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Onifade AA, Rychtář J, Taylor D. A dynamic game of lymphatic filariasis prevention by voluntary use of insecticide treated nets. J Theor Biol 2024; 585:111796. [PMID: 38522665 DOI: 10.1016/j.jtbi.2024.111796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 03/15/2024] [Accepted: 03/16/2024] [Indexed: 03/26/2024]
Abstract
Lymphatic filariasis (LF) has been targeted for elimination as a public health concern by 2030 with a goal to keep the prevalence of LF infections under the 1% threshold. While mass drug administration (MDA) is a primary strategy recommended by WHO, the use of insecticide treated nets (ITN) plays a crucial role as an alternative strategy when MDA cannot be used. In this paper, we use imitation dynamics to incorporate human behavior and voluntary use of ITN into the compartmental epidemiological model of LF transmission. We find the equilibrium states of the dynamics and the ITN usage as it depends on epidemiological parameters and the cost of ITNs. We investigate the conditions under which the voluntary use of ITNs can keep the LF prevalence under the 1% threshold. We found that when the cost of using the ITNs is about 105 smaller than the perceived cost of LF, then the voluntary use of ITNs will eliminate LF as a public health concern. Furthermore, when the ITNs are given away for free, our model predicts that over 80% of the population will use them which would eliminate LF completely in regions where Anopheles are the primary vectors.
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Affiliation(s)
- Akindele Akano Onifade
- Department of Computer Science and Mathematics, Mountain Top University, Ibafo, Nigeria.
| | - Jan Rychtář
- Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA, United States of America.
| | - Dewey Taylor
- Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA, United States of America.
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Vasconcelos A, King JD, Nunes-Alves C, Anderson R, Argaw D, Basáñez MG, Bilal S, Blok DJ, Blumberg S, Borlase A, Brady OJ, Browning R, Chitnis N, Coffeng LE, Crowley EH, Cucunubá ZM, Cummings DAT, Davis CN, Davis EL, Dixon M, Dobson A, Dyson L, French M, Fronterre C, Giorgi E, Huang CI, Jain S, James A, Kim SH, Kura K, Lucianez A, Marks M, Mbabazi PS, Medley GF, Michael E, Montresor A, Mutono N, Mwangi TS, Rock KS, Saboyá-Díaz MI, Sasanami M, Schwehm M, Spencer SEF, Srivathsan A, Stawski RS, Stolk WA, Sutherland SA, Tchuenté LAT, de Vlas SJ, Walker M, Brooker SJ, Hollingsworth TD, Solomon AW, Fall IS. Accelerating Progress Towards the 2030 Neglected Tropical Diseases Targets: How Can Quantitative Modeling Support Programmatic Decisions? Clin Infect Dis 2024; 78:S83-S92. [PMID: 38662692 PMCID: PMC11045030 DOI: 10.1093/cid/ciae082] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
Over the past decade, considerable progress has been made in the control, elimination, and eradication of neglected tropical diseases (NTDs). Despite these advances, most NTD programs have recently experienced important setbacks; for example, NTD interventions were some of the most frequently and severely impacted by service disruptions due to the coronavirus disease 2019 (COVID-19) pandemic. Mathematical modeling can help inform selection of interventions to meet the targets set out in the NTD road map 2021-2030, and such studies should prioritize questions that are relevant for decision-makers, especially those designing, implementing, and evaluating national and subnational programs. In September 2022, the World Health Organization hosted a stakeholder meeting to identify such priority modeling questions across a range of NTDs and to consider how modeling could inform local decision making. Here, we summarize the outputs of the meeting, highlight common themes in the questions being asked, and discuss how quantitative modeling can support programmatic decisions that may accelerate progress towards the 2030 targets.
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Affiliation(s)
- Andreia Vasconcelos
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
- Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
| | - Jonathan D King
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland
| | - Cláudio Nunes-Alves
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
| | - Roy Anderson
- London Centre for Neglected Tropical Disease Research, London, United Kingdom
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, St Mary's Campus, Imperial College London, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Daniel Argaw
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland
| | - Maria-Gloria Basáñez
- London Centre for Neglected Tropical Disease Research, London, United Kingdom
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, St Mary's Campus, Imperial College London, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Shakir Bilal
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | - David J Blok
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Seth Blumberg
- Francis I. Proctor Foundation, University of California, San Francisco, California, USA
| | - Anna Borlase
- Department of Biology, University of Oxford, Oxford, United Kingdom
| | - Oliver J Brady
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Raiha Browning
- The Department of Statistics, The University of Warwick, Coventry, United Kingdom
| | - Nakul Chitnis
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Luc E Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Emily H Crowley
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Zulma M Cucunubá
- Departamento de Epidemiología Clínica y Bioestadística, Facultad de Medicina, Universidad Pontificia Javeriana, Bogotá, Colombia
| | - Derek A T Cummings
- Department of Biology, University of Florida, Gainesville, Florida, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
| | - Christopher Neil Davis
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Emma Louise Davis
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Matthew Dixon
- London Centre for Neglected Tropical Disease Research, London, United Kingdom
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, St Mary's Campus, Imperial College London, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Andrew Dobson
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA
| | - Louise Dyson
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Michael French
- Schistosomiasis Control Initiative, Department of Infectious Disease Epidemiology, St Mary's Campus, Imperial College London, London, United Kingdom
- RTI International, Washington, D.C., USA
| | - Claudio Fronterre
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
| | - Emanuele Giorgi
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
| | - Ching-I Huang
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Saurabh Jain
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland
| | - Ananthu James
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sung Hye Kim
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland
| | - Klodeta Kura
- London Centre for Neglected Tropical Disease Research, London, United Kingdom
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, St Mary's Campus, Imperial College London, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Ana Lucianez
- Communicable Diseases Prevention, Control, and Elimination, Pan American Health Organization, Washington D.C., USA
| | - Michael Marks
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Pamela Sabina Mbabazi
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland
| | - Graham F Medley
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Edwin Michael
- College of Public Health, University of South Florida, Tampa, Florida, USA
| | - Antonio Montresor
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland
| | - Nyamai Mutono
- Centre for Epidemiological Modelling and Analysis, University of Nairobi, Nairobi, Kenya
- Paul G. Allen School for Global Health, Washington State University, Pullman, Washington, USA
| | - Thumbi S Mwangi
- Centre for Epidemiological Modelling and Analysis, University of Nairobi, Nairobi, Kenya
- Paul G. Allen School for Global Health, Washington State University, Pullman, Washington, USA
- Institute of Immunology and Infection Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Kat S Rock
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Martha-Idalí Saboyá-Díaz
- Communicable Diseases Prevention, Control, and Elimination, Pan American Health Organization, Washington D.C., USA
| | - Misaki Sasanami
- Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
| | - Markus Schwehm
- ExploSYS GmbH, Interdisciplinary Institute for Exploratory Systems, Leinfelden-Echterdingen, Germany
| | - Simon E F Spencer
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Ariktha Srivathsan
- Francis I. Proctor Foundation, University of California, San Francisco, California, USA
| | - Robert S Stawski
- Institute of Public Health and Wellbeing, School of Health and Social Care, University of Essex, Essex, United Kingdom
| | - Wilma A Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Samuel A Sutherland
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Warwick Medical School, The University of Warwick, Coventry, United Kingdom
| | | | - Sake J de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Martin Walker
- London Centre for Neglected Tropical Disease Research, London, United Kingdom
- Department of Pathobiology and Population Sciences, Royal Veterinary College, University of London, London, United Kingdom
| | | | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
| | - Anthony W Solomon
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland
| | - Ibrahima Socé Fall
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland
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James A, Coffeng LE, Blok DJ, King JD, de Vlas SJ, Stolk WA. Predictive Value of Microfilariae-Based Stop-MDA Thresholds After Triple Drug Therapy With IDA Against Lymphatic Filariasis in Treatment-Naive Indian Settings. Clin Infect Dis 2024; 78:S131-S137. [PMID: 38662696 PMCID: PMC11045019 DOI: 10.1093/cid/ciae019] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
Mass drug administration (MDA) of antifilarial drugs is the main strategy for the elimination of lymphatic filariasis (LF). Recent clinical trials indicated that the triple-drug therapy with ivermectin, diethylcarbamazine, and albendazole (IDA) is much more effective against LF than the widely used two-drug combinations (albendazole plus either ivermectin or diethylcarbamazine). For IDA-based MDA, the stop-MDA decision is made based on microfilariae (mf) prevalence in adults. In this study, we assess how the probability of eventually reaching elimination of transmission depends on the critical threshold used in transmission assessment surveys (TAS-es) to define whether transmission was successfully suppressed and triple-drug MDA can be stopped. This analysis focuses on treatment-naive Indian settings. We do this for a range of epidemiological and programmatic contexts, using the established LYMFASIM model for transmission and control of LF. Based on our simulations, a single TAS, one year after the last MDA round, provides limited predictive value of having achieved suppressed transmission, while a higher MDA coverage increases elimination probability, thus leading to a higher predictive value. Every additional TAS, conditional on previous TAS-es being passed with the same threshold, further improves the predictive value for low values of stop-MDA thresholds. An mf prevalence threshold of 0.5% corresponding to TAS-3 results in ≥95% predictive value even when the MDA coverage is relatively low.
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Affiliation(s)
- Ananthu James
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Luc E Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - David J Blok
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jonathan D King
- Department of Control of Neglected Tropical Diseases, World Health Organization, Geneva, Switzerland
| | - Sake J de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Wilma A Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
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Coffeng LE, Graham M, Browning R, Kura K, Diggle PJ, Denwood M, Medley GF, Anderson RM, de Vlas SJ. Improving the Cost-efficiency of Preventive Chemotherapy: Impact of New Diagnostics on Stopping Decisions for Control of Schistosomiasis. Clin Infect Dis 2024; 78:S153-S159. [PMID: 38662699 PMCID: PMC11045014 DOI: 10.1093/cid/ciae020] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Control of schistosomiasis (SCH) relies on the regular distribution of preventive chemotherapy (PC) over many years. For the sake of sustainable SCH control, a decision must be made at some stage to scale down or stop PC. These "stopping decisions" are based on population surveys that assess whether infection levels are sufficiently low. However, the limited sensitivity of the currently used diagnostic (Kato-Katz [KK]) to detect low-intensity infections is a concern. Therefore, the use of new, more sensitive, molecular diagnostics has been proposed. METHODS Through statistical analysis of Schistosoma mansoni egg counts collected from Burundi and a simulation study using an established transmission model for schistosomiasis, we investigated the extent to which more sensitive diagnostics can improve decision making regarding stopping or continuing PC for the control of S. mansoni. RESULTS We found that KK-based strategies perform reasonably well for determining when to stop PC at a local scale. Use of more sensitive diagnostics leads to a marginally improved health impact (person-years lived with heavy infection) and comes at a cost of continuing PC for longer (up to around 3 years), unless the decision threshold for stopping PC is adapted upward. However, if this threshold is set too high, PC may be stopped prematurely, resulting in a rebound of infection levels and disease burden (+45% person-years of heavy infection). CONCLUSIONS We conclude that the potential value of more sensitive diagnostics lies more in the reduction of survey-related costs than in the direct health impact of improved parasite control.
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Affiliation(s)
- Luc E Coffeng
- Department of Public Health, Erasmus Medical Center, University Medical Center Rotterdam, The Netherlands
| | - Matthew Graham
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford
| | | | - Klodeta Kura
- London Centre for Neglected Tropical Disease Research, School of Public Health, Imperial College London
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London
| | - Peter J Diggle
- Centre for Health Informatics, Computing, and Statistics, Lancaster University Medical School, United Kingdom
| | - Matthew Denwood
- Department of Veterinary and Animal Sciences, University of Copenhagen, Denmark
| | - Graham F Medley
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, United Kingdom
| | - Roy M Anderson
- London Centre for Neglected Tropical Disease Research, School of Public Health, Imperial College London
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London
| | - Sake J de Vlas
- Department of Public Health, Erasmus Medical Center, University Medical Center Rotterdam, The Netherlands
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Turner HC, Kura K, Roth B, Kuesel AC, Kinrade S, Basáñez MG. An Updated Economic Assessment of Moxidectin Treatment Strategies for Onchocerciasis Elimination. Clin Infect Dis 2024; 78:S138-S145. [PMID: 38662693 PMCID: PMC11045023 DOI: 10.1093/cid/ciae054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Concerns that annual mass administration of ivermectin, the predominant strategy for onchocerciasis control and elimination, may not lead to elimination of parasite transmission (EoT) in all endemic areas have increased interest in alternative treatment strategies. One such strategy is moxidectin. We performed an updated economic assessment of moxidectin- relative to ivermectin-based strategies. METHODS We investigated annual and biannual community-directed treatment with ivermectin (aCDTI, bCDTI) and moxidectin (aCDTM, bCDTM) with minimal or enhanced coverage (65% or 80% of total population taking the drug, respectively) in intervention-naive areas with 30%, 50%, or 70% microfilarial baseline prevalence (representative of hypo-, meso-, and hyperendemic areas). We compared programmatic delivery costs for the number of treatments achieving 90% probability of EoT (EoT90), calculated with the individual-based stochastic transmission model EPIONCHO-IBM. We used the costs for 40 years of program delivery when EoT90 was not reached earlier. The delivery costs do not include drug costs. RESULTS aCDTM and bCDTM achieved EoT90 with lower programmatic delivery costs than aCDTI with 1 exception: aCDTM with minimal coverage did not achieve EoT90 in hyperendemic areas within 40 years. With minimal coverage, bCDTI delivery costs as much or more than aCDTM and bCDTM. With enhanced coverage, programmatic delivery costs for aCDTM and bCDTM were lower than for aCDTI and bCDTI. CONCLUSIONS Moxidectin-based strategies could accelerate progress toward EoT and reduce programmatic delivery costs compared with ivermectin-based strategies. The costs of moxidectin to national programs are needed to quantify whether delivery cost reductions will translate into overall program cost reduction.
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Affiliation(s)
- Hugo C Turner
- UK Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Klodeta Kura
- UK Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Barbara Roth
- Medicines Development for Global Health, Melbourne, Victoria, Australia
| | - Annette C Kuesel
- UNICEF/United Nations Development Progamme/World Bank/World Health Organization Special Programme for Research and Training in Tropical Diseases, World Health Organization, Geneva, Switzerland (retired)
| | - Sally Kinrade
- Medicines Development for Global Health, Melbourne, Victoria, Australia
| | - Maria-Gloria Basáñez
- UK Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
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Wheeler J, Rosengart A, Jiang Z, Tan K, Treutle N, Ionides EL. Informing policy via dynamic models: Cholera in Haiti. PLoS Comput Biol 2024; 20:e1012032. [PMID: 38683863 PMCID: PMC11081515 DOI: 10.1371/journal.pcbi.1012032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 05/09/2024] [Accepted: 03/29/2024] [Indexed: 05/02/2024] Open
Abstract
Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. We assess current methodological approaches to these issues via a case study of the 2010-2019 cholera epidemic in Haiti. We consider three dynamic models developed by expert teams to advise on vaccination policies. We evaluate previous methods used for fitting these models, and we demonstrate modified data analysis strategies leading to improved statistical fit. Specifically, we present approaches for diagnosing model misspecification and the consequent development of improved models. Additionally, we demonstrate the utility of recent advances in likelihood maximization for high-dimensional nonlinear dynamic models, enabling likelihood-based inference for spatiotemporal incidence data using this class of models. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.
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Affiliation(s)
- Jesse Wheeler
- Statistics Department, University of Michigan, Ann Arbor, Michigan, United States of America
| | - AnnaElaine Rosengart
- Statistics and Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Zhuoxun Jiang
- Statistics Department, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Kevin Tan
- Wharton Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Noah Treutle
- Statistics Department, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Edward L. Ionides
- Statistics Department, University of Michigan, Ann Arbor, Michigan, United States of America
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Bowring AL, Ten Brink D, Martin-Hughes R, Fraser-Hurt N, Cheikh N, Scott N. Evaluation of the use of modelling in resource allocation decisions for HIV and TB. BMJ Glob Health 2024; 9:e012418. [PMID: 38232992 PMCID: PMC10806894 DOI: 10.1136/bmjgh-2023-012418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 11/25/2023] [Indexed: 01/19/2024] Open
Abstract
INTRODUCTION Globally, resources for health spending, including HIV and tuberculosis (TB), are constrained, and a substantial gap exists between spending and estimated needs. Optima is an allocative efficiency modelling tool that has been used since 2010 in over 50 settings to generate evidence for country-level HIV and TB resource allocation decisions. This evaluation assessed the utilisation of modelling to inform financing priorities from the perspective of country stakeholders and their international partners. METHODS In October to December 2021, the World Bank and Burnet Institute led 16 semi-structured small-group virtual interviews with 54 representatives from national governments and international health and funding organisations. Interviews probed participants' roles and satisfaction with Optima analyses and how model findings have had been used and impacted resource allocation. Interviewed stakeholders represented nine countries and 11 different disease programme-country contexts with prior Optima modelling analyses. Interview notes were thematically analysed to assess factors influencing the utilisation of modelling evidence in health policy and outcomes. RESULTS Common influences on utilisation of Optima findings encompassed the perceived validity of findings, health system financing mechanisms, the extent of stakeholder participation in the modelling process-including engagement of funding organisations, sociopolitical context and timeliness of the analysis. Using workshops can facilitate effective stakeholder engagement and collaboration. Model findings were often used conceptually to localise global evidence and facilitate discussion. Secondary outputs included informing strategic and financial planning, funding advocacy, grant proposals and influencing investment shifts. CONCLUSION Allocative efficiency modelling has supported evidence-informed decision-making in numerous contexts and enhanced the conceptual and practical understanding of allocative efficiency. Most immediately, greater involvement of country stakeholders in modelling studies and timing studies to key strategic and financial planning decisions may increase the impact on decision-making. Better consideration for integrated disease modelling, equity goals and financing constraints may improve relevance and utilisation of modelling findings.
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Affiliation(s)
| | | | | | | | | | - Nick Scott
- Burnet Institute, Melbourne, Victoria, Australia
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8
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Forbes K, Basáñez MG, Hollingsworth TD, Anderson RM. Introduction to the special issue: challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220272. [PMID: 37598699 PMCID: PMC10440167 DOI: 10.1098/rstb.2022.0272] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 07/20/2023] [Indexed: 08/22/2023] Open
Abstract
Twenty neglected tropical diseases (NTDs) are currently prioritised by the World Health Organization for eradication, elimination as a public health problem, elimination of transmission or control by 2030. This issue celebrates progress made since the 2012 London Declaration on NTDs and discusses challenges currently faced to achieve these goals. It comprises 14 contributions spanning NTDs tackled by intensified disease management to those addressed by preventive chemotherapy. Although COVID-19 negatively affected NTD programmes, it also served to spur new multisectoral approaches to strengthen school-based health systems. The issue highlights the needs to improve impact survey design, evaluate new diagnostics, understand the consequences of heterogeneous prevalence and human movement, the potential impact of alternative treatment strategies and the importance of zoonotic transmission. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.
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Affiliation(s)
- Kathryn Forbes
- London Centre for Neglected Tropical Disease Research (LCNTDR), Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London W2 1PG, UK
| | - Maria-Gloria Basáñez
- London Centre for Neglected Tropical Disease Research (LCNTDR), Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London W2 1PG, UK
- MRC Centre for Global Infectious Disease Analysis (MRC GIDA), Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London W2 1PG, UK
| | | | - Roy M. Anderson
- London Centre for Neglected Tropical Disease Research (LCNTDR), Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London W2 1PG, UK
- MRC Centre for Global Infectious Disease Analysis (MRC GIDA), Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London W2 1PG, UK
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9
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Barazanji M, Ngo JD, Powe JA, Schneider KP, Rychtář J, Taylor D. Modeling the "F" in "SAFE": The dynamic game of facial cleanliness in trachoma prevention. PLoS One 2023; 18:e0287464. [PMID: 37352249 PMCID: PMC10289400 DOI: 10.1371/journal.pone.0287464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 06/06/2023] [Indexed: 06/25/2023] Open
Abstract
Trachoma, a neglected tropical disease (NTDs) caused by bacterium Chlamydia trachomatis, is a leading cause of infectious blindness. Efforts are underway to eliminate trachoma as a public health problem by using the "SAFE" strategy. While mathematical models are now standard tools used to support elimination efforts and there are a variety of models studying different aspects of trachoma transmission dynamics, the "F" component of the strategy corresponding to facial cleanliness has received very little attention so far. In this paper, we incorporate human behavior into a standard epidemiological model and develop a dynamical game during which individuals practice facial cleanliness based on their epidemiological status and perceived benefits and costs. We found that the number of infectious individuals generally increases with the difficulty to access a water source. However, this increase happens only during three transition periods and the prevalence stays constant otherwise. Consequently, improving access to water can help eliminate trachoma, but the improvement needs to be significant enough to cross at least one of the three transition thresholds; otherwise the improved access will have no noticeable effect.
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Affiliation(s)
- Mary Barazanji
- Department of Kinesiology and Health Sciences, Virginia Commonwealth University, Richmond, VA, United States of America
| | - Janesah D. Ngo
- Department of Biology, Virginia Commonwealth University, Richmond, VA, United States of America
| | - Jule A. Powe
- Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA, United States of America
| | - Kimberley P. Schneider
- Department of Chemistry, Virginia Commonwealth University, Richmond, VA, United States of America
| | - Jan Rychtář
- Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA, United States of America
| | - Dewey Taylor
- Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA, United States of America
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10
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Huang CI, Crump RE, Crowley EH, Hope A, Bessell PR, Shampa C, Mwamba Miaka E, Rock KS. A modelling assessment of short- and medium-term risks of programme interruptions for gambiense human African trypanosomiasis in the DRC. PLoS Negl Trop Dis 2023; 17:e0011299. [PMID: 37115809 PMCID: PMC10171604 DOI: 10.1371/journal.pntd.0011299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 05/10/2023] [Accepted: 04/12/2023] [Indexed: 04/29/2023] Open
Abstract
Gambiense human African trypanosomiasis (gHAT) is a deadly vector-borne, neglected tropical disease found in West and Central Africa targeted for elimination of transmission (EoT) by 2030. The recent pandemic has illustrated how it can be important to quantify the impact that unplanned disruption to programme activities may have in achieving EoT. We used a previously developed model of gHAT fitted to data from the Democratic Republic of the Congo, the country with the highest global case burden, to explore how interruptions to intervention activities, due to e.g. COVID-19, Ebola or political instability, could impact progress towards EoT and gHAT burden. We simulated transmission and reporting dynamics in 38 regions within Kwilu, Mai Ndombe and Kwango provinces under six interruption scenarios lasting for nine or twenty-one months. Included in the interruption scenarios are the cessation of active screening in all scenarios and a reduction in passive detection rates and a delay or suspension of vector control deployments in some scenarios. Our results indicate that, even under the most extreme 21-month interruption scenario, EoT is not predicted to be delayed by more than one additional year compared to the length of the interruption. If existing vector control deployments continue, we predict no delay in achieving EoT even when both active and passive screening activities are interrupted. If passive screening remains as functional as in 2019, we expect a marginal negative impact on transmission, however this depends on the strength of passive screening in each health zone. We predict a pronounced increase in additional gHAT disease burden (morbidity and mortality) in many health zones if both active and passive screening were interrupted compared to the interruption of active screening alone. The ability to continue existing vector control during medical activity interruption is also predicted to avert a moderate proportion of disease burden.
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Affiliation(s)
- Ching-I Huang
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Ronald E. Crump
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Emily H. Crowley
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Andrew Hope
- Liverpool School of Tropical Medicine (LSTM), Liverpool, United Kingdom
| | | | - Chansy Shampa
- Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Kinshasa, Democratic Republic of the Congo
| | - Erick Mwamba Miaka
- Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Kinshasa, Democratic Republic of the Congo
| | - Kat S. Rock
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
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11
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Leão LPMO, de B Vieira N, Oliveira PPS, Chagas-Paula DA, Soares MG, Souza TB, Baldim JL, Costa-Silva TA, Tempone AG, Dias DF, Lago JHG. Structure-activity relationship study of antitrypanosomal analogues of gibbilimbol B using multivariate analysis and computation-aided drug design. Bioorg Med Chem Lett 2023; 83:129190. [PMID: 36805048 DOI: 10.1016/j.bmcl.2023.129190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 02/09/2023] [Accepted: 02/13/2023] [Indexed: 02/21/2023]
Abstract
Gibbilimbol B and analogues were isolated from the Brazilian plant Piper malacophyllum and displayed activity against trypomastigote forms of Trypanosoma cruzi as well as reduced toxicity against NCTC cells. These results stimulated the preparation of a series of 24 chemically related analogues to study the potential of these compounds against T. cruzi trypomastigotes and explore structure-activity relationships. Initially, 12 compounds were planned, maintaining the same extension of the linear side chain of gibbilimbol B and unsaturation on the C-4 position but changing the functional groups - ester and amide - and variating the substituent at the p-position in the aromatic ring. Other 12 compounds were prepared using a branched side chain containing an ethyl group at the C-2 position. Overall, these structurally-related analogues demonstrated promising activity against trypomastigote forms (EC50 < 20 μM) and no mammalian cytotoxicity to fibroblasts (CC50 > 200 μM). Using multivariate statistics and machine learning analysis, aspects associated with structure/activity were related to their three-dimensional structure and, mainly, to the substituents on the aromatic ring. Obtained results suggested that the presence of t-butyl or nitro groups at p-position with appropriate side chains causes an alteration in the electron topological state, Van der Waals volumes, surface areas, and polarizabilities of tested compounds which seem to be essential for biological activity against T. cruzi parasites.
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Affiliation(s)
- Luiz P M O Leão
- Institute of Chemistry, Federal University of Alfenas, Minas Gerais 37130-001, Brazil
| | - Nátalie de B Vieira
- Institute of Chemistry, Federal University of Alfenas, Minas Gerais 37130-001, Brazil
| | - Paula P S Oliveira
- Institute of Chemistry, Federal University of Alfenas, Minas Gerais 37130-001, Brazil
| | | | - Marisi G Soares
- Institute of Chemistry, Federal University of Alfenas, Minas Gerais 37130-001, Brazil
| | - Thiago B Souza
- Pharmacy School, Federal University of Ouro Preto, Minas Gerais 35400-000, Brazil
| | - João L Baldim
- Federal Institute of Education, Science and Technology of South of Minas Gerais, Minas Gerais 37890-000, Brazil
| | | | - Andre G Tempone
- Center for Parasitology and Mycology, Instituto Adolfo Lutz, São Paulo 01246-902, Brazil
| | - Danielle F Dias
- Institute of Chemistry, Federal University of Alfenas, Minas Gerais 37130-001, Brazil.
| | - João Henrique G Lago
- Center of Natural and Human Sciences, Federal University of ABC, São Paulo 09210-580, Brazil.
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12
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Stolk WA, Coffeng LE, Bolay FK, Eneanya OA, Fischer PU, Hollingsworth TD, Koudou BG, Méité A, Michael E, Prada JM, Caja Rivera RM, Sharma S, Touloupou P, Weil GJ, de Vlas SJ. Comparing antigenaemia- and microfilaraemia as criteria for stopping decisions in lymphatic filariasis elimination programmes in Africa. PLoS Negl Trop Dis 2022; 16:e0010953. [PMID: 36508458 PMCID: PMC9779720 DOI: 10.1371/journal.pntd.0010953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 12/22/2022] [Accepted: 11/14/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Mass drug administration (MDA) is the main strategy towards lymphatic filariasis (LF) elimination. Progress is monitored by assessing microfilaraemia (Mf) or circulating filarial antigenaemia (CFA) prevalence, the latter being more practical for field surveys. The current criterion for stopping MDA requires <2% CFA prevalence in 6- to 7-year olds, but this criterion is not evidence-based. We used mathematical modelling to investigate the validity of different thresholds regarding testing method and age group for African MDA programmes using ivermectin plus albendazole. METHODOLGY/PRINCIPAL FINDINGS We verified that our model captures observed patterns in Mf and CFA prevalence during annual MDA, assuming that CFA tests are positive if at least one adult worm is present. We then assessed how well elimination can be predicted from CFA prevalence in 6-7-year-old children or from Mf or CFA prevalence in the 5+ or 15+ population, and determined safe (>95% positive predictive value) thresholds for stopping MDA. The model captured trends in Mf and CFA prevalences reasonably well. Elimination cannot be predicted with sufficient certainty from CFA prevalence in 6-7-year olds. Resurgence may still occur if all children are antigen-negative, irrespective of the number tested. Mf-based criteria also show unfavourable results (PPV <95% or unpractically low threshold). CFA prevalences in the 5+ or 15+ population are the best predictors, and post-MDA threshold values for stopping MDA can be as high as 10% for 15+. These thresholds are robust for various alternative assumptions regarding baseline endemicity, biological parameters and sampling strategies. CONCLUSIONS/SIGNIFICANCE For African areas with moderate to high pre-treatment Mf prevalence that have had 6 or more rounds of annual ivermectin/albendazole MDA with adequate coverage, we recommend to adopt a CFA threshold prevalence of 10% in adults (15+) for stopping MDA. This could be combined with Mf testing of CFA positives to ensure absence of a significant Mf reservoir for transmission.
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Affiliation(s)
- Wilma A. Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- * E-mail:
| | - Luc E. Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Fatorma K. Bolay
- National Public Health Institute of Liberia (NPHIL), Monrovia, Liberia
| | - Obiora A. Eneanya
- Infectious Diseases Division, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Peter U. Fischer
- Infectious Diseases Division, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - T. Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Benjamin G. Koudou
- Centre Suisse de Recherches Scientifiques en Côte d’Ivoire, Abidjan, Abidjan, Côte d’Ivoire
- Laboratoire de Cytologie et Biologie Animale, UFR Science de la Nature, Université Nangui Abrogoua Abidjan, Abidjan, Côte d’Ivoire
| | - Aboulaye Méité
- Programme National de Lutte contre les Maladies Tropicales Négligées à Chimiothérapie Préventive, Abidjan, Côte d’Ivoire
| | - Edwin Michael
- Center for Global Health Infectious Disease Research, University of South Florida, Tampa, Florida, United States of America
| | - Joaquin M. Prada
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Rocio M. Caja Rivera
- Center for Global Health Infectious Disease Research, University of South Florida, Tampa, Florida, United States of America
| | - Swarnali Sharma
- Department of Biological Sciences, University of Notre Dame, South Bend, Indiana, United States of America
- Christian Medical College, IDA Scudder Rd, Vellore, Tamil Nadu, India
| | - Panayiota Touloupou
- Department of Statistics, University of Warwick, Coventry, United Kingdom
- School of Mathematics, University of Birmingham, Birmingham, United Kingdom
| | - Gary J. Weil
- Infectious Diseases Division, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Sake J. de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
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13
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Augsburger IB, Galanthay GK, Tarosky JH, Rychtář J, Taylor D. Voluntary vaccination may not stop monkeypox outbreak: A game-theoretic model. PLoS Negl Trop Dis 2022; 16:e0010970. [PMID: 36516113 DOI: 10.1371/journal.pntd.0010970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 11/21/2022] [Indexed: 12/15/2022] Open
Abstract
Monkeypox (MPX) is a viral zoonotic disease that was endemic to Central and West Africa. However, during the first half of 2022, MPX spread to almost 60 countries all over the world. Smallpox vaccines are about 85% effective in preventing MPX infections. Our objective is to determine whether the vaccines should be mandated or whether voluntary use of the vaccine could be enough to stop the MPX outbreak. We incorporate a standard SVEIR compartmental model of MPX transmission into a game-theoretical framework. We study a vaccination game in which individuals decide whether or not to vaccinate by assessing their benefits and costs. We solve the game for Nash equilibria, i.e., the vaccination rates the individuals would likely adopt without any outside intervention. We show that, without vaccination, MPX can become endemic in previously non-endemic regions, including the United States. We also show that to "not vaccinate" is often an optimal solution from the individual's perspective. Moreover, we demonstrate that, for some parameter values, there are multiple equilibria of the vaccination game, and they exhibit a backward bifurcation. Thus, without centrally mandated minimal vaccination rates, the population could easily revert to no vaccination scenario.
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Affiliation(s)
- Ian B Augsburger
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Grace K Galanthay
- Department of Mathematics and Computer Science, College of the Holy Cross, Worcester, Massachusetts, United States of America
| | - Jacob H Tarosky
- Department of Mathematical Sciences, High Point University, High Point, North Carolina, United States of America
| | - Jan Rychtář
- Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, Virginia, United States of America
| | - Dewey Taylor
- Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, Virginia, United States of America
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14
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Dial NJ, Croft SL, Chapman LAC, Terris-Prestholt F, Medley GF. Challenges of using modelling evidence in the visceral leishmaniasis elimination programme in India. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0001049. [PMID: 36962829 PMCID: PMC10021829 DOI: 10.1371/journal.pgph.0001049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 10/25/2022] [Indexed: 06/18/2023]
Abstract
As India comes closer to the elimination of visceral leishmaniasis (VL) as a public health problem, surveillance efforts and elimination targets must be continuously revised and strengthened. Mathematical modelling is a compelling research discipline for informing policy and programme design in its capacity to project incidence across space and time, the likelihood of achieving benchmarks, and the impact of different interventions. To gauge the extent to which modelling informs policy in India, this qualitative analysis explores how and whether policy makers understand, value, and reference recently produced VL modelling research. Sixteen semi-structured interviews were carried out with both users- and producers- of VL modelling research, guided by a knowledge utilisation framework grounded in knowledge translation theory. Participants reported that barriers to knowledge utilisation include 1) scepticism that models accurately reflect transmission dynamics, 2) failure of modellers to apply their analyses to specific programme operations, and 3) lack of accountability in the process of translating knowledge to policy. Political trust and support are needed to translate knowledge into programme activities, and employment of a communication intermediary may be a necessary approach to improve this process.
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Affiliation(s)
- Natalie J. Dial
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Simon L. Croft
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Lloyd A. C. Chapman
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Fern Terris-Prestholt
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Graham F. Medley
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
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15
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Rychtář J, Taylor D. A game-theoretic model of lymphatic filariasis prevention. PLoS Negl Trop Dis 2022; 16:e0010765. [PMID: 36137005 PMCID: PMC9498957 DOI: 10.1371/journal.pntd.0010765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 08/23/2022] [Indexed: 11/18/2022] Open
Abstract
Lymphatic filariasis (LF) is a mosquito-borne parasitic neglected tropical disease. In 2000, WHO launched the Global Programme to Eliminate Lymphatic Filariasis (GPELF) as a public health problem. In 2020, new goals for 2030 were set which includes a reduction to 0 of the total population requiring Mass Drug Administrations (MDA), a primary tool of GPELF. We develop a mathematical model to study what can happen at the end of MDA. We use a game-theoretic approach to assess the voluntary use of insect repellents in the prevention of the spread of LF through vector bites. Our results show that when individuals use what they perceive as optimal levels of protection, the LF incidence rates will become high. This is in striking difference to other vector-borne NTDs such as Chagas or zika. We conclude that the voluntary use of the protection alone will not be enough to keep LF eliminated as a public health problem and a more coordinated effort will be needed at the end of MDA.
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Affiliation(s)
- Jan Rychtář
- Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, Virginia, United States of America
- * E-mail:
| | - Dewey Taylor
- Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, Virginia, United States of America
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16
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Dixon MA, Winskill P, Harrison WE, Whittaker C, Schmidt V, Flórez Sánchez AC, Cucunuba ZM, Edia-Asuke AU, Walker M, Basáñez MG. Global variation in force-of-infection trends for human T aenia solium taeniasis/cysticercosis. eLife 2022; 11:76988. [PMID: 35984416 PMCID: PMC9391040 DOI: 10.7554/elife.76988] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 08/07/2022] [Indexed: 12/01/2022] Open
Abstract
Infection by Taenia solium poses a major burden across endemic countries. The World Health Organization (WHO) 2021–2030 Neglected Tropical Diseases roadmap has proposed that 30% of endemic countries achieve intensified T. solium control in hyperendemic areas by 2030. Understanding geographical variation in age-prevalence profiles and force-of-infection (FoI) estimates will inform intervention designs across settings. Human taeniasis (HTT) and human cysticercosis (HCC) age-prevalence data from 16 studies in Latin America, Africa, and Asia were extracted through a systematic review. Catalytic models, incorporating diagnostic performance uncertainty, were fitted to the data using Bayesian methods, to estimate rates of antibody (Ab)-seroconversion, infection acquisition and Ab-seroreversion or infection loss. HCC FoI and Ab-seroreversion rates were also estimated across 23 departments in Colombia from 28,100 individuals. Across settings, there was extensive variation in all-ages seroprevalence. Evidence for Ab-seroreversion or infection loss was found in most settings for both HTT and HCC and for HCC Ab-seroreversion in Colombia. The average duration until humans became Ab-seropositive/infected decreased as all-age (sero)prevalence increased. There was no clear relationship between the average duration humans remain Ab-seropositive and all-age seroprevalence. Marked geographical heterogeneity in T. solium transmission rates indicate the need for setting-specific intervention strategies to achieve the WHO goals.
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Affiliation(s)
- Matthew A Dixon
- Department of Infectious Disease Epidemiology and London Centre for Neglected Tropical Disease Research (LCNTDR), Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom.,MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom.,SCI Foundation, Edinburgh House, London, United Kingdom
| | - Peter Winskill
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom
| | | | - Charles Whittaker
- Department of Infectious Disease Epidemiology and London Centre for Neglected Tropical Disease Research (LCNTDR), Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom.,MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom
| | - Veronika Schmidt
- Department of Neurology, Center for Global Health, Technical University Munich (TUM), Munich, Germany.,Centre for Global Health, Institute of Health and Society, University of Oslo, Oslo, Norway
| | | | - Zulma M Cucunuba
- Department of Infectious Disease Epidemiology and London Centre for Neglected Tropical Disease Research (LCNTDR), Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom.,MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom
| | | | - Martin Walker
- Department of Pathobiology and Population Sciences and London Centre for Neglected Tropical Disease Research (LCNTDR), Royal Veterinary College, Hatfield, United Kingdom
| | - María-Gloria Basáñez
- Department of Infectious Disease Epidemiology and London Centre for Neglected Tropical Disease Research (LCNTDR), Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom.,MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom
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17
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Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease. PLoS Negl Trop Dis 2022; 16:e0010594. [PMID: 35853042 PMCID: PMC9337653 DOI: 10.1371/journal.pntd.0010594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/29/2022] [Accepted: 06/18/2022] [Indexed: 12/02/2022] Open
Abstract
Background Chagas disease is a long-lasting disease with a prolonged asymptomatic period. Cumulative indices of infection such as prevalence do not shed light on the current epidemiological situation, as they integrate infection over long periods. Instead, metrics such as the Force-of-Infection (FoI) provide information about the rate at which susceptible people become infected and permit sharper inference about temporal changes in infection rates. FoI is estimated by fitting (catalytic) models to available age-stratified serological (ground-truth) data. Predictive FoI modelling frameworks are then used to understand spatial and temporal trends indicative of heterogeneity in transmission and changes effected by control interventions. Ideally, these frameworks should be able to propagate uncertainty and handle spatiotemporal issues. Methodology/principal findings We compare three methods in their ability to propagate uncertainty and provide reliable estimates of FoI for Chagas disease in Colombia as a case study: two Machine Learning (ML) methods (Boosted Regression Trees (BRT) and Random Forest (RF)), and a Linear Model (LM) framework that we had developed previously. Our analyses show consistent results between the three modelling methods under scrutiny. The predictors (explanatory variables) selected, as well as the location of the most uncertain FoI values, were coherent across frameworks. RF was faster than BRT and LM, and provided estimates with fewer extreme values when extrapolating to areas where no ground-truth data were available. However, BRT and RF were less efficient at propagating uncertainty. Conclusions/significance The choice of FoI predictive models will depend on the objectives of the analysis. ML methods will help characterise the mean behaviour of the estimates, while LM will provide insight into the uncertainty surrounding such estimates. Our approach can be extended to the modelling of FoI patterns in other Chagas disease-endemic countries and to other infectious diseases for which serosurveys are regularly conducted for surveillance. Metrics such as the per susceptible rate of infection acquisition (Force-of-Infection) are crucial to understand the current epidemiological situation and the impact of control interventions for long-lasting diseases in which the infection event might have occurred many years previously, such as Chagas disease. FoI values are estimated from serological age profiles, often obtained in a few locations. However, when using predictive models to estimate the FoI over time and space (including areas where serosurveys had not been conducted), methods able to handle and propagate uncertainty must be implemented; otherwise, overconfident predictions may be obtained. Although Machine Learning (ML) methods are powerful tools, they may not be able to entirely handle this challenge. Therefore, the use of ML must be considered in relation to the aims of the analyses. ML will be more relevant to characterise the central trends of the estimates while Linear Models will help identify areas where further serosurveys should be conducted to improve the reliability of the predictions. Our approaches can be used to generate FoI predictions in other Chagas disease-endemic countries as well as in other diseases for which serological surveillance data are collected.
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18
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Malizia V, Giardina F, de Vlas SJ, Coffeng LE. Appropriateness of the current parasitological control target for hookworm morbidity: A statistical analysis of individual-level data. PLoS Negl Trop Dis 2022; 16:e0010279. [PMID: 35763498 PMCID: PMC9239476 DOI: 10.1371/journal.pntd.0010279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/25/2022] [Indexed: 11/18/2022] Open
Abstract
Background
Soil-transmitted helminths affect almost 2 billion people globally. Hookworm species contribute to most of the related morbidity. Hookworms mainly cause anaemia, due to blood loss at the site of the attachment of the adult worms to the human intestinal mucosa. The World Health Organization (WHO) aims to eliminate hookworm morbidity by 2030 through achieving a prevalence of moderate and heavy intensity (M&HI) infections below 2%. In this paper, we aim to assess the suitability of this threshold to reflect hookworm-attributable morbidity.
Methodology/Principal findings
We developed a hierarchical statistical model to simulate individual haemoglobin concentrations in association with hookworm burdens, accounting for low haemoglobin values attributable to other causes. The model was fitted to individual-level data within a Bayesian framework. Then, we generated different endemicity settings corresponding to infection prevalence ranging from 10% to 90% (0% to 55% M&HI prevalence), using 1, 2 or 4 Kato-Katz slides. For each scenario, we estimated the prevalence of anaemia due to hookworm. Our results showed that on average, haemoglobin falls below the WHO threshold for anaemia when intensities are above 2000 eggs per gram of faeces. For the different simulated scenarios, the estimated prevalence of anaemia attributable to hookworm ranges from 0% to 30% (95%-PI: 24% - 36%) being mainly associated to the prevalence of M&HI infections. Simulations show that a 2% prevalence of M&HI infections in adults corresponds to a prevalence of hookworm-attributable anaemia lower than 1%.
Conclusions/Significance
Our results support the use of the current WHO thresholds of 2% prevalence of M&HI as a proxy for hookworm morbidity. A single Kato-Katz slide may be sufficient to assess the achievement of the morbidity target. Further studies are needed to elucidate haemoglobin dynamics pre- and post- control, ideally using longitudinal data in adults and children.
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Affiliation(s)
- Veronica Malizia
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department for Health Evidence, Biostatistics Research Group, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
- * E-mail: (VM); (LEC)
| | - Federica Giardina
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department for Health Evidence, Biostatistics Research Group, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Sake J. de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Luc E. Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- * E-mail: (VM); (LEC)
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Kumar V, Siddiqui NA, Pollington TM, Mandal R, Das S, Kesari S, Das VR, Pandey K, Hollingsworth TD, Chapman LA, Das P. Impact of intensified control on visceral leishmaniasis in a highly-endemic district of Bihar, India: An interrupted time series analysis. Epidemics 2022; 39:100562. [PMID: 35561500 PMCID: PMC9188270 DOI: 10.1016/j.epidem.2022.100562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 11/16/2021] [Accepted: 04/04/2022] [Indexed: 11/22/2022] Open
Abstract
Visceral leishmaniasis (VL) is declining in India and the World Health Organization’s (WHO) 2020 ‘elimination as a public health problem’ target has nearly been achieved. Intensified combined interventions might help reach elimination, but their impact has not been assessed. WHO’s Neglected Tropical Diseases 2021–2030 roadmap provides an opportunity to revisit VL control strategies. We estimated the combined effect of a district-wide pilot of intensified interventions in the highly-endemic Vaishali district, where cases fell from 3,598 in 2012–2014 to 762 in 2015–2017. The intensified control approach comprised indoor residual spraying with improved supervision; VL-specific training for accredited social health activists to reduce onset-to-diagnosis time; and increased Information Education & Communication activities in the community. We compared the rate of incidence decrease in Vaishali to other districts in Bihar state via an interrupted time series analysis with a spatiotemporal model informed by previous VL epidemiological estimates. Changes in Vaishali’s rank among Bihar’s endemic districts in terms of monthly incidence showed a change pre-pilot (3rd highest out of 33 reporting districts) vs. during the pilot (9th) (p<1e-10). The rate of decline in Vaishali’s incidence saw no change in rank at 11th highest, both pre-pilot & during the pilot. Counterfactual model simulations suggest an estimated median of 352 cases (IQR 234–477) were averted by the Vaishali pilot between January 2015 and December 2017, which was robust to modest changes in the onset-to-diagnosis distribution. Strengthening control strategies may have precipitated a substantial change in VL incidence in Vaishali and suggests this approach should be piloted in other highly-endemic districts.
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Identifying regions for enhanced control of gambiense sleeping sickness in the Democratic Republic of Congo. Nat Commun 2022; 13:1448. [PMID: 35304479 PMCID: PMC8933483 DOI: 10.1038/s41467-022-29192-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 02/28/2022] [Indexed: 11/08/2022] Open
Abstract
Gambiense human African trypanosomiasis (sleeping sickness, gHAT) is a disease targeted for elimination of transmission by 2030. While annual new cases are at a historical minimum, the likelihood of achieving the target is unknown. We utilised modelling to study the impacts of four strategies using currently available interventions, including active and passive screening and vector control, on disease burden and transmission across 168 endemic health zones in the Democratic Republic of the Congo. Median projected years of elimination of transmission show only 98 health zones are on track despite significant reduction in disease burden under medical-only strategies (64 health zones if > 90% certainty required). Blanket coverage with vector control is impractical, but is predicted to reach the target in all heath zones. Utilising projected disease burden under the uniform medical-only strategy, we provide a priority list of health zones for consideration for supplementary vector control alongside medical interventions.
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21
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Janoušková E, Clark J, Kajero O, Alonso S, Lamberton PHL, Betson M, Prada JM. Public Health Policy Pillars for the Sustainable Elimination of Zoonotic Schistosomiasis. FRONTIERS IN TROPICAL DISEASES 2022. [DOI: 10.3389/fitd.2022.826501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Schistosomiasis is a parasitic disease acquired through contact with contaminated freshwater. The definitive hosts are terrestrial mammals, including humans, with some Schistosoma species crossing the animal-human boundary through zoonotic transmission. An estimated 12 million people live at risk of zoonotic schistosomiasis caused by Schistosoma japonicum and Schistosoma mekongi, largely in the World Health Organization’s Western Pacific Region and in Indonesia. Mathematical models have played a vital role in our understanding of the biology, transmission, and impact of intervention strategies, however, these have mostly focused on non-zoonotic Schistosoma species. Whilst these non-zoonotic-based models capture some aspects of zoonotic schistosomiasis transmission dynamics, the commonly-used frameworks are yet to adequately capture the complex epi-ecology of multi-host zoonotic transmission. However, overcoming these knowledge gaps goes beyond transmission dynamics modelling. To improve model utility and enhance zoonotic schistosomiasis control programmes, we highlight three pillars that we believe are vital to sustainable interventions at the implementation (community) and policy-level, and discuss the pillars in the context of a One-Health approach, recognising the interconnection between humans, animals and their shared environment. These pillars are: (1) human and animal epi-ecological understanding; (2) economic considerations (such as treatment costs and animal losses); and (3) sociological understanding, including inter- and intra-human and animal interactions. These pillars must be built on a strong foundation of trust, support and commitment of stakeholders and involved institutions.
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22
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Teerawattananon Y, Kc S, Chi YL, Dabak S, Kazibwe J, Clapham H, Lopez Hernandez C, Leung GM, Sharifi H, Habtemariam M, Blecher M, Nishtar S, Sarkar S, Wilson D, Chalkidou K, Gorgens M, Hutubessy R, Wibulpolprasert S. Recalibrating the notion of modelling for policymaking during pandemics. Epidemics 2022; 38:100552. [PMID: 35259693 PMCID: PMC8889889 DOI: 10.1016/j.epidem.2022.100552] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 01/19/2022] [Accepted: 03/01/2022] [Indexed: 11/16/2022] Open
Abstract
COVID-19 disease models have aided policymakers in low-and middle-income countries (LMICs) with many critical decisions. Many challenges remain surrounding their use, from inappropriate model selection and adoption, inadequate and untimely reporting of evidence, to the lack of iterative stakeholder engagement in policy formulation and deliberation. These issues can contribute to the misuse of models and hinder effective policy implementation. Without guidance on how to address such challenges, the true potential of such models may not be realised. The COVID-19 Multi-Model Comparison Collaboration (CMCC) was formed to address this gap. CMCC is a global collaboration between decision-makers from LMICs, modellers and researchers, and development partners. To understand the limitations of existing COVID-19 disease models (primarily from high income countries) and how they could be adequately support decision-making in LMICs, a desk review of modelling experience during the COVID-19 and past disease outbreaks, two online surveys, and regular online consultations were held among the collaborators. Three key recommendations from CMCC include: A ‘fitness-for-purpose’ flowchart, a tool that concurrently walks policymakers (or their advisors) and modellers through a model selection and development process. The flowchart is organised around the following: policy aims, modelling feasibility, model implementation, model reporting commitment. Holmdahl and Buckee (2020) A ‘reporting standards trajectory’, which includes three gradually increasing standard of reports, ‘minimum’, ‘acceptable’, and ‘ideal’, and seeks collaboration from funders, modellers, and decision-makers to enhance the quality of reports over time and accountability of researchers. Malla et al. (2018) A framework for “collaborative modelling for effective policy implementation and evaluation” which extends the definition of stakeholders to funders, ground-level implementers, public, and other researchers, and outlines how each can contribute to modelling. We advocate for standardisation of modelling processes and adoption of country-owned model through iterative stakeholder participation and discuss how they can enhance trust, accountability, and public ownership to decisions. COVID-19 models need appropriate adaptation to reflect contextual differences across settings. Upholding scientific standards is equally important as providing evidence for policymaking during pandemics. Wider stakeholder engagement with an iterative process for re-evaluating decisions is required for effective policy implementation.
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Affiliation(s)
- Yot Teerawattananon
- Health Intervention and Technology Assessment Program (HITAP), Department of Health, Ministry of Public Health, 6th Floor, 6th Building, Tiwanon Road, Nonthaburi 11000, Thailand; Saw Swee Hock School of Public Health (SSHSPH), National University of Singapore (NUS), 12 Science Drive 2, #10-01, 117549, Singapore
| | - Sarin Kc
- Health Intervention and Technology Assessment Program (HITAP), Department of Health, Ministry of Public Health, 6th Floor, 6th Building, Tiwanon Road, Nonthaburi 11000, Thailand.
| | - Y-Ling Chi
- Centre for Global Development Europe, Great Peter House, Abbey Gardens, Great College St, Westminster, London SW1P 3SE, UK
| | - Saudamini Dabak
- Health Intervention and Technology Assessment Program (HITAP), Department of Health, Ministry of Public Health, 6th Floor, 6th Building, Tiwanon Road, Nonthaburi 11000, Thailand
| | - Joseph Kazibwe
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London (ICL), Faculty of Medicine Building, St Mary's Campus, Norfolk Place, London W2 1PG, UK
| | - Hannah Clapham
- Saw Swee Hock School of Public Health (SSHSPH), National University of Singapore (NUS), 12 Science Drive 2, #10-01, 117549, Singapore
| | | | - Gabriel M Leung
- Li Ka Shing Faculty of Medicine (HKUMed), Hong Kong University, 21 Sassoon Rd, Pok Fu Lam, Hong Kong
| | - Hamid Sharifi
- HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences (KMU), Kerman 7616911320, Iran
| | - Mahlet Habtemariam
- Africa Centres for Disease Control and Prevention, African Union Commission, Roosevelt Streeet, Addis Ababa, Ethiopia
| | - Mark Blecher
- National Treasury, 120 Plein Street, Cape Town, Republic of South Africa
| | - Sania Nishtar
- Poverty Alleviation and Social Safety Division, Government of Pakistan, Cabinet Secretariat, 4th Floor, Evacuee Trust Complex, F-5/1, Islamabad, Pakistan
| | - Swarup Sarkar
- Indian Council for Medical Research (ICMR), Government of India, V. Ramalingaswami Bhawan, P.O. Box No. 4911, Ansari Nagar, New Delhi 110029, India
| | - David Wilson
- Bill and Melinda Gates Foundation (BMGF), 500 5th Ave N, Seattle, WA 98109, USA
| | - Kalipso Chalkidou
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London (ICL), Faculty of Medicine Building, St Mary's Campus, Norfolk Place, London W2 1PG, UK; The Global Fund to Fight AIDS, Tuberculosis and Malaria, Global Health Campus, Chemin du Pommier 40, 1218 Grand-Saconnex, Geneva, Switzerland
| | - Marelize Gorgens
- World Bank Group (WBG), 1818H Street, N.W., Washington, DC 20433, USA
| | - Raymond Hutubessy
- World Health Organisation (WHO), Avenue Appia 20, 1211 Geneva, Switzerland
| | - Suwit Wibulpolprasert
- Health Intervention and Technology Assessment Program (HITAP), Department of Health, Ministry of Public Health, 6th Floor, 6th Building, Tiwanon Road, Nonthaburi 11000, Thailand; International Health Policy Program (IHPP), Ministry of Public Health, Tiwanon Rd., Nonthaburi 11000, Thailand
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23
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Rock KS, Huang CI, Crump RE, Bessell PR, Brown PE, Tirados I, Solano P, Antillon M, Picado A, Mbainda S, Darnas J, Crowley EH, Torr SJ, Peka M. Update of transmission modelling and projections of gambiense human African trypanosomiasis in the Mandoul focus, Chad. Infect Dis Poverty 2022; 11:11. [PMID: 35074016 PMCID: PMC8785021 DOI: 10.1186/s40249-022-00934-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 01/03/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND In recent years, a programme of vector control, screening and treatment of gambiense human African trypanosomiasis (gHAT) infections led to a rapid decline in cases in the Mandoul focus of Chad. To represent the biology of transmission between humans and tsetse, we previously developed a mechanistic transmission model, fitted to data between 2000 and 2013 which suggested that transmission was interrupted by 2015. The present study outlines refinements to the model to: (1) Assess whether elimination of transmission has already been achieved despite low-level case reporting; (2) quantify the role of intensified interventions in transmission reduction; and (3) predict the trajectory of gHAT in Mandoul for the next decade under different strategies. METHOD Our previous gHAT transmission model for Mandoul was updated using human case data (2000-2019) and a series of model refinements. These include how diagnostic specificity is incorporated into the model and improvements to the fitting method (increased variance in observed case reporting and how underreporting and improvements to passive screening are captured). A side-by-side comparison of fitting to case data was performed between the models. RESULTS We estimated that passive detection rates have increased due to improvements in diagnostic availability in fixed health facilities since 2015, by 2.1-fold for stage 1 detection, and 1.5-fold for stage 2. We find that whilst the diagnostic algorithm for active screening is estimated to be highly specific (95% credible interval (CI) 99.9-100%, Specificity = 99.9%), the high screening and low infection levels mean that some recently reported cases with no parasitological confirmation might be false positives. We also find that the focus-wide tsetse reduction estimated through model fitting (95% CI 96.1-99.6%, Reduction = 99.1%) is comparable to the reduction previously measured by the decline in tsetse catches from monitoring traps. In line with previous results, the model suggests that transmission was interrupted in 2015 due to intensified interventions. CONCLUSIONS We recommend that additional confirmatory testing is performed in Mandoul to ensure the endgame can be carefully monitored. More specific measurement of cases, would better inform when it is safe to stop active screening and vector control, provided there is a strong passive surveillance system in place.
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Affiliation(s)
- Kat S Rock
- Mathematics Institute, University of Warwick, Academic Loop Road, Coventry, CV4 7AL, UK.
- Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Academic Loop Road, Coventry, CV4 7AL, UK.
| | - Ching-I Huang
- Mathematics Institute, University of Warwick, Academic Loop Road, Coventry, CV4 7AL, UK
- Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Academic Loop Road, Coventry, CV4 7AL, UK
| | - Ronald E Crump
- Mathematics Institute, University of Warwick, Academic Loop Road, Coventry, CV4 7AL, UK
- Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Academic Loop Road, Coventry, CV4 7AL, UK
| | | | - Paul E Brown
- Mathematics Institute, University of Warwick, Academic Loop Road, Coventry, CV4 7AL, UK
- Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Academic Loop Road, Coventry, CV4 7AL, UK
| | - Inaki Tirados
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Philippe Solano
- Institut de Recherche pour le Développement, UMR INTERTRYP IRD-CIRAD, Université de Montpellier, 34398, Montpellier, France
| | - Marina Antillon
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Albert Picado
- Foundation for Innovative New Diagnostics (FIND), Geneva, Switzerland
| | - Severin Mbainda
- Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Moundou, Chad
| | - Justin Darnas
- Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Moundou, Chad
| | - Emily H Crowley
- Mathematics Institute, University of Warwick, Academic Loop Road, Coventry, CV4 7AL, UK
- Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Academic Loop Road, Coventry, CV4 7AL, UK
| | - Steve J Torr
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Mallaye Peka
- Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Moundou, Chad
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24
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Ledien J, Cucunubá ZM, Parra-Henao G, Rodríguez-Monguí E, Dobson AP, Basáñez MG, Nouvellet P. Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study. BMC Med Res Methodol 2022; 22:13. [PMID: 35027002 PMCID: PMC8759231 DOI: 10.1186/s12874-021-01477-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 11/15/2021] [Indexed: 11/29/2022] Open
Abstract
Age-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in estimated FoI values when fitting models and evaluating their predictive ability. To assess how this uncertainty impact predictions, we compared three approaches with three levels of uncertainty integration. We propose a performance indicator to assess how predictions reflect initial uncertainty. In Colombia, 76 serosurveys (1980–2014) conducted at municipality level provided age-stratified Chagas disease prevalence data. The yearly FoI was estimated at the serosurvey level using a time-varying catalytic model. Environmental, demographic and entomological predictors were used to fit and predict the FoI at municipality level from 1980 to 2010 across Colombia. A stratified bootstrap method was used to fit the models without temporal autocorrelation at the serosurvey level. The predictive ability of each model was evaluated to select the best-fit models within urban, rural and (Amerindian) indigenous settings. Model averaging, with the 10 best-fit models identified, was used to generate predictions. Our analysis shows a risk of overconfidence in model predictions when median estimates of FoI alone are used to fit and evaluate models, failing to account for uncertainty in FoI estimates. Our proposed methodology fully propagates uncertainty in the estimated FoI onto the generated predictions, providing realistic assessments of both central tendency and current uncertainty surrounding exposure to Chagas disease.
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Affiliation(s)
- Julia Ledien
- School of Life Sciences, University of Sussex, Brighton, UK.
| | - Zulma M Cucunubá
- London Centre for Neglected Tropical Disease Research & MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.,Departamento de Epidemiología Clínica y Bioestadística, Facultad de Medicina, Universidad Javeriana, Bogotá, Colombia
| | - Gabriel Parra-Henao
- Centro de Investigación en Salud para el Trópico, Universidad Cooperativa de Colombia, Santa Marta, Colombia.,National Institute of Health, Bogotá, Colombia
| | - Eliana Rodríguez-Monguí
- Neglected, Tropical and Vector Borne Diseases Program, Pan American Health Organization (PAHO), Bogotá, Colombia
| | - Andrew P Dobson
- Ecology & Evolutionary Biology, Princeton University, Princeton, USA
| | - María-Gloria Basáñez
- London Centre for Neglected Tropical Disease Research & MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
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25
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Blok DJ, de Vlas SJ. Guiding policy towards zero leprosy: Challenges for modelling & economic evaluation. Indian J Med Res 2022; 155:7-10. [PMID: 35859423 PMCID: PMC9552368 DOI: 10.4103/ijmr.ijmr_220_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- David J Blok
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Sake J de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
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26
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Pollett S, Johansson MA, Reich NG, Brett-Major D, Del Valle SY, Venkatramanan S, Lowe R, Porco T, Berry IM, Deshpande A, Kraemer MUG, Blazes DL, Pan-ngum W, Vespigiani A, Mate SE, Silal SP, Kandula S, Sippy R, Quandelacy TM, Morgan JJ, Ball J, Morton LC, Althouse BM, Pavlin J, van Panhuis W, Riley S, Biggerstaff M, Viboud C, Brady O, Rivers C. Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines. PLoS Med 2021; 18:e1003793. [PMID: 34665805 PMCID: PMC8525759 DOI: 10.1371/journal.pmed.1003793] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research. METHODS AND FINDINGS We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies. CONCLUSIONS These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.
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Affiliation(s)
- Simon Pollett
- Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America
| | - Michael A. Johansson
- Division of Vector-Borne Diseases, Centers for Disease Control & Prevention, San Juan, Puerto Rico, United States of America
| | - Nicholas G. Reich
- University of Massachusetts–Amherst, School of Public Health and Health Sciences, Amherst, Massachusetts, United States of America
| | - David Brett-Major
- University of Nebraska Medical Center, Omaha, Nebraska, United States of America
| | - Sara Y. Del Valle
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Srinivasan Venkatramanan
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, Virginia, United States of America
| | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Diseases and Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Barcelona Institute for Global Health, Barcelona, Spain
| | - Travis Porco
- University of California at San Francisco, San Francisco, California, United States of America
| | - Irina Maljkovic Berry
- Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America
| | - Alina Deshpande
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | | | - David L. Blazes
- Bill and Melinda Gates Foundation, Seattle, Washington, United States of America
| | - Wirichada Pan-ngum
- Mahidol-Oxford Tropical Medicine Research Unit and Department of Tropical Hygiene, Mahidol University, Thailand
| | - Alessandro Vespigiani
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Suzanne E. Mate
- Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America
| | - Sheetal P. Silal
- Modelling and Simulation Hub, Africa, Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Sasikiran Kandula
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York City, New York, United States of America
| | - Rachel Sippy
- Institute for Global Health and Translational Science, State University of New York Upstate Medical University, Syracuse, New York, United States of America
| | - Talia M. Quandelacy
- Division of Vector-Borne Diseases, Centers for Disease Control & Prevention, San Juan, Puerto Rico, United States of America
| | - Jeffrey J. Morgan
- Catholic University of America, Washington, DC, United States of America
| | - Jacob Ball
- U.S. Army Public Health Center, Edgewood, Maryland, United States of America
| | - Lindsay C. Morton
- Armed Forces Health Surveillance Division, Global Emerging Infections Surveillance, Silver Spring, Maryland, United States of America
- George Washington University, Milken Institute School of Public Health, Washington, DC, United States of America
| | - Benjamin M. Althouse
- University of Washington, Seattle, Washington, United States of America
- Institute for Disease Modeling, Bellevue, Washington, United States of America
- New Mexico State University, Las Cruces, New Mexico, United States of America
| | - Julie Pavlin
- National Academies of Sciences, Engineering, and Medicine, Washington, DC, United States of America
| | - Wilbert van Panhuis
- University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, United States of America
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College, London, United Kingdom
| | - Matthew Biggerstaff
- Influenza Division, Centers for Disease Control & Prevention, Atlanta, Georgia, United States of America
| | - Cecile Viboud
- Fogarty International Center, National Institutes for Health, Bethesda, Maryland, United States of America
| | - Oliver Brady
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Caitlin Rivers
- Johns Hopkins Center for Health Security, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
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Abstract
PURPOSE OF REVIEW Mathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcare-associated infections. To contextualize recent findings, we offer a summative review of recent literature focused on modeling transmission of pathogens in healthcare settings. RECENT FINDINGS The COVID-19 pandemic has led to a dramatic shift in the modeling landscape as the healthcare community has raced to characterize the transmission dynamics of SARS-CoV-2 and develop effective interventions. Inequities in COVID-19 outcomes have inspired new efforts to quantify how structural bias impacts both health outcomes and model parameterization. Meanwhile, developments in the modeling of methicillin-resistant Staphylococcus aureus, Clostridioides difficile, and other nosocomial infections continue to advance. Machine learning continues to be applied in novel ways, and genomic data is being increasingly incorporated into modeling efforts. SUMMARY As the type and amount of data continues to grow, mathematical, statistical, and computational modeling will play an increasing role in healthcare epidemiology. Gaps remain in producing models that are generalizable to a variety of time periods, geographic locations, and populations. However, with effective communication of findings and interdisciplinary collaboration, opportunities for implementing models for clinical decision-making and public health decision-making are bound to increase.
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28
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Stolk WA, Blok DJ, Hamley JID, Cantey PT, de Vlas SJ, Walker M, Basáñez MG. Scaling-Down Mass Ivermectin Treatment for Onchocerciasis Elimination: Modeling the Impact of the Geographical Unit for Decision Making. Clin Infect Dis 2021; 72:S165-S171. [PMID: 33909070 PMCID: PMC8201558 DOI: 10.1093/cid/ciab238] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Background Due to spatial heterogeneity in onchocerciasis transmission, the duration of ivermectin mass drug administration (MDA) required for eliminating onchocerciasis will vary within endemic areas and the occurrence of transmission “hotspots” is inevitable. The geographical scale at which stop-MDA decisions are made will be a key driver in how rapidly national programs can scale down active intervention upon achieving the epidemiological targets for elimination. Methods We used 2 onchocerciasis models (EPIONCHO-IBM and ONCHOSIM) to predict the likelihood of achieving elimination by 2030 in Africa, accounting for variation in preintervention endemicity levels and histories of ivermectin treatment. We explore how decision making at contrasting geographical scales (community vs larger scale “project”) changes projections on populations still requiring MDA or transitioning to post-treatment surveillance. Results The total population considered grows from 118 million people in 2020 to 136 million in 2030. If stop-MDA decisions are made at project level, the number of people requiring treatment declines from 69–118 million in 2020 to 59–118 million in 2030. If stop-MDA decisions are made at community level, the numbers decline from 23–81 million in 2020 to 15–63 million in 2030. The lower estimates in these prediction intervals are based on ONCHOSIM, the upper limits on EPIONCHO-IBM. Conclusions The geographical scale at which stop-MDA decisions are made strongly determines how rapidly national onchocerciasis programs can scale down MDA programs. Stopping in portions of project areas or transmission zones would free up human and economic resources.
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Affiliation(s)
- Wilma A Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - David J Blok
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jonathan I D Hamley
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's Campus), Imperial College London, London, United Kingdom.,MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's Campus), Imperial College London, London, United Kingdom
| | - Paul T Cantey
- Division of Parasitic Diseases and Malaria, US Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Sake J de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Martin Walker
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's Campus), Imperial College London, London, United Kingdom.,London Centre for Neglected Tropical Disease Research, Department of Pathobiology and Population Sciences, Royal Veterinary College, University of London, Hatfield, United Kingdom
| | - María-Gloria Basáñez
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's Campus), Imperial College London, London, United Kingdom.,MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's Campus), Imperial College London, London, United Kingdom
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29
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Coffeng LE, Le Rutte EA, Munoz J, Adams E, de Vlas SJ. Antibody and Antigen Prevalence as Indicators of Ongoing Transmission or Elimination of Visceral Leishmaniasis: A Modeling Study. Clin Infect Dis 2021; 72:S180-S187. [PMID: 33906229 PMCID: PMC8201595 DOI: 10.1093/cid/ciab210] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Background Control of visceral leishmaniasis (VL) on the Indian subcontinent has been highly successful. Control efforts such as indoor residual spraying and active case detection will be scaled down or even halted over the coming years. We explored how after scale-down, potential recurrence of VL cases may be predicted based on population-based surveys of antibody or antigenemia prevalence. Methods Using a stochastic age-structured transmission model of VL, we predicted trends in case incidence and biomarker prevalence over time after scaling down control efforts when the target of 3 successive years without VL cases has been achieved. Next, we correlated biomarker prevalence with the occurrence of new VL cases within 10 years of scale-down. Results Occurrence of at least 1 new VL case in a population of 10 000 was highly correlated with the seroprevalence and antigenemia prevalence at the moment of scale-down, or 1 or 2 years afterward. Receiver operating characteristic curves indicated that biomarker prevalence in adults provided the most predictive information, and seroprevalence was a more informative predictor of new VL cases than antigenemia prevalence. Thresholds for biomarker prevalence to predict occurrence of new VL cases with high certainty were robust to variation in precontrol endemicity. Conclusions The risk of recrudescence of VL after scaling down control efforts can be monitored and mitigated by means of population-based surveys. Our findings highlight that rapid point-of-care diagnostic tools to assess (preferably) seroprevalence or (otherwise) antigenemia in the general population could be a key ingredient of sustainable VL control.
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Affiliation(s)
- Luc E Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam,The Netherlands
| | - Epke A Le Rutte
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam,The Netherlands.,Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel,Switzerland.,University of Basel, Basel, Switzerland
| | - Johanna Munoz
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam,The Netherlands
| | - Emily Adams
- Department of Tropical Disease Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
| | - Sake J de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam,The Netherlands
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30
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Blok DJ, Kamgno J, Pion SD, Nana-Djeunga HC, Niamsi-Emalio Y, Chesnais CB, Mackenzie CD, Klion AD, Fletcher DA, Nutman TB, de Vlas SJ, Boussinesq M, Stolk WA. Feasibility of Onchocerciasis Elimination Using a "Test-and-not-treat" Strategy in Loa loa Co-endemic Areas. Clin Infect Dis 2021; 72:e1047-e1055. [PMID: 33289025 PMCID: PMC8204788 DOI: 10.1093/cid/ciaa1829] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Indexed: 12/29/2022] Open
Abstract
Background Mass drug administration (MDA) with ivermectin is the main strategy for onchocerciasis elimination. Ivermectin is generally safe, but is associated with serious adverse events in individuals with high Loa loa microfilarial densities (MFD). Therefore, ivermectin MDA is not recommended in areas where onchocerciasis is hypo-endemic and L loa is co-endemic. To eliminate onchocerciasis in those areas, a test-and-not-treat (TaNT) strategy has been proposed. We investigated whether onchocerciasis elimination can be achieved using TaNT and the required duration. Methods We used the individual-based model ONCHOSIM to predict the impact of TaNT on onchocerciasis microfilarial (mf) prevalence. We simulated precontrol mf prevalence levels from 2% to 40%. The impact of TaNT was simulated under varying levels of participation, systematic nonparticipation, and exclusion from ivermectin resulting from high L loa MFD. For each scenario, we assessed the time to elimination, defined as bringing onchocerciasis mf prevalence below 1.4%. Results In areas with 30% to 40% precontrol mf prevalence, the model predicted that it would take between 14 and 16 years to bring the mf prevalence below 1.4% using conventional MDA, assuming 65% participation. TaNT would increase the time to elimination by up to 1.5 years, depending on the level of systematic nonparticipation and the exclusion rate. At lower exclusion rates (≤2.5%), the delay would be less than 6 months. Conclusions Our model predicts that onchocerciasis can be eliminated using TaNT in L loa co-endemic areas. The required treatment duration using TaNT would be only slightly longer than in areas with conventional MDA, provided that participation is good.
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Affiliation(s)
- David J Blok
- Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Joseph Kamgno
- Centre for Research on Filariasis and other Tropical Diseases (CRFilMT), Yaoundé, Cameroon
| | - Sebastien D Pion
- IRD UMI 233-INSERM U1175-Montpellier University, Montpellier, France
| | - Hugues C Nana-Djeunga
- Centre for Research on Filariasis and other Tropical Diseases (CRFilMT), Yaoundé, Cameroon
| | - Yannick Niamsi-Emalio
- Centre for Research on Filariasis and other Tropical Diseases (CRFilMT), Yaoundé, Cameroon
| | - Cedric B Chesnais
- IRD UMI 233-INSERM U1175-Montpellier University, Montpellier, France
| | | | - Amy D Klion
- Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, Bethesda, Maryland, United States
| | - Daniel A Fletcher
- Department of Bioengineering and the Biophysics Program, University of California, Berkeley, California, United States
| | - Thomas B Nutman
- Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, Bethesda, Maryland, United States
| | - Sake J de Vlas
- Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Michel Boussinesq
- IRD UMI 233-INSERM U1175-Montpellier University, Montpellier, France
| | - Wilma A Stolk
- Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
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31
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Fortunato AK, Glasser CP, Watson JA, Lu Y, Rychtář J, Taylor D. Mathematical modelling of the use of insecticide-treated nets for elimination of visceral leishmaniasis in Bihar, India. ROYAL SOCIETY OPEN SCIENCE 2021; 8:201960. [PMID: 34234949 PMCID: PMC8242840 DOI: 10.1098/rsos.201960] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 05/24/2021] [Indexed: 05/27/2023]
Abstract
Visceral leishmaniasis (VL) is a deadly neglected tropical disease caused by a parasite Leishmania donovani and spread by female sand flies Phlebotomus argentipes. There is conflicting evidence regarding the role of insecticide-treated nets (ITNs) on the prevention of VL. Numerous studies demonstrated the effectiveness of ITNs. However, KalaNet, a large trial in Nepal and India did not support those findings. The purpose of this paper is to gain insight into the situation by mathematical modelling. We expand a mathematical model of VL transmission based on the KalaNet trial and incorporate the use of ITNs explicitly into the model. One of the major contributions of this work is that we calibrate the model based on the available epidemiological data, generally independent of the KalaNet trial. We validate the model on data collected during the KalaNet trial. We conclude that in order to eliminate VL, the ITN usage would have to stay above 96%. This is higher than the 91% ITNs use at the end of the trial which may explain why the trial did not show a positive effect from ITNs. At the same time, our model indicates that asymptomatic individuals play a crucial role in VL transmission.
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Affiliation(s)
- Anna K. Fortunato
- Department of Mathematics, University of Richmond, Richmond, VA 23173, USA
| | - Casey P. Glasser
- Department of Mathematics, Virginia Tech, Blacksburg, VA 24061-1026, USA
| | - Joy A. Watson
- Department of Mathematics and Economics, Virginia State University, Petersburg, VA 23806, USA
| | - Yongjin Lu
- Department of Mathematics and Economics, Virginia State University, Petersburg, VA 23806, USA
| | - Jan Rychtář
- Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA 23284-2014, USA
| | - Dewey Taylor
- Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA 23284-2014, USA
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32
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Walker M, Hamley JID, Milton P, Monnot F, Kinrade S, Specht S, Pedrique B, Basáñez MG. Supporting drug development for neglected tropical diseases using mathematical modelling. Clin Infect Dis 2021; 73:e1391-e1396. [PMID: 33893482 PMCID: PMC8442785 DOI: 10.1093/cid/ciab350] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Indexed: 11/14/2022] Open
Abstract
Drug-based interventions are at the heart of global efforts to reach elimination as a public health problem (trachoma, soil-transmitted helminthiases, schistosomiasis, lymphatic filariasis) or elimination of transmission (onchocerciasis) for 5 of the most prevalent neglected tropical diseases tackled via the World Health Organization preventive chemotherapy strategy. While for some of these diseases there is optimism that currently available drugs will be sufficient to achieve the proposed elimination goals, for others—particularly onchocerciasis—there is a growing consensus that novel therapeutic options will be needed. Since in this area no high return of investment is possible, minimizing wasted money and resources is essential. Here, we use illustrative results to show how mathematical modeling can guide the drug development pathway, yielding resource-saving and efficiency payoffs, from the refinement of target product profiles and intended context of use to the design of clinical trials.
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Affiliation(s)
- Martin Walker
- Department of Pathobiology and Population Sciences and London Centre for Neglected Tropical Disease Research, Royal Veterinary College, UK.,MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology and London Centre for Neglected Tropical Disease Research, Imperial College London, UK
| | - Jonathan I D Hamley
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology and London Centre for Neglected Tropical Disease Research, Imperial College London, UK
| | - Philip Milton
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology and London Centre for Neglected Tropical Disease Research, Imperial College London, UK
| | - Frédéric Monnot
- Drugs for Neglected Diseases initiative (DNDi), Geneva, Switzerland
| | - Sally Kinrade
- Medicines Development for Global Health, Southbank VIC, Australia
| | - Sabine Specht
- Drugs for Neglected Diseases initiative (DNDi), Geneva, Switzerland
| | - Bélen Pedrique
- Drugs for Neglected Diseases initiative (DNDi), Geneva, Switzerland
| | - Maria-Gloria Basáñez
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology and London Centre for Neglected Tropical Disease Research, Imperial College London, UK
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33
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Davis CN, Rock KS, Antillón M, Miaka EM, Keeling MJ. Cost-effectiveness modelling to optimise active screening strategy for gambiense human African trypanosomiasis in endemic areas of the Democratic Republic of Congo. BMC Med 2021; 19:86. [PMID: 33794881 PMCID: PMC8017623 DOI: 10.1186/s12916-021-01943-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 02/16/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Gambiense human African trypanosomiasis (gHAT) has been brought under control recently with village-based active screening playing a major role in case reduction. In the approach to elimination, we investigate how to optimise active screening in villages in the Democratic Republic of Congo, such that the expenses of screening programmes can be efficiently allocated whilst continuing to avert morbidity and mortality. METHODS We implement a cost-effectiveness analysis using a stochastic gHAT infection model for a range of active screening strategies and, in conjunction with a cost model, we calculate the net monetary benefit (NMB) of each strategy. We focus on the high-endemicity health zone of Kwamouth in the Democratic Republic of Congo. RESULTS High-coverage active screening strategies, occurring approximately annually, attain the highest NMB. For realistic screening at 55% coverage, annual screening is cost-effective at very low willingness-to-pay thresholds (20.4 per disability adjusted life year (DALY) averted), only marginally higher than biennial screening (14.6 per DALY averted). We find that, for strategies stopping after 1, 2 or 3 years of zero case reporting, the expected cost-benefits are very similar. CONCLUSIONS We highlight the current recommended strategy-annual screening with three years of zero case reporting before stopping active screening-is likely cost-effective, in addition to providing valuable information on whether transmission has been interrupted.
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Affiliation(s)
- Christopher N Davis
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK.
- Zeeman Institute (SBIDER), University of Warwick, Coventry, CV4 7AL, UK.
| | - Kat S Rock
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
- Zeeman Institute (SBIDER), University of Warwick, Coventry, CV4 7AL, UK
| | - Marina Antillón
- Swiss Tropical and Public Health Institute, Socinstrasse 57, Basel, 4051, Switzerland
- University of Basel, Petersplatz 1, Basel, 4051, Switzerland
| | - Erick Mwamba Miaka
- Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Ave Coisement Liberation et Bd Triomphal No 1, Commune de Kasavubu, Kinshasa, Democratic Republic of Congo
| | - Matt J Keeling
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
- Zeeman Institute (SBIDER), University of Warwick, Coventry, CV4 7AL, UK
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
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34
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Le Rutte EA, Coffeng LE, Muñoz J, de Vlas SJ. Modelling the impact of COVID-19-related programme interruptions on visceral leishmaniasis in India. Trans R Soc Trop Med Hyg 2021; 115:229-235. [PMID: 33580952 PMCID: PMC7928630 DOI: 10.1093/trstmh/trab012] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 12/22/2020] [Accepted: 01/13/2021] [Indexed: 01/05/2023] Open
Abstract
Background In March 2020, India declared a nationwide lockdown to control the spread of coronavirus disease 2019. As a result, control efforts against visceral leishmaniasis (VL) were interrupted. Methods Using an established age-structured deterministic VL transmission model, we predicted the impact of a 6- to 24-month programme interruption on the timeline towards achieving the VL elimination target as well as on the increase of VL cases. We also explored the potential impact of a mitigation strategy after the interruption. Results Delays towards the elimination target are estimated to range between 0 and 9 y. Highly endemic settings where control efforts have been ongoing for 5–8 y are most affected by an interruption, for which we identified a mitigation strategy to be most relevant. However, more importantly, all settings can expect an increase in the number of VL cases. This increase is substantial even for settings with a limited expected delay in achieving the elimination target. Conclusions Besides implementing mitigation strategies, it is of great importance to try and keep the duration of the interruption as short as possible to prevent new individuals from becoming infected with VL and continue the efforts towards VL elimination as a public health problem in India.
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Affiliation(s)
- Epke A Le Rutte
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands.,Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Luc E Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands
| | - Johanna Muñoz
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands
| | - Sake J de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands
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35
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Blumberg S, Borlase A, Prada JM, Solomon AW, Emerson P, Hooper PJ, Deiner MS, Amoah B, Hollingsworth TD, Porco TC, Lietman TM. Implications of the COVID-19 pandemic in eliminating trachoma as a public health problem. Trans R Soc Trop Med Hyg 2021; 115:222-228. [PMID: 33449114 PMCID: PMC7928550 DOI: 10.1093/trstmh/traa170] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 11/07/2020] [Accepted: 01/09/2021] [Indexed: 11/13/2022] Open
Abstract
Background Progress towards elimination of trachoma as a public health problem has been substantial, but the coronavirus disease 2019 (COVID-19) pandemic has disrupted community-based control efforts. Methods We use a susceptible-infected model to estimate the impact of delayed distribution of azithromycin treatment on the prevalence of active trachoma. Results We identify three distinct scenarios for geographic districts depending on whether the basic reproduction number and the treatment-associated reproduction number are above or below a value of 1. We find that when the basic reproduction number is <1, no significant delays in disease control will be caused. However, when the basic reproduction number is >1, significant delays can occur. In most districts, 1 y of COVID-related delay can be mitigated by a single extra round of mass drug administration. However, supercritical districts require a new paradigm of infection control because the current strategies will not eliminate disease. Conclusions If the pandemic can motivate judicious, community-specific implementation of control strategies, global elimination of trachoma as a public health problem could be accelerated.
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Affiliation(s)
- Seth Blumberg
- Francis I Proctor Foundation, University of California, San Francisco, San Francisco, CA, USA
| | | | - Joaquin M Prada
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Anthony W Solomon
- Department of Control of Neglected Tropical Diseases, World Health Organization, Geneva, Switzerland
| | - Paul Emerson
- International Trachoma Initiative, Task Force for Global Health, Decatur, GA, USA
| | - Pamela J Hooper
- International Trachoma Initiative, Task Force for Global Health, Decatur, GA, USA
| | - Michael S Deiner
- Francis I Proctor Foundation, University of California, San Francisco, San Francisco, CA, USA
| | - Benjamin Amoah
- Lancaster Medical School, Lancaster University, Bailrigg, Lancaster, UK
| | | | - Travis C Porco
- Francis I Proctor Foundation, University of California, San Francisco, San Francisco, CA, USA.,Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Thomas M Lietman
- Francis I Proctor Foundation, University of California, San Francisco, San Francisco, CA, USA.,Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.,Institute for Global Health Sciences, University of California, San Francisco, San Francisco, CA, USA.,Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, USA
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36
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Malizia V, Giardina F, Vegvari C, Bajaj S, McRae-McKee K, Anderson RM, de Vlas SJ, Coffeng LE. Modelling the impact of COVID-19-related control programme interruptions on progress towards the WHO 2030 target for soil-transmitted helminths. Trans R Soc Trop Med Hyg 2021; 115:253-260. [PMID: 33313897 PMCID: PMC7798673 DOI: 10.1093/trstmh/traa156] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 10/21/2020] [Accepted: 11/23/2020] [Indexed: 12/21/2022] Open
Abstract
Background On 1 April 2020, the WHO recommended an interruption of all activities for the control of neglected tropical diseases, including soil-transmitted helminths (STH), in response to the COVID-19 pandemic. This paper investigates the impact of this disruption on the progress towards the WHO 2030 target for STH. Methods We used two stochastic individual-based models to simulate the impact of missing one or more preventive chemotherapy (PC) rounds in different endemicity settings. We also investigated the extent to which this impact can be lessened by mitigation strategies, such as semiannual or community-wide PC. Results Both models show that without a mitigation strategy, control programmes will catch up by 2030, assuming that coverage is maintained. The catch-up time can be up to 4.5 y after the start of the interruption. Mitigation strategies may reduce this time by up to 2 y and increase the probability of achieving the 2030 target. Conclusions Although a PC interruption will only temporarily impact the progress towards the WHO 2030 target, programmes are encouraged to restart as soon as possible to minimise the impact on morbidity. The implementation of suitable mitigation strategies can turn the interruption into an opportunity to accelerate progress towards reaching the target.
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Affiliation(s)
- Veronica Malizia
- Departmen t of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Federica Giardina
- Departmen t of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Carolin Vegvari
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.,Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Sumali Bajaj
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.,Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Kevin McRae-McKee
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.,Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Roy M Anderson
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.,Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.,The DeWorm3 Project, Natural History Museum, London, UK
| | - Sake J de Vlas
- Departmen t of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Luc E Coffeng
- Departmen t of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
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37
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Hollingsworth TD, Mwinzi P, Vasconcelos A, de Vlas SJ. Evaluating the potential impact of interruptions to neglected tropical disease programmes due to COVID-19. Trans R Soc Trop Med Hyg 2021; 115:201-204. [PMID: 33693894 PMCID: PMC7946803 DOI: 10.1093/trstmh/trab023] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 02/11/2021] [Indexed: 01/05/2023] Open
Affiliation(s)
- T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford OX3 7LF, UK
| | - Pauline Mwinzi
- Expanded Special Project for Elimination of Neglected Tropical Diseases (ESPEN), World Health Organization Regional Office for Africa, P.O.BOX 06 Cité du Djoué Brazzaville, Congo
| | - Andreia Vasconcelos
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford OX3 7LF, UK
| | - Sake J de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands
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38
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Hamley JID, Blok DJ, Walker M, Milton P, Hopkins AD, Hamill LC, Downs P, de Vlas SJ, Stolk WA, Basáñez MG. What does the COVID-19 pandemic mean for the next decade of onchocerciasis control and elimination? Trans R Soc Trop Med Hyg 2021; 115:269-280. [PMID: 33515042 PMCID: PMC7928565 DOI: 10.1093/trstmh/traa193] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 12/16/2020] [Accepted: 12/29/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Mass drug administration (MDA) of ivermectin for onchocerciasis has been disrupted by the coronavirus disease 2019 (COVID-19) pandemic. Mathematical modelling can help predict how missed/delayed MDA will affect short-term epidemiological trends and elimination prospects by 2030. METHODS Two onchocerciasis transmission models (EPIONCHO-IBM and ONCHOSIM) are used to simulate microfilarial prevalence trends, elimination probabilities and age profiles of Onchocerca volvulus microfilarial prevalence and intensity for different treatment histories and transmission settings, assuming no interruption, a 1-y (2020) interruption or a 2-y (2020-2021) interruption. Biannual MDA or increased coverage upon MDA resumption are investigated as remedial strategies. RESULTS Programmes with shorter MDA histories and settings with high pre-intervention endemicity will be the most affected. Biannual MDA is more effective than increasing coverage for mitigating COVID-19's impact on MDA. Programmes that had already switched to biannual MDA should be minimally affected. In high-transmission settings with short treatment history, a 2-y interruption could lead to increased microfilarial load in children (EPIONCHO-IBM) and adults (ONCHOSIM). CONCLUSIONS Programmes with shorter (annual MDA) treatment histories should be prioritised for remedial biannual MDA. Increases in microfilarial load could have short- and long-term morbidity and mortality repercussions. These results can guide decision-making to mitigate the impact of COVID-19 on onchocerciasis elimination.
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Affiliation(s)
- Jonathan I D Hamley
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's campus), Imperial College London, Norfolk Place, London W2 1PG, UK.,MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's campus), Imperial College London, Norfolk Place, London W2 1PG, UK
| | - David J Blok
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Martin Walker
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's campus), Imperial College London, Norfolk Place, London W2 1PG, UK.,London Centre for Neglected Tropical Disease Research (LCNTDR), Department of Pathobiology and Population Sciences, Royal Veterinary College, University of London, Hatfield AL9 7TA, UK
| | - Philip Milton
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's campus), Imperial College London, Norfolk Place, London W2 1PG, UK.,MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's campus), Imperial College London, Norfolk Place, London W2 1PG, UK
| | - Adrian D Hopkins
- Neglected and Disabling Diseases of Poverty Consultant, Kent, UK
| | - Louise C Hamill
- Sightsavers, 35 Perrymount Road, Haywards Heath, RH16 3BW, UK
| | - Philip Downs
- Sightsavers, 35 Perrymount Road, Haywards Heath, RH16 3BW, UK
| | - Sake J de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Wilma A Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Maria-Gloria Basáñez
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's campus), Imperial College London, Norfolk Place, London W2 1PG, UK.,MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's campus), Imperial College London, Norfolk Place, London W2 1PG, UK
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Vega Gomez MC, Rolón M, Coronel C, Pereira Carneiro JN, Lucas dos Santos AT, Almeida-Bezerra JW, Almeida de Menezes S, Everson da Silva L, Melo Coutinho HD, do Amaral W, Ribeiro-Filho J, Bezerra Morais-Braga MF. Antiparasitic effect of essential oils obtained from two species of Piper L. native to the Atlantic forest. BIOCATALYSIS AND AGRICULTURAL BIOTECHNOLOGY 2021. [DOI: 10.1016/j.bcab.2021.101958] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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40
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Jawahar S, Tricoche N, Bulman CA, Sakanari J, Lustigman S. Drugs that target early stages of Onchocerca volvulus: A revisited means to facilitate the elimination goals for onchocerciasis. PLoS Negl Trop Dis 2021; 15:e0009064. [PMID: 33600426 PMCID: PMC7891776 DOI: 10.1371/journal.pntd.0009064] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Several issues have been identified with the current programs for the elimination of onchocerciasis that target only transmission by using mass drug administration (MDA) of the drug ivermectin. Alternative and/or complementary treatment regimens as part of a more comprehensive strategy to eliminate onchocerciasis are needed. We posit that the addition of “prophylactic” drugs or therapeutic drugs that can be utilized in a prophylactic strategy to the toolbox of present microfilaricidal drugs and/or future macrofilaricidal treatment regimens will not only improve the chances of meeting the elimination goals but may hasten the time to elimination and also will support achieving a sustained elimination of onchocerciasis. These “prophylactic” drugs will target the infective third- (L3) and fourth-stage (L4) larvae of Onchocerca volvulus and consequently prevent the establishment of new infections not only in uninfected individuals but also in already infected individuals and thus reduce the overall adult worm burden and transmission. Importantly, an effective prophylactic treatment regimen can utilize drugs that are already part of the onchocerciasis elimination program (ivermectin), those being considered for MDA (moxidectin), and/or the potential macrofilaricidal drugs (oxfendazole and emodepside) currently under clinical development. Prophylaxis of onchocerciasis is not a new concept. We present new data showing that these drugs can inhibit L3 molting and/or inhibit motility of L4 at IC50 and IC90 that are covered by the concentration of these drugs in plasma based on the corresponding pharmacological profiles obtained in human clinical trials when these drugs were tested using various doses for the therapeutic treatments of various helminth infections.
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Affiliation(s)
- Shabnam Jawahar
- Molecular Parasitology, Lindsey F. Kimball Research Institute, New York Blood Center, New York, New York, United States of America
| | - Nancy Tricoche
- Molecular Parasitology, Lindsey F. Kimball Research Institute, New York Blood Center, New York, New York, United States of America
| | - Christina A Bulman
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, United States of America
| | - Judy Sakanari
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, United States of America
| | - Sara Lustigman
- Molecular Parasitology, Lindsey F. Kimball Research Institute, New York Blood Center, New York, New York, United States of America
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41
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Crump RE, Huang CI, Knock ES, Spencer SEF, Brown PE, Mwamba Miaka E, Shampa C, Keeling MJ, Rock KS. Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo. PLoS Comput Biol 2021; 17:e1008532. [PMID: 33513134 PMCID: PMC7899378 DOI: 10.1371/journal.pcbi.1008532] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 02/22/2021] [Accepted: 11/12/2020] [Indexed: 11/18/2022] Open
Abstract
Gambiense human African trypanosomiasis (gHAT) is a virulent disease declining in burden but still endemic in West and Central Africa. Although it is targeted for elimination of transmission by 2030, there remain numerous questions about the drivers of infection and how these vary geographically. In this study we focus on the Democratic Republic of Congo (DRC), which accounted for 84% of the global case burden in 2016, to explore changes in transmission across the country and elucidate factors which may have contributed to the persistence of disease or success of interventions in different regions. We present a Bayesian fitting methodology, applied to 168 endemic health zones (∼100,000 population size), which allows for calibration of a mechanistic gHAT model to case data (from the World Health Organization HAT Atlas) in an adaptive and automated framework. It was found that the model needed to capture improvements in passive detection to match observed trends in the data within former Bandundu and Bas Congo provinces indicating these regions have substantially reduced time to detection. Health zones in these provinces generally had longer burn-in periods during fitting due to additional model parameters. Posterior probability distributions were found for a range of fitted parameters in each health zone; these included the basic reproduction number estimates for pre-1998 (R0) which was inferred to be between 1 and 1.14, in line with previous gHAT estimates, with higher median values typically in health zones with more case reporting in the 2000s. Previously, it was not clear whether a fall in active case finding in the period contributed to the declining case numbers. The modelling here accounts for variable screening and suggests that underlying transmission has also reduced greatly-on average 96% in former Equateur, 93% in former Bas Congo and 89% in former Bandundu-Equateur and Bandundu having had the highest case burdens in 2000. This analysis also sets out a framework to enable future predictions for the country.
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Affiliation(s)
- Ronald E. Crump
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
- The School of Life Sciences, The University of Warwick, Coventry, United Kingdom
- * E-mail:
| | - Ching-I Huang
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Edward S. Knock
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- The Department of Statistics, The University of Warwick, Coventry, United Kingdom
| | - Simon E. F. Spencer
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- The Department of Statistics, The University of Warwick, Coventry, United Kingdom
| | - Paul E. Brown
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Erick Mwamba Miaka
- Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Kinshasa, D.R.C.
| | - Chansy Shampa
- Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Kinshasa, D.R.C.
| | - Matt J. Keeling
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
- The School of Life Sciences, The University of Warwick, Coventry, United Kingdom
| | - Kat S. Rock
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
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42
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Clark J, Stolk WA, Basáñez MG, Coffeng LE, Cucunubá ZM, Dixon MA, Dyson L, Hampson K, Marks M, Medley GF, Pollington TM, Prada JM, Rock KS, Salje H, Toor J, Hollingsworth TD. How modelling can help steer the course set by the World Health Organization 2021-2030 roadmap on neglected tropical diseases. Gates Open Res 2021; 5:112. [PMID: 35169682 PMCID: PMC8816801 DOI: 10.12688/gatesopenres.13327.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/28/2022] [Indexed: 01/12/2023] Open
Abstract
The World Health Organization recently launched its 2021-2030 roadmap, Ending the Neglect to Attain the Sustainable Development Goals , an updated call to arms to end the suffering caused by neglected tropical diseases. Modelling and quantitative analyses played a significant role in forming these latest goals. In this collection, we discuss the insights, the resulting recommendations and identified challenges of public health modelling for 13 of the target diseases: Chagas disease, dengue, gambiense human African trypanosomiasis (gHAT), lymphatic filariasis (LF), onchocerciasis, rabies, scabies, schistosomiasis, soil-transmitted helminthiases (STH), Taenia solium taeniasis/ cysticercosis, trachoma, visceral leishmaniasis (VL) and yaws. This piece reflects the three cross-cutting themes identified across the collection, regarding the contribution that modelling can make to timelines, programme design, drug development and clinical trials.
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Affiliation(s)
- Jessica Clark
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
- Institute of Biodiversity, Animal Health & Comparative Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Wilma A. Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3000 CA, The Netherlands
| | - María-Gloria Basáñez
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - Luc E. Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3000 CA, The Netherlands
| | - Zulma M. Cucunubá
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - Matthew A. Dixon
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- Schistosomiasis Control Initiative Foundation, London, SE11 5DP, UK
| | - Louise Dyson
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
| | - Katie Hampson
- Institute of Biodiversity, Animal Health & Comparative Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Michael Marks
- Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Graham F. Medley
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK
| | - Timothy M. Pollington
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
| | - Joaquin M. Prada
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, UK
| | - Kat S. Rock
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
| | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK
| | - Jaspreet Toor
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - T. Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
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43
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Clark J, Stolk WA, Basáñez MG, Coffeng LE, Cucunubá ZM, Dixon MA, Dyson L, Hampson K, Marks M, Medley GF, Pollington TM, Prada JM, Rock KS, Salje H, Toor J, Hollingsworth TD. How modelling can help steer the course set by the World Health Organization 2021-2030 roadmap on neglected tropical diseases. Gates Open Res 2021; 5:112. [PMID: 35169682 PMCID: PMC8816801 DOI: 10.12688/gatesopenres.13327.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2021] [Indexed: 01/12/2023] Open
Abstract
The World Health Organization recently launched its 2021-2030 roadmap, Ending the Neglect to Attain the Sustainable Development Goals , an updated call to arms to end the suffering caused by neglected tropical diseases. Modelling and quantitative analyses played a significant role in forming these latest goals. In this collection, we discuss the insights, the resulting recommendations and identified challenges of public health modelling for 13 of the target diseases: Chagas disease, dengue, gambiense human African trypanosomiasis (gHAT), lymphatic filariasis (LF), onchocerciasis, rabies, scabies, schistosomiasis, soil-transmitted helminthiases (STH), Taenia solium taeniasis/ cysticercosis, trachoma, visceral leishmaniasis (VL) and yaws. This piece reflects the three cross-cutting themes identified across the collection, regarding the contribution that modelling can make to timelines, programme design, drug development and clinical trials.
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Affiliation(s)
- Jessica Clark
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
- Institute of Biodiversity, Animal Health & Comparative Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Wilma A. Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3000 CA, The Netherlands
| | - María-Gloria Basáñez
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - Luc E. Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3000 CA, The Netherlands
| | - Zulma M. Cucunubá
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - Matthew A. Dixon
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- Schistosomiasis Control Initiative Foundation, London, SE11 5DP, UK
| | - Louise Dyson
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
| | - Katie Hampson
- Institute of Biodiversity, Animal Health & Comparative Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Michael Marks
- Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Graham F. Medley
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK
| | - Timothy M. Pollington
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
| | - Joaquin M. Prada
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, UK
| | - Kat S. Rock
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
| | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK
| | - Jaspreet Toor
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - T. Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
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44
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Lucas TCD, Pollington TM, Davis EL, Hollingsworth TD. Responsible modelling: Unit testing for infectious disease epidemiology. Epidemics 2020; 33:100425. [PMID: 33307443 PMCID: PMC7690327 DOI: 10.1016/j.epidem.2020.100425] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 10/21/2020] [Accepted: 11/21/2020] [Indexed: 11/30/2022] Open
Abstract
Infectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID-19 and Ebola and endemic diseases including malaria and tuberculosis. Yet a coding bug may bias results, yielding incorrect conclusions and actions causing avoidable harm. We are ethically obliged to make our code as free of error as possible. Unit testing is a coding method to avoid such bugs, but it is rarely used in epidemiology. We demonstrate how unit testing can handle the particular quirks of infectious disease models and aim to increase the uptake of this methodology in our field.
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Affiliation(s)
- Tim C D Lucas
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK. Centre for Environment and Health, School of Public Health, Imperial College, UK.
| | - Timothy M Pollington
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK. MathSys CDT, University of Warwick, UK
| | - Emma L Davis
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
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45
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Blumberg S, Borlase A, Prada JM, Solomon AW, Emerson P, Hooper PJ, Deiner MS, Amoah B, Hollingsworth D, Porco TC, Lietman TM. Implications of the COVID-19 pandemic on eliminating trachoma as a public health problem. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020. [PMID: 33140063 PMCID: PMC7605574 DOI: 10.1101/2020.10.26.20219691] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background: Progress towards elimination of trachoma as a public health problem has been substantial, but the COVID-19 pandemic has disrupted community-based control efforts. Methods: We use a susceptible-infected model to estimate the impact of delayed distribution of azithromycin treatment on the prevalence of active trachoma. Results: We identify three distinct scenarios for geographic districts depending on whether the basic reproduction number and the treatment-associated reproduction number are above or below a value of one. We find that when the basic reproduction number is below one, no significant delays in disease control will be caused. However, when the basic reproduction number is above one, significant delays can occur. In most districts a year of COVID-related delay can be mitigated by a single extra round of mass drug administration. However, supercritical districts require a new paradigm of infection control because the current strategies will not eliminate disease. Conclusion: If the pandemic can motivate judicious, community-specific implementation of control strategies, global elimination of trachoma as a public health problem could be accelerated.
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Affiliation(s)
| | | | - Joaquin M Prada
- Faculty of Health and Medical Sciences, University of Surrey, UK
| | - Anthony W Solomon
- Department of Control of Neglected Tropical Diseases, World Health Organization, Geneva, Switzerland
| | - Paul Emerson
- International Trachoma Initiative, The Task Force for Global Health, USA
| | - Pamela J Hooper
- International Trachoma Initiative, The Task Force for Global Health, USA
| | | | - Benjamin Amoah
- Lancaster Medical School, Lancaster University, Bailrigg, Lancaster, UK
| | | | - Travis C Porco
- Francis I Proctor Foundation, UCSF, USA.,Department of Epidemiology and Biostatistics, UCSF, USA
| | - Thomas M Lietman
- Francis I Proctor Foundation, UCSF, USA.,Department of Epidemiology and Biostatistics, UCSF, USA.,Institute for Global Health Sciences, UCSF, USA.,Department of Ophthalmology, UCSF, USA
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46
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Erdemir A, Mulugeta L, Ku JP, Drach A, Horner M, Morrison TM, Peng GCY, Vadigepalli R, Lytton WW, Myers JG. Credible practice of modeling and simulation in healthcare: ten rules from a multidisciplinary perspective. J Transl Med 2020; 18:369. [PMID: 32993675 PMCID: PMC7526418 DOI: 10.1186/s12967-020-02540-4] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 09/21/2020] [Indexed: 11/10/2022] Open
Abstract
The complexities of modern biomedicine are rapidly increasing. Thus, modeling and simulation have become increasingly important as a strategy to understand and predict the trajectory of pathophysiology, disease genesis, and disease spread in support of clinical and policy decisions. In such cases, inappropriate or ill-placed trust in the model and simulation outcomes may result in negative outcomes, and hence illustrate the need to formalize the execution and communication of modeling and simulation practices. Although verification and validation have been generally accepted as significant components of a model’s credibility, they cannot be assumed to equate to a holistic credible practice, which includes activities that can impact comprehension and in-depth examination inherent in the development and reuse of the models. For the past several years, the Committee on Credible Practice of Modeling and Simulation in Healthcare, an interdisciplinary group seeded from a U.S. interagency initiative, has worked to codify best practices. Here, we provide Ten Rules for credible practice of modeling and simulation in healthcare developed from a comparative analysis by the Committee’s multidisciplinary membership, followed by a large stakeholder community survey. These rules establish a unified conceptual framework for modeling and simulation design, implementation, evaluation, dissemination and usage across the modeling and simulation life-cycle. While biomedical science and clinical care domains have somewhat different requirements and expectations for credible practice, our study converged on rules that would be useful across a broad swath of model types. In brief, the rules are: (1) Define context clearly. (2) Use contextually appropriate data. (3) Evaluate within context. (4) List limitations explicitly. (5) Use version control. (6) Document appropriately. (7) Disseminate broadly. (8) Get independent reviews. (9) Test competing implementations. (10) Conform to standards. Although some of these are common sense guidelines, we have found that many are often missed or misconstrued, even by seasoned practitioners. Computational models are already widely used in basic science to generate new biomedical knowledge. As they penetrate clinical care and healthcare policy, contributing to personalized and precision medicine, clinical safety will require established guidelines for the credible practice of modeling and simulation in healthcare.
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Affiliation(s)
- Ahmet Erdemir
- Department of Biomedical Engineering and Computational Biomodeling (CoBi) Core, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue (ND20), Cleveland, OH, 44195, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Lealem Mulugeta
- InSilico Labs LLC, 2617 Bissonnet St. Suite 435, Houston, TX, 77005, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Joy P Ku
- Department of Bioengineering, Clark Center, Stanford University, 318 Campus Drive, Stanford, CA, 94305-5448, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Andrew Drach
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, 201 E. 24th st, Austin, TX, 78712, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Marc Horner
- ANSYS, Inc, 1007 Church Street, Suite 250, Evanston, IL, 60201, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Tina M Morrison
- Division of Applied Mechanics, United States Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Grace C Y Peng
- National Institute of Biomedical Imaging & Bioengineering, Suite 200, MSC 6707 Democracy Blvd5469, Bethesda, MD, 20892, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Rajanikanth Vadigepalli
- Department of Pathology, Anatomy and Cell Biology, Daniel Baugh Institute for Functional Genomics/Computational Biology, Thomas Jefferson University, 1020 Locust St, Philadelphia, PA, 19107, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - William W Lytton
- State University of New York, Kings County Hospital, 450 Clarkson Ave., MSC 31, Brooklyn, NY, 11203, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Jerry G Myers
- Human Research Program, Cross-Cutting Computational Modeling Project, National Aeronautics and Space Administration - John H. Glenn Research Center, 21000 Brookpark Road, Cleveland, OH, 44135, USA. .,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA.
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Saltelli A, Bammer G, Bruno I, Charters E, Di Fiore M, Didier E, Nelson Espeland W, Kay J, Lo Piano S, Mayo D, Pielke R, Portaluri T, Porter TM, Puy A, Rafols I, Ravetz JR, Reinert E, Sarewitz D, Stark PB, Stirling A, van der Sluijs J, Vineis P. Five ways to ensure that models serve society: a manifesto. Nature 2020; 582:482-484. [PMID: 32581374 DOI: 10.1038/d41586-020-01812-9] [Citation(s) in RCA: 145] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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