Severe acute respiratory disease SARS-CoV-2 has had a found impact on public
health systems and healthcare emergency response especially with respect to
making decisions on the most effective measures to be taken at any given time.
As demonstrated throughout the last three years with COVID-19, the prediction
of the number of positive cases can be an effective way to facilitate
decision-making. However, the limited availability of data and the highly
dynamic and uncertain nature of the virus transmissibility makes this task very
challenging. Aiming at investigating these challenges and in order to address
this problem, this work studies data-driven (learning, statistical) methods for
incrementally training models to adapt to these nonstationary conditions. An
extensive empirical study is conducted to examine various characteristics, such
as, performance analysis on a per virus wave basis, feature extraction,
"lookback" window size, memory size, all for next-, 7-, and 14-day forecasting
tasks. We demonstrate that the incremental learning framework can successfully
address the aforementioned challenges and perform well during outbreaks,
providing accurate predictions.

通过数据驱动的方法和增量学习，该研究针对 SARS-CoV-2 等疫情的不确定性，成功解决了病毒爆发期间的准确预测问题。

COVID-19 病例自适应预测的数据驱动方法研究

A Study of Data-driven Methods for Adaptive Forecasting of COVID-19  Cases

The global population at risk of mosquito-borne diseases such as dengue,
yellow fever, chikungunya and Zika is expanding. Infectious disease models
commonly incorporate environmental measures like temperature and precipitation.
Given increasing availability of high-resolution satellite imagery, here we
consider including landscape features from satellite imagery into infectious
disease prediction models. To do so, we implement a Convolutional Neural
Network (CNN) model trained on Imagenet data and labelled landscape features in
satellite data from London. We then incorporate landscape features from
satellite image data from Pakistan, labelled using the CNN, in a well-known
Susceptible-Infectious-Recovered epidemic model, alongside dengue case data
from 2012-2016 in Pakistan. We study improvement of the prediction model for
each of the individual landscape features, and assess the feasibility of using
image labels from a different place. We find that incorporating
satellite-derived landscape features can improve prediction of outbreaks, which
is important for proactive and strategic surveillance and control programmes.

此研究探讨了卫星图像中的景观特征在感染病预测模型中的应用，通过采用卷积神经网络从卫星数据中提取并标记的景观特征，结合巴基斯坦的登革热病例数据与 SIR 疫情模型，发现卫星图像中的景观特征能够显著提高疾病爆发的预测能力，对主动和战略性的监测和控制计划非常重要。