Data from: Ebola cases and health system demand in Liberia

In 2014, a major epidemic of human Ebola virus disease emerged in West Africa, where human-to-human transmission has now been sustained for greater than 12 months. In the summer of 2014, there was great uncertainty about the answers to several key policy questions concerning the path to containment. What is the relative importance of nosocomial transmission compared with community-acquired infection? How much must hospital capacity increase to provide care for the anticipated patient burden? To which interventions will Ebola transmission be most responsive? What must be done to achieve containment? In recent years, epidemic models have been used to guide public health interventions. But, model-based policy relies on high quality causal understanding of transmission, including the availability of appropriate dynamic transmission models and reliable reporting about the sequence of case incidence for model fitting, which were lacking for this epidemic. To investigate the range of potential transmission scenarios, we developed a multi-type branching process model that incorporates key heterogeneities and time-varying parameters to reflect changing human behavior and deliberate interventions in Liberia. Ensembles of this model were evaluated at a set of parameters that were both epidemiologically plausible and capable of reproducing the observed trajectory. Results of this model suggested that epidemic outcome would depend on both hospital capacity and individual behavior. Simulations suggested that if hospital capacity was not increased, then transmission might outpace the rate of isolation and the ability to provide care for the ill, infectious, and dying. Similarly, the model suggested that containment would require individuals to adopt behaviors that increase the rates of case identification and isolation and secure burial of the deceased. As of mid-October, it was unclear that this epidemic would be contained even by 99% hospitalization at the planned hospital capacity. A new version of the model, updated to reflect information collected during October and November 2014, predicts a significantly more constrained set of possible futures. This model suggests that epidemic outcome still depends very heavily on individual behavior. Particularly, if future patient hospitalization rates return to background levels (estimated to be around 70%), then transmission is predicted to remain just below the critical point around Reff = 1. At the higher hospitalization rate of 85%, this model predicts near complete elimination in March to June, 2015.

2014年，西非暴发大规模人类埃博拉病毒病（Ebola virus disease）疫情，目前人际持续传播已超过12个月。2014年夏季，围绕疫情遏制路径的若干关键政策问题存在极大不确定性：与社区获得性感染（community-acquired infection）相比，院内传播（nosocomial transmission）的相对重要性如何？为应对预期的患者收治量，医院收治能力需提升多少？哪些干预措施对埃博拉传播的响应最为显著？应采取何种措施实现疫情遏制？近年来，疫情模型已被用于指导公共卫生干预。但基于模型的政策制定依赖于对传播机制的高质量因果认知，包括是否具备合适的动态传播模型（dynamic transmission models），以及是否有可靠的病例发病率（case incidence）序列报告用于模型拟合（model fitting）——而本次疫情中这些条件均不具备。为探究潜在传播情景的范围，我们开发了一款多类型分支过程模型（multi-type branching process model），该模型纳入了关键异质性与时变参数（time-varying parameters），以反映利比里亚境内人类行为的变化与针对性干预措施。我们针对一系列兼具流行病学合理性且能够复现观测到的疫情走势的参数组合，对该模型的集成结果进行了评估。该模型的结果显示，疫情结局同时取决于医院收治能力与个体行为。模拟结果表明，若不提升医院收治能力，病毒传播速度可能超过隔离速度以及为患病、具有传染性和濒死患者提供救治的能力。同理，该模型显示，实现疫情遏制需要民众采取能够提升病例识别与隔离率、妥善处置逝者遗体的行为。截至2014年10月中旬，即便按照规划中的医院收治能力实现99%的患者收治率，本次疫情能否得到遏制仍不明确。2014年10月至11月间收集的信息更新后的新版模型，预测了一组受限程度显著提升的潜在未来情景。该模型仍显示，疫情结局依然高度依赖个体行为。具体而言，若未来患者收治率回落至基线水平（估算约为70%），则预测病毒传播将维持在有效再生数（Reff）≈1的临界点以下；若收治率提升至85%，该模型则预测疫情将在2015年3月至6月间基本完全消除。