Ultrasound treatment inhibits SARS-CoV-2 in vitro infectivity (preprint)

Background: COVID-19 (coronavirus disease 2019) is a disease caused by infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), affecting millions of people worldwide, with a high rate of deaths. The present study aims to evaluate ultrasound (US) as a physical method for virus inactivation. Materials and methods: The UV-transductor was exposed to the SARS-CoV-2 viral solution for 30 minutes. Vero-E6 cells were infected with medium exposure or not with the US, using 3-12, 5-10, or 6-18MHz as frequencies applied. We performed confocal microscopy to determine virus infection and replicative process. Moreover, we detected the virus particles with a titration assay. Results: We observed an effective infection of SARS-CoV-2 Wuhan, Delta, and Gamma strains in comparison with mock, an uninfected experimental group. The US treatment was able to inhibit the Wuhan strain in all applied frequencies. Interestingly, 3-12 and 6-18MHz did not inhibit SARS-CoV-2 delta and gamma variants infection, on the other hand, 5-10MHz was able to abrogate infection and replication in all experimental conditions. Conclusions: These results show that SARS-CoV-2 is susceptible to US exposure at a specific frequency 5-10MHz and could be a novel tool for reducing the incidence of SARS-CoV-2 infection. Keywords: Ultrasound, SARS-CoV-2, virucidal effect, COVID-19

Social Interaction Layers in Complex Networks for the Dynamical Epidemic Modeling of COVID-19 in Brazil (preprint)

We are currently living in a state of uncertainty due to the pandemic caused by the Sars-CoV-2 virus. There are several factors involved in the epidemic spreading such as the individual characteristics of each city/country. The true shape of the epidemic dynamics is a large, complex system such as most of the social systems. In this context, Complex networks are a great candidate to analyze these systems due to their ability to tackle structural and dynamical properties. Therefore this study presents a new approach to model the COVID-19 epidemic using a multi-layer complex network, where nodes represent people, edges are social contacts, and layers represent different social activities. The model improves the traditional SIR and it is applied to study the Brazilian epidemic by analyzing possible future actions and their consequences. The network is characterized using statistics of infection, death, and hospitalization time. To simulate isolation, social distancing, or precautionary measures we remove layers and/or reduce the intensity of social contacts. Results show that even taking various optimistic assumptions, the current isolation levels in Brazil still may lead to a critical scenario for the healthcare system and a considerable death toll (average of 149,000). If all activities return to normal, the epidemic growth may suffer a steep increase, and the demand for ICU beds may surpass 3 times the country's capacity. This would surely lead to a catastrophic scenario, as our estimation reaches an average of 212,000 deaths even considering that all cases are effectively treated. The increase of isolation (up to a lockdown) shows to be the best option to keep the situation under the healthcare system capacity, aside from ensuring a faster decrease of new case occurrences (months of difference), and a significantly smaller death toll (average of 87,000).