Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
BMC Infect Dis. 2024 Feb 14;24(1):204. doi: 10.1186/s12879-024-08986-x.
Recurring COVID-19 waves highlight the need for tools able to quantify transmission risk, and identify geographical areas at risk of outbreaks. Local outbreak risk depends on complex immunity patterns resulting from previous infections, vaccination, waning and immune escape, alongside other factors (population density, social contact patterns). Immunity patterns are spatially and demographically heterogeneous, and are challenging to capture in country-level forecast models.
We used a spatiotemporal regression model to forecast subnational case and death counts and applied it to three EU countries as test cases: France, Czechia, and Italy. Cases in local regions arise from importations or local transmission. Our model produces age-stratified forecasts given age-stratified data, and links reported case counts to routinely collected covariates (e.g. test number, vaccine coverage). We assessed the predictive performance of our model up to four weeks ahead using proper scoring rules and compared it to the European COVID-19 Forecast Hub ensemble model. Using simulations, we evaluated the impact of variations in transmission on the forecasts. We developed an open-source RShiny App to visualise the forecasts and scenarios.
At a national level, the median relative difference between our median weekly case forecasts and the data up to four weeks ahead was 25% (IQR: 12-50%) over the prediction period. The accuracy decreased as the forecast horizon increased (on average 24% increase in the median ranked probability score per added week), while the accuracy of death forecasts was more stable. Beyond two weeks, the model generated a narrow range of likely transmission dynamics. The median national case forecasts showed similar accuracy to forecasts from the European COVID-19 Forecast Hub ensemble model, but the prediction interval was narrower in our model. Generating forecasts under alternative transmission scenarios was therefore key to capturing the range of possible short-term transmission dynamics.
Our model captures changes in local COVID-19 outbreak dynamics, and enables quantification of short-term transmission risk at a subnational level. The outputs of the model improve our ability to identify areas where outbreaks are most likely, and are available to a wide range of public health professionals through the Shiny App we developed.
新冠疫情的反复凸显了人们对于能够量化传播风险、并识别存在疫情爆发风险的地理区域的工具的需求。局部疫情风险取决于先前感染、疫苗接种、衰减和免疫逃逸等因素所导致的复杂免疫模式。免疫模式在空间和人口统计学上存在异质性，难以在国家层面的预测模型中捕捉到。
我们使用时空回归模型来预测次国家级别的病例和死亡数，并将其应用于三个欧盟国家作为测试案例：法国、捷克和意大利。当地地区的病例是由输入或本地传播引起的。我们的模型根据年龄分层数据生成年龄分层预测，并将报告的病例数与常规收集的协变量（例如检测数量、疫苗接种覆盖率）联系起来。我们使用适当的评分规则评估了模型在未来四周的预测性能，并将其与欧洲 COVID-19 预测中心的综合模型进行了比较。通过模拟，我们评估了传播变化对预测的影响。我们开发了一个开源的 RShiny 应用程序来可视化预测结果和情景。
在国家层面，我们的每周病例中位数预测与数据之间的中位数相对差异在预测期内为 25%（IQR：12-50%）。随着预测时间的增加，准确性会降低（平均每增加一周，排名概率得分的中位数增加 24%），而死亡预测的准确性则更加稳定。超过两周后，模型生成了一个可能的传播动态的狭窄范围。国家层面的病例中位数预测与欧洲 COVID-19 预测中心的综合模型的预测结果具有相似的准确性，但我们的模型的预测区间更窄。因此，在替代传播情景下生成预测结果是捕捉短期传播动态的可能范围的关键。
我们的模型捕捉到了当地 COVID-19 疫情动态的变化，并能够在次国家级别的量化短期传播风险。模型的输出提高了我们识别最有可能爆发疫情的地区的能力，并通过我们开发的 Shiny 应用程序为广泛的公共卫生专业人员提供了这些信息。
