Interpreting models of infectious diseases in terms of integral input-to-state stability

Input-to-state stability and Lyapunov functions with explicit domains for SIR model of infectious diseases

This paper demonstrates input-to-state stability (ISS) of the SIR model of infectious diseases with respect to the disease-free equilibrium and the endemic equilibrium. Lyapunov functions are constructed to verify that both equilibria are individually robust with respect to perturbation of newborn/immigration rate which determines the eventual state of populations in epidemics. The construction and analysis are geometric and global in the space of the populations. In addition to the establishment of ISS, this paper shows how explicitly the constructed level sets reflect the flow of trajectorie…

Predicting and controlling the dynamics of infectious diseases

This paper introduces a new optimal control model to describe and control the dynamics of infectious diseases. In the present model, the average time of isolation (i.e. hospitalization) of infectious population is the main time-dependent parameter that defines the spread of infection. All the preventive measures aim to decrease the average time of isolation under given constraints.

传染病控制中连续状态马尔可夫决策过程的状态离散化

State Discretization for Continuous-State MDPs in Infectious Disease Control

针对传染病控制中连续状态MDP难以处理的问题，开发连续状态离散化算法以获取近似解，并通过合成示例和洛杉矶县COVID-19案例与均匀离散化进行性能基准测试，为最优控制策略确定提供支持。

Screening for an Infectious Disease as a Problem in Stochastic Control

There has been much recent interest in screening populations for an infectious disease. Here, we present a stochastic-control model, wherein the optimum screening policy is provably difficult to find, but wherein Thompson sampling has provably optimal performance guarantees in the form of Bayesian regret. Thompson sampling seems applicable especially to diseases, for which we do not understand the dynamics well, such as to the super-spreading COVID-19.

使用路径积分控制学习传染病缓解的公平策略

Learning Fair Policies for Infectious Diseases Mitigation using Path Integral Control

针对传染病缓解政策的公平性问题，提出基于随机多组SIR模型的序贯决策框架，采用路径积分控制算法学习公平的疫苗接种和封锁策略，案例研究显示其公平性优于传统方法，可为政策制定者提供参考。

在动态条件下优化传染病防控

Optimizing infectious disease mitigation under dynamic conditions

在动态条件下，研究发现最优防控策略需严格或完全不采取，季节性变化影响防控强度，且延迟防控会显著增加成本。