Virus transmission by ultrasonic scaler and its prevention by antiviral agent: An in vitro study.

BACKGROUND: It is well recognized that dental procedures represent a potential way of infection transmission. With the COVID-19 pandemic, the focus of dental procedure associated transmission has rapidly changed from bacteria to viruses. The aim was to develop an experimental setup for testing the spread of viruses by ultrasonic scaler (USS) generated dental spray and evaluate its mitigation by antiviral coolants. METHODS: In a virus transmission tunnel, the dental spray was generated by USS with saline coolant and suspension of Equine Arteritis Virus (EAV) delivered to the USS tip. Virus transmission by settled droplets was evaluated with adherent RK13 cell lines culture monolayer. The suspended droplets were collected by a cyclone aero-sampler. Antiviral activity of 0.25% NaOCl and electrolyzed oxidizing water (EOW) was tested using a suspension test. Antiviral agents' transmission prevention ability was evaluated by using them as a coolant. RESULTS: In the suspension test with 0.25% NaOCl or EOW, the TCID50/mL was below the detection limit after 5 seconds. With saline coolant, the EAV-induced cytopathic effect on RK13 cells was found up to the distance of 45 cm, with the number of infected cells decreasing with distance. By aero-sampler, viral particles were detected in concentration ≤4.2 TCID50/mL. With both antiviral agents used as coolants, no EAV-associated RK-13 cell infection was found. CONCLUSION: We managed to predictably demonstrate EAV spread by droplets because of USS action. More importantly, we managed to mitigate the spread by a simple substitution of the USS coolant with NaOCl or EOW.

COVID-19 in Slovenia, from a Success Story to Disaster: What Lessons Can Be Learned?

During the first wave of the COVID-19 pandemic in spring 2020, Slovenia was among the least affected countries, but the situation became drastically worse during the second wave in autumn 2020 with high numbers of deaths per number of inhabitants, ranking Slovenia among the most affected countries. This was true even though strict non-pharmaceutical interventions (NPIs) to control the progression of the epidemic were being enforced. Using a semi-parametric Bayesian model developed for the purpose of this study, we explore if and how the changes in mobility, their timing and the activation of contact tracing can explain the differences in the epidemic progression of the two waves. To fit the model, we use data on daily numbers of deaths, patients in hospitals, intensive care units, etc., and allow transmission intensity to be affected by contact tracing and mobility (data obtained from Google Mobility Reports). Our results imply that though there is some heterogeneity not explained by mobility levels and contact tracing, implementing interventions at a similar stage as in the first wave would keep the death toll and the health system burden low in the second wave as well. On the other hand, sticking to the same timeline of interventions as observed in the second wave and focusing on enforcing a higher decrease in mobility would not be as beneficial. According to our model, the 'dance' strategy, i.e., first allowing the numbers to rise and then implementing strict interventions to make them drop again, has been played at too-late stages of the epidemic. In contrast, a 15-20% reduction of mobility compared to pre-COVID level, if started at the beginning and maintained for the entire duration of the second wave and coupled with contact tracing, could suffice to control the epidemic. A very important factor in this result is the presence of contact tracing; without it, the reduction in mobility needs to be substantially larger. The flexibility of our proposed model allows similar analyses to be conducted for other regions even with slightly different data sources for the progression of the epidemic; the extension to more than two waves is straightforward. The model could help policymakers worldwide to make better decisions in terms of the timing and severity of the adopted NPIs.

Modeling COVID-19 pandemic using Bayesian analysis with application to Slovene data.

In the paper, we propose a semiparametric framework for modeling the COVID-19 pandemic. The stochastic part of the framework is based on Bayesian inference. The model is informed by the actual COVID-19 data and the current epidemiological findings about the disease. The framework combines many available data sources (number of positive cases, number of patients in hospitals and in intensive care, etc.) to make outputs as accurate as possible and incorporates the times of non-pharmaceutical governmental interventions which were adopted worldwide to slow-down the pandemic. The model estimates the reproduction number of SARS-CoV-2, the number of infected individuals and the number of patients in different disease progression states in time. It can be used for estimating current infection fatality rate, proportion of individuals not detected and short term forecasting of important indicators for monitoring the state of the healthcare system. With the prediction of the number of patients in hospitals and intensive care units, policy makers could make data driven decisions to potentially avoid overloading the capacities of the healthcare system. The model is applied to Slovene COVID-19 data showing the effectiveness of the adopted interventions for controlling the epidemic by reducing the reproduction number of SARS-CoV-2. It is estimated that the proportion of infected people in Slovenia was among the lowest in Europe (0.350%, 90% CI [0.245-0.573]%), that infection fatality rate in Slovenia until the end of first wave was 1.56% (90% CI [0.94-2.21]%) and the proportion of unidentified cases was 88% (90% CI [83-93]%). The proposed framework can be extended to more countries/regions, thus allowing for comparison between them. One such modification is exhibited on data for Slovene hospitals.