Assessment of event-triggered policies of nonpharmaceutical interventions based on epidemiological indicators.

Nonpharmaceutical interventions (NPI) such as banning public events or instituting lockdowns have been widely applied around the world to control the current COVID-19 pandemic. Typically, this type of intervention is imposed when an epidemiological indicator in a given population exceeds a certain threshold. Then, the nonpharmaceutical intervention is lifted when the levels of the indicator used have decreased sufficiently. What is the best indicator to use? In this paper, we propose a mathematical framework to try to answer this question. More specifically, the proposed framework permits to assess and compare different event-triggered controls based on epidemiological indicators. Our methodology consists of considering some outcomes that are consequences of the nonpharmaceutical interventions that a decision maker aims to make as low as possible. The peak demand for intensive care units (ICU) and the total number of days in lockdown are examples of such outcomes. If an epidemiological indicator is used to trigger the interventions, there is naturally a trade-off between the outcomes that can be seen as a curve parameterized by the trigger threshold to be used. The computation of these curves for a group of indicators then allows the selection of the best indicator the curve of which dominates the curves of the other indicators. This methodology is illustrated with indicators in the context of COVID-19 using deterministic compartmental models in discrete-time, although the framework can be adapted for a larger class of models.

Bridging the gap between respiratory research and health literacy: an interactive web-based platform.

Many patients with respiratory disease lack an understanding of basic respiratory physiology and the changes occurring in their lungs due to disease. Describing how the lungs work using realistic 3D visualisation of lung structure and function will improve communication of complicated concepts, resulting in improved health literacy. We developed a web-based platform, using anatomically realistic 3D lung models, to create an interactive visualisation tool to improve health literacy for patients with respiratory disease. A small amount of non-identifying personal information including gender, age, weight, height and smoking history can be used to customise the visualisation to an individual user. 3D computer modelling was used to create a web-based application that helps people understand how their lungs work in health and disease. The web-based application includes pages describing and visualising how the lungs work and the changes that occur during asthma and damage that smoking may be doing to their lungs. The application is freely available and located at https://sites.bioeng.auckland.ac.nz/silo6/lung_new/. This application bridges the gap between computational modelling and patient education, giving a visually compelling view into the patient's body that cannot be provided with any existing tools, hence providing a novel platform for enhancing patient-clinician interaction.