Nonparametric comparison of epidemic time trends: the case of COVID-19

Probabilistic and mean-field model of COVID-19 epidemics with user mobility and contact tracing

We propose a detailed discrete-time model of COVID-19 epidemics coming in two flavours, mean-field and probabilistic. The main contribution lies in several extensions of the basic model that capture i) user mobility - distinguishing routing, i.e. change of residence, from commuting, i.e. daily mobility - and ii) contact tracing procedures. We confront this model to public data on daily hospitalizations, and discuss its application as well as underlying estimation procedures.

时变流行病模型的非参数推断框架

Nonparametric Inference Framework for Time-dependent Epidemic Models

本文为时变感染率的随机SIR模型构建了非参数推断框架，通过似似然近似、B样条建模和参数自举法进行参数估计与置信区间构建，并成功应用于安大略省COVID-19多波次数据。

Bayesian nonparametrics for stochastic epidemic models

The vast majority of models for the spread of communicable diseases are parametric in nature and involve underlying assumptions about how the disease spreads through a population. In this article we consider the use of Bayesian nonparametric approaches to analysing data from disease outbreaks. Specifically we focus on methods for estimating the infection process in simple models under the assumption that this process has an explicit time-dependence.

Nonparametric estimation of highest density regions for COVID-19

Highest density regions refer to level sets containing points of relatively high density. Their estimation from a random sample, generated from the underlying density, allows to determine the clusters of the corresponding distribution. This task can be accomplished considering different nonparametric perspectives. From a practical point of view, reconstructing highest density regions can be interpreted as a way of determining hot-spots, a crucial task for understanding COVID-19 space-time evolution. In this work, we compare the behavior of classical plug-in methods and a recently proposed hybr…

Bayesian inference of heterogeneous epidemic models: Application to COVID-19 spread accounting for long-term care facilities

We propose a high dimensional Bayesian inference framework for learning heterogeneous dynamics of a COVID-19 model, with a specific application to the dynamics and severity of COVID-19 inside and outside long-term care (LTC) facilities. We develop a heterogeneous compartmental model that accounts for the heterogeneity of the time-varying spread and severity of COVID-19 inside and outside LTC facilities, which is characterized by time-dependent stochastic processes and time-independent parameters in $\sim$1500 dimensions after discretization. To infer these parameters, we use reported data on t…

基于接触间隔的流行病数据非参数生存分析

Nonparametric survival analysis of epidemic data

本文提出基于接触间隔的流行病数据非参数生存分析方法，利用Nelson-Aalen估计量及EM算法处理传播网络观测不确定性，并证明其可扩展链二项模型至连续时间。