On the Transmission Dynamics of SARS-CoV-2 in a Temperate Climate

On modelling airborne infection risk

Airborne infection risk analysis is usually performed for enclosed spaces where susceptible individuals are exposed to infectious airborne respiratory droplets by inhalation. It is usually based on exponential, dose-response models of which a widely used variant is the Wells-Riley (WR) model. We revisit this infection-risk estimate and extend it to the population level. We use an epidemiological model where the mode of pathogen transmission, airborne or contact, is explicitly considered. We illustrate the link between epidemiological models and the WR and the Gammaitoni and Nucci models. We argue that airborne infection quanta are, up to an overall density, airborne infectious respiratory droplets modified by a parameter that depends on biological properties of the pathogen, physical properties of the droplet and behavioural properties of the individual. We calculate the time-dependent risk of being infected for two scenarios. We show how the epidemic infection risk depends on the viral latent period and the event time, the time infection occurs. Infection risk follows the dynamics of the infected population. As the latent period decreases, infection risk increases. The longer a susceptible is present in the epidemic, the higher its risk of infection for equal exposure time to the pathogen is.

Parametric analysis of the transmission dynamics during indigenous aggregated outbreaks caused by five SARS-CoV-2 strains in Nanjing, China

Background

SARS-CoV-2 strains have been of great concern due to their high infectivity and antibody evasion.

Methods

In this study, data were collected on indigenous aggregated outbreaks in Nanjing from January 2020 to December 2022, caused by five strains including the original strain, the Delta variant, and the Omicron variant (BA.2, BA.5.2, and BF.7). The basic epidemiological characteristics of infected individuals were described and then parametric analysis of transmission dynamics was performed, including the calculation of incubation period, serial interval (SI), the basic reproductive number (R0), and the household secondary attack rate (HSAR). Finally, we compared the trends of transmission dynamic parameters of different strains.

Results

The incubation period for the original strain, the Delta variant, Omicron BA.2, Omicron BA.5.2, and Omicron BF.7 were 6 d (95% CI: 3.5-7.5 d), 5 d (95% CI: 4.0-6.0 d), 3 d (95% CI: 3.0-4.0 d), 3 d (95% CI: 3.0-3.0 d), and 2 d (95% CI: 2.0-3.0 d), respectively; Also, the SI of the five strains were 5.69 d, 4.79 d, 2.7 d, 2.12 d, and 2.43 d, respectively. Notably, the incubation period and SI of the five had both a progressive shortening trend (p < 0.001); Moreover, R0 of the five were 2.39 (95% CI: 1.30-4.29), 3.73 (95% CI: 2.66-5.15), 5.28 (95% CI: 3.52-8.10), 5.54 (95% CI: 2.69-11.17), 7.39 (95% CI: 2.97-18.76), with an increasing trend gradually (p < 0.01); HSAR of the five were 25.5% (95% CI: 20.1-31.7%), 27.4% (95% CI: 22.0-33.4%), 42.9% (95% CI: 34.3-51.8%), 53.1% (95% CI: 45.0-60.9%), 41.4% (95% CI, 25.5-59.3%), also with an increasing trend (p < 0.001).

Conclusion

Compared to the original strain, the incubation period and SI decreased while R0 and HSAR increased, suggesting that transmission in the population was faster and the scope of the population was wider. Overall, it's crucial to keep implementing comprehensive measures like monitoring and alert systems, herd immunization plans, and outbreak control.

Different Continuous Training Intensities Improve Echocardiographic Parameters, Quality of Life, and Functional Capacity in Heart Failure Patients with Reduced Ejection Fraction

Background

Multiple comorbidities and physiological changes play a role in a range of heart failure conditions and influence the most effective approach to exercise-based rehabilitation. This research aimed to examine and compare the outcomes of continuous training at three different intensities, focusing on left ventricular (LV) remodeling, functional capacity, and quality of life among patients with heart failure with reduced ejection fraction (HFrEF).

Methods

In this randomized control trial, a total of 60 male patients (average age: 54.33 ±2.35 years) with HFrEF were randomly allocated into three groups: 1) High-intensity continuous training group (HICT), 2) Moderate-intensity continuous training group (MICT), and 3) Low-intensity continuous training group (LICT). All the training was performed on a bicycle ergometer 3 times/week for 12 weeks. Echocardiographic parameters (left ventricular ejection fraction, left ventricular end-diastolic dimension, left ventricular end-systolic dimension, N-terminal pro-B-type natriuretic peptide (NT-proBNP), quality of life (Minnesota Living with Heart Failure Questionnaire), and functional capacity (6-minute walking test) were assessed before and the end of the study.

Results

The HICT group demonstrated the greatest improvements in all measured variables when compared to the other two groups (P < 0.05). These findings were consistent across all measured outcomes.

Conclusion

It was determined that HICT appears to yield the most favorable outcomes in enhancing echocardiographic measures, NT-proBNP levels, quality of life, and functional capacity among HFrEF patients.

Reaction-diffusion spatial modeling of COVID-19: Greece and Andalusia as case examples

We examine the spatial modeling of the outbreak of COVID-19 in two regions: the autonomous community of Andalusia in Spain and the mainland of Greece. We start with a zero-dimensional (0D; ordinary-differential-equation-level) compartmental epidemiological model consisting of Susceptible, Exposed, Asymptomatic, (symptomatically) Infected, Hospitalized, Recovered, and deceased populations (SEAIHR model). We emphasize the importance of the viral latent period (reflected in the exposed population) and the key role of an asymptomatic population. We optimize model parameters for both regions by comparing predictions to the cumulative number of infected and total number of deaths, the reported data we found to be most reliable, via minimizing the ℓ^{2} norm of the difference between predictions and observed data. We consider the sensitivity of model predictions on reasonable variations of model parameters and initial conditions, and we address issues of parameter identifiability. We model both the prequarantine and postquarantine evolution of the epidemic by a time-dependent change of the viral transmission rates that arises in response to containment measures. Subsequently, a spatially distributed version of the 0D model in the form of reaction-diffusion equations is developed. We consider that, after an initial localized seeding of the infection, its spread is governed by the diffusion (and 0D model "reactions") of the asymptomatic and symptomatically infected populations, which decrease with the imposed restrictive measures. We inserted the maps of the two regions, and we imported population-density data into the finite-element software package COMSOL Multiphysics®, which was subsequently used to numerically solve the model partial differential equations. Upon discussing how to adapt the 0D model to this spatial setting, we show that these models bear significant potential towards capturing both the well-mixed, zero-dimensional description and the spatial expansion of the pandemic in the two regions. Veins of potential refinement of the model assumptions towards future work are also explored.