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Pickles M, Cori A, Probert WJM, Sauter R, Hinch R, Fidler S, Ayles H, Bock P, Donnell D, Wilson E, Piwowar-Manning E, Floyd S, Hayes RJ, Fraser C. PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial. PLoS Comput Biol 2021; 17:e1009301. [PMID: 34473700 PMCID: PMC8478209 DOI: 10.1371/journal.pcbi.1009301] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 09/28/2021] [Accepted: 07/22/2021] [Indexed: 11/23/2022] Open
Abstract
Mathematical models are powerful tools in HIV epidemiology, producing quantitative projections of key indicators such as HIV incidence and prevalence. In order to improve the accuracy of predictions, such models need to incorporate a number of behavioural and biological heterogeneities, especially those related to the sexual network within which HIV transmission occurs. An individual-based model, which explicitly models sexual partnerships, is thus often the most natural type of model to choose. In this paper we present PopART-IBM, a computationally efficient individual-based model capable of simulating 50 years of an HIV epidemic in a large, high-prevalence community in under a minute. We show how the model calibrates within a Bayesian inference framework to detailed age- and sex-stratified data from multiple sources on HIV prevalence, awareness of HIV status, ART status, and viral suppression for an HPTN 071 (PopART) study community in Zambia, and present future projections of HIV prevalence and incidence for this community in the absence of trial intervention. In this paper we present PopART-IBM, an individual-based model used to simulate HIV transmission in communities in high prevalence settings. We show that PopART-IBM can simulate transmission over a span of decades in a large community in less than a minute. This computational efficiency allows us to calibrate the model within an inference framework, and we show an illustrative example of calibration using an adaptive population Monte Carlo Approximate Bayesian Computation algorithm for a community in Zambia that was part of the HPTN-071 (PopART) trial. We compare the detailed model output to real-world data collected during the trial from this community. Finally, we project how the HIV epidemic would have changed over time in this community if no intervention from the trial had occurred.
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Affiliation(s)
- Michael Pickles
- Medical Research Council Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
- * E-mail:
| | - Anne Cori
- Medical Research Council Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - William J. M. Probert
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Rafael Sauter
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Robert Hinch
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Sarah Fidler
- Department of Infectious Disease, Imperial College London, London, United Kingdom
- Imperial College NIHR BRC, London, United Kingdom
| | - Helen Ayles
- Zambart, School of Public Health, University of Zambia, Ridgeway Campus, Lusaka, Zambia
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Peter Bock
- Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Stellenbosch University, Cape Town, South Africa
| | - Deborah Donnell
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Ethan Wilson
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Estelle Piwowar-Manning
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland United States of America
| | - Sian Floyd
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Richard J. Hayes
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
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Murray EJ, Robins JM, Seage GR, Freedberg KA, Hernán MA. The Challenges of Parameterizing Direct Effects in Individual-Level Simulation Models. Med Decis Making 2020; 40:106-111. [PMID: 31975656 DOI: 10.1177/0272989x19894940] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Eleanor J Murray
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - James M Robins
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.,Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - George R Seage
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Kenneth A Freedberg
- Divisions of General Internal Medicine and Infectious Disease and the Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, MA, USA.,Department of Health Policy and Management, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Miguel A Hernán
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.,Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA.,Harvard-MIT Division of Health Sciences and Technology, Boston, MA, USA
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3
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Avanceña ALV, Hutton DW. Optimization Models for HIV/AIDS Resource Allocation: A Systematic Review. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2020; 23:1509-1521. [PMID: 33127022 DOI: 10.1016/j.jval.2020.08.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 07/23/2020] [Accepted: 08/07/2020] [Indexed: 06/11/2023]
Abstract
OBJECTIVE This study reviews optimization models for human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS) resource allocation. METHODS We searched 2 databases for peer-reviewed articles published from January 1985 through August 2019 that describe optimization models for resource allocation in HIV/AIDS. We included models that consider 2 or more competing HIV/AIDS interventions. We extracted data on selected characteristics and identified similarities and differences across models. We also assessed the quality of mathematical disease transmission models based on the best practices identified by a 2010 task force. RESULTS The final qualitative synthesis included 23 articles that used 14 unique optimization models. The articles shared several characteristics, including the use of dynamic transmission modeling to estimate health benefits and the inclusion of specific high-risk groups in the study population. The models explored similar HIV/AIDS interventions that span primary and secondary prevention and antiretroviral treatment. Most articles were focused on sub-Saharan African countries (57%) and the United States (39%). There was notable variation in the types of optimization objectives across the articles; the most common was minimizing HIV incidence or maximizing infections averted (87%). Articles that utilized mathematical modeling of HIV disease and transmission displayed variable quality. CONCLUSIONS This systematic review of the literature identified examples of optimization models that have been applied in different settings, many of which displayed similar features. There were similarities in objective functions across optimization models, but they did not align with global HIV/AIDS goals or targets. Future work should be applied in countries facing the largest declines in HIV/AIDS funding.
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Affiliation(s)
- Anton L V Avanceña
- Department of Health Management and Policy, University of Michigan, Ann Arbor, MI, USA.
| | - David W Hutton
- Department of Health Management and Policy and Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA
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Jalali MS, Botticelli M, Hwang RC, Koh HK, McHugh RK. The opioid crisis: need for systems science research. Health Res Policy Syst 2020; 18:88. [PMID: 32771004 PMCID: PMC7414582 DOI: 10.1186/s12961-020-00598-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 06/29/2020] [Indexed: 01/07/2023] Open
Abstract
The opioid epidemic in the United States has had a devastating impact on millions of people as well as on their families and communities. The increased prevalence of opioid misuse, use disorder and overdose in recent years has highlighted the need for improved public health approaches for reducing the tremendous harms of this illness. In this paper, we explain and call for the need for more systems science approaches, which can uncover the complexities of the opioid crisis, and help evaluate, analyse and forecast the effectiveness of ongoing and new policy interventions. Similar to how a stream of systems science research helped policy development in infectious diseases and obesity, more systems science research is needed in opioids.
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Affiliation(s)
- Mohammad S. Jalali
- grid.38142.3c000000041936754XMGH Institute for Technology Assessment, Harvard Medical School, 101 Merrimac St, Suite 1010, Boston, MA 02114 United States of America ,grid.116068.80000 0001 2341 2786MIT Sloan School of Management, 100 Main St, Cambridge, MA 02142 United States of America
| | - Michael Botticelli
- grid.239424.a0000 0001 2183 6745Grayken Center for Addiction, Boston Medical Center, Boston, MA United States of America
| | - Rachael C. Hwang
- grid.116068.80000 0001 2341 2786MIT Sloan School of Management, 100 Main St, Cambridge, MA 02142 United States of America
| | - Howard K. Koh
- grid.38142.3c000000041936754XT.H. Chan School of Public Health, Harvard
University, Boston, MA United States of America ,grid.38142.3c000000041936754XHarvard Kennedy School, Harvard University, Cambridge, MA United States of America
| | - R. Kathryn McHugh
- grid.38142.3c000000041936754XMGH Institute for Technology Assessment, Harvard Medical School, 101 Merrimac St, Suite 1010, Boston, MA 02114 United States of America ,grid.240206.20000 0000 8795 072XDivision of Alcohol and Drug Abuse, McLean Hospital, Belmont, MA United States of America
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Hazelbag CM, Dushoff J, Dominic EM, Mthombothi ZE, Delva W. Calibration of individual-based models to epidemiological data: A systematic review. PLoS Comput Biol 2020; 16:e1007893. [PMID: 32392252 PMCID: PMC7241852 DOI: 10.1371/journal.pcbi.1007893] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 05/21/2020] [Accepted: 04/21/2020] [Indexed: 01/24/2023] Open
Abstract
Individual-based models (IBMs) informing public health policy should be calibrated to data and provide estimates of uncertainty. Two main components of model-calibration methods are the parameter-search strategy and the goodness-of-fit (GOF) measure; many options exist for each of these. This review provides an overview of calibration methods used in IBMs modelling infectious disease spread. We identified articles on PubMed employing simulation-based methods to calibrate IBMs informing public health policy in HIV, tuberculosis, and malaria epidemiology published between 1 January 2013 and 31 December 2018. Articles were included if models stored individual-specific information, and calibration involved comparing model output to population-level targets. We extracted information on parameter-search strategies, GOF measures, and model validation. The PubMed search identified 653 candidate articles, of which 84 met the review criteria. Of the included articles, 40 (48%) combined a quantitative GOF measure with an algorithmic parameter-search strategy–either an optimisation algorithm (14/40) or a sampling algorithm (26/40). These 40 articles varied widely in their choices of parameter-search strategies and GOF measures. For the remaining 44 (52%) articles, the parameter-search strategy could either not be identified (32/44) or was described as an informal, non-reproducible method (12/44). Of these 44 articles, the majority (25/44) were unclear about the GOF measure used; of the rest, only five quantitatively evaluated GOF. Only a minority of the included articles, 14 (17%) provided a rationale for their choice of model-calibration method. Model validation was reported in 31 (37%) articles. Reporting on calibration methods is far from optimal in epidemiological modelling studies of HIV, malaria and TB transmission dynamics. The adoption of better documented, algorithmic calibration methods could improve both reproducibility and the quality of inference in model-based epidemiology. There is a need for research comparing the performance of calibration methods to inform decisions about the parameter-search strategies and GOF measures. Calibration—that is, “fitting” the model to data—is a crucial part of using mathematical models to better forecast and control the population-level spread of infectious diseases. Evidence that the mathematical model is well-calibrated improves confidence that the model provides a realistic picture of the consequences of health policy decisions. To make informed decisions, Policymakers need information about uncertainty: i.e., what is the range of likely outcomes (rather than just a single prediction). Thus, modellers should also strive to provide accurate measurements of uncertainty, both for their model parameters and for their predictions. This systematic review provides an overview of the methods used to calibrate individual-based models (IBMs) of the spread of HIV, malaria, and tuberculosis. We found that less than half of the reviewed articles used reproducible, non-subjective calibration methods. For the remaining articles, the method could either not be identified or was described as an informal, non-reproducible method. Only one-third of the articles obtained estimates of parameter uncertainty. We conclude that the adoption of better-documented, algorithmic calibration methods could improve both reproducibility and the quality of inference in model-based epidemiology.
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Affiliation(s)
- C. Marijn Hazelbag
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
- * E-mail:
| | - Jonathan Dushoff
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
- Department of Biology, Department of Mathematics and Statistics, Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - Emanuel M. Dominic
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
| | - Zinhle E. Mthombothi
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
| | - Wim Delva
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
- School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
- Center for Statistics, I-BioStat, Hasselt University, Diepenbeek, Belgium
- Department of Global Health, Faculty of Medicine and Health, Stellenbosch University, Stellenbosch, South Africa
- International Centre for Reproductive Health, Ghent University, Ghent, Belgium
- Rega Institute for Medical Research, KU Leuven, Leuven, Belgium
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Behrend MR, Basáñez MG, Hamley JID, Porco TC, Stolk WA, Walker M, de Vlas SJ. Modelling for policy: The five principles of the Neglected Tropical Diseases Modelling Consortium. PLoS Negl Trop Dis 2020; 14:e0008033. [PMID: 32271755 PMCID: PMC7144973 DOI: 10.1371/journal.pntd.0008033] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Affiliation(s)
- Matthew R. Behrend
- Neglected Tropical Diseases, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America
- Blue Well 8, Seattle, Washington, United States of America
- * E-mail:
| | - María-Gloria Basáñez
- MRC Centre for Global Infectious Disease Analysis and London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Jonathan I. D. Hamley
- MRC Centre for Global Infectious Disease Analysis and London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Travis C. Porco
- Francis I. Proctor Foundation for Research in Ophthalmology, Department of Epidemiology and Biostatistics, and Department of Ophthalmology, University of California, San Francisco, United States of America
| | - Wilma A. Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Martin Walker
- London Centre for Neglected Tropical Disease Research, Department of Pathobiology and Population Sciences, Royal Veterinary College, Hatfield, Hertfordshire, United Kingdom
- London Centre for Neglected Tropical Disease Research and Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Sake J. de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
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7
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Modelling the epidemiologic impact of achieving UNAIDS fast-track 90-90-90 and 95-95-95 targets in South Africa. Epidemiol Infect 2020; 147:e122. [PMID: 30869008 PMCID: PMC6452860 DOI: 10.1017/s0950268818003497] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
UNAIDS established fast-track targets of 73% and 86% viral suppression among human immunodeficiency virus (HIV)-positive individuals by 2020 and 2030, respectively. The epidemiologic impact of achieving these goals is unknown. The HIV-Calibrated Dynamic Model, a calibrated agent-based model of HIV transmission, is used to examine scenarios of incremental improvements to the testing and antiretroviral therapy (ART) continuum in South Africa in 2015. The speed of intervention availability is explored, comparing policies for their predicted effects on incidence, prevalence and achievement of fast-track targets in 2020 and 2030. Moderate (30%) improvements in the continuum will not achieve 2020 or 2030 targets and have modest impacts on incidence and prevalence. Improving the continuum by 80% and increasing availability reduces incidence from 2.54 to 0.80 per 100 person-years (-1.73, interquartile range (IQR): -1.42, -2.13) and prevalence from 26.0 to 24.6% (-1.4 percentage points, IQR: -0.88, -1.92) from 2015 to 2030 and achieves fast track targets in 2020 and 2030. Achieving 90-90-90 in South Africa is possible with large improvements to the testing and treatment continuum. The epidemiologic impact of these improvements depends on the balance between survival and transmission benefits of ART with the potential for incidence to remain high.
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Murray EJ, Robins JM, Seage GR, Lodi S, Hyle EP, Reddy KP, Freedberg KA, Hernán MA. Using Observational Data to Calibrate Simulation Models. Med Decis Making 2018; 38:212-224. [PMID: 29141153 PMCID: PMC5771959 DOI: 10.1177/0272989x17738753] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Individual-level simulation models are valuable tools for comparing the impact of clinical or public health interventions on population health and cost outcomes over time. However, a key challenge is ensuring that outcome estimates correctly reflect real-world impacts. Calibration to targets obtained from randomized trials may be insufficient if trials do not exist for populations, time periods, or interventions of interest. Observational data can provide a wider range of calibration targets but requires methods to adjust for treatment-confounder feedback. We propose the use of the parametric g-formula to estimate calibration targets and present a case-study to demonstrate its application. METHODS We used the parametric g-formula applied to data from the HIV-CAUSAL Collaboration to estimate calibration targets for 7-y risks of AIDS and/or death (AIDS/death), as defined by the Center for Disease Control and Prevention under 3 treatment initiation strategies. We compared these targets to projections from the Cost-effectiveness of Preventing AIDS Complications (CEPAC) model for treatment-naïve individuals presenting to care in the following year ranges: 1996 to 1999, 2000 to 2002, or 2003 onwards. RESULTS The parametric g-formula estimated a decreased risk of AIDS/death over time and with earlier treatment. The uncalibrated CEPAC model successfully reproduced targets obtained via the g-formula for baseline 1996 to 1999, but over-estimated calibration targets in contemporary populations and failed to reproduce time trends in AIDS/death risk. Calibration to g-formula targets improved CEPAC model fit for contemporary populations. CONCLUSION Individual-level simulation models are developed based on best available information about disease processes in one or more populations of interest, but these processes can change over time or between populations. The parametric g-formula provides a method for using observational data to obtain valid calibration targets and enables updating of simulation model inputs when randomized trials are not available.
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Affiliation(s)
- Eleanor J Murray
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA (EJM, JMR, GRS, SL, MAH)
| | - James M Robins
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA (EJM, JMR, GRS, SL, MAH)
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA (JMR, MAH)
| | - George R Seage
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA (EJM, JMR, GRS, SL, MAH)
| | - Sara Lodi
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA (EJM, JMR, GRS, SL, MAH)
| | - Emily P Hyle
- Division of Infectious Disease, Massachusetts General Hospital, Boston, MA, USA (EPH, KAF)
| | - Krishna P Reddy
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA (KPR)
| | - Kenneth A Freedberg
- Division of Infectious Disease, Massachusetts General Hospital, Boston, MA, USA (EPH, KAF)
- Department of Health Policy and Management, Harvard School of Public Health, Boston, MA, USA (KAF)
- Center for AIDS Research, Harvard University, Boston, MA, USA (KAF)
| | - Miguel A Hernán
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA (EJM, JMR, GRS, SL, MAH)
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA (JMR, MAH)
- Harvard-MIT Division of Health Sciences and Technology, Boston, MA, USA (MAH)
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Murray EJ, Robins JM, Seage GR, Freedberg KA, Hernán MA. A Comparison of Agent-Based Models and the Parametric G-Formula for Causal Inference. Am J Epidemiol 2017; 186:131-142. [PMID: 28838064 PMCID: PMC5860229 DOI: 10.1093/aje/kwx091] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Accepted: 02/01/2017] [Indexed: 12/20/2022] Open
Abstract
Decision-making requires choosing from treatments on the basis of correctly estimated outcome distributions under each treatment. In the absence of randomized trials, 2 possible approaches are the parametric g-formula and agent-based models (ABMs). The g-formula has been used exclusively to estimate effects in the population from which data were collected, whereas ABMs are commonly used to estimate effects in multiple populations, necessitating stronger assumptions. Here, we describe potential biases that arise when ABM assumptions do not hold. To do so, we estimated 12-month mortality risk in simulated populations differing in prevalence of an unknown common cause of mortality and a time-varying confounder. The ABM and g-formula correctly estimated mortality and causal effects when all inputs were from the target population. However, whenever any inputs came from another population, the ABM gave biased estimates of mortality-and often of causal effects even when the true effect was null. In the absence of unmeasured confounding and model misspecification, both methods produce valid causal inferences for a given population when all inputs are from that population. However, ABMs may result in bias when extrapolated to populations that differ on the distribution of unmeasured outcome determinants, even when the causal network linking variables is identical.
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Elmore SA, Chipman RB, Slate D, Huyvaert KP, VerCauteren KC, Gilbert AT. Management and modeling approaches for controlling raccoon rabies: The road to elimination. PLoS Negl Trop Dis 2017; 11:e0005249. [PMID: 28301480 PMCID: PMC5354248 DOI: 10.1371/journal.pntd.0005249] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Rabies is an ancient viral disease that significantly impacts human and animal health throughout the world. In the developing parts of the world, dog bites represent the highest risk of rabies infection to people, livestock, and other animals. However, in North America, where several rabies virus variants currently circulate in wildlife, human contact with the raccoon rabies variant leads to the highest per capita population administration of post-exposure prophylaxis (PEP) annually. Previous rabies variant elimination in raccoons (Canada), foxes (Europe), and dogs and coyotes (United States) demonstrates that elimination of the raccoon variant from the eastern US is feasible, given an understanding of rabies control costs and benefits and the availability of proper tools. Also critical is a cooperatively produced strategic plan that emphasizes collaborative rabies management among agencies and organizations at the landscape scale. Common management strategies, alone or as part of an integrated approach, include the following: oral rabies vaccination (ORV), trap-vaccinate-release (TVR), and local population reduction. As a complement, mathematical and statistical modeling approaches can guide intervention planning, such as through contact networks, circuit theory, individual-based modeling, and others, which can be used to better understand and predict rabies dynamics through simulated interactions among the host, virus, environment, and control strategy. Strategies derived from this ecological lens can then be optimized to produce a management plan that balances the ecological needs and program financial resources. This paper discusses the management and modeling strategies that are currently used, or have been used in the past, and provides a platform of options for consideration while developing raccoon rabies virus elimination strategies in the US.
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Affiliation(s)
- Stacey A. Elmore
- United States Department of Agriculture, National Wildlife Research Center, Fort Collins, Colorado, United States of America
| | - Richard B. Chipman
- United States Department of Agriculture, Wildlife Services, National Rabies Management Program, Concord, New Hampshire, United States of America
| | - Dennis Slate
- United States Department of Agriculture, Wildlife Services, National Rabies Management Program, Concord, New Hampshire, United States of America
| | - Kathryn P. Huyvaert
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, United States of America
| | - Kurt C. VerCauteren
- United States Department of Agriculture, National Wildlife Research Center, Fort Collins, Colorado, United States of America
| | - Amy T. Gilbert
- United States Department of Agriculture, National Wildlife Research Center, Fort Collins, Colorado, United States of America
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Balzer L, Staples P, Onnela JP, DeGruttola V. Using a network-based approach and targeted maximum likelihood estimation to evaluate the effect of adding pre-exposure prophylaxis to an ongoing test-and-treat trial. Clin Trials 2017; 14:201-210. [PMID: 28124579 DOI: 10.1177/1740774516679666] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Several cluster-randomized trials are underway to investigate the implementation and effectiveness of a universal test-and-treat strategy on the HIV epidemic in sub-Saharan Africa. We consider nesting studies of pre-exposure prophylaxis within these trials. Pre-exposure prophylaxis is a general strategy where high-risk HIV- persons take antiretrovirals daily to reduce their risk of infection from exposure to HIV. We address how to target pre-exposure prophylaxis to high-risk groups and how to maximize power to detect the individual and combined effects of universal test-and-treat and pre-exposure prophylaxis strategies. METHODS We simulated 1000 trials, each consisting of 32 villages with 200 individuals per village. At baseline, we randomized the universal test-and-treat strategy. Then, after 3 years of follow-up, we considered four strategies for targeting pre-exposure prophylaxis: (1) all HIV- individuals who self-identify as high risk, (2) all HIV- individuals who are identified by their HIV+ partner (serodiscordant couples), (3) highly connected HIV- individuals, and (4) the HIV- contacts of a newly diagnosed HIV+ individual (a ring-based strategy). We explored two possible trial designs, and all villages were followed for a total of 7 years. For each village in a trial, we used a stochastic block model to generate bipartite (male-female) networks and simulated an agent-based epidemic process on these networks. We estimated the individual and combined intervention effects with a novel targeted maximum likelihood estimator, which used cross-validation to data-adaptively select from a pre-specified library the candidate estimator that maximized the efficiency of the analysis. RESULTS The universal test-and-treat strategy reduced the 3-year cumulative HIV incidence by 4.0% on average. The impact of each pre-exposure prophylaxis strategy on the 4-year cumulative HIV incidence varied by the coverage of the universal test-and-treat strategy with lower coverage resulting in a larger impact of pre-exposure prophylaxis. Offering pre-exposure prophylaxis to serodiscordant couples resulted in the largest reductions in HIV incidence (2% reduction), and the ring-based strategy had little impact (0% reduction). The joint effect was larger than either individual effect with reductions in the 7-year incidence ranging from 4.5% to 8.8%. Targeted maximum likelihood estimation, data-adaptively adjusting for baseline covariates, substantially improved power over the unadjusted analysis, while maintaining nominal confidence interval coverage. CONCLUSION Our simulation study suggests that nesting a pre-exposure prophylaxis study within an ongoing trial can lead to combined intervention effects greater than those of universal test-and-treat alone and can provide information about the efficacy of pre-exposure prophylaxis in the presence of high coverage of treatment for HIV+ persons.
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Affiliation(s)
- Laura Balzer
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Patrick Staples
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Victor DeGruttola
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Dahabreh IJ, Wong JB, Trikalinos TA. Validation and calibration of structural models that combine information from multiple sources. Expert Rev Pharmacoecon Outcomes Res 2017; 17:27-37. [PMID: 28043174 DOI: 10.1080/14737167.2017.1277143] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Mathematical models that attempt to capture structural relationships between their components and combine information from multiple sources are increasingly used in medicine. Areas covered: We provide an overview of methods for model validation and calibration and survey studies comparing alternative approaches. Expert commentary: Model validation entails a confrontation of models with data, background knowledge, and other models, and can inform judgments about model credibility. Calibration involves selecting parameter values to improve the agreement of model outputs with data. When the goal of modeling is quantitative inference on the effects of interventions or forecasting, calibration can be viewed as estimation. This view clarifies issues related to parameter identifiability and facilitates formal model validation and the examination of consistency among different sources of information. In contrast, when the goal of modeling is the generation of qualitative insights about the modeled phenomenon, calibration is a rather informal process for selecting inputs that result in model behavior that roughly reproduces select aspects of the modeled phenomenon and cannot be equated to an estimation procedure. Current empirical research on validation and calibration methods consists primarily of methodological appraisals or case-studies of alternative techniques and cannot address the numerous complex and multifaceted methodological decisions that modelers must make. Further research is needed on different approaches for developing and validating complex models that combine evidence from multiple sources.
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Affiliation(s)
- Issa J Dahabreh
- a Center for Evidence Synthesis in Health, School of Public Health , Brown University , Providence , RI , USA.,b Department of Health Services, Policy & Practice, School of Public Health , Brown University , Providence , RI , USA.,c Department of Epidemiology, School of Public Health , Brown University , Providence , RI , USA
| | - John B Wong
- d Division of Clinical Decision Making, Department of Medicine , Tufts Medical Center , Boston , MA , USA
| | - Thomas A Trikalinos
- a Center for Evidence Synthesis in Health, School of Public Health , Brown University , Providence , RI , USA.,b Department of Health Services, Policy & Practice, School of Public Health , Brown University , Providence , RI , USA
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Abstract
Effective HIV prevention requires knowledge of the structure and dynamics of the social networks across which infections are transmitted. These networks most commonly comprise chains of sexual relationships, but in some populations, sharing of contaminated needles is also an important, or even the main mechanism that connects people in the network. Whereas network data have long been collected during survey interviews, new data sources have become increasingly common in recent years, because of advances in molecular biology and the use of partner notification services in HIV prevention and treatment programmes. We review current and emerging methods for collecting HIV-related network data, as well as modelling frameworks commonly used to infer network parameters and map potential HIV transmission pathways within the network. We discuss the relative strengths and weaknesses of existing methods and models, and we propose a research agenda for advancing network analysis in HIV epidemiology. We make the case for a combination approach that integrates multiple data sources into a coherent statistical framework.
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Abuelezam NN, McCormick AW, Fussell T, Afriyie AN, Wood R, DeGruttola V, Freedberg KA, Lipsitch M, Seage GR. Can the Heterosexual HIV Epidemic be Eliminated in South Africa Using Combination Prevention? A Modeling Analysis. Am J Epidemiol 2016; 184:239-48. [PMID: 27416841 DOI: 10.1093/aje/kwv344] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Accepted: 12/08/2015] [Indexed: 12/26/2022] Open
Abstract
Little is known about how combining efficacious interventions for human immunodeficiency virus (HIV) prevention could lead to HIV elimination. We used an agent-based simulation model, the HIV calibrated dynamic model, to assess the potential for HIV elimination in South Africa. We examined several scenarios (from continuation of the current status quo to perfect achievement of targets) with differing combinations of male condom use, adult male circumcision, HIV testing, and early antiretroviral therapy (ART). We varied numerous parameters, including the proportion of adult males circumcised, the frequency of condom use during sex acts, acceptance of HIV testing, linkage to health care, criteria for ART initiation, ART viral suppression rates, and loss to follow-up. Maintaining current levels of combination prevention would lead to increasing HIV incidence and prevalence in South Africa, while the perfect combination scenario was projected to eliminate HIV on a 50-year time scale from 2013 to 2063. Perfecting testing and treatment, without changing condom use or circumcision rates, resulted in an 89% reduction in HIV incidence but not elimination. Universal adult male circumcision alone resulted in a 21% incidence reduction within 20 years. Substantial decreases in HIV incidence are possible from sufficient uptake of both primary prevention and ART, but with continuation of the status quo, HIV elimination in South Africa is unlikely within a 50-year time scale.
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Understanding the effects of different HIV transmission models in individual-based microsimulation of HIV epidemic dynamics in people who inject drugs. Epidemiol Infect 2016; 144:1683-700. [PMID: 26753627 DOI: 10.1017/s0950268815003180] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
We investigated how different models of HIV transmission, and assumptions regarding the distribution of unprotected sex and syringe-sharing events ('risk acts'), affect quantitative understanding of HIV transmission process in people who inject drugs (PWID). The individual-based model simulated HIV transmission in a dynamic sexual and injecting network representing New York City. We constructed four HIV transmission models: model 1, constant probabilities; model 2, random number of sexual and parenteral acts; model 3, viral load individual assigned; and model 4, two groups of partnerships (low and high risk). Overall, models with less heterogeneity were more sensitive to changes in numbers risk acts, producing HIV incidence up to four times higher than that empirically observed. Although all models overestimated HIV incidence, micro-simulations with greater heterogeneity in the HIV transmission modelling process produced more robust results and better reproduced empirical epidemic dynamics.
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Childs LM, Abuelezam NN, Dye C, Gupta S, Murray MB, Williams BG, Buckee CO. Modelling challenges in context: lessons from malaria, HIV, and tuberculosis. Epidemics 2015; 10:102-7. [PMID: 25843394 PMCID: PMC4451070 DOI: 10.1016/j.epidem.2015.02.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2014] [Revised: 02/09/2015] [Accepted: 02/09/2015] [Indexed: 02/08/2023] Open
Abstract
Malaria, HIV, and tuberculosis (TB) collectively account for several million deaths each year, with all three ranking among the top ten killers in low-income countries. Despite being caused by very different organisms, malaria, HIV, and TB present a suite of challenges for mathematical modellers that are particularly pronounced in these infections, but represent general problems in infectious disease modelling, and highlight many of the challenges described throughout this issue. Here, we describe some of the unifying challenges that arise in modelling malaria, HIV, and TB, including variation in dynamics within the host, diversity in the pathogen, and heterogeneity in human contact networks and behaviour. Through the lens of these three pathogens, we provide specific examples of the other challenges in this issue and discuss their implications for informing public health efforts.
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Affiliation(s)
- Lauren M Childs
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States
| | - Nadia N Abuelezam
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States
| | - Christopher Dye
- Office of the Director General, World Health Organization, Avenue Appia, 1211 Geneva 27, Switzerland
| | - Sunetra Gupta
- Department of Zoology, University of Oxford, Oxford OX1 3PS, United Kingdom
| | - Megan B Murray
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States; Division of Global Health Equity, Brigham & Women's Hospital, Boston, MA 02115, United States
| | - Brian G Williams
- South African Centre for Epidemiological Modelling and Analysis, Stellenbosch, South Africa; Wits Reproductive Health and HIV Institute, University of the Witwatersrand, Johannesburg, South Africa
| | - Caroline O Buckee
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States.
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Christley S, Cockrell C, An G. Computational Studies of the Intestinal Host-Microbiota Interactome. COMPUTATION (BASEL, SWITZERLAND) 2015; 3:2-28. [PMID: 34765258 PMCID: PMC8580329 DOI: 10.3390/computation3010002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
A large and growing body of research implicates aberrant immune response and compositional shifts of the intestinal microbiota in the pathogenesis of many intestinal disorders. The molecular and physical interaction between the host and the microbiota, known as the host-microbiota interactome, is one of the key drivers in the pathophysiology of many of these disorders. This host-microbiota interactome is a set of dynamic and complex processes, and needs to be treated as a distinct entity and subject for study. Disentangling this complex web of interactions will require novel approaches, using a combination of data-driven bioinformatics with knowledge-driven computational modeling. This review describes the computational approaches for investigating the host-microbiota interactome, with emphasis on the human intestinal tract and innate immunity, and highlights open challenges and existing gaps in the computation methodology for advancing our knowledge about this important facet of human health.
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Affiliation(s)
- Scott Christley
- Department of Surgery, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA
| | - Chase Cockrell
- Department of Surgery, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA
| | - Gary An
- Department of Surgery, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA
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McCormick AW, Abuelezam NN, Rhode ER, Hou T, Walensky RP, Pei PP, Becker JE, DiLorenzo MA, Losina E, Freedberg KA, Lipsitch M, Seage GR. Development, calibration and performance of an HIV transmission model incorporating natural history and behavioral patterns: application in South Africa. PLoS One 2014; 9:e98272. [PMID: 24867402 PMCID: PMC4035281 DOI: 10.1371/journal.pone.0098272] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2014] [Accepted: 04/28/2014] [Indexed: 11/19/2022] Open
Abstract
Understanding HIV transmission dynamics is critical to estimating the potential population-wide impact of HIV prevention and treatment interventions. We developed an individual-based simulation model of the heterosexual HIV epidemic in South Africa and linked it to the previously published Cost-Effectiveness of Preventing AIDS Complications (CEPAC) International Model, which simulates the natural history and treatment of HIV. In this new model, the CEPAC Dynamic Model (CDM), the probability of HIV transmission per sexual encounter between short-term, long-term and commercial sex worker partners depends upon the HIV RNA and disease stage of the infected partner, condom use, and the circumcision status of the uninfected male partner. We included behavioral, demographic and biological values in the CDM and calibrated to HIV prevalence in South Africa pre-antiretroviral therapy. Using a multi-step fitting procedure based on Bayesian melding methodology, we performed 264,225 simulations of the HIV epidemic in South Africa and identified 3,750 parameter sets that created an epidemic and had behavioral characteristics representative of a South African population pre-ART. Of these parameter sets, 564 contributed 90% of the likelihood weight to the fit, and closely reproduced the UNAIDS HIV prevalence curve in South Africa from 1990–2002. The calibration was sensitive to changes in the rate of formation of short-duration partnerships and to the partnership acquisition rate among high-risk individuals, both of which impacted concurrency. Runs that closely fit to historical HIV prevalence reflect diverse ranges for individual parameter values and predict a wide range of possible steady-state prevalence in the absence of interventions, illustrating the value of the calibration procedure and utility of the model for evaluating interventions. This model, which includes detailed behavioral patterns and HIV natural history, closely fits HIV prevalence estimates.
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Affiliation(s)
- Alethea W. McCormick
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
- * E-mail:
| | - Nadia N. Abuelezam
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Erin R. Rhode
- Divisions of General Medicine and Infectious Disease and the Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Taige Hou
- Divisions of General Medicine and Infectious Disease and the Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Rochelle P. Walensky
- Divisions of General Medicine and Infectious Disease and the Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Division of Infectious Diseases, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Center for AIDS Research, Harvard University, Boston, Massachusetts, United States of America
| | - Pamela P. Pei
- Divisions of General Medicine and Infectious Disease and the Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Jessica E. Becker
- Divisions of General Medicine and Infectious Disease and the Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Madeline A. DiLorenzo
- Divisions of General Medicine and Infectious Disease and the Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Elena Losina
- Departments of Biostatistics and Epidemiology, Boston University School of Public Health, Boston, Massachusetts, United States of America
- Department of Orthopedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Kenneth A. Freedberg
- Divisions of General Medicine and Infectious Disease and the Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Center for AIDS Research, Harvard University, Boston, Massachusetts, United States of America
- Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Marc Lipsitch
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
- Center for Communicable Disease Dynamics and Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - George R. Seage
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
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