How does the proportion of never treatment influence the success of mass drug administration programmes for the elimination of lymphatic filariasis?
KURA, K.; Stolk, W.; Basanez, M.-G.; Collyer, B.; de Vlas, S. J.; Diggle, P. J.; Gass, K.; Graham, M.; Hollingsworth, D.; King, J.; Krentel, A.; Anderson, R.; Coffeng, L. E.
Show abstract
BackgroundMass drug administration (MDA) is the cornerstone for the elimination of lymphatic filariasis (LF). The proportion of the population that is never treated (NT) is a crucial determinant of whether this goal is achieved within reasonable timeframes. MethodsUsing two individual-based stochastic LF transmission models, we assess the maximum permissible level of NT for which the 1% mf prevalence threshold can be achieved (with 90% probability) within 10 years under different scenarios of annual MDA coverage, drug combination and transmission setting. ResultsFor Anopheles-transmission settings, we find that treating 80% of the eligible population annually with ivermectin+albendazole (IA) can achieve the 1% mf prevalence threshold within 10 years of annual treatment when baseline mf prevalence is 10%, as long as NT <10%. Higher proportions of NT are acceptable when more efficacious treatment regimens are used. For Culex-transmission settings with a low (5%) baseline mf prevalence and Diethylcarbamazine+Albendazole (DA) or Ivermectin+Diethylcarbamazine+Albendazole (IDA) treatment, elimination can be reached if treatment coverage among eligibles is 80% or higher. For 10% baseline mf prevalence, the target can be achieved when the annual coverage is 80% and NT [≤]15%. Higher infection prevalence or levels of NT would make achieving the target more difficult. ConclusionsThe proportion of people never treated in MDA programmes for LF can strongly influence the achievement of elimination and the impact of NT is greater in high transmission areas. This study provides a starting point for further development of criteria for the evaluation of NT.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A modelling assessment of short- and medium-term risks of programme interruptions for gambiense human African trypanosomiasis in the DRC 97%
- Modelling to infer the role of animals in gambiense human African trypanosomiasis transmission and elimination in DRC 97%
- Modelling the impact of fexinidazole use on human African trypanosomiasis transmission in the Democratic Republic of Congo 96%
Similar papers in this journal
- Predicting the impact of disruptions in lymphatic filariasis elimination programmes due to the outbreak of coronavirus disease (COVID-19) and possible mitigation strategies 98%
- Disruptions to schistosomiasis programmes due to COVID-19: an analysis of potential impact and mitigation strategies 97%
- What does the COVID-19 pandemic mean for the next decade of onchocerciasis control and elimination? 97%
Similar papers in this journal
- Reducing malaria burden and accelerating elimination with long-lasting systemic insecticides: a modeling study of three potential use cases 97%
- Population replacement gene drive characteristics for malaria elimination in a range of seasonal transmission settings: a modeling study 96%
- Quantifying malaria acquired during travel and its role in malaria elimination on Bioko Island 95%
Similar papers in this journal
- Impact of four years of annually repeated indoor residual spraying (IRS) with Actellic 300CS on routinely reported malaria cases in an agricultural setting in Malawi 95%
- Assessing yellow fever outbreak potential and implications for vaccine strategy 95%
- Long-term effects of increased adoption of artemisinin combination therapies in Burkina Faso 95%
Similar papers in this journal
- Modelling the impact of larviciding as a supplementary malaria vector control intervention in rural south-eastern Tanzania: A district-level simulation study 96%
- Assessing the potential of plains zebra to maintain African horse sickness in the Western Cape Province, South Africa 95%
- The basic reproduction number of COVID-19 across Africa 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.