Integrating Phylodynamics and Epidemiology to Estimate Transmission Diversity in Viral Epidemics

Magiorkinis, Gkikas; Sypsa, Vana; Magiorkinis, Emmanouil; Paraskevis, Dimitrios; Katsoulidou, Antigoni; Belshaw, Robert; Fraser, Christophe; Pybus, Oliver G.; Hatzakis, Angelos

doi:10.1371/journal.pcbi.1002876

Cited by 64 publications

(57 citation statements)

References 63 publications

(60 reference statements)

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“…The prevalence found in this study falls within the range of previous estimates [24, 45,46]. Furthermore, the estimated number of secondary infections resulting from PWID belonging to the medium and high risks groups (4.3 and 7.3, respectively) is similar to other findings by Magiorkinis et al, (2013), who calculated R 0 in Greece for HCV subtypes using epidemiologic (surveillance) data and phylodynamic modeling [47]. For subtypes with a higher proportion of PWID, they estimated that the number of secondary infections were 3.4, 11.5 and 2.4 for subtypes 1a, 3a and 4a, respectively; the subtype with the highest proportion of PWID (47 percent) was subtype 3a [47].…”

Section: Model Validationsupporting

confidence: 90%

A quasi-markov model for transmission and disease elimination: Hepatitis C among people who inject drugs

Hart-Malloy¹,

DiRienzo²

2014

J Med Stat Inform

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Background: The use of mathematic modeling to better understand the spread of Hepatitis C and the impact of interventions can be invaluable for localities, states and countries with a large burden of injection drug use. New York State (NYS) is estimated to be home to a large number of people who inject drugs (PWID), however the burden of hepatitis C among this population and the impact of interventions are not known and/or fully understood. Since accurate modeling can be complex and require costly data analysis software to implement, the purpose of this paper was to derive a methodology to accurately model the prevalence of hepatitis C at the state level that is tractable and easily implemented with free software. Methods: A non-stationary quasi-Markov model, is proposed that is implemented using free software R © . The methodology aims to estimate hepatitis C prevalence and evaluate impacts of interventions on disease burden among PWID in NYS. The approach is "quasi-Markov" because transition probabilities among states change in time and depend on past information. Interventions evaluated aimed to: reduce sharing needles and/or other drug use paraphernalia; increase needle disinfection rates; and increase availability of clean needles and other drug use paraphernalia. Results:The quasi-Markov model estimated hepatitis C prevalence among PWID reached an equilibrium value of 63.6 percent after 50 years. In order to eliminate disease, all proposed interventions were needed, resulting in an estimated prevalence less than 1.0 percent, 56 years after implementation. Using parameters defined for an alternate study modeling hepatitis C prevalence found similar results, serving to reinforce the validity of the proposed methods. Conclusion:The results of this analysis demonstrate the feasibility of using a non-stationary quasiMarkov methodology to model the spread of hepatitis C among PWID using free programming software. Coding provided can be used by other researchers to use and modify for their own purposes and could further impact this field of research as well as inform and promote additional education prevention and interventions for PWID.

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Section: Model Validationsupporting

confidence: 90%

A quasi-markov model for transmission and disease elimination: Hepatitis C among people who inject drugs

Hart-Malloy¹,

DiRienzo²

2014

J Med Stat Inform

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“…(iii) Onward transmission per capita was more homogeneous and, on average, less frequent after the estimated transition (95% BCI: 1952–1968). This counterintuitive result arises because the lineage coalescence rate will be faster when only a small fraction of infections generate the majority of new cases and when the viral generation time is reduced (48). To discriminate among these hypotheses, we first reconstructed the epidemic history of lineages that maintained ancestry within Kinshasa (i.e., 84 taxa in Fig.…”

Section: Divergent Epidemic Dynamics Of Hiv-1 Groups M and Omentioning

confidence: 99%

The early spread and epidemic ignition of HIV-1 in human populations

Faria

Rambaut

Suchard

et al. 2014

Science

562

459

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Thirty years after the discovery of HIV-1, the early transmission, dissemination, and establishment of the virus in human populations remain unclear. Using statistical approaches applied to HIV-1 sequence data from central Africa, we show that from the 1920s Kinshasa (in what is now the Democratic Republic of Congo) was the focus of early transmission and the source of pre-1960 pandemic viruses elsewhere. Location and dating estimates were validated using the earliest HIV-1 archival sample, also from Kinshasa. The epidemic histories of HIV-1 group M and nonpandemic group O were similar until ~1960, after which group M underwent an epidemiological transition and outpaced regional population growth. Our results reconstruct the early dynamics of HIV-1 and emphasize the role of social changes and transport networks in the establishment of this virus in human populations.

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“…To obtain estimations of some of the epidemiological parameters, we used our previously described methods [16]. We estimated the number of secondary infections per primary infection in a completely susceptible population as…”

Section: Estimates Of Epidemiological Parametersmentioning

confidence: 99%

Reducing HIV infection in people who inject drugs is impossible without targeting recently-infected subjects

2018

Aids

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a Objective: Although our understanding of viral transmission among people who inject drugs (PWID) has improved, we still know little about when and how many times each injector transmits HIV throughout the duration of infection. We describe HIV dynamics in PWID to evaluate which preventive strategies can be efficient.Design: Due to the notably scarce interventions, HIV-1 spread explosively in Russia and Ukraine in 1990s. By studying this epidemic between 1995 and 2005, we characterized naturally occurring transmission dynamics of HIV among PWID. Method:We combined publicly available HIV pol and env sequences with prevalence estimates from Russia and Ukraine under an evolutionary epidemiology framework to characterize HIV transmissibility between PWID. We then constructed compartmental models to simulate HIV spread among PWID.Results: In the absence of interventions, each injector transmits on average to 10 others. Half of the transmissions take place within 1 month after primary infection, suggesting that the epidemic will expand even after blocking all the post-first month transmissions. Primary prevention can realistically target the first month of infection, and we show that it is very efficient to control the spread of HIV-1 in PWID. Treating acutely infected on top of primary prevention is notably effective. Conclusion:As a large proportion of transmissions among PWID occur within 1 month after infection, reducing and delaying transmissions through scale-up of harm reduction programmes should always form the backbone of HIV control strategies in PWID. Growing PWID populations in the developing world, where primary prevention is scarce, constitutes a public health time bomb.

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Integrating Phylodynamics and Epidemiology to Estimate Transmission Diversity in Viral Epidemics

Cited by 64 publications

References 63 publications

A quasi-markov model for transmission and disease elimination: Hepatitis C among people who inject drugs

A quasi-markov model for transmission and disease elimination: Hepatitis C among people who inject drugs

The early spread and epidemic ignition of HIV-1 in human populations

Reducing HIV infection in people who inject drugs is impossible without targeting recently-infected subjects

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