Sewershed surveillance as a tool for smart management of a pandemic in threshold countries. Case study: Tracking SARS-CoV-2 during COVID-19 pandemic in a major urban metropolis in northwestern Argentina.

Wastewater-based epidemiology is an economical and effective tool for monitoring the COVID-19 pandemic. In this study we proposed sampling campaigns that addressed spatial-temporal trends within a metropolitan area. This is a local study of detection and quantification of SARS-CoV-2 in wastewater during the onset, rise, and decline of COVID-19 cases in Salta city (Argentina) over the course of a twenty-one-week period (13 Aug to 30 Dec) in 2020. Wastewater samples were gathered from 13 sewer manholes specific to each sewershed catchment, prior to convergence or mixing with other sewer lines, resulting in samples specific to individual catchments with defined areas. The 13 sewershed catchments selected comprise 118,832 connections to the network throughout the city, representing 84.7 % (534,747 individuals) of the total population. The number of COVID19-related exposure and symptoms cases in each area were registered using an application developed for smartphones by the provincial government. Geographical coordinates provided by the devices were recorded, and consequently, it was possible to geolocalise all app-cases and track them down to which of the 13 sampling catchments belonged. RNA fragments of SARS-CoV-2 were detected in every site since the beginning of the monitoring, anticipating viral circulation in the population. Over the course of the 21-week study, the concentrations of SARS-CoV-2 ranged between 1.77 × 104 and 4.35 × 107 genome copies/L. There was a correspondence with the highest viral load in wastewater and the peak number of cases reported by the app for each catchment. The associations were evaluated with correlation analysis. The viral loads of SARS-CoV-2 in wastewater were a feasible means to describe the trends of COVID-19 infections. Surveillance at sewershed scale, provided reliable and strategic information that could be used by local health stakeholders to manage the COVID-19 pandemic.

Virtual screening of plant-derived compounds against SARS-CoV-2 viral proteins using computational tools.

The new SARS-CoV-2, responsible for the COVID-19 pandemic, has been threatening public health worldwide for more than a year. The aim of this work was to evaluate compounds of natural origin, mainly from medicinal plants, as potential SARS-CoV-2 inhibitors through docking studies. The viral spike (S) glycoprotein and the main protease Mpro, involved in the recognition of virus by host cells and in viral replication, respectively, were the main molecular targets in this study. Molecular docking was performed using AutoDock, which allowed us to select the plant actives with the highest affinity towards the viral targets and to identify the interaction molecular sites with the SARS-CoV2 proteins. The best energy binding values for S protein were, in kcal/mol: -19.22 for glycyrrhizin, -17.84 for gitoxin, -12.05 for dicumarol, -10.75 for diosgenin, and -8.12 for delphinidin. For Mpro were, in kcal/mol: -9.36 for spirostan, -8.75 for N-(3-acetylglycyrrhetinoyl)-2-amino-propanol, -8.41 for α-amyrin, -8.35 for oleanane, -8.11 for taraxasterol, and -8.03 for glycyrrhetinic acid. In addition, the synthetic drugs umifenovir, chloroquine, and hydroxychloroquine were used as controls for S protein, while atazanavir and nelfinavir were used for Mpro. Key hydrogen bonds and hydrophobic interactions between natural compounds and the respective viral proteins were identified, allowing us to explain the great affinity obtained in those compounds with the lowest binding energies. These results suggest that these natural compounds could potentially be useful as drugs to be experimentally evaluated against COVID-19.

[Proposal for the control and mitigation of COVID-19 dissemination: strategic management of the disease]. / Propuestas para el control y la mitigación de la diseminación de COVID-19: un manejo estratégico de la enfermedad.

The rapid spread of COVID-19 throughout the world, has led most of the affected countries to close their borders and implement some form of lockdown. Six months after the pandemic started, many countries made decisions tending to relax the lockdown, although without a vaccine or treatment capable of confronting SARS-CoV-2 infection, the situation could be reversed at any time. In this context, the aim of this work was to propose a decision algorithm that will allow to optimize asymptomatic case detections and strategically manage quarantine to prevent the spread of the virus and drive the transition to a managed new normal. This tentative proposal was developed for optimizing and ordering the number of tests for the detection of SARS-CoV-2, analyzing composite samples (group analysis) combining with those samples individually taken from asymptomatic members of cohorts of interest. Cohorts were defined according to their critical role in society and/or their vulnerability. The algorithm includes variables such as cohort priority, number of cohort members in the analysis groups, intra-and intergroup contact, vulnerability to contagion due to the activity performed, and time elapsed since last testing. The proposed tool was illustrated with defined hypothetical cohorts, in which, for the sake of simplification, only one analysis group was considered. The application of this tool allowed to establish in a rational way a priority order to test critical groups in society. Furthermore, this tool would help to optimize resources, reducing the impact on a region's health, society, and economy.