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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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Davis CN, Crump RE, Sutherland SA, Spencer SEF, Corbella A, Chansy S, Lebuki J, Miaka EM, Rock KS. Comparison of stochastic and deterministic models for gambiense sleeping sickness at different spatial scales: A health area analysis in the DRC. PLoS Comput Biol 2024; 20:e1011993. [PMID: 38557869 PMCID: PMC11008881 DOI: 10.1371/journal.pcbi.1011993] [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: 08/21/2023] [Revised: 04/11/2024] [Accepted: 03/11/2024] [Indexed: 04/04/2024] Open
Abstract
The intensification of intervention activities against the fatal vector-borne disease gambiense human African trypanosomiasis (gHAT, sleeping sickness) in the last two decades has led to a large decline in the number of annually reported cases. However, while we move closer to achieving the ambitious target of elimination of transmission (EoT) to humans, pockets of infection remain, and it becomes increasingly important to quantitatively assess if different regions are on track for elimination, and where intervention efforts should be focused. We present a previously developed stochastic mathematical model for gHAT in the Democratic Republic of Congo (DRC) and show that this same formulation is able to capture the dynamics of gHAT observed at the health area level (approximately 10,000 people). This analysis was the first time any stochastic gHAT model has been fitted directly to case data and allows us to better quantify the uncertainty in our results. The analysis focuses on utilising a particle filter Markov chain Monte Carlo (MCMC) methodology to fit the model to the data from 16 health areas of Mosango health zone in Kwilu province as a case study. The spatial heterogeneity in cases is reflected in modelling results, where we predict that under the current intervention strategies, the health area of Kinzamba II, which has approximately one third of the health zone's cases, will have the latest expected year for EoT. We find that fitting the analogous deterministic version of the gHAT model using MCMC has substantially faster computation times than fitting the stochastic model using pMCMC, but produces virtually indistinguishable posterior parameterisation. This suggests that expanding health area fitting, to cover more of the DRC, should be done with deterministic fits for efficiency, but with stochastic projections used to capture both the parameter and stochastic variation in case reporting and elimination year estimations.
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Affiliation(s)
- Christopher N. Davis
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- School of Life Sciences, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Ronald E. Crump
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Samuel A. Sutherland
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Warwick Medical School, The University of Warwick, Coventry, United Kingdom
| | - Simon E. F. Spencer
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Department of Statistics, The University of Warwick, Coventry, United Kingdom
| | - Alice Corbella
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Department of Statistics, The University of Warwick, Coventry, United Kingdom
| | - Shampa Chansy
- Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Kinshasa, Democratic Republic of the Congo
| | - Junior Lebuki
- 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 Systems 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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Antillon M, Huang CI, Sutherland SA, Crump RE, Bessell PR, Shaw APM, Tirados I, Picado A, Biéler S, Brown PE, Solano P, Mbainda S, Darnas J, Wang-Steverding X, Crowley EH, Peka M, Tediosi F, Rock KS. Health economic evaluation of strategies to eliminate gambiense human African trypanosomiasis in the Mandoul disease focus of Chad. PLoS Negl Trop Dis 2023; 17:e0011396. [PMID: 37498938 PMCID: PMC10409297 DOI: 10.1371/journal.pntd.0011396] [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: 07/28/2022] [Revised: 08/08/2023] [Accepted: 05/22/2023] [Indexed: 07/29/2023] Open
Abstract
Human African trypanosomiasis, caused by the gambiense subspecies of Trypanosoma brucei (gHAT), is a deadly parasitic disease transmitted by tsetse. Partners worldwide have stepped up efforts to eliminate the disease, and the Chadian government has focused on the previously high-prevalence setting of Mandoul. In this study, we evaluate the economic efficiency of the intensified strategy that was put in place in 2014 aimed at interrupting the transmission of gHAT, and we make recommendations on the best way forward based on both epidemiological projections and cost-effectiveness. In our analysis, we use a dynamic transmission model fit to epidemiological data from Mandoul to evaluate the cost-effectiveness of combinations of active screening, improved passive screening (defined as an expansion of the number of health posts capable of screening for gHAT), and vector control activities (the deployment of Tiny Targets to control the tsetse vector). For cost-effectiveness analyses, our primary outcome is disease burden, denominated in disability-adjusted life-years (DALYs), and costs, denominated in 2020 US$. Although active and passive screening have enabled more rapid diagnosis and accessible treatment in Mandoul, the addition of vector control provided good value-for-money (at less than $750/DALY averted) which substantially increased the probability of reaching the 2030 elimination target for gHAT as set by the World Health Organization. Our transmission modelling and economic evaluation suggest that the gains that have been made could be maintained by passive screening. Our analysis speaks to comparative efficiency, and it does not take into account all possible considerations; for instance, any cessation of ongoing active screening should first consider that substantial surveillance activities will be critical to verify the elimination of transmission and to protect against the possible importation of infection from neighbouring endemic foci.
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Affiliation(s)
- Marina Antillon
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Ching-I Huang
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Samuel A. Sutherland
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Warwick Medical School, University of Warwick, Coventry, United Kingdom
| | - Ronald E. Crump
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | | | - Alexandra P. M. Shaw
- Infection Medicine, Deanery of Biomedical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, United Kingdom
- AP Consultants, Walworth Enterprise Centre, Andover, United Kingdom
| | - Iñaki Tirados
- Department of Vector Biology, Liverpool School of Tropical Medicine, United Kingdom
| | - Albert Picado
- Foundation for Innovative New Diagnostics (FIND), Geneva, Switzerland
| | - Sylvain Biéler
- Foundation for Innovative New Diagnostics (FIND), Geneva, Switzerland
| | - Paul E. Brown
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Philippe Solano
- Institut de Recherche pour le Développement, UMR INTERTRYP IRD-CIRAD, Université de Montpellier, Montpellier, France
| | - 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
| | - Xia Wang-Steverding
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Warwick Medical School, University of Warwick, Coventry, United Kingdom
| | - Emily H. Crowley
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Mallaye Peka
- Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Moundou, Chad
| | - Fabrizio Tediosi
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Kat S. Rock
- Zeeman Institute for Systems 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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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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Borlase A, Le Rutte EA, Castaño S, Blok DJ, Toor J, Giardina F, Davis EL. Evaluating and mitigating the potential indirect effect of COVID-19 on control programmes for seven neglected tropical diseases: a modelling study. Lancet Glob Health 2022; 10:e1600-e1611. [PMID: 36240827 PMCID: PMC9579354 DOI: 10.1016/s2214-109x(22)00360-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/25/2022] [Accepted: 08/03/2022] [Indexed: 11/06/2022]
Abstract
Background In line with movement restrictions and physical distancing essential for the control of the COVID-19 pandemic, WHO recommended postponement of all neglected tropical disease (NTD) control activities that involve community-based surveys, active case finding, and mass drug administration in April, 2020. Following revised guidance later in 2020, and after interruptions to NTD programmes of varying lengths, NTD programmes gradually restarted in the context of an ongoing pandemic. However, ongoing challenges and service gaps have been reported. This study aimed to evaluate the potential effect of the programmatic interruptions and strategies to mitigate this effect. Methods For seven NTDs, namely soil-transmitted helminths, schistosomiasis, lymphatic filariasis, onchocerciasis, trachoma, visceral leishmaniasis, and human African trypanosomiasis, we used mathematical transmission models to simulate the effect of programme interruptions on the dynamics of each of these diseases in different endemic settings. We also explored the potential benefit of implementing mitigation strategies, primarily in terms of minimising the delays to control targets. Findings We show that the effect of the COVID-19-induced interruption in terms of delay to achieving elimination goals might in some cases be much longer than the duration of the interruption. For schistosomiasis, onchocerciasis, trachoma, and visceral leishmaniasis, a mean delay of 2–3 years for a 1-year interruption is predicted in areas of highest prevalence. We also show that these delays can largely be mitigated by measures such as additional mass drug administration or enhanced case-finding. Interpretation The COVID-19 pandemic has brought infectious disease control to the forefront of global consciousness. It is essential that the NTDs, so long neglected in terms of research and financial support, are not overlooked, and remain a priority in health service planning and funding. Funding Bill & Melinda Gates Foundation, Medical Research Council, and the UK Foreign, Commonwealth & Development Office.
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Affiliation(s)
- Anna Borlase
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Epke A Le Rutte
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands; Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland
| | - Soledad Castaño
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland; LYO-X, Allschwil, Switzerland
| | - David J Blok
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Jaspreet Toor
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Federica Giardina
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands; Department of Health Evidence, Radboud University Medical Center, Nijmegen, Netherlands
| | - Emma L Davis
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Mathematics Institute, University of Warwick, Coventry, UK.
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Crump RE, Huang CI, Spencer SEF, Brown PE, Shampa C, Mwamba Miaka E, Rock KS. Modelling to infer the role of animals in gambiense human African trypanosomiasis transmission and elimination in the DRC. PLoS Negl Trop Dis 2022; 16:e0010599. [PMID: 35816487 PMCID: PMC9302778 DOI: 10.1371/journal.pntd.0010599] [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: 12/23/2021] [Revised: 07/21/2022] [Accepted: 06/22/2022] [Indexed: 11/18/2022] Open
Abstract
Gambiense human African trypanosomiasis (gHAT) has been targeted for elimination of transmission (EoT) to humans by 2030. Whilst this ambitious goal is rapidly approaching, there remain fundamental questions about the presence of non-human animal transmission cycles and their potential role in slowing progress towards, or even preventing, EoT. In this study we focus on the country with the most gHAT disease burden, the Democratic Republic of Congo (DRC), and use mathematical modelling to assess whether animals may contribute to transmission in specific regions, and if so, how their presence could impact the likelihood and timing of EoT. By fitting two model variants-one with, and one without animal transmission-to the human case data from 2000-2016 we estimate model parameters for 158 endemic health zones of the DRC. We evaluate the statistical support for each model variant in each health zone and infer the contribution of animals to overall transmission and how this could impact predicted time to EoT. We conclude that there are 24/158 health zones where there is substantial to decisive statistical support for some animal transmission. However-even in these regions-we estimate that animals would be extremely unlikely to maintain transmission on their own. Animal transmission could hamper progress towards EoT in some settings, with projections under continuing interventions indicating that the number of health zones expected to achieve EoT by 2030 reduces from 68/158 to 61/158 if animal transmission is included in the model. With supplementary vector control (at a modest 60% tsetse reduction) added to medical screening and treatment interventions, the predicted number of health zones meeting the goal increases to 147/158 for the model including animal transmission. This is due to the impact of vector reduction on transmission to and from all hosts.
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Affiliation(s)
- Ronald E. Crump
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Ching-I Huang
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Simon E. F. Spencer
- Zeeman Institute for Systems 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 Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, 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 Systems 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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7
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Compaoré CFA, Kaboré J, Ilboudo H, Thomas LF, Falzon LC, Bamba M, Sakande H, Koné M, Kaba D, Bougouma C, Adama I, Amathe O, Belem AMG, Fèvre EM, Büscher P, Lejon V, Jamonneau V. Monitoring the elimination of gambiense human African trypanosomiasis in the historical focus of Batié, South-West Burkina Faso. Parasite 2022; 29:25. [PMID: 35543528 PMCID: PMC9093133 DOI: 10.1051/parasite/2022024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 04/19/2022] [Indexed: 11/21/2022] Open
Abstract
The World Health Organisation has targeted the elimination of human African trypanosomiasis (HAT) as zero transmission by 2030. Continued surveillance needs to be in place for early detection of re-emergent cases. In this context, the performance of diagnostic tests and testing algorithms for detection of the re-emergence of Trypanosoma brucei gambiense HAT remains to be assessed. We carried out a door-to-door active medical survey for HAT in the historical focus of Batié, South–West Burkina Faso. Screening was done using three rapid diagnostic tests (RDTs). Two laboratory tests (ELISA/T. b. gambiense and immune trypanolysis) and parasitological examination were performed on RDT positives only. In total, 5883 participants were screened, among which 842 (14%) tested positive in at least one RDT. Blood from 519 RDT positives was examined microscopically but no trypanosomes were observed. The HAT Sero-K-Set test showed the lowest specificity of 89%, while the specificities of SD Bioline HAT and rHAT Sero-Strip were 92% and 99%, respectively. The specificity of ELISA/T. b. gambiense and trypanolysis was 99% (98–99%) and 100% (99–100%), respectively. Our results suggest that T. b. gambiense is no longer circulating in the study area and that zero transmission has probably been attained. While a least cost analysis is still required, our study showed that RDT preselection followed by trypanolysis may be a useful strategy for post-elimination surveillance in Burkina Faso.
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Affiliation(s)
- Charlie Franck Alfred Compaoré
- Centre International de Recherche-Développement sur l'Élevage en zone Subhumide, Unité de recherche sur les maladies à vecteurs et biodiversité, 01 BP 454 Bobo-Dioulasso 01, Burkina Faso - Université Nazi Boni, Unité de Formation et de Recherche Sciences et Techniques, 01 BP 1091 Bobo-Dioulasso, Burkina-Faso
| | - Jacques Kaboré
- Centre International de Recherche-Développement sur l'Élevage en zone Subhumide, Unité de recherche sur les maladies à vecteurs et biodiversité, 01 BP 454 Bobo-Dioulasso 01, Burkina Faso - Université Nazi Boni, Unité de Formation et de Recherche Sciences et Techniques, 01 BP 1091 Bobo-Dioulasso, Burkina-Faso
| | - Hamidou Ilboudo
- Institut de Recherche en Sciences de la Santé, Unité de Recherche Clinique de Nanoro, 11 BP 218 Ouagadougou CMS 11, Burkina Faso
| | - Lian Francesca Thomas
- International Livestock Research Institute, PO Box 30709, Nairobi 00100, Kenya - University of Liverpool, Institute of Infection, Veterinary and Ecological Sciences, Liverpool L69 3BX, United Kingdom
| | - Laura Cristina Falzon
- International Livestock Research Institute, PO Box 30709, Nairobi 00100, Kenya - University of Liverpool, Institute of Infection, Veterinary and Ecological Sciences, Liverpool L69 3BX, United Kingdom
| | - Mohamed Bamba
- Centre International de Recherche-Développement sur l'Élevage en zone Subhumide, Unité de recherche sur les maladies à vecteurs et biodiversité, 01 BP 454 Bobo-Dioulasso 01, Burkina Faso
| | - Hassane Sakande
- Centre International de Recherche-Développement sur l'Élevage en zone Subhumide, Unité de recherche sur les maladies à vecteurs et biodiversité, 01 BP 454 Bobo-Dioulasso 01, Burkina Faso
| | - Minayégninrin Koné
- Institut Pierre Richet, Unité de Recherche et de Formation Trypanosomoses et Leishmanioses, 01 BP 1500 Bouake, Côte d'Ivoire
| | - Dramane Kaba
- Institut Pierre Richet, Unité de Recherche et de Formation Trypanosomoses et Leishmanioses, 01 BP 1500 Bouake, Côte d'Ivoire
| | - Clarisse Bougouma
- Programme National de Lutte contre les Maladies Tropicales Négligées, 03 BP 7009 Ouagadougou 03, Burkina Faso
| | - Ilboudo Adama
- Programme National de Lutte contre les Maladies Tropicales Négligées, 03 BP 7009 Ouagadougou 03, Burkina Faso
| | - Ouedraogo Amathe
- Programme National de Lutte contre les Maladies Tropicales Négligées, 03 BP 7009 Ouagadougou 03, Burkina Faso
| | - Adrien Marie Gaston Belem
- Université Nazi Boni, Unité de Formation et de Recherche Sciences et Techniques, 01 BP 1091 Bobo-Dioulasso, Burkina-Faso
| | - Eric Maurice Fèvre
- International Livestock Research Institute, PO Box 30709, Nairobi 00100, Kenya - University of Liverpool, Institute of Infection, Veterinary and Ecological Sciences, Liverpool L69 3BX, United Kingdom
| | - Philippe Büscher
- Institute of Tropical Medicine, Department of Biomedical Sciences, Nationalestraat 155, 2000 Antwerp, Belgium
| | - Veerle Lejon
- Institut de Recherche pour le Développement, UMR INTERTRYP IRD-CIRAD, Université de Montpellier, TA A-17/G, Campus International de Baillarguet, 34398 Montpellier, France
| | - Vincent Jamonneau
- Institut Pierre Richet, Unité de Recherche et de Formation Trypanosomoses et Leishmanioses, 01 BP 1500 Bouake, Côte d'Ivoire - Institut de Recherche pour le Développement, UMR INTERTRYP IRD-CIRAD, Université de Montpellier, TA A-17/G, Campus International de Baillarguet, 34398 Montpellier, France
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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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Cost-effectiveness of sleeping sickness elimination campaigns in five settings of the Democratic Republic of Congo. Nat Commun 2022; 13:1051. [PMID: 35217656 PMCID: PMC8881616 DOI: 10.1038/s41467-022-28598-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 01/28/2022] [Indexed: 11/08/2022] Open
Abstract
Gambiense human African trypanosomiasis (gHAT) is marked for elimination of transmission by 2030, but the disease persists in several low-income countries. We couple transmission and health outcomes models to examine the cost-effectiveness of four gHAT elimination strategies in five settings – spanning low- to high-risk – of the Democratic Republic of Congo. Alongside passive screening in fixed health facilities, the strategies include active screening at average or intensified coverage levels, alone or with vector control with a scale-back algorithm when no cases are reported for three consecutive years. In high or moderate-risk settings, costs of gHAT strategies are primarily driven by active screening and, if used, vector control. Due to the cessation of active screening and vector control, most investments (75-80%) are made by 2030 and vector control might be cost-saving while ensuring elimination of transmission. In low-risk settings, costs are driven by passive screening, and minimum-cost strategies consisting of active screening and passive screening lead to elimination of transmission by 2030 with high probability. Gambiense human African trypanosomiasis has been targeted for elimination of transmission by 2030. Here, the authors assess the cost-effectiveness of elimination strategies in the Democratic Republic of the Congo and find that those which lead to elimination of transmission might also be considered cost-effective by conventional thresholds.
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10
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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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11
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Das AM, Chitnis N, Burri C, Paris DH, Patel S, Spencer SEF, Miaka EM, Castaño MS. Modelling the impact of fexinidazole use on human African trypanosomiasis (HAT) transmission in the Democratic Republic of the Congo. PLoS Negl Trop Dis 2021; 15:e0009992. [PMID: 34843475 PMCID: PMC8659363 DOI: 10.1371/journal.pntd.0009992] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 12/09/2021] [Accepted: 11/12/2021] [Indexed: 11/18/2022] Open
Abstract
Gambiense human African trypanosomiasis is a deadly disease that has been declining in incidence since the start of the Century, primarily due to increased screening, diagnosis and treatment of infected people. The main treatment regimen currently in use requires a lumbar puncture as part of the diagnostic process to determine disease stage and hospital admission for drug administration. Fexinidazole is a new oral treatment for stage 1 and non-severe stage 2 human African trypanosomiasis. The World Health Organization has recently incorporated fexinidazole into its treatment guidelines for human African trypanosomiasis. The treatment does not require hospital admission or a lumbar puncture for all patients, which is likely to ease access for patients; however, it does require concomitant food intake, which is likely to reduce adherence. Here, we use a mathematical model calibrated to case and screening data from Mushie territory, in the Democratic Republic of the Congo, to explore the potential negative impact of poor compliance to an oral treatment, and potential gains to be made from increases in the rate at which patients seek treatment. We find that reductions in compliance in treatment of stage 1 cases are projected to result in the largest increase in further transmission of the disease, with failing to cure stage 2 cases also posing a smaller concern. Reductions in compliance may be offset by increases in the rate at which cases are passively detected. Efforts should therefore be made to ensure good adherence for stage 1 patients to treatment with fexinidazole and to improve access to care. Sleeping sickness is a parasitic disease present in parts of Central and West Africa that is fatal if left untreated. Current case management requires unpleasant procedures such as a lumbar puncture and intravenous drug administration, but has high compliance rates as the treatment is given by hospital staff to patients. In this study, we explore the impact of a new oral treatment on compliance rates for treatment using a mathematical model fitted to data on sleeping sickness cases and screening activities. We also look at the possibility of patients being more likely to seek and access treatment since the new treatment can be used without a lumbar puncture if the patient does not display clinically severe symptoms. We find that reduced compliance, especially from patients suffering from the first less severe stage of the disease, will lead to more sleeping sickness cases and delay elimination, but increases in the number of patients seeking treatment will likely counter effects of reduced compliance.
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Affiliation(s)
- Aatreyee M. Das
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
- * E-mail:
| | - Nakul Chitnis
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Christian Burri
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Daniel H. Paris
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Swati Patel
- Department of Statistics, University of Warwick, Coventry, United Kingdom
- Department of Mathematics, Oregon State University, Corvallis, Oregon, United States of America
| | | | - Erick M. Miaka
- Programme National de Lutte contre la Trypanosomiase Humaine Africaine, Kinshasa, the Democratic Republic of the Congo
| | - M. Soledad Castaño
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
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12
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Modifications and hybrids of 1,2,3,4-tetrahydropyridinium salts and their antiprotozoal potencies. MONATSHEFTE FUR CHEMIE 2021. [DOI: 10.1007/s00706-021-02847-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
AbstractThe antiprotozoal activity of 1-benzyltetrahydropyridin-4-yliden iminium salts is reported. This paper describes the preparation of a series of analogs from dihydropyridines or dihydrothiopyrans as educts. The new compounds were investigated for their activity against Plasmodium falciparum NF54, a causative organism of Malaria tropica and Trypanosoma brucei rhodesiense, the causative organism of Human African Trypanosomiasis (sleeping sickness). Several structure–activity relationships were detected. Both the substituents in ring positions 1 and 4 of the tetrahydropyridinium moiety had a strong impact on the antiprotozoal activities as well as on the cytotoxicity of compounds against L-6 cells (rat skeletal myoblasts). All new compounds were characterized using FT-IR spectroscopy, HRMS, and NMR spectroscopy.
Graphic abstract
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13
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Davis CN, Keeling MJ, Rock KS. Modelling gambiense human African trypanosomiasis infection in villages of the Democratic Republic of Congo using Kolmogorov forward equations. J R Soc Interface 2021; 18:20210419. [PMID: 34610258 PMCID: PMC8492173 DOI: 10.1098/rsif.2021.0419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 09/14/2021] [Indexed: 11/29/2022] Open
Abstract
Stochastic methods for modelling disease dynamics enable the direct computation of the probability of elimination of transmission. For the low-prevalence disease of human African trypanosomiasis (gHAT), we develop a new mechanistic model for gHAT infection that determines the full probability distribution of the gHAT infection using Kolmogorov forward equations. The methodology allows the analytical investigation of the probabilities of gHAT elimination in the spatially connected villages of different prevalence health zones of the Democratic Republic of Congo, and captures the uncertainty using exact methods. Our method provides a more realistic approach to scaling the probability of elimination of infection between single villages and much larger regions, and provides results comparable to established models without the requirement of detailed infection structure. The novel flexibility allows the interventions in the model to be implemented specific to each village, and this introduces the framework to consider the possible future strategies of test-and-treat or direct treatment of individuals living in villages where cases have been found, using a new drug.
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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
| | - 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
| | - Kat S. Rock
- Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
- Zeeman Institute (SBIDER), University of Warwick, Coventry CV4 7AL, UK
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14
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Aliee M, Keeling MJ, Rock KS. Modelling to explore the potential impact of asymptomatic human infections on transmission and dynamics of African sleeping sickness. PLoS Comput Biol 2021; 17:e1009367. [PMID: 34516544 PMCID: PMC8459990 DOI: 10.1371/journal.pcbi.1009367] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 09/23/2021] [Accepted: 08/20/2021] [Indexed: 01/20/2023] Open
Abstract
Gambiense human African trypanosomiasis (gHAT, sleeping sickness) is one of several neglected tropical diseases (NTDs) where there is evidence of asymptomatic human infection but there is uncertainty of the role it plays in transmission and maintenance. To explore possible consequences of asymptomatic infections, particularly in the context of elimination of transmission—a goal set to be achieved by 2030—we propose a novel dynamic transmission model to account for the asymptomatic population. This extends an established framework, basing infection progression on a number of experimental and observation gHAT studies. Asymptomatic gHAT infections include those in people with blood-dwelling trypanosomes, but no discernible symptoms, or those with parasites only detectable in skin. Given current protocols, asymptomatic infection with blood parasites may be diagnosed and treated, based on observable parasitaemia, in contrast to many other diseases for which treatment (and/or diagnosis) may be based on symptomatic infection. We construct a model in which exposed people can either progress to either asymptomatic skin-only parasite infection, which would not be diagnosed through active screening algorithms, or blood-parasite infection, which is likely to be diagnosed if tested. We add extra parameters to the baseline model including different self-cure, recovery, transmission and detection rates for skin-only or blood infections. Performing sensitivity analysis suggests all the new parameters introduced in the asymptomatic model can impact the infection dynamics substantially. Among them, the proportion of exposures resulting in initial skin or blood infection appears the most influential parameter. For some plausible parameterisations, an initial fall in infection prevalence due to interventions could subsequently stagnate even under continued screening due to the formation of a new, lower endemic equilibrium. Excluding this scenario, our results still highlight the possibility for asymptomatic infection to slow down progress towards elimination of transmission. Location-specific model fitting will be needed to determine if and where this could pose a threat. Gambiense African sleeping sickness is an infectious disease targeted for elimination of transmission by 2030. Despite this there is still some uncertainty how frequently some infected people who may not have symptoms could “self-cure” without ever having disease and whether some types of infections, such as infections only in the skin, but not the blood, could still contribute to transmission, yet go undiagnosed. To explore how problematic these asymptomatic infections could be in terms of the elimination goal, we use a mathematical model which quantitatively describes changes to infection and transmission over time and includes these different types of infection. We use results of published experimental or field studies as inputs for the model parameters governing asymptomatic infections. We examined the impact of asymptomatic infections when control interventions are put in place. Compared to a baseline model with no asymptomatics, including asymptomatic infection using plausible biological parameters can have a profound impact on transmission and slow progress towards elimination. In some instances it could be possible that even after initial decline in sleeping sickness cases, progress could stagnate without reaching the elimination goal at all, however location-specific modelling will be needed to determine if and where this could pose a threat.
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Affiliation(s)
- Maryam Aliee
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
- * E-mail:
| | - Matt J. Keeling
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | - Kat S. Rock
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
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15
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Davis CN, Castaño MS, Aliee M, Patel S, Miaka EM, Keeling MJ, Spencer SEF, Chitnis N, Rock KS. Modelling to Quantify the Likelihood that Local Elimination of Transmission has Occurred Using Routine Gambiense Human African Trypanosomiasis Surveillance Data. Clin Infect Dis 2021; 72:S146-S151. [PMID: 33905480 PMCID: PMC8201550 DOI: 10.1093/cid/ciab190] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Background The gambiense human African trypanosomiasis (gHAT) elimination programme in the Democratic Republic of Congo (DRC) routinely collects case data through passive surveillance and active screening, with several regions reporting no cases for several years, despite being endemic in the early 2000s. Methods We use mathematical models fitted to longitudinal data to estimate the probability that selected administrative regions have already achieved elimination of transmission (EOT) of gHAT. We examine the impact of active screening coverage on the certainty of model estimates for transmission and therefore the role of screening in the measurement of EOT. Results In 3 example health zones of Sud-Ubangi province, we find there is a moderate (>40%) probability that EOT has been achieved by 2018, based on 2000–2016 data. Budjala and Mbaya reported zero cases during 2017–18, and this further increases our respective estimates to 99.9% and 99.6% (model S) and to 87.3% and 92.1% (model W). Bominenge had recent case reporting, however, that if zero cases were found in 2021, it would substantially raise our certainty that EOT has been met there (99.0% for model S and 88.5% for model W); this could be higher with 50% coverage screening that year (99.1% for model S and 94.0% for model W). Conclusions We demonstrate how routine surveillance data coupled with mechanistic modeling can estimate the likelihood that EOT has already been achieved. Such quantitative assessment will become increasingly important for measuring local achievement of EOT as 2030 approaches.
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Affiliation(s)
- Christopher N Davis
- Mathematics Institute, University of Warwick, Coventry, United Kingdom.,Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, United Kingdom
| | - María Soledad Castaño
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Maryam Aliee
- Mathematics Institute, University of Warwick, Coventry, United Kingdom.,Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, United Kingdom
| | - Swati Patel
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, United Kingdom.,Department of Statistics, University of Warwick, Coventry, United Kingdom
| | - Erick Mwamba Miaka
- Programme National de Lutte contre la Trypanosomiase Humaine Africaine, Kinshasa, the Democratic Republic of the Congo
| | - Matt J Keeling
- Mathematics Institute, University of Warwick, Coventry, United Kingdom.,Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, United Kingdom.,School of Life Science, University of Warwick, Coventry, United Kingdom
| | - Simon E F Spencer
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, United Kingdom.,Department of Statistics, University of Warwick, Coventry, United Kingdom
| | - Nakul Chitnis
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Kat S Rock
- Mathematics Institute, University of Warwick, Coventry, United Kingdom.,Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, United Kingdom
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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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17
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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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18
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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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