Inferring Contagion Patterns in Social Contact Networks with Limited Infection Data

TLQP: Early-stage transportation lock-down and quarantine problem

The advent of COVID-19 is a sensible reminder of the vulnerability of our society to pandemics. We need to be better prepared for finding ways to stem such outbreaks. Except from social distancing and wearing face masks, restricting the movement of people is one important measure necessary to control the spread. Such decisions on the lock-down/reduction of movement should be made in an informed way and, accordingly, modeled as an optimization problem. We propose the Early-stage Transportation Lock-down and Quarantine Problem (TLQP), which can help to decide which parts of the transportation infrastructure of a country should be restricted in early stages. On top of the network-based Susceptible-Exposed-Infectious-Recovered (SEIR) model, we establish a decision recommendation framework, which considers the lock-down of cross-border traffic, internal traffic, and movement inside individual populations. The combinatorial optimization problem aims to find the best set of actions which minimize the social cost of a lock-down. Given the inherent intractability of this problem, we develop a highly-efficient heuristic based on the Effective Distance (ED) path and the Cost-Effective Lazy Forward (CELF) algorithm. We perform and report experiments on the global spread of COVID-19 and show how individual countries may protect their population by taking appropriate measures against the threatening pandemic. We believe that our study contributes to the orchestration of measures for dealing with current and future epidemic outbreaks.

Methodology of emergency medical logistics for public health emergencies

This work presents a novel model of emergency medical logistics for quick response to public health emergencies. The proposed methodology consists of two recursive mechanisms: (1) the time-varying forecasting of medical relief demand and (2) relief distribution. The medical demand associated with each epidemic area is forecast according to a modified susceptible-exposed-infected-recovered model. A linear programming approach is then applied to facilitate distribution decision-making. The physical and psychological fragility of affected people are discussed. Numerical studies are conducted. Results show that the consideration of survivor psychology significantly reduces the psychological fragility of affected people, but it barely influences physical fragility.