用异方差高斯过程对疾病发病率的唯象预测：登革热个案研究
Phenomenological forecasting of disease incidence using heteroskedastic Gaussian processes: a dengue case study
摘要：
2015年，美国联邦政府利用秘鲁伊基托斯和波多黎各圣胡安的历史病例数据赞助了一场登革热预测比赛。竞争对手进行了评估的几个方面的样本外预测，包括高峰周的目标，高峰期发病率在该周和总季节发病率在每个几个季节。我们的团队是该比赛中表现最好的团队之一，在多个目标/本地比赛中表现优于所有其他团队。在本文中，我们报告了我们的方法，令人惊讶的是，其中很大一部分忽略了流行病的已知生物学-特别是登革热传播与环境因素之间的关系-而是依赖于灵活的非参数非线性高斯过程（GP）回归拟合，"记住"过去季节的轨迹，然后将即将展开的季节的动态与过去的实时动态进行"匹配"。我们的现象学方法在疾病动力学不太清楚的情况下具有优势，例如，在疾病历史较短的地点（如伊基托斯），或在辅助协变量（如降水量）的测量和预测不可用和/或与病例的关联强度尚不清楚的地方。特别是，我们表明，GP方法一般优于一个更经典的广义线性（自回归）模型（GLM），我们开发利用丰富的协变量信息。我们说明了我们的方法（S）在两个基准区域的变化，以及其他竞赛竞争对手提交的结果的完整摘要。
英文摘要：
In 2015 the US federal government sponsored a dengue forecasting competition using historical case data from Iquitos, Peru and San Juan, Puerto Rico. Competitors were evaluated on several aspects of out-of-sample forecasts including the targets of peak week, peak incidence during that week and total season incidence across each of several seasons. Our team was one of the top performers of that competition, outperforming all other teams in multiple targets/locals. In this paper we report on our methodology, a large component of which, surprisingly, ignores the known biology of epidemics at large---in particular relationships between dengue transmission and environmental factors---and instead relies on flexible nonparametric nonlinear Gaussian process (GP) regression fits that "memorize" the trajectories of past seasons, and then "match" the dynamics of the unfolding season to past ones in real-time. Our phenomenological approach has advantages in situations where disease dynamics are less well understood, e.g., at sites with shorter histories of disease (such as Iquitos), or where measurements and forecasts of ancillary covariates like precipitation are unavailable and/or where the strength of association with cases are as yet unknown. In particular, we show that the GP approach generally outperforms a more classical generalized linear (autoregressive) model (GLM) that we developed to utilize abundant covariate information. We illustrate variations of our method(s) on the two benchmark locales alongside a full summary of results submitted by other contest competitors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
