Modeling COVID-19 transmission between age groups in the United States considering virus mutations, vaccinations, and reinfection.

The in-depth understanding of the dynamics of COVID-19 transmission among different age groups is of great interest for governments and health authorities so that strategies can be devised to reduce the pandemic's detrimental effects. We developed the SIRDV-Virulence (Susceptible-Infected-Recovered-Dead-Vaccinated-Virulence) epidemiological model based on a population balance equation to study the effects virus mutants, vaccination strategies, 'Anti/Non Vaxxer' proportions, and reinfection rates to provide methods to mitigate COVID-19 transmission among the United States population. Based on publicly available data, we obtain the key parameters governing the spread of the pandemic. The results show that a large fraction of infected cases comes from the adult and children populations in the presence of a highly infectious COVID-19 mutant. Given the situation at the end of July 2021, the results show that prioritizing children and adult vaccinations over that of seniors can contain the spread of the active cases, thereby preventing the healthcare system from being overwhelmed and minimizing subsequent deaths. The model suggests that the only option to curb the effects of this pandemic is to reduce the population of unvaccinated individuals. A higher fraction of 'Anti/Non-vaxxers' and a higher reinfection rate can both independently lead to the resurgence of the pandemic.

Effect of the App-Based Video Guidance on Prenatal Pelvic Floor Muscle Training Combined with Global Postural Re-education for Stress Urinary Incontinence Prevention: A Protocol for a Multicenter, Randomized Controlled Trial.

BACKGROUND: As the effectiveness on stress urinary incontinence (SUI) prevention of pelvic floor muscle training (PFMT) for pregnant women has been inconclusive, we are planning to conduct a trial to evaluate a video program designed for prevention of SUI developed through combining PFMT with global postural reeducation (GPR). METHODS: As a randomized controlled trial, eligible participants will be randomized (1:1) into an exercise group and a control group to perform PFMT regularly following video guidance or with no intervention, respectively. The experimental stage will be from the 16th gestation week (GW) to the 12th month postpartum, with eight appointments at the 16th, 28th, 37th GW, delivery, the 6th week and the 3rd, 6th, and 12th month postpartum. Data will be collected regarding urinary leakage symptoms, the stress test, the modified Oxford Scale, pelvic floor ultrasound, perineal laceration classification at delivery, neonatal Apgar score, and questionnaires (PISQ-12, ICIQ-UI SF, I-QOL, OABSS). The primary outcome is the occurrence of the symptomatic SUI and positive stress test at the 6th week postpartum. DISCUSSION: This protocol is anticipated to evaluate the efficacy of the intervention via video app for the design of a future randomized control trial (RCT). TRIAL REGISTRATION: The trial has been registered at Chinese Clinical Trial Registry (registration number: ChiCTR2000029618).

A model to rate strategies for managing disease due to COVID-19 infection.

Considering looming fatality and economic recession, effective policy making based on ongoing COVID-19 pandemic is an urgent and standing issue. Numerous issues for controlling infection have arisen from public discussion led by medical professionals. Yet understanding of these factors has been necessarily qualitative and control measures to correct unfavorable trends specific to an infection area have been lacking. The logical implement for control is a large scale stochastic model with countless parameters lacking robustness and requiring enormous data. This paper presents a remedy for this vexing problem by proposing an alternative approach. Machine learning has come to play a widely circulated role in the study of complex data in recent times. We demonstrate that when machine learning is employed together with the mechanistic framework of a mathematical model, there can be a considerably enhanced understanding of complex systems. A mathematical model describing the viral infection dynamics reveals two transmissibility parameters influenced by the management strategies in the area for the control of the current pandemic. Both parameters readily yield the peak infection rate and means for flattening the curve, which is correlated to different management strategies by employing machine learning, enabling comparison of different strategies and suggesting timely alterations. Treatment of population data with the model shows that restricted non-essential business closure, school closing and strictures on mass gathering influence the spread of infection. While a rational strategy for initiation of an economic reboot would call for a wider perspective of the local economics, the model can speculate on its timing based on the status of the infection as reflected by its potential for an unacceptably renewed viral onslaught.