Bridging the gap with Pando

The Pando app is UK based and part of the NHS Clinical Communication Procurement Framework, which is designed to provide continuity of care with virtual patient management (https://www.bad.org.uk/healthcare-professionals/covid-19/re mote-dermatology-guidance), and drive tech-enabled connectivity across the National Health Service (NHS). This has also been used in the British Army to help defence medical staff connect with and seek advice from their colleagues in the UK while in the field (www.hellopando.com). Lack of on-site medical illustration, the COVID-19 pandemic and plastic surgeons operating in a NHS-funded private setting with no access to Picture Archiving and Communication System (PACS) in our Trust prompted use of the Pando app to capture prebiopsy pictures, avoid wrong-site surgery and improve interdepartmental communication. We present our multidisciplinary quality improvement project, involving dermatology and plastic surgery, evaluating the use of the Pando app from September to December 2020, mostly from 2-week-wait skin cancer clinics. All dermatology and plastic surgery colleagues downloaded the Pando app to their mobile phones and created a group entitled 'Dermatology/Plastics' to share their patient photos with identity labels. Patient photos could also be emailed to the clinicians' NHS email addresses - all done with patient consent. We evaluated our project with pre-and post-Pando feedback questionnaires. In the pre-Pando questionnaires, the majority of 14 colleagues involved were concerned with the varying quality of photos emailed by patients, the time lag in photos being uploaded to PACS and any likelihood of compromising patient safety. With post-Pando questionnaires, the majority found the app to be user-friendly, that the photographs taken were of superior quality, that there were no reported concerns with patient consent and they preferred using the app to the previous pathway. Comments suggested the Pando app to be invaluable for site recognition in patients with cognitive impairment, multiple lesions, difficult-to-see areas, medicolegal, educational and audit purposes, and local cancer multidisciplinary discussions. The drawbacks were the lack of seamless connection between the app and PACS, the inability to search for pictures in the app with patient identification and lack of access to previously shared pictures for new users. Despite some limitations, the Pando app has immensely improved patient safety and proved to be invaluable for our joint dermatology and plastic surgery interactions. However, there is an unmet need for a system with the ability to instantly transfer pictures to PACS and patient electronic records, to improve things further.

Stochastic compartmental models of the COVID-19 pandemic must have temporally correlated uncertainties

Compartmental models are an important quantitative tool in epidemiology, enabling us to forecast the course of a communicable disease. However, the model parameters, such as the infectivity rate of the disease, are riddled with uncertainties, which has motivated the development and use of stochastic compartmental models. Here, we first show that a common stochastic model, which treats the uncertainties as white noise, is fundamentally flawed since it erroneously implies that greater parameter uncertainties will lead to the eradication of the disease. Then, we present a principled modelling of the uncertainties based on reasonable assumptions on the contacts of each individual. Using the central limit theorem and Doob's theorem on Gaussian Markov processes, we prove that the correlated Ornstein-Uhlenbeck (OU) process is the appropriate tool for modelling uncertainties in the infectivity rate. We demonstrate our results using a compartmental model of the COVID-19 pandemic and the available US data from the Johns Hopkins University COVID-19 database. In particular, we show that the white noise stochastic model systematically underestimates the severity of the Omicron variant of COVID-19, whereas the OU model correctly forecasts the course of this variant. Moreover, using an SIS model of sexually transmitted disease, we derive an exact closed-form solution for the final distribution of infected individuals. This analytical result shows that the white noise model underestimates the severity of the pandemic because of unrealistic noise-induced transitions. Our results strongly support the need for temporal correlations in modelling of uncertainties in compartmental models of infectious disease. © 2023 The Authors.