Maximising HIV prevention by balancing the opportunities of today with the promises of tomorrow: a modelling study

SummaryBackgroundMany ways of preventing HIV infection have been proposed and more are being developed. We sought to construct a strategic approach to HIV prevention that would use limited resources to achieve the greatest possible prevention impact through the use of interventions available today and in the coming years.MethodsWe developed a deterministic compartmental model of heterosexual HIV transmission in South Africa and formed assumptions about the costs and effects of a range of interventions, encompassing the further scale-up of existing interventions (promoting condom use, male circumcision, early antiretroviral therapy [ART] initiation for all [including increased HIV testing and counselling activities], and oral pre-exposure prophylaxis [PrEP]), the introduction of new interventions in the medium term (offering intravaginal rings, long-acting injectable antiretroviral drugs) and long term (vaccine, broadly neutralising antibodies [bNAbs]). We examined how available resources could be allocated across these interventions to achieve maximum impact, and assessed how this would be affected by the failure of the interventions to be developed or scaled up.FindingsIf all interventions are available, the optimum mix would place great emphasis on the following: scale-up of male circumcision and early ART initiation with outreach testing, as these are available immediately and assumed to be low cost and highly efficacious; intravaginal rings targeted to sex workers; and vaccines, as these can achieve a large effect if scaled up even if imperfectly efficacious. The optimum mix would rely less on longer term developments, such as long-acting antiretroviral drugs and bNAbs, unless the costs of these reduced. However, if impossible to scale up existing interventions to the extent assumed, emphasis on oral PrEP, intravaginal rings, and long-acting antiretroviral drugs would increase. The long-term effect on the epidemic is most affected by scale-up of existing interventions and the successful development of a vaccine.InterpretationWith current information, a strategic approach in which limited resources are used to maximise prevention impact would focus on strengthening the scale-up of existing interventions, while pursuing a workable vaccine and developing other approaches that can be used if further scale-up of existing interventions is limited.FundingBill & Melinda Gates Foundation

PrEP as a feature in the optimal landscape of combination HIV prevention in sub-Saharan Africa

INTRODUCTION: The new WHO guidelines recommend offering pre-exposure prophylaxis (PrEP) to people who are at substantial risk of HIV infection. However, where PrEP should be prioritised, and for which population groups, remains an open question. The HIV landscape in sub-Saharan Africa features limited prevention resources, multiple options for achieving cost saving, and epidemic heterogeneity. This paper examines what role PrEP should play in optimal prevention in this complex and dynamic landscape. METHODS: We use a model that was previously developed to capture subnational HIV transmission in sub-Saharan Africa. With this model, we can consider how prevention funds could be distributed across and within countries throughout sub-Saharan Africa to enable optimal HIV prevention (that is, avert the greatest number of infections for the lowest cost). Here, we focus on PrEP to elucidate where, and to whom, it would optimally be offered in portfolios of interventions (alongside voluntary medical male circumcision, treatment as prevention, and behaviour change communication). Over a range of continental expenditure levels, we use our model to explore prevention patterns that incorporate PrEP, exclude PrEP, or implement PrEP according to a fixed incidence threshold. RESULTS: At low-to-moderate levels of total prevention expenditure, we find that the optimal intervention portfolios would include PrEP in only a few regions and primarily for female sex workers (FSW). Prioritisation of PrEP would expand with increasing total expenditure, such that the optimal prevention portfolios would offer PrEP in more subnational regions and increasingly for men who have sex with men (MSM) and the lower incidence general population. The marginal benefit of including PrEP among the available interventions increases with overall expenditure by up to 14% (relative to excluding PrEP). The minimum baseline incidence for the optimal offer of PrEP declines for all population groups as expenditure increases. We find that using a fixed incidence benchmark to guide PrEP decisions would incur considerable losses in impact (up to 7%) compared with an approach that uses PrEP more flexibly in light of prevailing budget conditions. CONCLUSIONS: Our findings suggest that, for an optimal distribution of prevention resources, choices of whether to implement PrEP in subnational regions should depend on the scope for impact of other possible interventions, local incidence in population groups, and total resources available. If prevention funding were to become restricted in the future, it may be suboptimal to use PrEP according to a fixed incidence benchmark, and other prevention modalities may be more cost-effective. In contrast, expansions in funding could permit PrEP to be used to its full potential in epidemiologically driven prevention portfolios and thereby enable a more cost-effective HIV response across Africa

A general reaction-diffusion model of acidity in cancer invasion

We model the metabolism and behaviour of a developing cancer tumour in the context of its microenvironment, with the aim of elucidating the consequences of altered energy metabolism. Of particular interest is the Warburg Effect, a widespread preference in tumours for cytosolic glycolysis rather than oxidative phosphorylation for glucose breakdown, as yet incompletely understood. We examine a candidate explanation for the prevalence of the Warburg Effect in tumours, the acid-mediated invasion hypothesis, by generalising a canonical non-linear reaction–diffusion model of acid-mediated tumour invasion to consider additional biological features of potential importance. We apply both numerical methods and a non-standard asymptotic analysis in a travelling wave framework to obtain an explicit understanding of the range of tumour behaviours produced by the model and how fundamental parameters govern the speed and shape of invading tumour waves. Comparison with conclusions drawn under the original system—a special case of our generalised system—allows us to comment on the structural stability and predictive power of the modelling framework

Projected impact, costs and savings from the VMMC program in a 'status quo' background context, relative to the counterfactual scenario of no VMMC program ever.

<p>Projected impact, costs and savings from the VMMC program in a 'status quo' background context, relative to the counterfactual scenario of no VMMC program ever.</p

Projected impact of the VMMC program over 2016–2030, with VMMC scale-up embedded within broader scale-up of HIV prevention and treatment, according to the global Fast Track targets, evaluated relative to the counterfactual scenario in which the Fast Track targets for other interventions are met without VMMC.

<p>Projected impact of the VMMC program over 2016–2030, with VMMC scale-up embedded within broader scale-up of HIV prevention and treatment, according to the global Fast Track targets, evaluated relative to the counterfactual scenario in which the Fast Track targets for other interventions are met without VMMC.</p

VMMC scale-up scenarios evaluated.

<p>VMMC scale-up scenarios evaluated.</p

Modeled circumcisions.

<p><b>(A) Number of new VMMCs occurring each year and (B) the resulting percentage of men ages 15–49 who are circumcised, by scenario.</b> The 2009–2016 new VMMCs are from program data; the 2008–2009 circumcision coverage was the modelers’ estimate based on the 2005 and 2010 DHS. The projected results (VMMC numbers and circumcision coverage over 2010–2030) shown here are from the Goals model; the ICL and EMOD models projected similar numbers (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0199453#pone.0199453.t002" target="_blank">Table 2</a>) and coverages (not shown).</p