Department of Mathematics and Statistics, UiT-The Arctic University of Norway, Tromsø, 9019, Norway.
Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0202, USA.
Nat Commun. 2019 May 30;10(1):2374. doi: 10.1038/s41467-019-10099-y.
For dengue fever and other seasonal epidemics we show how the stability of the preceding inter-outbreak period can predict subsequent total outbreak magnitude, and that a feasible stability metric can be computed from incidence data alone. As an observable of a dynamical system, incidence data contains information about the underlying mechanisms: climatic drivers, changing serotype pools, the ecology of the vector populations, and evolving viral strains. We present mathematical arguments to suggest a connection between stability measured in incidence data during the inter-outbreak period and the size of the effective susceptible population. The method is illustrated with an analysis of dengue incidence in San Juan, Puerto Rico, where forecasts can be made as early as three to four months ahead of an outbreak. These results have immediate significance for public health planning, and can be used in combination with existing forecasting methods and more comprehensive dengue models.
对于登革热和其他季节性流行病,我们展示了前一次疫情爆发间隔期的稳定性如何预测随后的总爆发规模,以及可以仅从发病率数据计算出可行的稳定性指标。作为动态系统的一个可观测指标,发病率数据包含了有关潜在机制的信息:气候驱动因素、不断变化的血清型池、媒介种群的生态学以及不断演变的病毒株。我们提出了数学论据,以表明在疫情爆发间隔期内发病率数据中测量的稳定性与有效易感人群的规模之间存在联系。该方法通过对波多黎各圣胡安的登革热发病率进行分析进行了说明,可以在疫情爆发前三到四个月提前进行预测。这些结果对公共卫生规划具有直接意义,并且可以与现有的预测方法和更全面的登革热模型结合使用。