Modelling the dynamics of population viral load measures under HIV treatment as prevention

In 2011 the Centers for Disease Control and Prevention(CDC)published guidelines for the use of population viral load(PVL),community viral load(CVL)and monitored viral load(MVL),defined as the average viral load(VL)of all HIV infected individuals in a population,of all diagnosed individuals,and of all individuals on antiretroviral treatment(ART),respectively.Since then,CVL has been used to assess the effectiveness of ART on HIV transmission and as a proxy for HIV incidence.The first objective of this study was to investigate how aggregate VL measures change with the HIV epidemic phase and the drivers behind these changes using a mathematical transmission model.Secondly,we aimed to give some insight into how well CVL correlates with HIV incidence during the course of the epidemic and roll out of ART.We developed a compartmental model for disease progression and HIV transmission with disease stages that differ in viral loads for epidemiological scenarios relevant to a concentrated epidemic in a population of men who have sex with men(MSM)in Western Europe(WE)and to a generalized epidemic in a heterosexual population in Sub-Saharan Africa(SSA).The model predicts that PVL and CVL change with the epidemic phase,while MVL stays constant.These dynamics are linked to the dynamics of infected subgroups(undiagnosed,diagnosed untreated and treated)in different disease stages(primary,chronic and AIDS).In particular,CVL decreases through all epidemic stages:before ART,since chronic population builds up faster than AIDS population and after ART,due to the build-up of treated population with low VL.The trends in CVL and incidence can be both opposing and coinciding depending on the epidemic phase.Before ART is scaled up to sufficiently high levels,incidence increases while CVL decreases.After this point,CVL is a useful indicator of changes in HIV incidence.The model predicts that during the ART scale-up HIV transmission is driven by undiagnosed and diagnosed untreated individuals,and that new infections decline due to the increase in the number of treated.Although CVL is not able to capture the contribution of undiagnosed population to HIV transmission,it declines due to the increase of people on ART too.In the scenarios described by our model,the present epidemic phase corresponds to declining trends in CVL and incidence.

Pair formation models for sexually transmitted infections:A primer

Formodelling sexually transmitted infections,duration of partnerships can strongly influence the transmission dynamics of the infection.If partnerships are monogamous,pairs of susceptible individuals are protected from becoming infected,while pairs of infected individuals delay onward transmission of the infection as long as they persist.In addition,for curable infections re-infection froman infected partnermay occur.Furthermore,interventions based on contact tracing rely on the possibility of identifying and treating partners of infected individuals.To reflect these features in a mathematical model,pair formation models were introduced tomathematical epidemiology in the 1980's.They have since been developed into a widely used tool in modelling sexually transmitted infections and the impact of interventions.Here we give a basic introduction to the concepts of pair formation models for a susceptibleinfected-susceptible(SIS)epidemic.We review some results and applications of pair formation models mainly in the context of chlamydia infection.

Contact tracing-Old models and new challenges

Contact tracing is an effective method to control emerging infectious diseases.Since the 1980’s,modellers are developing a consistent theory for contact tracing,with the aim to find effective and efficient implementations,and to assess the effects of contact tracing on the spread of an infectious disease.Despite the progress made in the area,there remain important open questions.In addition,technological developments,especially in the field of molecular biology(genetic sequencing of pathogens)and modern communication(digital contact tracing),have posed new challenges for the modelling community.In the present paper,we discuss modelling approaches for contact tracing and identify some of the current challenges for the field.