Is West Africa Approaching a Catastrophic Phase or is the 2014 Ebola Epidemic Slowing Down? Different Models Yield Different Answers for Liberia.
Is West Africa Approaching a Catastrophic Phase or is the 2014 Ebola Epidemic Slowing Down? Different Models Yield Different Answers for Liberia.
复制标题
DOI：
10.1371/currents.outbreaks.b4690859d91684da963dc40e00f3da81
复制
发表时间：
2014-11-20
期刊：
影响因子：
--
通讯作者：
Kuang, Yang
中科院分区：
文献类型：
--
作者：
Chowell, Gerardo;Simonsen, Lone;Kuang, Yang
自2013年12月左右以来，扎伊尔埃博拉病毒（EBOV）的空前流行影响了西非，几内亚、塞拉利昂和利比里亚正在发生严重传播，国际影响日益严重。事实证明，数学模型有助于预测预期的感染和死亡人数，并量化控制传播所需的干预措施的强度;然而，由于流行病学数据有限，干预措施变化迅速，因此根据正在发生的疫情校准机械传播模型是一项具有挑战性的任务。在这里，我们通过将逻辑增长模型拟合到累计病例数来预测利比里亚EBOV流行的轨迹。我们的模型预测与截至10月23日的最新流行病学报告一致，并表明利比里亚的指数增长阶段已经结束，预计最终发病率为0.1- 0.12%。我们的研究结果表明，简单的现象学模型可以提供对疫情动态的补充见解，并捕捉人口行为和干预措施变化的早期迹象。特别是，我们的研究结果强调，需要把易感人群的有效规模作为一个动态变量，而不是一个固定的数量，由于在整个疫情传播的反应性变化。我们发现，从逻辑模型的预测是更可变的流行病的早期阶段（如埃博拉病毒在塞拉利昂和几内亚的流行）。在公共卫生当局充分利用这种预测之前，有必要进行更多的研究来比较疾病预测的机械方法和现象学方法的性能。
An unprecedented epidemic of Zaire ebolavirus (EBOV) has affected West Africa since approximately December 2013, with intense transmission on-going in Guinea, Sierra Leone and Liberia and increasingly important international repercussions. Mathematical models are proving instrumental to forecast the expected number of infections and deaths and quantify the intensity of interventions required to control transmission; however, calibrating mechanistic transmission models to an on-going outbreak is a challenging task owing to limited availability of epidemiological data and rapidly changing interventions. Here we project the trajectory of the EBOV epidemic in Liberia by fitting logistic growth models to the cumulative number of cases. Our model predictions align well with the latest epidemiological reports available as of October 23, and indicates that the exponential growth phase is over in Liberia, with an expected final attack rate of ~0.1-0.12%. Our results indicate that simple phenomenological models can provide complementary insights into the dynamics of an outbreak and capture early signs of changes in population behavior and interventions. In particular, our results underscore the need to treat the effective size of the susceptible population as a dynamic variable rather than a fixed quantity, due to reactive changes in transmission throughout the outbreak. We show that predictions from the logistic model are more variable in the earlier stages of an epidemic (such as the EBOV epidemics in Sierra Leone and Guinea). More research is warranted to compare the performances of mechanistic and phenomenological approaches for disease forecasts, before such predictions can be fully used by public health authorities.