Chisholm Rebecca H, Campbell Patricia T, Wu Yue, Tong Steven Y C, McVernon Jodie, Geard Nicholas
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
Modelling and Simulation Research Group, Murdoch Childrens Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia.
R Soc Open Sci. 2018 Feb 14;5(2):172341. doi: 10.1098/rsos.172341. eCollection 2018 Feb.
For infectious pathogens such as and , some hosts may carry the pathogen and transmit it to others, yet display no symptoms themselves. These asymptomatic carriers contribute to the spread of disease but go largely undetected and can therefore undermine efforts to control transmission. Understanding the natural history of carriage and its relationship to disease is important for the design of effective interventions to control transmission. Mathematical models of infectious diseases are frequently used to inform decisions about control and should therefore accurately capture the role played by asymptomatic carriers. In practice, incorporating asymptomatic carriers into models is challenging due to the sparsity of direct evidence. This absence of data leads to uncertainty in estimates of model parameters and, more fundamentally, in the selection of an appropriate model structure. To assess the implications of this uncertainty, we systematically reviewed published models of carriage and propose a new model of disease transmission with asymptomatic carriage. Analysis of our model shows how different assumptions about the role of asymptomatic carriers can lead to different conclusions about the transmission and control of disease. Critically, selecting an inappropriate model structure, even when parameters are correctly estimated, may lead to over- or under-estimates of intervention effectiveness. Our results provide a more complete understanding of the role of asymptomatic carriers in transmission and highlight the importance of accurately incorporating carriers into models used to make decisions about disease control.
对于诸如[病原体名称1]和[病原体名称2]等传染性病原体，一些宿主可能携带病原体并将其传播给他人，但自身却没有症状。这些无症状携带者会导致疾病传播，但很大程度上未被发现，因此可能会破坏控制传播的努力。了解携带病原体的自然史及其与疾病的关系对于设计有效的控制传播干预措施至关重要。传染病的数学模型经常被用于为控制决策提供信息，因此应该准确地反映无症状携带者所起的作用。在实际操作中，由于直接证据稀少，将无症状携带者纳入模型具有挑战性。这种数据缺失导致模型参数估计的不确定性，更根本的是，导致难以选择合适的模型结构。为了评估这种不确定性的影响，我们系统地回顾了已发表的携带病原体模型，并提出了一种带有无症状携带情况的疾病传播新模型。对我们模型的分析表明，关于无症状携带者作用的不同假设如何导致关于疾病传播和控制的不同结论。至关重要的是，即使参数估计正确，选择不适当的模型结构也可能导致对干预效果的高估或低估。我们的结果提供了对无症状携带者在传播中作用的更全面理解，并强调了在用于做出疾病控制决策的模型中准确纳入携带者的重要性。
