Minter Amanda, Pellis Lorenzo, Medley Graham F, Hollingsworth T Déirdre
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom.
Department of Mathematics, University of Manchester, Manchester, United Kingdom.
Clin Infect Dis. 2021 Jun 14;72(Suppl 3):S129-S133. doi: 10.1093/cid/ciab188.
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.
随着各项计划朝着世界卫生组织(WHO)降低发病率、消除作为公共卫生问题的疾病或消除传播的目标迈进,各国将面临在低流行环境中规划下一阶段监测和控制工作的问题。被忽视热带病(NTDs)的数学模型需要超越预测不同治疗方案对这些目标的影响,进而预测这些成果能否得以维持。最重要的挑战之一将是确定长期干预措施和监测的政策目标及适当限制,因为单一政策选项无法实现所有目标——例如,无法同时实现发病率最小化和成本最小化。随着被忽视热带病迈向2030年及以后,更细致入微的干预选择将由适应国家情况的定量分析提供依据。