4.7 Article

What Can Modeling Tell Us About Sustainable End Points for Neglected Tropical Diseases?

Journal

CLINICAL INFECTIOUS DISEASES
Volume 72, Issue -, Pages S129-S133

Publisher

OXFORD UNIV PRESS INC
DOI: 10.1093/cid/ciab188

Keywords

sustainable control; elimination; neglected tropical diseases

Funding

  1. NTD Modeling Consortium by the Bill and Melinda Gates Foundation [OPP1184344]
  2. Wellcome Trust [202562/Z/16/Z]
  3. Royal Society [202562/Z/16/Z]
  4. Wellcome Trust [202562/Z/16/Z] Funding Source: Wellcome Trust
  5. Wellcome Trust [202562/A/16/Z] Funding Source: researchfish

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As countries strive to meet WHO goals for reducing morbidity and eliminating disease transmission, they will need to plan for the next stages of surveillance and control in low prevalence settings. Going beyond simple predictions of treatment program effectiveness, more nuanced intervention choices informed by quantitative analyses will be necessary for sustainable gains in the long term. Identifying policy goals and finding the right balance between intervention and surveillance constraints will be key challenges in achieving multiple aims such as minimizing morbidity and costs simultaneously.
As programs move closer toward the World Health Organization (WHO) goals of reduction in morbidity, elimination as a public health problem or elimination of transmission, countries will be faced with planning the next stages of surveillance and control in low prevalence settings. Mathematical models of neglected tropical diseases (NTDs) will need to go beyond predicting the effect of different treatment programs on these goals and on to predicting whether the gains can be sustained. One of the most important challenges will be identifying the policy goal and the right constraints on interventions and surveillance over the long term, as a single policy option will not achieve all aims-for example, minimizing morbidity and minimizing costs cannot both be achieved. As NTDs move toward 2030 and beyond, more nuanced intervention choices will be informed by quantitative analyses which are adapted to national context.

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