Within epidemiological modeling, the majority of analyses assume a single
epidemic process for generating ground-truth data. However, this assumed data
generation process can be unrealistic, since data sources for epidemics are
often aggregated across geographic regions and communities. As a result,
state-of-the-art models for estimating epidemiological parameters,
e.g.~transmission rates, can be inappropriate when faced with complex systems.
Our work empirically demonstrates some limitations of applying epidemiological
models to aggregated datasets. We generate three complex outbreak scenarios by
combining incidence curves from multiple epidemics that are independently
simulated via SEIR models with different sets of parameters. Using these
scenarios, we assess the robustness of a state-of-the-art Bayesian inference
method that estimates the epidemic trajectory from viral load surveillance
data. We evaluate two data-generating models within this Bayesian inference
framework: a simple exponential growth model and a highly flexible Gaussian
process prior model. Our results show that both models generate accurate
transmission rate estimates for the combined incidence curve at the cost of
generating biased estimates for each underlying epidemic, reflecting highly
heterogeneous underlying population dynamics. The exponential growth model,
while interpretable, is unable to capture the complexity of the underlying
epidemics. With sufficient surveillance data, the Gaussian process prior model
captures the shape of complex trajectories, but is imprecise for periods of low
data coverage. Thus, our results highlight the potential pitfalls of neglecting
complexity and heterogeneity in the data generation process, which can mask
underlying location- and population-specific epidemic dynamics.

本研究通过结合多个不同参数的 SEIR 模型的流行病曲线，生成了三个复杂的流行病情景，并考察了流行病学参数估计的目前现状和局限性，结果表明忽略数据生成过程中的复杂性和异质性会掩盖底层地理和人口特定的流行病动态。

聚合异质数据的流行病学建模的差异

Discrepancies in Epidemiological Modeling of Aggregated Heterogeneous  Data

Objective: The COVID-19 pandemic has created many challenges that need
immediate attention. Various epidemiological and deep learning models have been
developed to predict the COVID-19 outbreak, but all have limitations that
affect the accuracy and robustness of the predictions. Our method aims at
addressing these limitations and making earlier and more accurate pandemic
outbreak predictions by (1) using patients' EHR data from different counties
and states that encode local disease status and medical resource utilization
condition; (2) considering demographic similarity and geographical proximity
between locations; and (3) integrating pandemic transmission dynamics into deep
learning models. Materials and Methods: We proposed a spatio-temporal attention
network (STAN) for pandemic prediction. It uses an attention-based graph
convolutional network to capture geographical and temporal trends and predict
the number of cases for a fixed number of days into the future. We also
designed a physical law-based loss term for enhancing long-term prediction.
STAN was tested using both massive real-world patient data and open source
COVID-19 statistics provided by Johns Hopkins university across all U.S.
counties. Results: STAN outperforms epidemiological modeling methods such as
SIR and SEIR and deep learning models on both long-term and short-term
predictions, achieving up to 87% lower mean squared error compared to the best
baseline prediction model. Conclusions: By using information from real-world
patient data and geographical data, STAN can better capture the disease status
and medical resource utilization information and thus provides more accurate
pandemic modeling. With pandemic transmission law based regularization, STAN
also achieves good long-term prediction performance.

本文通过使用真实世界患者数据和地理数据以及传染病传播定律设计的损失项来提高预测准确性，提出了一种基于定位注意力和空间 - 时间图卷积网络的流行病预测方法，并在 COVID-19 数据集上进行了测试。结果表明，这种方法在短期和长期预测中均优于现有方法，提高了预测的准确性。