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Vasconcelos Andreia
King Jonathan D
Nunes-Alves Cláudio
Anderson Roy
Argaw Daniel
Basáñez Maria-Gloria
Bilal Shakir
Blok David J
Blumberg Seth
Borlase Anna
Brady Oliver J
Browning Raiha
Chitnis Nakul
Coffeng Luc E
Crowley Emily H
Cucunubá Zulma M
Cummings Derek A T
Davis Christopher Neil
Davis Emma Louise
Dixon Matthew
Dobson Andrew
Dyson Louise
French Michael
Fronterre Claudio
Giorgi Emanuele
Huang Ching-I
Jain Saurabh
James Ananthu
Kim Sung Hye
Kura Klodeta
Lucianez Ana
Marks Michael
Mbabazi Pamela Sabina
Medley Graham F
Michael Edwin
Montresor Antonio
Mutono Nyamai
Mwangi Thumbi S
Rock Kat S
Saboyá-Díaz Martha-Idalí
Sasanami Misaki
Schwehm Markus
Spencer Simon E F
Srivathsan Ariktha
Stawski Robert S
Stolk Wilma A
Sutherland Samuel A
Tchuenté Louis-Albert Tchuem
de Vlas Sake J
Walker Martin
Brooker Simon J
Hollingsworth T Déirdre
Solomon Anthony W
Fall Ibrahima Socé
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Accelerating Progress Towards the 2030 Neglected Tropical Diseases Targets: How Can Quantitative Modeling Support Programmatic Decisions?
Abstract
Over the past decade, considerable progress has been made in the control, elimination, and eradication of neglected tropical diseases (NTDs). Despite these advances, most NTD programs have recently experienced important setbacks; for example, NTD interventions were some of the most frequently and severely impacted by service disruptions due to the coronavirus disease 2019 (COVID-19) pandemic. Mathematical modeling can help inform selection of interventions to meet the targets set out in the NTD road map 2021-2030, and such studies should prioritize questions that are relevant for decision-makers, especially those designing, implementing, and evaluating national and subnational programs. In September 2022, the World Health Organization hosted a stakeholder meeting to identify such priority modeling questions across a range of NTDs and to consider how modeling could inform local decision making. Here, we summarize the outputs of the meeting, highlight common themes in the questions being asked, and discuss how quantitative modeling can support programmatic decisions that may accelerate progress towards the 2030 targets.