Modelling the first wave of COVID-19 in India

COVID-19 Mortality and Remdesivir - A Retrospective Cohort in Intensive Care Setting

BACKGROUND

Remdesivir is a broad-spectrum antiviral drug that received emergency use authorization in the first wave of the COVID-19 pandemic. However, its effectiveness in preventing mortality in COVID-19 patients who required intensive care was unclear.

PATIENTS AND METHODS

We retrospectively analyzed clinical data of 302 patients from intensive care units of a quaternary care center with moderate to severe COVID-19 illness and followed them until discharge between March 2020 and February 2021. Participants who received at least five doses of Remdesivir were compared against participants who received standard care. The primary outcome was all-cause mortality. Secondary outcomes included invasive mechanical ventilation, clinical worsening, and intensive care stay.

RESULTS

Remdesivir use was not associated with all-cause mortality in this cohort (age and sex-adjusted OR = 0.76, 95% CI 0.4 -1.5, p = 0.409). However, when stratified for clinical severity and steroid use, Remdesivir demonstrated a strong negative association with all-cause mortality in severely ill patients (OR 0.3, 95% CI 0.1 - 0.6, p = 0.003) or when used along with intravenous Methylprednisolone (Infusion/Bolus, OR 0.2/0.3, 95% CI 0.1 - 0.9 p = 0.06). Remdesivir use was not significantly associated with invasive mechanical ventilation or clinical worsening but with prolonged ICU stay.

CONCLUSION

While Remdesivir use may not affect all-cause mortality in moderate to severely ill COVID-19 ICU patients, it may still benefit severely ill patients or when used with intravenous steroids. However, the limitations of the present study necessitate a randomized controlled trial to test this combined intervention strategy.

An adaptive testing strategy for efficient utilization of healthcare resources during an epidemic

Lockdowns are found to be effective against rapidly spreading epidemics like COVID-19. Two downsides to strategies rooted in social distancing and lockdowns are that they adversely affect the economy and prolong the duration of the epidemic. The extended duration observed in these strategies is often due to the under-utilization of medical facilities. Even though an under-utilized health care system is preferred over an overwhelmed one, an alternate strategy could be to maintain medical facilities close to their capacity, with a factor of safety. We explore the practicality of this alternate mitigation strategy and show that it can be achieved by varying the testing rate. We present an algorithm to calculate the number of tests per day to maintain medical facilities close to their capacity. We illustrate the efficacy of our strategy by showing that it reduced the epidemic duration by 40% in comparison to lockdown-based strategies.

A mathematical model for transmission dynamics of COVID-19 infection

In this paper, a mathematical model of COVID-19 has been proposed to study the transmission dynamics of infection by taking into account the role of symptomatic and asymptomatic infected individuals. The model has also considered the effect of non-pharmaceutical interventions (NPIs) in controlling the spread of virus. The basic reproduction number ( R 0 ) has been computed and the analysis shows that for R 0 < 1 , the disease-free state becomes globally stable. The conditions of existence and stability for two other equilibrium states have been obtained. Transcritical bifurcation occurs when basic reproduction number is one (i.e. R 0 = 1 ). It is found that when asymptomatic cases get increased, infection will persist in the population. However, when symptomatic cases get increased as compared to asymptomatic ones, the endemic state will become unstable and infection may eradicate from the population. Increasing NPIs decrease the basic reproduction number and hence, the epidemic can be controlled. As the COVID-19 transmission is subject to environmental fluctuations, the effect of white noise has been considered in the deterministic model. The stochastic differential equation model has been solved numerically by using the Euler-Maruyama method. The stochastic model gives large fluctuations around the respective deterministic solutions. The model has been fitted by using the COVID-19 data of three waves of India. A good match is obtained between the actual data and the predicted trajectories of the model in all three waves of COVID-19. The findings of this model may assist policymakers and healthcare professionals in implementing the most effective measures to prevent the transmission of COVID-19 in different settings.