Competition between RSV and influenza: Limits of modelling inference from surveillance data.
Competition between RSV and influenza: Limits of modelling inference from surveillance data.
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DOI：
10.1016/j.epidem.2021.100460
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发表时间：
2021-06
期刊：
影响因子：
3.8
通讯作者：
Eggo RM
中科院分区：
文献类型：
--
作者：
Waterlow NR;Flasche S;Minter A;Eggo RM
呼吸道合胞病毒（RSV）和流感病毒可以通过先天免疫相互作用。相互作用可能导致短期交叉保护。数学模型可用于从监测数据中识别交叉保护。模型推断必须准确和精确地识别参数值。英国的呼吸季节可以作为一个案例研究，以评估后推。呼吸道合胞病毒（RSV）和流感造成巨大的疾病负担。它们通过临时交叉保护相互作用的证据表明，预防一种可能无意中导致另一种负担的增加。然而，这种相互作用的公共卫生影响的证据是稀疏的，主要来自监测数据的峰值转移的生态分析。为了测试RSV和流感病毒之间相互作用参数估计值的稳健性，我们进行了模拟和反向推理研究。我们开发了一个两种病原体相互作用模型，参数化模拟RSV和流感流行病学在英国。使用感染模型结合类似监视的随机观察过程，我们生成了一系列可能的RSV和流感轨迹，然后使用马尔可夫链蒙特卡罗（MCMC）方法来反向推断参数，包括描述竞争的参数。我们发现，在大多数情况下，RSV和流感相互作用的强度和持续时间可以从模拟监测数据中合理地估计。然而，推断的稳健性在接近合理参数范围的极端时下降，结果具有误导性。例如，不可能区分低/中度相互作用和无相互作用。总之，我们的研究结果表明，在一个合理的参数范围内，RSV和流感相互作用的强度可以从一个单一的季节的高质量监测数据估计，但也强调了在这种情况下先验测试参数可识别性的重要性。
Respiratory Syncytial Virus (RSV) and Influenza may interact through innate immunity. Interaction can result in short-term cross-protection. Mathematical models can be used to identify cross-protection from surveillance data. Model inference must accurately and precisely identify parameter values. The UK respiratory season can be used as a case study to evaluate back-inference. Respiratory Syncytial Virus (RSV) and Influenza cause a large burden of disease. Evidence of their interaction via temporary cross-protection implies that prevention of one could inadvertently lead to an increase in the burden of the other. However, evidence for the public health impact of such interaction is sparse and largely derives from ecological analyses of peak shifts in surveillance data. To test the robustness of estimates of interaction parameters between RSV and Influenza from surveillance data we conducted a simulation and back-inference study. We developed a two-pathogen interaction model, parameterised to simulate RSV and Influenza epidemiology in the UK. Using the infection model in combination with a surveillance-like stochastic observation process we generated a range of possible RSV and Influenza trajectories and then used Markov Chain Monte Carlo (MCMC) methods to back-infer parameters including those describing competition. We find that in most scenarios both the strength and duration of RSV and Influenza interaction could be estimated from the simulated surveillance data reasonably well. However, the robustness of inference declined towards the extremes of the plausible parameter ranges, with misleading results. It was for instance not possible to tell the difference between low/moderate interaction and no interaction. In conclusion, our results illustrate that in a plausible parameter range, the strength of RSV and Influenza interaction can be estimated from a single season of high-quality surveillance data but also highlights the importance to test parameter identifiability a priori in such situations.
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