Beams Alexander B, Earn David J D, Colijn Caroline
Department of Mathematics, Simon Fraser University, 8888 University Dr W, Burnaby, BC V5A 1S6, Canada.
Department of Mathematics & Statistics, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada.
J R Soc Interface. 2024 Dec;21(221):20240438. doi: 10.1098/rsif.2024.0438. Epub 2024 Dec 11.
As SARS-CoV-2 has transitioned from a novel pandemic-causing pathogen into an established seasonal respiratory virus, focus has shifted to post-acute sequelae of COVID-19 (PASC, colloquially 'long COVID'). We use compartmental mathematical models simulating emergence of new variants to help identify key sources of uncertainty in PASC trajectories. Some parameters (such as the duration and equilibrium prevalence of infection, as well as the fraction of infections that develop PASC) matter more than others (such as the duration of immunity and secondary vaccine efficacy against PASC). Even if newer variants carry the same risk of PASC as older types, the dynamics of selection can give rise to greater PASC prevalence. However, identifying plausible PASC prevalence trajectories requires accurate knowledge of the transmission potential of COVID-19 variants in the endemic phase. Precise estimates for secondary vaccine efficacy and duration of immunity will not greatly improve forecasts for PASC prevalence. Researchers involved with Living Evidence Synthesis, or other similar initiatives focused on PASC, are well advised to ascertain primary efficacy against infection, duration of infection and prevalence of active infection in order to facilitate predictions.
随着严重急性呼吸综合征冠状病毒2(SARS-CoV-2)从一种新型的大流行致病病原体转变为一种既定的季节性呼吸道病毒,重点已转向新冠后遗症(PASC,通俗地称为“长新冠”)。我们使用模拟新变种出现的分区数学模型,以帮助确定PASC轨迹中不确定性的关键来源。一些参数(如感染的持续时间和平衡患病率,以及出现PASC的感染比例)比其他参数(如免疫持续时间和针对PASC的二次疫苗效力)更为重要。即使新变种携带与旧变种相同的PASC风险,选择动态也可能导致更高的PASC患病率。然而,要确定合理的PASC患病率轨迹,需要准确了解新冠病毒变种在流行阶段的传播潜力。对二次疫苗效力和免疫持续时间的精确估计不会大幅改善对PASC患病率的预测。参与“实时证据综合”或其他专注于PASC的类似项目的研究人员,最好确定疫苗对感染的主要效力、感染持续时间和活跃感染患病率,以便进行预测。