论文标题
Crisp：基于接触数据的个人级别COVID-19感染风险估算的概率模型
CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact Data
论文作者
论文摘要
我们提出了Crisp（COVID-19风险评分预测），这是一种基于SEIR模型的人群传播的Covid-19感染的概率图形模型，我们假设跨时间之间的（1）个个体之间的相互接触跨越各种频道（例如，蓝齿接触痕迹）的跨时间（例如，bluetoots接触痕迹），以及（2）测试，以及（2）测试，以及（2），以实现感染和暴露于感染，并进行感染，并进行感染，并进行感染。我们的微型模型在每个时间点都跟踪每个人的感染状态，从易感性，暴露，感染性到恢复。我们既开发蒙特卡洛EM，又开发传递算法的消息来推断接触通道特定的感染传输概率。鉴于所有接触和测试结果数据的潜在感染状态，我们的蒙特卡洛算法使用gibbs采样在整个分析时间内绘制每个人的潜在感染状态的样本。模拟数据的实验结果表明，我们的清晰模型可以通过繁殖因子$ r_0 $参数化，并展示了与经典SEIR模型相似的人群水平的传染性和恢复时间序列。但是，由于单个接触数据，该模型允许精细的粒度控制和推断各种COVID-19缓解和抑制政策指标。此外，Block-GiBBS采样算法能够支持在测试过程隔离方法中的有效测试，以包含COVID-19的感染扩散。据我们所知，这是第一个基于个人水平的接触数据对Covid-19感染有效推断的模型；大多数流行病模型是在整个人群中推理的宏观模型。 Crisp的实现可在Python和C ++中获得，网址为https://github.com/zalandoresearch/crisp。
We present CRISP (COVID-19 Risk Score Prediction), a probabilistic graphical model for COVID-19 infection spread through a population based on the SEIR model where we assume access to (1) mutual contacts between pairs of individuals across time across various channels (e.g., Bluetooth contact traces), as well as (2) test outcomes at given times for infection, exposure and immunity tests. Our micro-level model keeps track of the infection state for each individual at every point in time, ranging from susceptible, exposed, infectious to recovered. We develop both a Monte Carlo EM as well as a message passing algorithm to infer contact-channel specific infection transmission probabilities. Our Monte Carlo algorithm uses Gibbs sampling to draw samples of the latent infection status of each individual over the entire time period of analysis, given the latent infection status of all contacts and test outcome data. Experimental results with simulated data demonstrate our CRISP model can be parametrized by the reproduction factor $R_0$ and exhibits population-level infectiousness and recovery time series similar to those of the classical SEIR model. However, due to the individual contact data, this model allows fine grained control and inference for a wide range of COVID-19 mitigation and suppression policy measures. Moreover, the block-Gibbs sampling algorithm is able to support efficient testing in a test-trace-isolate approach to contain COVID-19 infection spread. To the best of our knowledge, this is the first model with efficient inference for COVID-19 infection spread based on individual-level contact data; most epidemic models are macro-level models that reason over entire populations. The implementation of CRISP is available in Python and C++ at https://github.com/zalandoresearch/CRISP.
