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Crump RE, Aliee M, Sutherland SA, Huang CI, Crowley EH, Spencer SEF, Keeling MJ, Shampa C, Mwamba Miaka E, Rock KS. Modelling timelines to elimination of sleeping sickness in the Democratic Republic of Congo, accounting for possible cryptic human and animal transmission. Parasit Vectors 2024; 17:332. [PMID: 39123265 PMCID: PMC11313002 DOI: 10.1186/s13071-024-06404-4] [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: 03/20/2024] [Accepted: 07/13/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Sleeping sickness (gambiense human African trypanosomiasis, gHAT) is a vector-borne disease targeted for global elimination of transmission (EoT) by 2030. There are, however, unknowns that have the potential to hinder the achievement and measurement of this goal. These include asymptomatic gHAT infections (inclusive of the potential to self-cure or harbour skin-only infections) and whether gHAT infection in animals can contribute to the transmission cycle in humans. METHODS Using modelling, we explore how cryptic (undetected) transmission impacts the monitoring of progress towards and the achievement of the EoT goal. We have developed gHAT models that include either asymptomatic or animal transmission, and compare these to a baseline gHAT model without either of these transmission routes, to explore the potential role of cryptic infections on the EoT goal. Each model was independently calibrated to five different health zones in the Democratic Republic of the Congo (DRC) using available historical human case data for 2000-2020 (obtained from the World Health Organization's HAT Atlas). We applied a novel Bayesian sequential updating approach for the asymptomatic model to enable us to combine statistical information about this type of transmission from each health zone. RESULTS Our results suggest that, when matched to past case data, we estimated similar numbers of new human infections between model variants, although human infections were slightly higher in the models with cryptic infections. We simulated the continuation of screen-confirm-and-treat interventions, and found that forward projections from the animal and asymptomatic transmission models produced lower probabilities of EoT than the baseline model; however, cryptic infections did not prevent EoT from being achieved eventually under this approach. CONCLUSIONS This study is the first to simulate an (as-yet-to-be available) screen-and-treat strategy and found that removing a parasitological confirmation step was predicted to have a more noticeable benefit to transmission reduction under the asymptomatic model compared with the others. Our simulations suggest vector control could greatly impact all transmission routes in all models, although this resource-intensive intervention should be carefully prioritised.
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Affiliation(s)
- Ronald E Crump
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Academic Loop Road, CV4 7AL, Coventry, UK
- Mathematics Institute, University of Warwick, Academic Loop Road, Coventry, UK
| | - Maryam Aliee
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Academic Loop Road, CV4 7AL, Coventry, UK
- Mathematics Institute, University of Warwick, Academic Loop Road, Coventry, UK
| | - Samuel A Sutherland
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Academic Loop Road, CV4 7AL, Coventry, UK
- Warwick Medical School, University of Warwick, Gibbet Hill Road, Coventry, UK
| | - Ching-I Huang
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Academic Loop Road, CV4 7AL, Coventry, UK
- Mathematics Institute, University of Warwick, Academic Loop Road, Coventry, UK
| | - Emily H Crowley
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Academic Loop Road, CV4 7AL, Coventry, UK
- Mathematics Institute, University of Warwick, Academic Loop Road, Coventry, UK
| | - Simon E F Spencer
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Academic Loop Road, CV4 7AL, Coventry, UK
- Department of Statistics, University of Warwick, Academic Loop Road, Coventry, UK
| | - Matt J Keeling
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Academic Loop Road, CV4 7AL, Coventry, UK
- Mathematics Institute, University of Warwick, Academic Loop Road, Coventry, UK
- School of Life Sciences, University of Warwick, Gibbet Hill Road, Coventry, UK
| | - Chansy Shampa
- Programme National de Lutte Contre la Trypanosomiase Humaine Africaine (PNLTHA)-DRC, Kinshasa, Democratic Republic of Congo
| | - Erick Mwamba Miaka
- Programme National de Lutte Contre la Trypanosomiase Humaine Africaine (PNLTHA)-DRC, Kinshasa, Democratic Republic of Congo
| | - Kat S Rock
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Academic Loop Road, CV4 7AL, Coventry, UK.
- Mathematics Institute, University of Warwick, Academic Loop Road, Coventry, UK.
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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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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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Bobba V, Li Y, Afrin M, Dano R, Zhang W, Li B, Su B. Synthesis and biological evaluation of imidamide analogs as selective anti-trypanosomal agents. Bioorg Med Chem 2022; 61:116740. [PMID: 35396128 PMCID: PMC9074797 DOI: 10.1016/j.bmc.2022.116740] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/28/2022] [Accepted: 03/30/2022] [Indexed: 11/18/2022]
Abstract
Human African trypanosomiasis is caused by a protozoan parasite Trypanosoma brucei majorly infecting people living in sub-Saharan Africa. Current limited available treatments suffer from drug resistance, severe adverse effects, low efficacy, and costly administrative procedures in African countries with limited medical resources. Therefore, there is always a perpetual demand for advanced drug development and invention of new strategies to combat the disease. Previous work in our lab generated a library of sulfonamide analogs as selective tubulin inhibitors, based on the structural difference between mammalian and trypanosome tubulin proteins. Further lead derivatization was performed in the current study and generated 25 potential drug candidates to improve the drug efficacy and uptake by selectively targeting the parasite's P2 membrane transporter protein with imidamide moiety. One of the newly synthesized analogs, compound 25 with a di-imidamide moiety, has shown greater potency with an IC50 of 1 nM to selectively inhibit the growth of trypanosome cells without affecting the viability of mammalian cells. Western blot analyses reveal that the compound suppressed tubulin polymerization in T. brucei cells. A detailed structure-activity relationship (SAR) was summarized that will be used to guide future lead optimization.
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Affiliation(s)
- Viharika Bobba
- Department of Chemistry, Center for Gene Regulation in Health and Disease, College of Sciences and Health Professions, Cleveland State University, 2121 Euclid Ave., Cleveland, OH, 44115, USA
| | - Yaxin Li
- Department of Chemistry, Center for Gene Regulation in Health and Disease, College of Sciences and Health Professions, Cleveland State University, 2121 Euclid Ave., Cleveland, OH, 44115, USA
| | - Marjia Afrin
- Department of Biological, Geological, and Environmental Sciences, Center for Gene Regulation in Health and Disease, College of Sciences and Health Professions, Cleveland State University, 2121 Euclid Ave., Cleveland, OH, 44115, USA
| | - Raina Dano
- Department of Chemistry, Center for Gene Regulation in Health and Disease, College of Sciences and Health Professions, Cleveland State University, 2121 Euclid Ave., Cleveland, OH, 44115, USA
| | - Wenjing Zhang
- Department of Chemistry, Center for Gene Regulation in Health and Disease, College of Sciences and Health Professions, Cleveland State University, 2121 Euclid Ave., Cleveland, OH, 44115, USA
| | - Bibo Li
- Department of Biological, Geological, and Environmental Sciences, Center for Gene Regulation in Health and Disease, College of Sciences and Health Professions, Cleveland State University, 2121 Euclid Ave., Cleveland, OH, 44115, USA.
| | - Bin Su
- Department of Chemistry, Center for Gene Regulation in Health and Disease, College of Sciences and Health Professions, Cleveland State University, 2121 Euclid Ave., Cleveland, OH, 44115, USA.
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Huang CI, Crump RE, Brown PE, Spencer SEF, Miaka EM, Shampa C, Keeling MJ, Rock KS. 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] [Key Words] [MESH Headings] [Grants] [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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Affiliation(s)
- Ching-I Huang
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, UK.
- Mathematics Institute, The University of Warwick, Coventry, UK.
| | - Ronald E Crump
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, UK
- Mathematics Institute, The University of Warwick, Coventry, UK
- The School of Life Sciences, The University of Warwick, Coventry, UK
| | - Paul E Brown
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, UK
- Mathematics Institute, The University of Warwick, Coventry, UK
| | - Simon E F Spencer
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, UK
- The Department of Statistics, The University of Warwick, Coventry, UK
| | - Erick Mwamba Miaka
- Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Kinshasa, Democratic Republic of the Congo
| | - Chansy Shampa
- Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Kinshasa, Democratic Republic of the Congo
| | - Matt J Keeling
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, UK
- Mathematics Institute, The University of Warwick, Coventry, UK
- The School of Life Sciences, The University of Warwick, Coventry, UK
| | - Kat S Rock
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, UK
- Mathematics Institute, The University of Warwick, Coventry, UK
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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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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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9
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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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10
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Toor J, Hamley JID, Fronterre C, Castaño MS, Chapman LAC, Coffeng LE, Giardina F, Lietman TM, Michael E, Pinsent A, Le Rutte EA, Hollingsworth TD. Strengthening data collection for neglected tropical diseases: What data are needed for models to better inform tailored intervention programmes? PLoS Negl Trop Dis 2021; 15:e0009351. [PMID: 33983937 PMCID: PMC8118349 DOI: 10.1371/journal.pntd.0009351] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Locally tailored interventions for neglected tropical diseases (NTDs) are becoming increasingly important for ensuring that the World Health Organization (WHO) goals for control and elimination are reached. Mathematical models, such as those developed by the NTD Modelling Consortium, are able to offer recommendations on interventions but remain constrained by the data currently available. Data collection for NTDs needs to be strengthened as better data are required to indirectly inform transmission in an area. Addressing specific data needs will improve our modelling recommendations, enabling more accurate tailoring of interventions and assessment of their progress. In this collection, we discuss the data needs for several NTDs, specifically gambiense human African trypanosomiasis, lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminths (STH), trachoma, and visceral leishmaniasis. Similarities in the data needs for these NTDs highlight the potential for integration across these diseases and where possible, a wider spectrum of diseases.
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Affiliation(s)
- Jaspreet Toor
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom
- * E-mail:
| | - Jonathan I. D. Hamley
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Claudio Fronterre
- Centre for Health Informatics, Computing and Statistics, Lancaster University, Lancaster, United Kingdom
| | - María Soledad Castaño
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Lloyd A. C. Chapman
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, United Kingdom
- Department of Medicine, University of California, San Francisco, California, United States of America
| | - Luc E. Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Federica Giardina
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Thomas M. Lietman
- Francis I Proctor Foundation, University of California, San Francisco, California, United States of America
- Department of Ophthalmology, University of California, San Francisco, California, United States of America
- Department of Epidemiology & Biostatistics, University of California, San Francisco, California, United States of America
| | - Edwin Michael
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Amy Pinsent
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Epke A. Le Rutte
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - T. Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom
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11
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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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12
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Aliee M, Castaño S, Davis CN, Patel S, Miaka EM, Spencer SEF, Keeling MJ, Chitnis N, Rock KS. Predicting the impact of COVID-19 interruptions on transmission of gambiense human African trypanosomiasis in two health zones of the Democratic Republic of Congo. Trans R Soc Trop Med Hyg 2021; 115:245-252. [PMID: 33611586 PMCID: PMC7928583 DOI: 10.1093/trstmh/trab019] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 01/18/2021] [Accepted: 01/19/2021] [Indexed: 12/28/2022] Open
Abstract
Many control programmes against neglected tropical diseases have been interrupted due to the coronavirus disease 2019 (COVID-19) pandemic, including those that rely on active case finding. In this study we focus on gambiense human African trypanosomiasis (gHAT), where active screening was suspended in the Democratic Republic of Congo (DRC) due to the pandemic. We use two independent mathematical models to predict the impact of COVID-19 interruptions on transmission and reporting and achievement of the 2030 elimination of transmission (EOT) goal for gHAT in two moderate-risk regions of the DRC. We consider different interruption scenarios, including reduced passive surveillance in fixed health facilities, and whether this suspension lasts until the end of 2020 or 2021. Our models predict an increase in the number of new infections in the interruption period only if both active screening and passive surveillance were suspended, and with a slowed reduction—but no increase—if passive surveillance remains fully functional. In all scenarios, the EOT may be slightly pushed back if no mitigation, such as increased screening coverage, is put in place. However, we emphasise that the biggest challenge will remain in the higher-prevalence regions where EOT is already predicted to be behind schedule without interruptions unless interventions are bolstered.
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Affiliation(s)
- Maryam Aliee
- Zeeman Institute (SBIDER), University of Warwick, Mathematical Sciences Building, Gibbet Hill Road, Coventry, CV4 7AL, UK
| | - Soledad Castaño
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, Postfach, CH-4002 Basel, Switzerland
| | - Christopher N Davis
- Zeeman Institute (SBIDER), University of Warwick, Mathematical Sciences Building, Gibbet Hill Road, Coventry, CV4 7AL, UK
| | - Swati Patel
- Department of Statistics, University of Warwick, Mathematical Sciences Building, Gibbet Hill Road, Coventry, CV4 7AL, UK
| | - Erick Mwamba Miaka
- Programme National de lutte contre la THA (PNLTHA), Kinshasa 2, Democratic Republic of the Congo
| | - Simon E F Spencer
- Department of Statistics, University of Warwick, Mathematical Sciences Building, Gibbet Hill Road, Coventry, CV4 7AL, UK
| | - Matt J Keeling
- Zeeman Institute (SBIDER), University of Warwick, Mathematical Sciences Building, Gibbet Hill Road, Coventry, CV4 7AL, UK
| | - Nakul Chitnis
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, Postfach, CH-4002 Basel, Switzerland
| | - Kat S Rock
- Zeeman Institute (SBIDER), University of Warwick, Mathematical Sciences Building, Gibbet Hill Road, Coventry, CV4 7AL, UK
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13
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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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Insights from quantitative and mathematical modelling on the proposed 2030 goal for gambiense human African trypanosomiasis (gHAT). Gates Open Res 2020; 3:1553. [PMID: 32411945 PMCID: PMC7193711 DOI: 10.12688/gatesopenres.13070.2] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/06/2020] [Indexed: 11/20/2022] Open
Abstract
Gambiense human African trypanosomiasis (gHAT) is a parasitic, vector-borne neglected tropical disease that has historically affected populations across West and Central Africa and can result in death if untreated. Following from the success of recent intervention programmes against gHAT, the World Health Organization (WHO) has defined a 2030 goal of global elimination of transmission (EOT). The key proposed indicator to measure achievement of the goal is zero reported cases. Results of previous mathematical modelling and quantitative analyses are brought together to explore both the implications of the proposed indicator and the feasibility of achieving the WHO goal. Whilst the indicator of zero case reporting is clear and measurable, it is an imperfect proxy for EOT and could arise either before or after EOT is achieved. Lagging reporting of infection and imperfect diagnostic specificity could result in case reporting after EOT, whereas the converse could be true due to underreporting, lack of coverage, and cryptic human and animal reservoirs. At the village-scale, the WHO recommendation of continuing active screening until there are three years of zero cases yields a high probability of local EOT, but extrapolating this result to larger spatial scales is complex. Predictive modelling of gHAT has consistently found that EOT by 2030 is unlikely across key endemic regions if current medical-only strategies are not bolstered by improved coverage, reduced time to detection and/or complementary vector control. Unfortunately, projected costs for strategies expected to meet EOT are high in the short term and strategies that are cost-effective in reducing burden are unlikely to result in EOT by 2030. Future modelling work should aim to provide predictions while taking into account uncertainties in stochastic dynamics and infection reservoirs, as well as assessment of multiple spatial scales, reactive strategies, and measurable proxies of EOT.
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