus unlikely to be described well by a single distribution; instead we suggest a bimodal distribution model for the number of secondary infections (see Eq.3-5) that can represent both types of transmission behavior.
We can use Equation 4 to test whether our model is congruent with epidemiological data. Equation 4 predicts that PTP increases with the proportion of "transmitters" in the population of infected individuals (provided that the proportion of transmitters is <50%, which is the case for all the HCV epidemics in this study). Regression of PTP against the percentage of IDU infections for each HCV subtype is strongly significant (Figure 2) whereas the regressions for other risk groups are not (Table S4). This suggests that the estimates of PTP are compatible with the known epidemiology of HCV. However, we note that this regression contains only 4 points and therefore data from more sub-epidemics are required to strengthen this finding.
Estimation of the generation time (T)
There is no previously-available estimate for the generation time (T) of HCV since tracking of secondary infections is very difficult and date of infection is in most cases unknown. Some workers have suggested approximating T using the duration of infectiousness (1/(γ+μ)) , which for HCV is around 25 years (i.e 1/γ = 40 years and 1/μ = 70 years) (Table S3). If we assume that secondary infections follow a Poisson process within the duration of infectiousness (1/(γ+μ)) (i.e. if we perform a simulation of random secondary infections within 25 years of infectiousness), then the mean average time between primary and the subtending secondary infections is similarly high (~12.5 years) regardless of the average number of secondary infections. Such values are epidemiologically and empirically unrealistic for many HCV epidemics: we know that IDUs usually get infected within 2 years after initiating injection .
We assume that T is constant, which is reasonable for the exponential phase of the epidemic that we focus on -. Equation (5) shows that T is maximized at the smallest plausible value of u. The known epidemiology of HCV in IDUs suggests that the proportion of the transmitters (u) will not be smaller than the proportion of the IDUs (i.e. every IDU is likely to have transmitted), at least in our subtype 1a, 3a and 4a outbreaks, which are driven by intravenous drug use. Thus an epidemiologically-meaningful maximum T value can be obtained by setting u equal to the proportion of IDUs in the population (Figure 3).
Using Greek surveillance data on the proportion of HCV infections of each subtype associated with IDU  we estimate that the maximum T (Figure 3, Table 3) for subtype 1a (IDU: 26%) is 1.4 years, for subtype 3a (IDU: 47%) is 3.7 years and for subtype 4a (IDU: 20%) is 0.9 years. For the iatrogenic (non IDU-driven) epidemic of 1b (IDU:<10%) we estimate the maximum T close to the approximate duration of infectiousness (~20 years) [Note that we use IDU as transmitters even if the epidemic is non-IDU driven; this is due to their engagement in repeated paid blood donation up to the end of the 1970s.] .
These estimates of T for subtypes 1a, 3a and 4a are more compatible with the natural history of the disease than those based on the duration of infectiousness (~12.5 years). The probability of secondary infection per contact is expected to be higher during the first year of infection, when viral load is 10 times greater than later in infection , . Also, in the first year patients are less likely to have ceased or reduced the high-risk behavior (e.g. IDU) that led them to be infected. Taken together, this suggests that secondary infections are more likely during the first year of infection. For subtype 1b the estimated T is artificially inflated due to its transmission route (see below).
Analysing the transmission diversity of HCV epidemics
We used equations (3) and (4) to estimate the basic reproductive number of the transmitters (R0,a) and the variability in onward transmission, given the values for u, PTP, R0 and T obtained above (Table 2). We estimate that for HCV subtypes 1a, 1b, 3a and 4a the R0,a values ranged from 12 to 74 and the 99th percentile SSE from 18 to 83 secondary infections (Table 2, Figure 4, Figure S4). Compared to directly-transmitted pathogens, HCV epidemics generally have large 99th percentile SSE values, at least at the levels of SARS and Smallpox. For outbreaks of subtypes 1a, 1b, 3a and 4a investigated here, we estimate that 80% of the infections are caused by approximately 20%, 5%, 35% and 15% of the most infectious individuals, respectively (Figure 5).
The subtype 1b epidemic is the oldest and most prevalent in Greece, characterised by a small proportion of IDUs (6%) and was spread due to the use of contaminated blood and blood products. The very large number of secondary infections for each member of the transmitter population (R0,a = 75), the high degree of superspreading (SSE 99th percentile = 83) and the long generation time (T~20 years) are compatible with the expected transmission dynamics of blood transfusions in the 1960s and 1970s. Historically, subtype 1b infections in Greece are attributed to the use of imported pooled plasma products, a practice that increased the probability of contaminating dozens of individuals from a single contaminated batch; the plasma products could be stored and distributed over many years leading to an artificially large "generation time". Moreover, within Greece, infected IDUs during the 1960s and 1970s practiced repeated paid blood donations as a source of income. The reported dynamics of HCV-1b are typical of older (pre-1990s) HCV epidemics and do not apply to contemporary transmission (except in rare instances when transfusion safety breaks down. Similar trends in blood transfusion as a risk factor for HCV have been documented in many developed countries , -.
On the other hand, the epidemics of subtypes 1a, 3a and 4a epidemics have higher proportions of IDUs (26%, 47% and 20% respectively)  and are typical of the modern HCV epidemics in the Western societies. For these epidemics the higher proportion of IDUs resulted in almost proportionally higher mean and variance in the number of secondary infections. The dynamics of these epidemics are still operating in the developed world and the estimated transmission parameters can be used to design mitigating strategies.
Limitations of the study
Phylogenetic analysis suggests the sub-epidemics of HCV in Greece are the result of multiple introductions (i.e. non-monophyletic; Figure S1) suggesting that estimates of Ne(t)T near the root of the each subtype phylogeny may be biased upwards (because lineages fail to coalesce due to population structure). Two arguments suggest this is not a significant issue in our analysis. First, the trajectories of N(t) and Ne(t)T, which were estimated from separate data sources, closely correspond in four independent epidemics (in scale and shape) and N was obtained from epidemiological surveillance data of wholly Greek origin. Second, it is reasonable to assume that coalescent events within the exponential phase (the period during which we compared N(t) and Ne(t)T) did occur within Greece. That is, coalescences close to the root of each phylogeny (which may represent transmission outside Greece) were not used in our analysis. In the worst case scenario - that Ne(t)T has been overestimated - our estimate of PTP can be considered a lower bound and that variation in onward transmission might be even greater than reported here.
A second limitation of our study is that our estimate of PTP does not incorporate statistical uncertainty in the estimation of N(t) and Ne(t)T. In the future, we aim to develop a Bayesian approach to incorporate both sources of uncertainty and provide a proper posterior distribution for PTP.
Our approach provides information about superspreading from analytical relationships between the rate of coalescence (Ne), viral generation time (T), and prevalence (N) and thus is independent of phylogenetic topology. It is therefore complementary to alternative approaches that investigate how non-random contact structures affect the topology of a transmission tree . At this point we should emphasize that further exploration and extension of the approach is required. For example a zero-inflated Poisson distribution of secondary infections does not fit most of the HIV-1 epidemics. A power-law distribution resulting from sexual-contact analysis would provide a more realistic approximation, for which a detailed analysis of the effect of network structure on PTP needs to be performed. Finally, simulation studies could explore the robustness of the approach under a wider range of epidemiologic scenarios, whilst larger datasets could empirically replicate our findings to support wider applicability of this approach e.g. to inform Public Health policies.
We have shown that phylodynamic methods can be combined with epidemiological surveillance data to estimate the variability in ongoing transmission of a chronic viral epidemic, and to investigate its generation time. Both parameters are critical to the design of effective control measures but are very difficult to estimate from surveillance data alone. We tested the framework on a well-characterised set of HCV epidemic in Greece, showing that the results are epidemiologically coherent and suggesting that this approach could be a new tool for public health. We expect our approach to be most readily adapted to other chronic viral diseases such as HIV, but could also be applied to directly transmitted (e.g. Influenza) or vector-borne (e.g. Dengue) viral epidemics, for which superspreading events and generation times are largely unknown.