TY - JOUR
T1 - Modelling hepatitis C transmission over a social network of injecting drug users
AU - Rolls, D A
AU - Daraganova, G
AU - Sacks-Davis, Rachel
AU - Hellard, Margaret E
AU - Jenkinson, Robin
AU - McBryde, E
AU - Pattison, P E
AU - Robins, G L
PY - 2012
Y1 - 2012
N2 - Hepatitis C virus (HCV) is a blood-borne virus that disproportionately affects people who inject drugs (PWIDs). Based on extensive interview and blood test data from a longitudinal study in Melbourne, Australia, we describe an individual-based transmission model for HCV spread amongst PWID. We use this model to simulate the transmission of HCV on an empirical social network of PWID. A feature of our model is that sources of infection can be both network neighbours and non-neighbours via importing . Data-driven estimates of sharing frequency and rate of importing are provided. Compared to an appropriately calibrated fully connected network, the empirical network provides some protective effect on the time to primary infection. We also illustrate heterogeneities in incidence rate of infection, both across and within node degrees (i.e., number of network partners). We explore the reduced risk of infection from spontaneously clearing cutpoint nodes whose infection status oscillates, both in theory and in simulation. Further, we show our model-based estimate of per-event transmission probability largely agrees with previous estimates at the lower end of the range 1-3 commonly cited.
AB - Hepatitis C virus (HCV) is a blood-borne virus that disproportionately affects people who inject drugs (PWIDs). Based on extensive interview and blood test data from a longitudinal study in Melbourne, Australia, we describe an individual-based transmission model for HCV spread amongst PWID. We use this model to simulate the transmission of HCV on an empirical social network of PWID. A feature of our model is that sources of infection can be both network neighbours and non-neighbours via importing . Data-driven estimates of sharing frequency and rate of importing are provided. Compared to an appropriately calibrated fully connected network, the empirical network provides some protective effect on the time to primary infection. We also illustrate heterogeneities in incidence rate of infection, both across and within node degrees (i.e., number of network partners). We explore the reduced risk of infection from spontaneously clearing cutpoint nodes whose infection status oscillates, both in theory and in simulation. Further, we show our model-based estimate of per-event transmission probability largely agrees with previous estimates at the lower end of the range 1-3 commonly cited.
UR - http://ac.els-cdn.com.ezproxy.lib.monash.edu.au/S0022519311006175/1-s2.0-S0022519311006175-main.pdf?_tid=4bf98fc2-9684-11e2-9b0e-00000aacb362&acdnat=1
U2 - 10.1016/j.jtbi.2011.12.008
DO - 10.1016/j.jtbi.2011.12.008
M3 - Article
VL - 297
SP - 73
EP - 87
JO - Journal of Theoretical Biology
JF - Journal of Theoretical Biology
SN - 0022-5193
ER -