Decoding the global outbreak of COVID-19: the nature is behind the scene

Antiviral potential of nanoparticles for the treatment of Coronavirus infections

BACKGROUND

On 31st December 2019 in Wuhan, China, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), was acknowledged. This virus spread quickly throughout the world causing a global pandemic. The World Health Organization declared COVID-19 a pandemic disease on 11th March 2020. Since then, the whole world has come together and have developed several vaccines against this deadly virus. Similarly, several alternative searches for pandemic disease therapeutics are still ongoing. One of them has been identified as nanotechnology. It has demonstrated significant promise for detecting and inhibiting a variety of viruses, including coronaviruses. Several nanoparticles, including gold nanoparticles, silver nanoparticles, quantum dots, carbon dots, graphene oxide nanoparticles, and zinc oxide nanoparticles, have previously demonstrated remarkable antiviral activity against a diverse array of viruses.

OBJECTIVE

This review aims to provide a basic and comprehensive overview of COVID-19's initial global outbreak and its mechanism of infiltration into human host cells, as well as the detailed mechanism and inhibitory effects of various nanoparticles against this virus. In addition to nanoparticles, this review focuses on the role of several antiviral drugs used against COVID-19 to date.

CONCLUSION

COVID-19 has severely disrupted the social and economic lives of people all over the world. Due to a lack of adequate medical facilities, countries have struggled to maintain control of the situation. Neither a drug nor a vaccine has a 100% efficacy rate. As a result, nanotechnology may be a better therapeutic alternative for this pandemic disease.

Linear and non-linear dynamics of the epidemics: System identification based parametric prediction models for the pandemic outbreaks

Coronavirus disease 2019 (COVID-19) has endured constituting formidable economic, social, educational, and phycological challenges for the societies. Moreover, during pandemic outbreaks, the hospitals are overwhelmed with patients requiring more intensive care units and intubation equipment. Therein, to cope with these urgent healthcare demands, the state authorities seek ways to develop policies based on the estimated future casualties. These policies are mainly non-pharmacological policies including the restrictions, curfews, closures, and lockdowns. In this paper, we construct three model structures of the SpInItIbD-N (suspicious Sp, infected In, intensive care It, intubated Ib, and dead D together with the non-pharmacological policies N) holding two key targets. The first one is to predict the future COVID-19 casualties including the intensive care and intubated ones, which directly determine the need for urgent healthcare facilities, and the second one is to analyse the linear and non-linear dynamics of the COVID-19 pandemic under the non-pharmacological policies. In this respect, we have modified the non-pharmacological policies and incorporated them within the models whose parameters are learned from the available data. The trained models with the data released by the Turkish Health Ministry confirmed that the linear SpInItIbD-N model yields more accurate results under the imposed non-pharmacological policies. It is important to note that the non-pharmacological policies have a damping effect on the pandemic casualties and this can dominate the non-linear dynamics. Herein, a model without pharmacological or non-pharmacological policies might have more dominant non-linear dynamics. In addition, the paper considers two machine learning approaches to optimize the unknown parameters of the constructed models. The results show that the recursive neural network has superior performance for learning nonlinear dynamics. However, the batch least squares outperforms in the presence of linear dynamics and stochastic data. The estimated future pandemic casualties with the linear SpInItIbD-N model confirm that the suspicious, infected, and dead casualties converge to zero from 200000, 1400, 200 casualties, respectively. The convergences occur in 120 days under the current conditions.

Interrelationship between daily COVID-19 cases and average temperature as well as relative humidity in Germany

COVID-19 pandemic continues to obstruct social lives and the world economy other than questioning the healthcare capacity of many countries. Weather components recently came to notice as the northern hemisphere was hit by escalated incidence in winter. This study investigated the association between COVID-19 cases and two components, average temperature and relative humidity, in the 16 states of Germany. Three main approaches were carried out in this study, namely temporal correlation, spatial auto-correlation, and clustering-integrated panel regression. It is claimed that the daily COVID-19 cases correlate negatively with the average temperature and positively with the average relative humidity. To extract the spatial auto-correlation, both global Moran's [Formula: see text] and global Geary's [Formula: see text] were used whereby no significant difference in the results was observed. It is evident that randomness overwhelms the spatial pattern in all the states for most of the observations, except in recent observations where either local clusters or dispersion occurred. This is further supported by Moran's scatter plot, where states' dynamics to and fro cold and hot spots are identified, rendering a traveling-related early warning system. A random-effects model was used in the sense of case-weather regression including incidence clustering. Our task is to perceive which ranges of the incidence that are well predicted by the existing weather components rather than seeing which ranges of the weather components predicting the incidence. The proposed clustering-integrated model associated with optimal barriers articulates the data well whereby weather components outperform lag incidence cases in the prediction. Practical implications based on marginal effects follow posterior to model diagnostics.

Selective Pressure-Free Treatments for COVID-19

The new severe acute respiratory syndrome coronavirus (SARS-CoV-2) has caused more than 40 million human infections since December 2019, when a cluster of unexplained pneumonia cases was first reported in Wuhan, China. Just a few days after the coronavirus was officially recognized, it was identified as the causative agent of this mysterious pneumonia. This paper discusses the pros and cons of antiviral drugs from the selective pressure and possible drug resistance point of view. We also address the key advantages of potential selective pressure-free treatment methods such as the use of sparsely and densely ionizing low-dose radiation (LDR). It is known that LDR has the capacity to modulate excessive inflammatory responses, regulate lymphocyte counts and control bacterial co-infections in patients with COVID-19 and different modalities. Substantial evidence shows that viruses are constantly mutating and evolving. When an antiviral immune response is unable to eliminate a virus, viral evolution is promoted. Therefore, it is of crucial importance to limit the use of antivirals/vaccines against SARS-CoV-2 when their effects on viral fitness are not fully understood. Furthermore, to limit the spread of the virus, it is essential to develop a vaccine that is available for as many people as possible. However, with the advent of vaccines or new therapies, the new situation may force the virus to evolve. Given this consideration, selective pressure-free treatments for COVID-19 are of great importance.