Stochastic epidemic dynamics on extremely heterogeneous networks

Emergency ventilator for COVID-19

The COVID-19 pandemic disrupted the world in 2020 by spreading at unprecedented rates and causing tens of thousands of fatalities within a few months. The number of deaths dramatically increased in regions where the number of patients in need of hospital care exceeded the availability of care. Many COVID-19 patients experience Acute Respiratory Distress Syndrome (ARDS), a condition that can be treated with mechanical ventilation. In response to the need for mechanical ventilators, designed and tested an emergency ventilator (EV) that can control a patient's peak inspiratory pressure (PIP) and breathing rate, while keeping a positive end expiratory pressure (PEEP). This article describes the rapid design, prototyping, and testing of the EV. The development process was enabled by rapid design iterations using additive manufacturing (AM). In the initial design phase, iterations between design, AM, and testing enabled a working prototype within one week. The designs of the 16 different components of the ventilator were locked by additively manufacturing and testing a total of 283 parts having parametrically varied dimensions. In the second stage, AM was used to produce 75 functional prototypes to support engineering evaluation and animal testing. The devices were tested over more than two million cycles. We also developed an electronic monitoring system and with automatic alarm to provide for safe operation, along with training materials and user guides. The final designs are available online under a free license. The designs have been transferred to more than 70 organizations in 15 countries. This project demonstrates the potential for ultra-fast product design, engineering, and testing of medical devices needed for COVID-19 emergency response.

Optimizing HIV Interventions for Multiplex Social Networks via Partition-Based Random Search

There are multiple modes for human immunodeficiency virus (HIV) transmissions, each of which is usually associated with a certain key population (e.g., needle sharing among people who inject drugs). Recent field studies revealed the merging trend of multiple key populations, making HIV intervention difficult because of the existence of multiple transmission modes in such complex multiplex social networks. In this paper, we aim to address this challenge by developing a multiplex social network framework to capture the multimode transmission across two key populations. Based on the multiplex social network framework, we propose a new random search method, named partition-based random search with network and memory prioritization (PRS-NMP), to identify the optimal subset of high-value individuals in the social network for interventions. Numerical experiments demonstrated that the proposed PRS-NMP-based interventions could effectively reduce the scale of HIV transmissions. The performance of PRS-NMP-based interventions is consistently better than the benchmark nested partitions method and network-based metrics.