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In 2020, the WHO launched its first global strategy to accelerate the elimination of cervical cancer, outlining an ambitious set of targets for countries to achieve over the next decade. At the same time, new tools, technologies, and strategies are in the pipeline that may improve screening performance, expand the reach of prophylactic vaccines, and prevent the acquisition, persistence and progression of oncogenic HPV. Detailed mechanistic modelling can help identify the combinations of current and future strategies to combat cervical cancer. Open-source modelling tools are needed to shift the capacity for such evaluations in-country. Here, we introduce the Human papillomavirus simulator (HPVsim), a new open-source software package for creating flexible agent-based models parameterised with country-specific vital dynamics, structured sexual networks, and co-transmitting HPV genotypes. HPVsim includes a novel methodology for modelling cervical disease progression, designed to be readily adaptable to new forms of screening. The software itself is implemented in Python, has built-in tools for simulating commonly-used interventions, includes a comprehensive set of tests and documentation, and runs quickly (seconds to minutes) on a laptop. Performance is greatly enhanced by HPVsim's multi-scale modelling functionality. HPVsim is open source under the MIT License and available via both the Python Package Index (via pip install) and GitHub (hpvsim.org). Author summary: Mathematical models have been integral in setting ambitious goals for cervical cancer elimination, along with determining intermediate targets for vaccination, screening, and treatment. However, given that elimination strategies encompass decade-long timelines, these targets will inevitably need updating over time as the landscape of available technologies for combating cervical cancer evolves. In this work, we introduce a new model, HPVsim, which can rapidly evaluate pathways toward elimination under complex combinations of new and existing interventions. We demonstrate several of the model's key features, including a novel multiscale modelling feature for reducing variability, and outputs from fitting the model to data from India. HPVsim is a community-driven software project which continues to evolve as collaborators strengthen the software for ongoing work. We present some illustrative workflows for conducting an analysis with the HPVsim software, to highlight how the model can be adapted to specific user needs. [ABSTRACT FROM AUTHOR]

Introduction: In 2014, the Government of Thailand recommended pre-exposure prophylaxis (PrEP) as an additional HIV prevention programme within Thailand's National Guidelines on HIV/AIDS Treatment Prevention. However, to date implementation and uptake of PrEP programmes have been limited, and evidence on the costs and the epidemiological and economic impact is not available. Methods: We estimated the costs associated with PrEP provision among men having sex with men (MSM) participating in a facility-based, prospective observational cohort study: the Test, Treat and Prevent HIV Programme in Thailand. We created a suite of scenarios to estimate the cost-effectiveness of PrEP and sensitivity of the results to the model input parameters, including PrEP programme effectiveness, PrEP uptake among high-risk and low-risk MSM, baseline and future antiretroviral therapy (ART) coverage, condom use, unit cost of delivering PrEP, and the discount rate. Results: Drug costs accounted for 82.5% of the total cost of providing PrEP, followed by lab testing (8.2%) and personnel costs (7.8%). The estimated costs of providing the PrEP package in accordance with the national recommendation ranges from US$223 to US$311 per person per year. Based on our modelling results, we estimate that PrEP would be cost-effective when provided to either high-risk or all MSM. However, we found that the programme would be approximately 32% more cost-effective if offered to high-risk MSM than it would be if offered to all MSM, with an incremental cost-effectiveness ratio of US $4,836 per disability-adjusted life years (DALY) averted and US$7,089 per DALY averted respectively. Cost-effectiveness acceptability curves demonstrate that 80% of scenarios would be cost-effective when PrEP is provided solely to higher-risk MSM. Conclusion: We provide the first estimates on cost and cost-effectiveness of PrEP in the Asia-Pacific region, and offer insights on how to deliver PrEP in combination with ART. While the high drug cost poses a budgeting challenge, incorporating PrEP delivery into an existing ART programme could be a cost-effective strategy to prevent HIV infections among MSM in Thailand. Keywords: AIDS; cost; cost-effectiveness analysis; HIV; modelling; pre-exposure prophylaxis; Thailand, 1 | INTRODUCTION HIV pre-exposure prophylaxis (PrEP) can reduce sexual and parenteral transmission of HIV. The World Health Organization recommends PrEP as part of a comprehensive HIV prevention package including [...]

Introduction: With limited funds available, meeting global health targets requires countries to both mobilize and prioritize their health spending. Within this context, countries have recognized the importance of allocating funds for HIV as efficiently as possible to maximize impact. Over the past six years, the governments of 23 countries in Africa, Asia, Eastern Europe and Latin America have used the Optima HIV tool to estimate the optimal allocation of HIV resources. Methods: Each study commenced with a request by the national government for technical assistance in conducting an HIV allocative efficiency study using Optima HIV. Each study team validated the required data, calibrated the Optima HIV epidemic model to produce HIV epidemic projections, agreed on cost functions for interventions, and used the model to calculate the optimal allocation of available funds to best address national strategic plan targets. From a review and analysis of these 23 country studies, we extract common themes around the optimal allocation of HIV funding in different epidemiological contexts. Results and discussion: The optimal distribution of HIV resources depends on the amount of funding available and the characteristics of each country's epidemic, response and targets. Universally, the modelling results indicated that scaling up treatment coverage is an efficient use of resources. There is scope for efficiency gains by targeting the HIV response towards the populations and geographical regions where HIV incidence is highest. Across a range of countries, the model results indicate that a more efficient allocation of HIV resources could reduce cumulative new HIV infections by an average of 18% over the years to 2020 and 25% over the years to 2030, along with an approximately 25% reduction in deaths for both timelines. However, in most countries this would still not be sufficient to meet the targets of the national strategic plan, with modelling results indicating that budget increases of up to 185% would be required. Conclusions: Greater epidemiological impact would be possible through better targeting of existing resources, but additional resources would still be required to meet targets. Allocative efficiency models have proven valuable in improving the HIV planning and budgeting process. Keywords: HIV modeling; allocative efficiency; cost-effectiveness; optimal HIV investment; resource allocation; resource needs, 1 | INTRODUCTION If decisions on the allocation of health resources were guided by the principals of health economics alone, funds would be allocated in ways intended to lead to [...]

Introduction: In 2014, city leaders from around the world endorsed the Paris Declaration on Fast-Track Cities, pledging to achieve the 2020 and 2030 HIV targets championed by UNAIDS. The City of Johannesburg--one of South Africa's metropolitan municipalities and also a health district--has over 600,000 people living with HIV (PLHIV), more than any other city worldwide. We estimate what it would take in terms of programmatic targets and costs for the City of Johannesburg to meet the Fast-Track targets, and demonstrate the impact that this would have. Methods: We applied the Optima HIV epidemic and resource allocation model to demographic, epidemiological and behavioural data on 26 sub-populations in Johannesburg. We used data on programme costs and coverage to produce baseline projections. We calculated how many people must be diagnosed, put onto treatment and maintained with viral suppression to achieve the 2020 and 2030 targets. We also estimated how treatment needs--and therefore fiscal commitments--could be reduced if the treatment targets are combined with primary HIV prevention interventions (voluntary medical male circumcision (VMMC), an expanded condom programme, and comprehensive packages for female sex workers (FSW) and young females). Results: If current programmatic coverage were maintained, Johannesburg could expect 303,000 new infections and 96,000 AIDS-related deaths between 2017 and 2030 and 769,000 PLHIV by 2030. Achieving the Fast-Track targets would require an additional 135,000 diagnoses and 232,000 people on treatment by 2020 (an increase in around 80% over 2016 treatment numbers), but would avert 176,000 infections and 56,500 deaths by 2030. Assuming stable ART unit costs, this would require ZAR 29 billion (USD 2.15 billion) in cumulative treatment investments over the 14 years to 2030. Plausible scale-ups of other proven interventions (VMMC, condom distribution and FSW strategies) could yield additional reductions in new infections (between 4 and 15%), and in overall treatment investment needs. Scaling up VMMC in line with national targets is found to be cost-effective in the medium term. Conclusions: The scale-up in testing and treatment programmes over this decade has been rapid, but these efforts must be doubled to reach 2020 targets. Strategic investments in proven interventions will help Johannesburg achieve the treatment targets and be on track to end AIDS by 2030. Keywords: Fast-Track targets; ending AIDS; Johannesburg; HIV modelling; allocative efficiency, 1 | INTRODUCTION In 2014, city leaders from around the world endorsed the Paris Declaration on Fast-Track Cities, pledging to achieve the 2020 and 2030 HIV targets championed by UNAIDS [...]

Despite the push towards evidence-based health policy, decisions about how to allocate health resources are all too often made on the basis of political forces or a continuation of the status quo. This results in wastage in health systems and loss of potential population health. However, if health systems are to serve people best, then they must operate efficiently and equitably, and appropriate valuation methods are needed to determine how to do this. With the advances in computing power over the past few decades, advanced mathematical optimization algorithms can now be run on personal computers, and can be used to provide comprehensive, evidence-based recommendations for policy makers on how to prioritize health spending considering policy objectives, interactions of interventions, real-world system constraints, and budget envelopes. Such methods provide an invaluable complement to traditional or extended cost-effectiveness analyses (CEA or ECEA) or league tables. In this paper, we describe how such methods work, how policy makers and program managers can access them and implement their recommendations, and how they have changed health spending in the world to date. [ABSTRACT FROM AUTHOR]