Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, UK.
Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, UK.
Epidemics. 2021 Jun;35:100460. doi: 10.1016/j.epidem.2021.100460. Epub 2021 Mar 26.
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.
呼吸道合胞病毒（RSV）和流感都会造成很大的疾病负担。有证据表明，它们通过暂时的交叉保护相互作用，预防其中一种疾病可能会无意中导致另一种疾病负担增加。然而，关于这种相互作用对公共卫生影响的证据很少，而且主要来自对监测数据中峰值转移的生态分析。为了测试从监测数据中估计 RSV 和流感相互作用参数的稳健性，我们进行了一项模拟和反推研究。我们开发了一个双病原体相互作用模型，对 RSV 和流感在英国的流行病学进行了参数化模拟。我们使用感染模型结合类似于监测的随机观察过程，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）方法对参数进行反推，包括描述竞争的参数。我们发现，在大多数情况下，从模拟监测数据中可以很好地估计 RSV 和流感相互作用的强度和持续时间。然而，推断的稳健性朝着合理参数范围的极端情况下降，导致误导性的结果。例如，无法区分低/中度相互作用和无相互作用。总之，我们的研究结果表明，在合理的参数范围内，可以从一个高质量监测季节的数据中估计 RSV 和流感相互作用的强度，但也强调了在这种情况下，事先测试参数可识别性的重要性。
