Effect of the early use of antivirals on the COVID-19 pandemic. A computational network modeling approach

Eco-friendly spectrophotometric methods for determination of remdesivir and favipiravir; the recently approved antivirals for COVID-19 treatment

Remdesivir (REM) and Favipiravir (FAV) are recently approved antivirals prescribed in severely ill COVID-19 patients. Therefore, development of new, simple, rapid, sensitive, and selective methods for analysis of such drugs in their pharmaceutical formulations will be highly advantageous. Herein, we have developed different spectrophotometric methods for analysis of the studied analytes. Method I is based on direct spectrophotometric analysis of REM and FAV in ethanol at λ_max 244 and 323 nm, respectively. For simultaneous quantitation of REM and FAV, methods II-V were followed. Method II is based on derivative spectrophotometry in which REM was determined in second-order derivative spectra at 248 nm (the zero-crossing wavelength for FAV), while FAV was measured in first-order derivative spectra at 337 nm (the zero-crossing point for REM). Method III is the dual-wavelength method in which spectral intensities were subtracted at 244-207 nm for REM and at 330-400 nm for FAV. Method IV is the ratio subtraction in which ratio spectra were obtained by a suitable divisor followed by subtraction of intensities at 272-340 nm and 335-222 nm for REM and FAV, respectively. Method V is the derivative ratio method in which the obtained ratio spectra in method IV were converted to first-order derivative and then REM and FAV were recorded at 280 and 340 nm, respectively. Calibration graphs were linear in the ranges of 1-10 µg/mL for REM through all methods and 1-20 µg/mL for FAV in methods I and II, and 2-20 µg/mL by the other methods. The evolved methods were applied to pharmaceutical dosage forms of REM and FAV. All the proposed methods were further applied to human plasma samples containing both drugs with acceptable mean recoveries.

Mathematical Modeling of SARS-CoV-2 Omicron Wave under Vaccination Effects

Over the course of the COVID-19 pandemic millions of deaths and hospitalizations have been reported. Different SARS-CoV-2 variants of concern have been recognized during this pandemic and some of these variants of concern have caused uncertainty and changes in the dynamics. The Omicron variant has caused a large amount of infected cases in the US and worldwide. The average number of deaths during the Omicron wave toll increased in comparison with previous SARS-CoV-2 waves. We studied the Omicron wave by using a highly nonlinear mathematical model for the COVID-19 pandemic. The novel model includes individuals who are vaccinated and asymptomatic, which influences the dynamics of SARS-CoV-2. Moreover, the model considers the waning of the immunity and efficacy of the vaccine against the Omicron strain. This study uses the facts that the Omicron strain has a higher transmissibility than the previous circulating SARS-CoV-2 strain but is less deadly. Preliminary studies have found that Omicron has a lower case fatality rate compared to previous circulating SARS-CoV-2 strains. The simulation results show that even if the Omicron strain is less deadly it might cause more deaths, hospitalizations and infections. We provide a variety of scenarios that help to obtain insight about the Omicron wave and its consequences. The proposed mathematical model, in conjunction with the simulations, provides an explanation for a large Omicron wave under various conditions related to vaccines and transmissibility. These results provide an awareness that new SARS-CoV-2 variants can cause more deaths even if their fatality rate is lower.

Mathematical modeling to study the impact of immigration on the dynamics of the COVID-19 pandemic: A case study for Venezuela

We propose two different mathematical models to study the effect of immigration on the COVID-19 pandemic. The first model does not consider immigration, whereas the second one does. Both mathematical models consider five different subpopulations: susceptible, exposed, infected, asymptomatic carriers, and recovered. We find the basic reproduction number R₀ using the next-generation matrix method for the mathematical model without immigration. This threshold parameter is paramount because it allows us to characterize the evolution of the disease and identify what parameters substantially affect the COVID-19 pandemic outcome. We focus on the Venezuelan scenario, where immigration and emigration have been important over recent years, particularly during the pandemic. We show that the estimation of the transmission rates of the SARS-CoV-2 are affected when the immigration of infected people is considered. This has an important consequence from a public health perspective because if the basic reproduction number is less than unity, we can expect that the SARS-CoV-2 would disappear. Thus, if the basic reproduction number is slightly above one, we can predict that some mild non-pharmaceutical interventions would be enough to decrease the number of infected people. The results show that the dynamics of the spread of SARS-CoV-2 through the population must consider immigration to obtain better insight into the outcomes and create awareness in the population regarding the population flow.

How and to what extent does the anti-social behavior of violating self-quarantine measures increase the spread of disease?

COVID-19 has shown that quarantine (or self-isolation) may be the only available tool against an unknown infectious disease if neither an effective vaccine nor anti-viral medication is available. Motivated by the fact that a considerable number of people were not compliant with the request for self-quarantine made by public authorities, this study used a multi-agent simulation model, whose results were validated by theory work, which highlights how and to what extent such an anti-social behavior hampers the confinement of a disease. Our framework quantifies two important scenarios: in one scenario a certain number of individuals totally ignore quarantine, whereas in the second scenario a larger number of individuals partially ignore the imposed policy. Our results reveal that the latter scenario can be more hazardous even if the total amount of social deficit of activity-measured by the total number of severed links in a physical network-would be same as the former scenario has, of which quantitative extent is dependent on the fraction of asymptomatic infected cases and the level of quarantine intensity the government imposing. Our findings have significance not only to epidemiology but also to research in the broader field of network science.

PACS numbers

Theory and modeling; computer simulation, 87.15.Aa; Dynamics of evolution, 87.23.Kg.

Antiviral Efficacy of Molnupiravir for COVID-19 Treatment

The ongoing global pandemic of COVID-19 poses unprecedented public health risks for governments and societies around the world, which have been exacerbated by the emergence of SARS-CoV-2 variants. Pharmaceutical interventions with high antiviral efficacy are expected to delay and contain the COVID-19 pandemic. Molnupiravir, as an oral antiviral prodrug, is active against SARS-CoV-2 and is now (23 February 2022) one of the seven widely-used coronavirus treatments. To estimate its antiviral efficacy of Molnupiravir, we built a granular mathematical within-host model. We find that the antiviral efficacy of Molnupiravir to stop the growth of the virus is 0.56 (95% CI: 0.49, 0.64), which could inhibit 56% of the replication of infected cells per day. There has been good progress in developing high-efficacy antiviral drugs that rapidly reduce viral load and may also reduce the infectiousness of treated cases if administered as early as possible.

Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges

Since late 2019, the beginning of coronavirus disease 2019 (COVID-19) pandemic, transmission dynamics models have achieved great development and were widely used in predicting and policy making. Here, we provided an introduction to the history of disease transmission, summarized transmission dynamics models into three main types: compartment extension, parameter extension and population-stratified extension models, highlight the key contribution of transmission dynamics models in COVID-19 pandemic: estimating epidemiological parameters, predicting the future trend, evaluating the effectiveness of control measures and exploring different possibilities/scenarios. Finally, we pointed out the limitations and challenges lie ahead of transmission dynamics models.

Complex network model for COVID-19: Human behavior, pseudo-periodic solutions and multiple epidemic waves

We propose a mathematical model for the transmission dynamics of SARS-CoV-2 in a homogeneously mixing non constant population, and generalize it to a model where the parameters are given by piecewise constant functions. This allows us to model the human behavior and the impact of public health policies on the dynamics of the curve of active infected individuals during a COVID-19 epidemic outbreak. After proving the existence and global asymptotic stability of the disease-free and endemic equilibrium points of the model with constant parameters, we consider a family of Cauchy problems, with piecewise constant parameters, and prove the existence of pseudo-oscillations between a neighborhood of the disease-free equilibrium and a neighborhood of the endemic equilibrium, in a biologically feasible region. In the context of the COVID-19 pandemic, this pseudo-periodic solutions are related to the emergence of epidemic waves. Then, to capture the impact of mobility in the dynamics of COVID-19 epidemics, we propose a complex network with six distinct regions based on COVID-19 real data from Portugal. We perform numerical simulations for the complex network model, where the objective is to determine a topology that minimizes the level of active infected individuals and the existence of topologies that are likely to worsen the level of infection. We claim that this methodology is a tool with enormous potential in the current pandemic context, and can be applied in the management of outbreaks (in regional terms) but also to manage the opening/closing of borders.

All-cause mortality among patients treated with repurposed antivirals and antibiotics for COVID-19 in Mexico City: A real-world observational study

The aim of our study was to evaluate all-cause mortality risk in patients with laboratory-confirmed COVID-19 in Mexico City treated with repurposed antivirals and antibiotics. This real-world retrospective cohort study contemplated 395,343 patients evaluated for suspected COVID-19 between February 24 and September 14, 2020 in 688 primary-to-tertiary medical units in Mexico City. Patients were included with a positive RT-PCR for SARS-CoV-2; those receiving unspecified antivirals, excluded; and groups of antivirals prescribed in < 30 patients, eliminated. Survival and mortality risks were determined for patients receiving antivirals, antibiotics, both, or none. We assessed the effect of early (<2 days) versus late (>2 days) use of antivirals on mortality in a sub-cohort of patients. Multivariable adjustment, propensity score matching, generalized estimating equations, and calculation of E-values were performed to limit confounding. 136,855 patients were analyzed; mean age 44.2 (SD:16.8) years; 51.3 % were men. 16.6 % received antivirals (3 %), antibiotics (10 %), or both (3.6 %). Antivirals studied were Oseltamivir (n=8414), Amantadine (n=319), Lopinavir-Ritonavir (n=100), Rimantadine (n=61), Zanamivir (n=39), and Acyclovir (n=36). Survival with antivirals (73.7 %, p<0.0001) and antibiotics (85.8 %, p<0.0001) was lower than no antiviral/antibiotic (93.6 %). After multivariable adjustment, increased risk of death occurred with antivirals (HR=1.72, 95 % CI: 1.61-1.84) in ambulatory (HR=4.7, 95 % CI: 3.94-5.62) and non-critical (HR=2.03, 95 % CI: 1.86-2.21) patients. Oseltamivir increased mortality risk in the general population (HR=1.72, 95 % CI: 1.61-1.84), ambulatory (HR=4.79, 95 % CI: 4.01-5.75), non-critical (HR=2.05, 95 % CI: 1.88-2.23), and pregnancy (HR=8.35, 95 % CI: 1.77-39.30); as well as hospitalized (HR=1.13, 95 % CI: 1.01-1.26) and critical patients (HR=1.22, 95 % CI: 1.05-1.43) after propensity score-matching. Early versus late oseltamivir did not modify the risk. Antibiotics were a risk factor in general population (HR=1.13, 95 % CI: 1.08-1.19) and pediatrics (HR=4.22, 95 % CI: 2.01-8.86), but a protective factor in hospitalized (HR=0.81, 95 % CI: 0.77-0.86) and critical patients (HR=0.67, 95 % CI: 0.63-0.72). No significant benefit for repurposed antivirals was observed; oseltamivir was associated with increased mortality. Antibiotics increased mortality risk in the general population but may increase survival in hospitalized and critical patients.