Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA.
UNICEF, New York, NY, USA.
Nat Commun. 2021 Sep 10;12(1):5379. doi: 10.1038/s41467-021-25695-0.
Probabilistic forecasts play an indispensable role in answering questions about the spread of newly emerged pathogens. However, uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among alternative model structures and assumptions. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about human mobility, spatiotemporal variation in transmission potential, and the number of virus introductions. We found that which model assumptions had the most ensemble weight changed through time. We additionally identified a trade-off whereby some individual models outperformed ensemble models early in the epidemic, but on average the ensembles outperformed all individual models. Our results suggest that multiple models spanning uncertainty across alternative assumptions are necessary to obtain robust forecasts for emerging infectious diseases.
概率预测在回答关于新出现病原体传播的问题方面发挥着不可或缺的作用。然而，对新兴病原体的流行病学的不确定性可能会使在替代模型结构和假设之间做出选择变得困难。为了评估新兴病原体的不确定性对其传播预测的潜在影响，我们在哥伦比亚 2015-2016 年寨卡疫情的背景下评估了 16 种预测模型的性能。每个模型都具有不同的关于人类流动性、传播潜力的时空变化以及病毒传入数量的假设组合。我们发现，随着时间的推移，具有最大集合权重的模型假设发生了变化。我们还发现了一种权衡，即一些单个模型在疫情早期的表现优于集合模型，但平均而言，集合模型优于所有单个模型。我们的研究结果表明，对于新兴传染病，需要使用多个涵盖不确定性的模型来获得稳健的预测。
