Calibration and analysis of complex models: methodological development and application to explore the impact of HAART in AfricaMR/J014362/1

复杂模型的校准和分析：探索HAART对非洲影响的方法开发和应用MR/J014362/1

• 批准号: MR/J005088/1
• 负责人: Richard White
• 金额: $ 65.7万
• 依托单位: London School of Hygiene & Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FJ005088%2F1
• 关键词: Calibration; analysis; complex; models; methodological

Mathematical models are used routinely in government, the public sector, industry and academia to help predict the future and make policy decisions, such as what is the impact of ocean currents on the climate and how should we best prepare for the next flu pandemic? The usefulness of models in helping to make decision relies on how well they are can be matched to real data (model calibration) and how well we can analyse the models to find out how confident we should be about the predictions they make. In recent years models have become increasingly complex, but we don't yet have good methods to calibrate and analyse these more complicated models. The lack of these methods is a major obstacle to improving our predictions, and the policy decisions that affect all of us in the UK. HIV/AIDS remains the leading public health problem in much of sub-Saharan Africa. While increased access to treatment for HIV (highly active anti-retroviral therapy or HAART) has successfully reduced suffering and deaths among people living with HIV/AIDS, it is unknown how best HAART can be used to reduce the number of new HIV infections. Models would help us determine the best strategy, but we need improved methods of model calibration and analysis to do this.In this project we will develop new methods to calibrate and analyse complex individual-based stochastic models (these models are very computer-intensive as every individual is explicitly tracked through time). First we will develop two new model calibration methods specifically for stochastic models and also develop new methods that allow us to calibrate multiple models outputs at the same time (eg the number of people infected with HIV and the number of deaths from AIDS). Then we will compare these new methods with existing methods to see how they perform. We will go on to develop hybrid calibration methods that combines the strengths of our new and existing methods, to get the best of both. Finally we will apply our new methods to calibrate a complex model to data on HIV/AIDS trends in Africa, and use our fitted models to predict the impact of HAART on HIV/AIDS in Africa. The development and evaluation of robust methods for the calibration and analysis of complex models should result in the generation of more reliable scientific evidence and better health policy and funding decisions. In addition, an effective method of assessing the likely impact of HAART should lead to better informed policies and improved public health in Africa. If the models are accurate, the benefits may include fewer people dying and being infected with HIV, alleviating the substantial social and economic burden of the epidemic as well as reducing the burgeoning costs of HIV-related health care.An international multidisciplinary team will carry out this research. Statistical expertise is available from investigators from Durham University, Sheffield University and Cambridge University; expertise in the development and application of complex HIV models is available from investigators in the London School of Hygiene and Tropical Medicine; and virologic expertise and uniquely detailed high quality empirical data is available from MRC/UVRI in Uganda.

数学模型在政府、公共部门、工业界和学术界经常使用，以帮助预测未来和做出政策决策，例如洋流对气候的影响以及我们应该如何为下一次流感大流行做好准备。模型在帮助决策方面的有用性取决于它们与真实的数据（模型校准）的匹配程度，以及我们对模型的分析程度，以了解我们对模型所做预测的信心。近年来，模型变得越来越复杂，但我们还没有好的方法来校准和分析这些更复杂的模型。缺乏这些方法是改善我们的预测以及影响我们所有英国人的政策决定的主要障碍。艾滋病毒/艾滋病仍然是撒哈拉以南非洲大部分地区的主要公共卫生问题。虽然艾滋病毒治疗（高效抗逆转录病毒疗法）的普及成功地减少了艾滋病毒/艾滋病感染者的痛苦和死亡，但尚不清楚如何最好地利用高效抗逆转录病毒疗法来减少新的艾滋病毒感染人数。模型将帮助我们确定最佳策略，但我们需要改进模型校准和分析的方法来做到这一点。在这个项目中，我们将开发新的方法来校准和分析复杂的基于个体的随机模型（这些模型是非常计算机密集型的，因为每个个体都是明确跟踪的）。首先，我们将开发两种新的模型校准方法，专门用于随机模型，并开发新的方法，使我们能够同时校准多个模型输出（例如感染艾滋病毒的人数和艾滋病死亡人数）。然后我们将这些新方法与现有方法进行比较，看看它们的性能如何。我们将继续开发混合校准方法，结合我们的新方法和现有方法的优势，以充分利用两者。最后，我们将应用我们的新方法来校准一个复杂的模型，非洲艾滋病毒/艾滋病趋势的数据，并使用我们的拟合模型来预测HAART对非洲艾滋病毒/艾滋病的影响。制定和评价用于校准和分析复杂模型的可靠方法，应能产生更可靠的科学证据，并改进卫生政策和供资决定。此外，评估高效抗逆转录病毒疗法可能产生的影响的有效方法应有助于制定更明智的政策，并改善非洲的公共卫生。如果这些模型是准确的，其好处可能包括更少的人死亡和感染艾滋病毒，减轻这一流行病的巨大社会和经济负担，以及减少艾滋病毒相关医疗保健的迅速增长的成本。一个国际多学科小组将开展这项研究。达勒姆大学、谢菲尔德大学和剑桥大学的研究人员提供统计学专门知识;伦敦卫生和热带医学院的研究人员提供开发和应用复杂艾滋病毒模型的专门知识;乌干达MRC/UVRI提供病毒学专门知识和独特详细的高质量经验数据。

项目成果

Transmission events revealed in tuberculosis contact investigations in London.

• DOI: 10.1038/s41598-018-25149-6
• 发表时间: 2018-04-27
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Cavany SM;Vynnycky E;Sumner T;Macdonald N;Thomas HL;White J;White RG;Maguire H;Anderson C
• 通讯作者: Anderson C

Impact and cost-effectiveness of current and future tuberculosis diagnostics: the contribution of modelling.

• DOI: 10.5588/ijtld.13.0851
• 发表时间: 2014-09
• 期刊: The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease
• 作者: Dowdy DW;Houben R;Cohen T;Pai M;Cobelens F;Vassall A;Menzies NA;Gomez GB;Langley I;Squire SB;White R;TB MAC meeting participants
• 通讯作者: TB MAC meeting participants

Effect of HSV-2 on population-level trends in HIV incidence in Uganda between 1990 and 2007.

• DOI: 10.1111/tmi.12176
• 发表时间: 2013-10
• 期刊: Tropical medicine & international health : TM & IH
• 作者: Biraro S;Kamali A;White R;Karabarinde A;Nsiimire Ssendagala J;Grosskurth H;Weiss HA
• 通讯作者: Weiss HA

Predicting the long-term impact of antiretroviral therapy scale-up on population incidence of tuberculosis.

• DOI: 10.1371/journal.pone.0075466
• 发表时间: 2013
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Dodd PJ;Knight GM;Lawn SD;Corbett EL;White RG
• 通讯作者: White RG

Efficient History Matching of a High Dimensional Individual-Based HIV Transmission Model

• DOI: 10.1137/16m1093008
• 发表时间: 2017-01-01
• 期刊: SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION
• 影响因子: 2
• 作者: Andrianakis, Loannis;McCreesh, Nicky;White, Richard G.
• 通讯作者: White, Richard G.