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Janoušková E, Clark J, Kajero O, Alonso S, Lamberton PHL, Betson M, Prada JM. Public Health Policy Pillars for the Sustainable Elimination of Zoonotic Schistosomiasis. FRONTIERS IN TROPICAL DISEASES 2022. [DOI: 10.3389/fitd.2022.826501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Schistosomiasis is a parasitic disease acquired through contact with contaminated freshwater. The definitive hosts are terrestrial mammals, including humans, with some Schistosoma species crossing the animal-human boundary through zoonotic transmission. An estimated 12 million people live at risk of zoonotic schistosomiasis caused by Schistosoma japonicum and Schistosoma mekongi, largely in the World Health Organization’s Western Pacific Region and in Indonesia. Mathematical models have played a vital role in our understanding of the biology, transmission, and impact of intervention strategies, however, these have mostly focused on non-zoonotic Schistosoma species. Whilst these non-zoonotic-based models capture some aspects of zoonotic schistosomiasis transmission dynamics, the commonly-used frameworks are yet to adequately capture the complex epi-ecology of multi-host zoonotic transmission. However, overcoming these knowledge gaps goes beyond transmission dynamics modelling. To improve model utility and enhance zoonotic schistosomiasis control programmes, we highlight three pillars that we believe are vital to sustainable interventions at the implementation (community) and policy-level, and discuss the pillars in the context of a One-Health approach, recognising the interconnection between humans, animals and their shared environment. These pillars are: (1) human and animal epi-ecological understanding; (2) economic considerations (such as treatment costs and animal losses); and (3) sociological understanding, including inter- and intra-human and animal interactions. These pillars must be built on a strong foundation of trust, support and commitment of stakeholders and involved institutions.
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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 2022; 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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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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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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Insights from mathematical modelling and quantitative analysis on the proposed 2030 goals for trachoma. Gates Open Res 2021; 3:1721. [PMID: 34027309 PMCID: PMC8111938 DOI: 10.12688/gatesopenres.13086.2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/04/2021] [Indexed: 01/21/2023] Open
Abstract
Trachoma is a neglected tropical disease and the leading infectious cause of blindness worldwide. The current World Health Organization goal for trachoma is elimination as a public health problem, defined as reaching a prevalence of trachomatous inflammation-follicular below 5% in children (1-9 years) and a prevalence of trachomatous trichiasis in adults below 0.2%. Current targets to achieve elimination were set to 2020 but are being extended to 2030. Mathematical and statistical models suggest that 2030 is a realistic timeline for elimination as a public health problem in most trachoma endemic areas. Although the goal can be achieved, it is important to develop appropriate monitoring tools for surveillance after having achieved the elimination target to check for the possibility of resurgence. For this purpose, a standardized serological approach or the use of multiple diagnostics in complement would likely be required.
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Insights from mathematical modelling and quantitative analysis on the proposed 2030 goals for trachoma. Gates Open Res 2021; 3:1721. [PMID: 34027309 DOI: 10.12688/gatesopenres.13086.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/12/2019] [Indexed: 11/20/2022] Open
Abstract
Trachoma is a neglected tropical disease and the leading infectious cause of blindness worldwide. The current World Health Organization goal for trachoma is elimination as a public health problem, defined as reaching a prevalence of trachomatous inflammation-follicular below 5% in children (1-9 years) and a prevalence of trachomatous trichiasis in adults below 0.2%. Current targets to achieve elimination were set to 2020 but are being extended to 2030. Mathematical and statistical models suggest that 2030 is a realistic timeline for elimination as a public health problem in most trachoma endemic areas. Although the goal can be achieved, it is important to develop appropriate monitoring tools for surveillance after having achieved the elimination target to check for the possibility of resurgence. For this purpose, a standardized serological approach or the use of multiple diagnostics in complement would likely be required.
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Borlase A, Blumberg S, Callahan EK, Deiner MS, Nash SD, Porco TC, Solomon AW, Lietman TM, Prada JM, Hollingsworth TD. Modelling trachoma post-2020: opportunities for mitigating the impact of COVID-19 and accelerating progress towards elimination. Trans R Soc Trop Med Hyg 2021; 115:213-221. [PMID: 33596317 PMCID: PMC7928577 DOI: 10.1093/trstmh/traa171] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 11/10/2020] [Accepted: 02/12/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has disrupted planned annual antibiotic mass drug administration (MDA) activities that have formed the cornerstone of the largely successful global efforts to eliminate trachoma as a public health problem. METHODS Using a mathematical model we investigate the impact of interruption to MDA in trachoma-endemic settings. We evaluate potential measures to mitigate this impact and consider alternative strategies for accelerating progress in those areas where the trachoma elimination targets may not be achievable otherwise. RESULTS We demonstrate that for districts that were hyperendemic at baseline, or where the trachoma elimination thresholds have not already been achieved after three rounds of MDA, the interruption to planned MDA could lead to a delay to reaching elimination targets greater than the duration of interruption. We also show that an additional round of MDA in the year following MDA resumption could effectively mitigate this delay. For districts where the probability of elimination under annual MDA was already very low, we demonstrate that more intensive MDA schedules are needed to achieve agreed targets. CONCLUSION Through appropriate use of additional MDA, the impact of COVID-19 in terms of delay to reaching trachoma elimination targets can be effectively mitigated. Additionally, more frequent MDA may accelerate progress towards 2030 goals.
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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
| | | | - E Kelly Callahan
- Trachoma Control Program, The Carter Center, Atlanta, Georgia, USA
| | | | - Scott D Nash
- Trachoma Control Program, The Carter Center, Atlanta, Georgia, USA
| | | | - Anthony W Solomon
- Department of Control of Neglected Tropical Diseases, World Health Organisation, Geneva, Switzerland
| | | | - Joaquin M Prada
- Faculty of Health and Medical Sciences, University of Surrey, UK
| | - T Dèirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
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Lessons learned for surveillance strategies for trachoma elimination as a public health problem, from the evaluation of approaches utilised by Guinea worm and onchocerciasis programmes: A literature review. PLoS Negl Trop Dis 2021; 15:e0009082. [PMID: 33507903 PMCID: PMC7872237 DOI: 10.1371/journal.pntd.0009082] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 02/09/2021] [Accepted: 12/17/2020] [Indexed: 11/18/2022] Open
Abstract
Introduction A number of neglected tropical diseases are targeted for elimination or eradication. An effective surveillance system is critical to determine if these goals have been achieved and maintained. Trachoma has two related but morphologically different presentations that are monitored for elimination, the active infectious form of trachoma and trachomatous trichiasis (TT), the progression of the disease. There are a number of lessons learnt from the Guinea worm surveillance system that are particularly compatible for TT surveillance and the onchocerciasis surveillance system which can provide insights for surveillance of the infectious form of trachoma. Methods/Principal findings A literature search of peer-reviewed published papers and grey literature was conducted using PUBMED and Google Scholar for articles relating to dracunculiasis or Guinea worm, onchocerciasis and trachoma, along with surveillance or elimination or eradication. The abstracts of relevant papers were read and inclusion was determined based on specified inclusion and exclusion criteria. The credibility and bias of relevant papers were also critically assessed using published criteria. A total of 41 papers were identified that were eligible for inclusion into the review. The Guinea worm programme is designed around a surveillance-containment strategy and combines both active and passive surveillance approaches, with a focus on village-based surveillance and reporting. Although rumour reporting and a monetary incentive for the identification of confirmed Guinea worm cases have been reported as successful for identifying previously unknown transmission there is little unbiased evidence to support this conclusion. More rigorous evidence through a randomised controlled trial, influenced by motivational factors identified through formative research, would be necessary in order to consider applicability for TT case finding in an elimination setting. The onchocerciasis surveillance strategy focuses on active surveillance through sentinel surveillance of villages and breeding sites. It relies on an entomological component, monitoring infectivity rates of black flies and an epidemiological component, tracking exposure to infection in humans. Challenges have included the introduction of relatively complex diagnostics that are not readily available in onchocerciasis endemic countries and target thresholds, which are practically unattainable with current diagnostic tests. Although there is utility in monitoring for infection and serological markers in trachoma surveillance, it is important that adequate considerations are made to ensure evidence-based and achievable guidelines for their utility are put in place. Conclusions/Significance The experiences of both the Guinea worm and onchocerciasis surveillance strategies have very useful lessons for trachoma surveillance, pre- and post-validation. The use of a monetary reward for identification of TT cases and further exploration into the use of infection and serological indicators particularly in a post-validation setting to assist in identifying recrudescence would be of particular relevance. The next step would be a real-world evaluation of their relative applicability for trachoma surveillance. The design of a surveillance system needs to be carefully thought out to ensure it provides sufficient evidence to determine if a disease or infection is eliminated or eradicated. If inappropriate it can lead to on-going transmission and resurgence of infection or disease or the unnecessary continuation of interventions, wasting valuable resources. Guinea worm is a disease that is painful and debilitating, for which there is no drug or vaccine. The aim is to eradicate the disease and as such the Guinea worm programme is designed around a strategy of identification of cases and their containment to prevent onward transmission. Onchocerciasis if left untreated can lead to blindness. The aim is to eliminate the disease through the interruption of transmission. A literature review was conducted to determine available evidence and identify lessons that can be learnt from the surveillance of both diseases for the design of trachoma surveillance strategies in the endgame. The potential utility of rumour reporting and a monetary incentive for the identification of a confirmed case of Guinea worm could be explored for trichiasis case finding. Trichiasis is the progression of trachoma and leads to significant ocular morbidity. The introduction of tests for infection and antibodies and the utility of sentinel surveillance as utilised for onchocerciasis are interesting considerations for active trachoma surveillance post-validation and has potential to identify recrudescence cost-effectively. The experiences of both the Guinea worm and onchocerciasis surveillance strategies have very useful lessons that can be trialled for trachoma surveillance. However, their real-world applicability and implications for trachoma need to be evaluated before any changes in guidelines are proposed.
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