1
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Shaw C, McLure A, Glass K. The effects of variable spatial aggregation on lymphatic filariasis transmission. Parasit Vectors 2025; 18:3. [PMID: 39780258 PMCID: PMC11716132 DOI: 10.1186/s13071-024-06582-1] [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: 07/29/2024] [Accepted: 11/11/2024] [Indexed: 01/11/2025] Open
Abstract
BACKGROUND Elimination of lymphatic filariasis (LF) is a World Health Organization goal, with several countries at or near prevalence thresholds. Where LF cases remain after mass drug administration, they tend to be spatially clustered, with an overdispersed individual worm burden. Both individual and spatial heterogeneities can cause aggregation of infection; however, few studies have investigated the drivers of heterogeneity and implications for disease elimination. METHODS We used a spatially explicit lymphatic filariasis model to investigate LF transmission in American Samoa at three spatial scales - a territory-level model, a village model with 64 groups and a subvillage model with 316 groups. RESULTS To reproduce American Samoan survey data, models with less spatial structure required increased individual-level bite aggregation. Threshold behaviour was present in the territory model but less evident in the models with spatial structure. As such, mass drug administration was most effective in the territory model, while in the spatially structured models, successive rounds of mass drug administration only gradually increased the likelihood of elimination. With the addition of spatial structure, residual infections remained in limited groups, and infection resurgence was slowed. CONCLUSIONS Due to the impacts on potential intervention and surveillance strategies, it is critical that studies incorporate individual and spatial sources of heterogeneity to accurately model transmission and inform potential policy decisions.
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Affiliation(s)
- Callum Shaw
- National Centre for Epidemiology and Population Health, Australian National University, 62 Mills Road, Canberra, 2601, ACT, Australia.
| | - Angus McLure
- National Centre for Epidemiology and Population Health, Australian National University, 62 Mills Road, Canberra, 2601, ACT, Australia
| | - Kathryn Glass
- National Centre for Epidemiology and Population Health, Australian National University, 62 Mills Road, Canberra, 2601, ACT, Australia
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2
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Coffeng LE, Graham M, Browning R, Kura K, Diggle PJ, Denwood M, Medley GF, Anderson RM, de Vlas SJ. Improving the Cost-efficiency of Preventive Chemotherapy: Impact of New Diagnostics on Stopping Decisions for Control of Schistosomiasis. Clin Infect Dis 2024; 78:S153-S159. [PMID: 38662699 PMCID: PMC11045014 DOI: 10.1093/cid/ciae020] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Control of schistosomiasis (SCH) relies on the regular distribution of preventive chemotherapy (PC) over many years. For the sake of sustainable SCH control, a decision must be made at some stage to scale down or stop PC. These "stopping decisions" are based on population surveys that assess whether infection levels are sufficiently low. However, the limited sensitivity of the currently used diagnostic (Kato-Katz [KK]) to detect low-intensity infections is a concern. Therefore, the use of new, more sensitive, molecular diagnostics has been proposed. METHODS Through statistical analysis of Schistosoma mansoni egg counts collected from Burundi and a simulation study using an established transmission model for schistosomiasis, we investigated the extent to which more sensitive diagnostics can improve decision making regarding stopping or continuing PC for the control of S. mansoni. RESULTS We found that KK-based strategies perform reasonably well for determining when to stop PC at a local scale. Use of more sensitive diagnostics leads to a marginally improved health impact (person-years lived with heavy infection) and comes at a cost of continuing PC for longer (up to around 3 years), unless the decision threshold for stopping PC is adapted upward. However, if this threshold is set too high, PC may be stopped prematurely, resulting in a rebound of infection levels and disease burden (+45% person-years of heavy infection). CONCLUSIONS We conclude that the potential value of more sensitive diagnostics lies more in the reduction of survey-related costs than in the direct health impact of improved parasite control.
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Affiliation(s)
- Luc E Coffeng
- Department of Public Health, Erasmus Medical Center, University Medical Center Rotterdam, The Netherlands
| | - Matthew Graham
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford
| | | | - Klodeta Kura
- London Centre for Neglected Tropical Disease Research, School of Public Health, Imperial College London
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London
| | - Peter J Diggle
- Centre for Health Informatics, Computing, and Statistics, Lancaster University Medical School, United Kingdom
| | - Matthew Denwood
- Department of Veterinary and Animal Sciences, University of Copenhagen, Denmark
| | - Graham F Medley
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, United Kingdom
| | - Roy M Anderson
- London Centre for Neglected Tropical Disease Research, School of Public Health, Imperial College London
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London
| | - Sake J de Vlas
- Department of Public Health, Erasmus Medical Center, University Medical Center Rotterdam, The Netherlands
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3
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Crawford KE, Hedtke SM, Doyle SR, Kuesel AC, Armoo S, Osei-Atweneboana MY, Grant WN. Genome-based tools for onchocerciasis elimination: utility of the mitochondrial genome for delineating Onchocerca volvulus transmission zones. Int J Parasitol 2024; 54:171-183. [PMID: 37993016 DOI: 10.1016/j.ijpara.2023.11.002] [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: 05/03/2023] [Revised: 09/21/2023] [Accepted: 11/13/2023] [Indexed: 11/24/2023]
Abstract
National programs in Africa have expanded their objectives from control of onchocerciasis (river blindness) as a public health problem to elimination of parasite transmission, motivated by the reduction of Onchocerca volvulus infection prevalence in many African meso- and hyperendemic areas due to mass drug administration of ivermectin (MDAi). Given the large, contiguous hypo-, meso-, and hyperendemic areas, sustainable elimination of onchocerciasis in sub-Saharan Africa requires delineation of geographic boundaries for parasite transmission zones, so that programs can consider the risk of parasite re-introduction through vector or human migration from areas with ongoing transmission when making decisions to stop MDAi. We propose that transmission zone boundaries can be delineated by characterising the parasite genetic population structure within and between potential zones. We analysed whole mitochondrial genome sequences of 189 O. volvulus adults to determine the pattern of genetic similarity across three West African countries: Ghana, Mali, and Côte d'Ivoire. Population genetic structure indicates that parasites from villages near the Pru, Daka, and Black Volta rivers in central Ghana belong to one parasite population, indicating that the assumption that river basins constitute individual transmission zones is not supported by the data. Parasites from Mali and Côte d'Ivoire are genetically distinct from those from Ghana. This research provides the basis for developing tools for elimination programs to delineate transmission zones, to estimate the risk of parasite re-introduction via vector or human movement when intervention is stopped in one area while transmission is ongoing in others, to identify the origin of infections detected post-treatment cessation, and to investigate whether persisting prevalence despite ongoing interventions in one area is due to parasites imported from others.
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Affiliation(s)
- Katie E Crawford
- Department of Animal, Plant and Soil Sciences, La Trobe University, Bundoora, Victoria, Australia
| | - Shannon M Hedtke
- Department of Animal, Plant and Soil Sciences, La Trobe University, Bundoora, Victoria, Australia; Department of Environment and Genetics, La Trobe University, Bundoora, Victoria, Australia.
| | - Stephen R Doyle
- Department of Animal, Plant and Soil Sciences, La Trobe University, Bundoora, Victoria, Australia
| | - Annette C Kuesel
- UNICEF/UNDP/World Bank/World Health Organization Special Programme for Research and Training in Tropical Diseases (TDR), World Health Organization, Geneva, Switzerland
| | - Samuel Armoo
- Biomedical and Public Health Research Unit, CSIR-Water Research Institute, Council for Scientific and Industrial Research, Council Close, Accra, Ghana
| | - Mike Y Osei-Atweneboana
- Biomedical and Public Health Research Unit, CSIR-Water Research Institute, Council for Scientific and Industrial Research, Council Close, Accra, Ghana
| | - Warwick N Grant
- Department of Animal, Plant and Soil Sciences, La Trobe University, Bundoora, Victoria, Australia; Department of Environment and Genetics, La Trobe University, Bundoora, Victoria, Australia
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4
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Coffeng LE, Stolk WA, de Vlas SJ. Predicting the risk and speed of drug resistance emerging in soil-transmitted helminths during preventive chemotherapy. Nat Commun 2024; 15:1099. [PMID: 38321011 PMCID: PMC10847116 DOI: 10.1038/s41467-024-45027-2] [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: 09/14/2023] [Accepted: 01/12/2024] [Indexed: 02/08/2024] Open
Abstract
Control of soil-transmitted helminths relies heavily on regular large-scale deworming of high-risk groups (e.g., children) with benzimidazole derivatives. Although drug resistance has not yet been documented in human soil-transmitted helminths, regular deworming of cattle and sheep has led to widespread benzimidazole resistance in veterinary helminths. Here we predict the population dynamics of human soil-transmitted helminth infections and drug resistance during 20 years of regular preventive chemotherapy, using an individual-based model. With the current preventive chemotherapy strategy of mainly targeting children in schools, drug resistance may evolve in soil-transmitted helminths within a decade. More intense preventive chemotherapy strategies increase the prospects of soil-transmitted helminths elimination, but also increase the speed at which drug efficacy declines, especially when implementing community-based preventive chemotherapy (population-wide deworming). If during the last decade, preventive chemotherapy against soil-transmitted helminths has led to resistance, we may not have detected it as drug efficacy has not been structurally monitored, or incorrectly so. These findings highlight the need to develop and implement strategies to monitor and mitigate the evolution of benzimidazole resistance.
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Affiliation(s)
- Luc E Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
| | - Wilma A Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sake J de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
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5
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Clark J, Davis EL, Prada JM, Gass K, Krentel A, Hollingsworth TD. How correlations between treatment access and surveillance inclusion impact neglected tropical disease monitoring and evaluation-A simulated study. PLoS Negl Trop Dis 2023; 17:e0011582. [PMID: 37672518 PMCID: PMC10506705 DOI: 10.1371/journal.pntd.0011582] [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: 09/08/2022] [Revised: 09/18/2023] [Accepted: 08/09/2023] [Indexed: 09/08/2023] Open
Abstract
Neglected tropical diseases (NTDs) largely impact marginalised communities living in tropical and subtropical regions. Mass drug administration is the leading intervention method for five NTDs; however, it is known that there is lack of access to treatment for some populations and demographic groups. It is also likely that those individuals without access to treatment are excluded from surveillance. It is important to consider the impacts of this on the overall success, and monitoring and evaluation (M&E) of intervention programmes. We use a detailed individual-based model of the infection dynamics of lymphatic filariasis to investigate the impact of excluded, untreated, and therefore unobserved groups on the true versus observed infection dynamics and subsequent intervention success. We simulate surveillance in four groups-the whole population eligible to receive treatment, the whole eligible population with access to treatment, the TAS focus of six- and seven-year-olds, and finally in >20-year-olds. We show that the surveillance group under observation has a significant impact on perceived dynamics. Exclusion to treatment and surveillance negatively impacts the probability of reaching public health goals, though in populations that do reach these goals there are no signals to indicate excluded groups. Increasingly restricted surveillance groups over-estimate the efficacy of MDA. The presence of non-treated groups cannot be inferred when surveillance is only occurring in the group receiving treatment.
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Affiliation(s)
- Jessica Clark
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow, Scotland
- Big Data Institute, Neglected Tropical Disease Modelling Consortium, University of Oxford, Oxford, England
| | - Emma L. Davis
- Big Data Institute, Neglected Tropical Disease Modelling Consortium, University of Oxford, Oxford, England
| | - Joaquin M. Prada
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, England
| | - Katherine Gass
- Neglected Tropical Diseases Support Center, Task Force for Global Health, Decatur, Georgia, United States of America
| | - Alison Krentel
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
- Bruyère Research Institute, Ottawa, Canada
| | - T. Déirdre Hollingsworth
- Big Data Institute, Neglected Tropical Disease Modelling Consortium, University of Oxford, Oxford, England
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6
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Stolk WA, Coffeng LE, Bolay FK, Eneanya OA, Fischer PU, Hollingsworth TD, Koudou BG, Méité A, Michael E, Prada JM, Caja Rivera RM, Sharma S, Touloupou P, Weil GJ, de Vlas SJ. Comparing antigenaemia- and microfilaraemia as criteria for stopping decisions in lymphatic filariasis elimination programmes in Africa. PLoS Negl Trop Dis 2022; 16:e0010953. [PMID: 36508458 PMCID: PMC9779720 DOI: 10.1371/journal.pntd.0010953] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 12/22/2022] [Accepted: 11/14/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Mass drug administration (MDA) is the main strategy towards lymphatic filariasis (LF) elimination. Progress is monitored by assessing microfilaraemia (Mf) or circulating filarial antigenaemia (CFA) prevalence, the latter being more practical for field surveys. The current criterion for stopping MDA requires <2% CFA prevalence in 6- to 7-year olds, but this criterion is not evidence-based. We used mathematical modelling to investigate the validity of different thresholds regarding testing method and age group for African MDA programmes using ivermectin plus albendazole. METHODOLGY/PRINCIPAL FINDINGS We verified that our model captures observed patterns in Mf and CFA prevalence during annual MDA, assuming that CFA tests are positive if at least one adult worm is present. We then assessed how well elimination can be predicted from CFA prevalence in 6-7-year-old children or from Mf or CFA prevalence in the 5+ or 15+ population, and determined safe (>95% positive predictive value) thresholds for stopping MDA. The model captured trends in Mf and CFA prevalences reasonably well. Elimination cannot be predicted with sufficient certainty from CFA prevalence in 6-7-year olds. Resurgence may still occur if all children are antigen-negative, irrespective of the number tested. Mf-based criteria also show unfavourable results (PPV <95% or unpractically low threshold). CFA prevalences in the 5+ or 15+ population are the best predictors, and post-MDA threshold values for stopping MDA can be as high as 10% for 15+. These thresholds are robust for various alternative assumptions regarding baseline endemicity, biological parameters and sampling strategies. CONCLUSIONS/SIGNIFICANCE For African areas with moderate to high pre-treatment Mf prevalence that have had 6 or more rounds of annual ivermectin/albendazole MDA with adequate coverage, we recommend to adopt a CFA threshold prevalence of 10% in adults (15+) for stopping MDA. This could be combined with Mf testing of CFA positives to ensure absence of a significant Mf reservoir for transmission.
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Affiliation(s)
- Wilma A. Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- * E-mail:
| | - Luc E. Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Fatorma K. Bolay
- National Public Health Institute of Liberia (NPHIL), Monrovia, Liberia
| | - Obiora A. Eneanya
- Infectious Diseases Division, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Peter U. Fischer
- Infectious Diseases Division, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - T. Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Benjamin G. Koudou
- Centre Suisse de Recherches Scientifiques en Côte d’Ivoire, Abidjan, Abidjan, Côte d’Ivoire
- Laboratoire de Cytologie et Biologie Animale, UFR Science de la Nature, Université Nangui Abrogoua Abidjan, Abidjan, Côte d’Ivoire
| | - Aboulaye Méité
- Programme National de Lutte contre les Maladies Tropicales Négligées à Chimiothérapie Préventive, Abidjan, Côte d’Ivoire
| | - Edwin Michael
- Center for Global Health Infectious Disease Research, University of South Florida, Tampa, Florida, United States of America
| | - Joaquin M. Prada
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Rocio M. Caja Rivera
- Center for Global Health Infectious Disease Research, University of South Florida, Tampa, Florida, United States of America
| | - Swarnali Sharma
- Department of Biological Sciences, University of Notre Dame, South Bend, Indiana, United States of America
- Christian Medical College, IDA Scudder Rd, Vellore, Tamil Nadu, India
| | - Panayiota Touloupou
- Department of Statistics, University of Warwick, Coventry, United Kingdom
- School of Mathematics, University of Birmingham, Birmingham, United Kingdom
| | - Gary J. Weil
- Infectious Diseases Division, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Sake J. de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
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7
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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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9
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The burden of skin disease and eye disease due to onchocerciasis in countries formerly under the African Programme for Onchocerciasis Control mandate for 1990, 2020, and 2030. PLoS Negl Trop Dis 2021; 15:e0009604. [PMID: 34310602 PMCID: PMC8312930 DOI: 10.1371/journal.pntd.0009604] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 06/29/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Onchocerciasis ("river blindness") can cause severe morbidity, including vision loss and various skin manifestations, and is targeted for elimination using ivermectin mass drug administration (MDA). We calculated the number of people with Onchocerca volvulus infection and onchocercal skin and eye disease as well as disability-adjusted life years (DALYs) lost from 1990 through to 2030 in areas formerly covered by the African Programme for Onchocerciasis Control. METHODS Per MDA implementation unit, we collated data on the pre-control distribution of microfilariae (mf) prevalence and the history of control. Next, we predicted trends in infection and morbidity over time using the ONCHOSIM simulation model. DALY estimates were calculated using disability weights from the Global Burden of Disease Study. RESULTS In 1990, prior to MDA implementation, the total population at risk was 79.8 million with 26.0 million (32.5%) mf-positive individuals, of whom 17.5 million (21.9%) had some form of onchocercal skin or eye disease (2.5 million DALYs lost). By 2030, the total population was predicted to increase to 236.1 million, while the number of mf-positive cases (about 6.8 million, 2.9%), people with skin or eye morbidity (4.2 million, 1.8%), and DALYs lost (0.7 million) were predicted to decline. CONCLUSIONS MDA has had a remarkable impact on the onchocerciasis burden in countries previously under the APOC mandate. In the few countries where we predict continued transmission between now and 2030, intensified MDA could be combined with local vector control efforts, or the introduction of new drugs for mopping up residual cases of infection and morbidity.
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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: 2.0] [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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de Vos AS, Stolk WA, Coffeng LE, de Vlas SJ. The impact of mass drug administration expansion to low onchocerciasis prevalence settings in case of connected villages. PLoS Negl Trop Dis 2021; 15:e0009011. [PMID: 33979331 PMCID: PMC8143415 DOI: 10.1371/journal.pntd.0009011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 05/24/2021] [Accepted: 04/26/2021] [Indexed: 11/21/2022] Open
Abstract
Background The existence of locations with low but stable onchocerciasis prevalence is not well understood. An often suggested yet poorly investigated explanation is that the infection spills over from neighbouring locations with higher infection densities. Methodology We adapted the stochastic individual based model ONCHOSIM to enable the simulation of multiple villages, with separate blackfly (intermediate host) and human populations, which are connected through the regular movement of the villagers and/or the flies. With this model we explore the impact of the type, direction and degree of connectedness, and of the impact of localized or full-area mass drug administration (MDA) over a range of connected village settings. Principal findings In settings with annual fly biting rates (ABR) below the threshold needed for stable local transmission, persistence of onchocerciasis prevalence can well be explained by regular human traffic and/or fly movement from locations with higher ABR. Elimination of onchocerciasis will then theoretically be reached by only implementing MDA in the higher prevalence area, although lingering infection in the low prevalence location can trigger resurgence of transmission in the total region when MDA is stopped too soon. Expanding MDA implementation to the lower ABR location can therefore shorten the duration of MDA needed. For example, when prevalence spill-over is due to human traffic, and both locations have about equal populations, then the MDA duration can be shortened by up to three years. If the lower ABR location has twice as many inhabitants, the reduction can even be up to six years, but if spill-over is due to fly movement, the expected reduction is less than a year. Conclusions/Significance Although MDA implementation might not always be necessary in locations with stable low onchocerciasis prevalence, in many circumstances it is recommended to accelerate achieving elimination in the wider area. When infected by onchocerciasis worm parasites, people can eventually develop blindness or severe skin morbidity. Over the past decades, in most places with high onchocerciasis prevalence, annual mass drug administration has become freely available for all inhabitants, regardless of their infection status. This policy has been highly successful in decreasing morbidity. For the next aim, to eliminate onchocerciasis, this intervention is now being expanded to lower prevalence locations. We have adapted an existing simulation model of the spread of onchocerciasis to allow us to model settings where multiple villages are connected, through movement of either humans or blackflies, the intermediate host. By this connection, worms could spill over from a high prevalence village to neighbouring villages with lower prevalence. For such situations, we have examined the impact of implementing treatment only in the high prevalence village, or also in one or two lower prevalence villages. We conclude that for elimination of onchocerciasis transmission, treatment in the lower prevalence villages may not actually be needed, but the total duration of mass drug administration in the entire area can be significantly decreased by expanding treatment to these villages.
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Affiliation(s)
- Anneke S. de Vos
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- * E-mail:
| | - Wilma A. Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Luc E. Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sake J. de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
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The roadmap towards elimination of lymphatic filariasis by 2030: insights from quantitative and mathematical modelling. Gates Open Res 2019; 3:1538. [PMID: 31728440 PMCID: PMC6833911 DOI: 10.12688/gatesopenres.13065.1] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/03/2019] [Indexed: 01/26/2023] Open
Abstract
The Global Programme to Eliminate Lymphatic Filariasis was launched in 2000 to eliminate lymphatic filariasis (LF) as a public health problem by 1) interrupting transmission through mass drug administration (MDA) and 2) offering basic care to those suffering from lymphoedema or hydrocele due to the infection. Although impressive progress has been made, the initial target year of 2020 will not be met everywhere. The World Health Organization recently proposed 2030 as the new target year for elimination of lymphatic filariasis (LF) as a public health problem. In this letter, LF modelers of the Neglected Tropical Diseases (NTDs) Modelling Consortium reflect on the proposed targets for 2030 from a quantitative perspective. While elimination as a public health problem seems technically and operationally feasible, it is uncertain whether this will eventually also lead to complete elimination of transmission. The risk of resurgence needs to be mitigated by strong surveillance after stopping interventions and sometimes perhaps additional interventions.
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Coffeng LE, Stolk WA, Golden A, de los Santos T, Domingo GJ, de Vlas SJ. Predictive Value of Ov16 Antibody Prevalence in Different Subpopulations for Elimination of African Onchocerciasis. Am J Epidemiol 2019; 188:1723-1732. [PMID: 31062838 PMCID: PMC6735885 DOI: 10.1093/aje/kwz109] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 04/25/2019] [Accepted: 04/26/2019] [Indexed: 12/02/2022] Open
Abstract
The World Health Organization currently recommends assessing elimination of onchocerciasis by testing whether Ov16 antibody prevalence in children aged 0–9 years is below 0.1%. However, the certainty of evidence for this recommendation is considered to be low. We used the established ONCHOSIM model to investigate the predictive value of different Ov16-antibody prevalence thresholds in various age groups for elimination of onchocerciasis in a variety of endemic settings and for various mass drug administration scenarios. According to our simulations, the predictive value of Ov16 antibody prevalence for elimination depends highly on the precontrol epidemiologic situation, history of mass drug administration, the age group that is sampled, and the chosen Ov16-antibody prevalence threshold. The Ov16 antibody prevalence in children aged 5–14 years performs best in predicting elimination. Appropriate threshold values for this age group start at 2.0% for very highly endemic areas; for lower-endemic areas, even higher threshold values are safe to use. Guidelines can be improved by sampling school-aged children, which also is operationally more feasible than targeting children under age 10 years. The use of higher threshold values allows sampling of substantially fewer children. Further improvement can be achieved by taking a differentiated sampling approach based on precontrol endemicity.
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Affiliation(s)
- Luc E Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Wilma A Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | | | | | | | - Sake J de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
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Perkins TA, Reiner RC, España G, ten Bosch QA, Verma A, Liebman KA, Paz-Soldan VA, Elder JP, Morrison AC, Stoddard ST, Kitron U, Vazquez-Prokopec GM, Scott TW, Smith DL. An agent-based model of dengue virus transmission shows how uncertainty about breakthrough infections influences vaccination impact projections. PLoS Comput Biol 2019; 15:e1006710. [PMID: 30893294 PMCID: PMC6443188 DOI: 10.1371/journal.pcbi.1006710] [Citation(s) in RCA: 16] [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: 03/12/2018] [Revised: 04/01/2019] [Accepted: 12/11/2018] [Indexed: 01/26/2023] Open
Abstract
Prophylactic vaccination is a powerful tool for reducing the burden of infectious diseases, due to a combination of direct protection of vaccinees and indirect protection of others via herd immunity. Computational models play an important role in devising strategies for vaccination by making projections of its impacts on public health. Such projections are subject to uncertainty about numerous factors, however. For example, many vaccine efficacy trials focus on measuring protection against disease rather than protection against infection, leaving the extent of breakthrough infections (i.e., disease ameliorated but infection unimpeded) among vaccinees unknown. Our goal in this study was to quantify the extent to which uncertainty about breakthrough infections results in uncertainty about vaccination impact, with a focus on vaccines for dengue. To realistically account for the many forms of heterogeneity in dengue virus (DENV) transmission, which could have implications for the dynamics of indirect protection, we used a stochastic, agent-based model for DENV transmission informed by more than a decade of empirical studies in the city of Iquitos, Peru. Following 20 years of routine vaccination of nine-year-old children at 80% coverage, projections of the proportion of disease episodes averted varied by a factor of 1.76 (95% CI: 1.54-2.06) across the range of uncertainty about breakthrough infections. This was equivalent to the range of vaccination impact projected across a range of uncertainty about vaccine efficacy of 0.268 (95% CI: 0.210-0.329). Until uncertainty about breakthrough infections can be addressed empirically, our results demonstrate the importance of accounting for it in models of vaccination impact.
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Affiliation(s)
- T. Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, United States of America
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
| | - Robert C. Reiner
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
- Department of Epidemiology and Biostatistics, Indiana University, Bloomington, IN, United States of America
| | - Guido España
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, United States of America
| | - Quirine A. ten Bosch
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, United States of America
| | - Amit Verma
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA
| | - Kelly A. Liebman
- Department of Entomology and Nematology, University of California, Davis, CA, United States of America
| | - Valerie A. Paz-Soldan
- Department of Global Community Health and Behavioral Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States of America
| | - John P. Elder
- Institute for Behavioral and Community Health, Graduate School of Public Health, San Diego State University, San Diego, CA, United States of America
| | - Amy C. Morrison
- Department of Entomology and Nematology, University of California, Davis, CA, United States of America
| | - Steven T. Stoddard
- Institute for Behavioral and Community Health, Graduate School of Public Health, San Diego State University, San Diego, CA, United States of America
| | - Uriel Kitron
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
- Department of Environmental Sciences, Emory University, Atlanta, GA, United States of America
| | - Gonzalo M. Vazquez-Prokopec
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
- Department of Environmental Sciences, Emory University, Atlanta, GA, United States of America
| | - Thomas W. Scott
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
- Department of Entomology and Nematology, University of California, Davis, CA, United States of America
| | - David L. Smith
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States of America
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