ABSTRACT
Objective: To develop a diversified recruitment model for the ongoing Trial of Parkinson's and Zoledronic Acid (TOPAZ) during the COVID-19 pandemic. Background: TOPAZ is a home-based trial examining the efficacy of zoledronate in preventing fractures in people with neurodegenerative parkinsonisms, who have up to 4-fold increased fracture risk. Design/Methods: Consent is obtained online (https://www.topazstudy.org). After eligibility is determined by movement disorders specialists using medical records and/or telemedicine, study drug is infused by research nurses at home. Fractures are ascertained by email or telephone. The 2/2020 onset of recruitment coincided with COVID-19 restrictions, with a nearly 7 months pause. To randomize 3,500 participants by 12/2023, we developed multiple methods to recruit potential participants via: 1) 46 Parkinson Study Group (PSG) sites, 2) 11 health care systems with integrated research networks, 3) community outreach organizations (i.e. support groups, social media, newsletters, etc.), 4) outreach by the Parkinson's Foundation (PF), 5) Fox Trial Finder (FTF), and 6) the 23andMe Parkinson's disease research program. Results: By 10/1/2021, 2002 had registered on the website, 1333 consented, 992 were eligible per expert diagnostic confirmation, and 632 were randomized, exceeding our goal of 600 for 9/30/21. Registered participants came from the multiple sources: 1) 609 (27.7%) from PSG sites, 2) 529 (24%) from health care systems with integrated research networks, 3) 213 (9.7%) from community outreach, 4) 34 (1.5%) from PF, 5) 16 (0.7%) from FTF and 6) 601 (27.3%) from 23andMe. The largest source of recruitment was PSG. A single study invitation emailed from 23andMe to its 19,733 PD research participants led to nearly the same number of referrals as PSG but in only a few weeks'time. Conclusions: Using diverse referral sources to the TOPAZ study website, we are succeeding in achieving enrollment targets for a Parkinson's trial amidst the challenges of the COVID-19 pandemic.
ABSTRACT
In this paper, we present a new method for successfully simulating the dynamics of COVID-19, experimentally focusing on the third wave. This method, namely, the Method of Parallel Trajectories (MPT), is based on the recently introduced self-organized diffusion model. According to this method, accurate simulation of the dynamics of the COVID-19 infected population evolution is accomplished by considering not the total data for the infected population, but successive segments of it. By changing the initial conditions with which each segment of the simulation is produced, we achieve close and detailed monitoring of the evolution of the pandemic, providing a tool for evaluating the overall situation and the fine-tuning of the restrictive measures. Finally, the application of the proposed MPT on simulating the pandemic's third wave dynamics in Greece and Italy is presented, verifying the method's effectiveness.
Subject(s)
COVID-19 , Computer Simulation , Diffusion , Humans , Italy , SARS-CoV-2ABSTRACT
Recently, it has been successfully shown that the temporal evolution of the fraction of COVID-19 infected people possesses the same dynamics as the ones demonstrated by a self-organizing diffusion model over a lattice, in the frame of universality. In this brief, the relevant emerging dynamics are further investigated. Evidence that this nonlinear model demonstrates critical dynamics is scrutinized within the frame of the physics of critical phenomena. Additionally, the concept of criticality over the infected population fraction in epidemics (or a pandemic) is introduced and its importance is discussed, highlighting the emergence of the critical slowdown phenomenon. A simple method is proposed for estimating how far away a population is from this "singular" state, by utilizing the theory of critical phenomena. Finally, a dynamic approach applying the self-organized diffusion model is proposed, resulting in more accurate simulations, which can verify the effectiveness of restrictive measures. All the above are supported by real epidemic data case studies.