School of Civil and Environment Engineering, UNSW Sydney, Sydney, NSW, Australia.
School of Women's and Children's Health, UNSW Sydney, Sydney, NSW, Australia.
BMC Med. 2019 Sep 2;17(1):171. doi: 10.1186/s12916-019-1389-3.
In 2015, the Zika virus spread from Brazil throughout the Americas, posing an unprecedented challenge to the public health community. During the epidemic, international public health officials lacked reliable predictions of the outbreak's expected geographic scale and prevalence of cases, and were therefore unable to plan and allocate surveillance resources in a timely and effective manner.
In this work, we present a dynamic neural network model to predict the geographic spread of outbreaks in real time. The modeling framework is flexible in three main dimensions (i) selection of the chosen risk indicator, i.e., case counts or incidence rate; (ii) risk classification scheme, which defines the high-risk group based on a relative or absolute threshold; and (iii) prediction forecast window (1 up to 12 weeks). The proposed model can be applied dynamically throughout the course of an outbreak to identify the regions expected to be at greatest risk in the future.
The model is applied to the recent Zika epidemic in the Americas at a weekly temporal resolution and country spatial resolution, using epidemiological data, passenger air travel volumes, and vector habitat suitability, socioeconomic, and population data for all affected countries and territories in the Americas. The model performance is quantitatively evaluated based on the predictive accuracy of the model. We show that the model can accurately predict the geographic expansion of Zika in the Americas with the overall average accuracy remaining above 85% even for prediction windows of up to 12 weeks.
Sensitivity analysis illustrated the model performance to be robust across a range of features. Critically, the model performed consistently well at various stages throughout the course of the outbreak, indicating its potential value at any time during an epidemic. The predictive capability was superior for shorter forecast windows and geographically isolated locations that are predominantly connected via air travel. The highly flexible nature of the proposed modeling framework enables policy makers to develop and plan vector control programs and case surveillance strategies which can be tailored to a range of objectives and resource constraints.
2015 年，寨卡病毒从巴西传播到整个美洲，给公共卫生界带来了前所未有的挑战。在疫情期间，国际公共卫生官员缺乏对疫情预期地理范围和病例流行程度的可靠预测，因此无法及时有效地规划和分配监测资源。
在这项工作中，我们提出了一个实时预测疫情地理传播的动态神经网络模型。该建模框架在三个主要方面具有灵活性：（i）选择所选风险指标，即病例数或发病率；（ii）风险分类方案，基于相对或绝对阈值定义高风险组；以及（iii）预测预测窗口（1 到 12 周）。所提出的模型可以在疫情发生的整个过程中动态应用，以识别未来风险最大的地区。
该模型每周以时间分辨率和国家空间分辨率应用于美洲最近的寨卡疫情，使用流行病学数据、旅客航空旅行量以及所有受影响的美洲国家和地区的病媒栖息地适宜性、社会经济和人口数据。根据模型的预测准确性对模型性能进行定量评估。我们表明，该模型可以准确预测美洲寨卡病毒的地理扩张，整体平均准确率甚至在长达 12 周的预测窗口内仍保持在 85%以上。
敏感性分析表明，该模型在一系列特征下表现稳健。至关重要的是，该模型在疫情发生的各个阶段都表现良好，表明其在疫情期间的任何时候都具有潜在价值。较短的预测窗口和主要通过航空旅行连接的地理隔离地区的预测能力更强。所提出的建模框架的高度灵活性使决策者能够制定和规划病媒控制计划和病例监测策略，可以根据各种目标和资源限制进行定制。
