The existence, spread, and strategies for environmental monitoring and control of SARS-CoV-2 in environmental media

Development of a reverse transcriptase digital droplet polymerase chain reaction-based approach for SARS-CoV-2 variant surveillance in wastewater

An urgent need for effective surveillance strategies arose due to the global emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although vaccines and antivirals are available, concerns persist about the evolution of new variants with potentially increased infectivity, transmissibility, and immune evasion. Therefore, variant monitoring is crucial for public health decision-making. Wastewater-based surveillance has proven to be an effective tool to monitor SARS-CoV-2 variants within populations. Specific SARS-CoV-2 variants are detected and quantified in wastewater in this study using a reverse transcriptase digital droplet polymerase chain reaction (RT-ddPCR) approach. The 11 designed assays were first validated in silico using a substantial dataset of high-quality SARS-CoV-2 genomes to ensure comprehensive variant coverage. The assessment of the sensitivity and specificity with reference material showed the capability of the developed assays to reliably identify target mutations while minimizing false positives and false negatives. The applicability of the assays was evaluated using wastewater samples from a wastewater treatment plant in Ghent, Belgium. The quantification of the specific mutations linked to the variants of concern present in these samples was calculated using these assays based on the detection of single mutations, which confirms their use for real-world variant surveillance. In conclusion, this study provides an adaptable protocol to monitor SARS-CoV-2 variants in wastewater with high sensitivity and specificity. Its potential for broader application in other viral surveillance contexts highlights its added value for rapid response to emerging infectious diseases. PRACTITIONER POINTS: Robust RT-ddPCR methodology for specific SARS-CoV-2 variants of concern detection in wastewater. Rigorous validation that demonstrates high sensitivity and specificity. Demonstration of real-world applicability using wastewater samples. Valuable tool for rapid response to emerging infectious diseases.

Early warning of emerging infectious diseases based on multimodal data

The coronavirus disease 2019 (COVID-19) pandemic has dramatically increased the awareness of emerging infectious diseases. The advancement of multiomics analysis technology has resulted in the development of several databases containing virus information. Several scientists have integrated existing data on viruses to construct phylogenetic trees and predict virus mutation and transmission in different ways, providing prospective technical support for epidemic prevention and control. This review summarized the databases of known emerging infectious viruses and techniques focusing on virus variant forecasting and early warning. It focuses on the multi-dimensional information integration and database construction of emerging infectious viruses, virus mutation spectrum construction and variant forecast model, analysis of the affinity between mutation antigen and the receptor, propagation model of virus dynamic evolution, and monitoring and early warning for variants. As people have suffered from COVID-19 and repeated flu outbreaks, we focused on the research results of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses. This review comprehensively viewed the latest virus research and provided a reference for future virus prevention and control research.

Predicting the concentrations of enteric viruses in urban rivers running through the city center via an artificial neural network

Viral waterborne diseases are widespread in cities due largely to the occurrence of enteric viruses in urban rivers, which pose a significant concern to human health. Yet, the application of rapid detection technology for enteric viruses in environmental water remains undeveloped globally. Here, multiple linear regression (MLR) modeling and artificial neural network (ANN) modeling, which used frequently measured physicochemical parameters in river water, were constructed to predict the concentration of enteric viruses including human enteroviruses (EnVs), rotaviruses (HRVs), astroviruses (AstVs), noroviruses GⅡ (HuNoVs GⅡ), and adenoviruses (HAdVs) in rivers. After training, testing, and validating, ANN models showed better performance than any MLR model for predicting the viral concentration in Jinhe River. All determined R-values for ANN models exceeded 0.89, suggesting a strong correlation between the predicted and measured outputs for target enteric viruses. Furthermore, ANN models provided a better congruence between the observed and predicted concentrations of each virus than MLR models did. Together, these findings strongly suggest that ANN modeling can provide more accurate and timely predictions of viral concentrations based on frequent (or routine) measurements of physicochemical parameters in river water, which would improve assessments of waterborne disease prevalence in cities.

Social Media Efficacy in Crisis Management: Effectiveness of Non-pharmaceutical Interventions to Manage COVID-19 Challenges

The new identified virus COVID-19 has become one of the most contagious diseases in human history. The ongoing coronavirus has created severe threats to global mental health, which have resulted in crisis management challenges and international concerns related to health issues. As of September 9, 2021, there were over 223.4 million patients with COVID-19, including 4.6 million deaths and over 200 million recovered patients reported worldwide, which has made the COVID-19 outbreak one of the deadliest pandemics in human history. The aggressive public health implementations endorsed various precautionary safety and preventive strategies to suppress and minimize COVID-19 disease transmission. The second, third, and fourth waves of COVID-19 continue to pose global challenges to crisis management, as its evolution and implications are still unfolding. This study posits that examining the strategic ripostes and pandemic experiences sheds light on combatting this global emergency. This study recommends two model strategies that help reduce the adverse effects of the pandemic on the immune systems of the general population. This present paper recommends NPI interventions (non-pharmaceutical intervention) to combine various measures, such as the suppression strategy (lockdown and restrictions) and mitigation model to decrease the burden on health systems. The current COVID-19 health crisis has influenced all vital economic sectors and developed crisis management problems. The global supply of vaccines is still not sufficient to manage this global health emergency. In this crisis, NPIs are helpful to manage the spillover impacts of the pandemic. It articulates the prominence of resilience and economic and strategic agility to resume economic activities and resolve healthcare issues. This study primarily focuses on the role of social media to tackle challenges and crises posed by COVID-19 on economies, business activities, healthcare burdens, and government support for societies to resume businesses, and implications for global economic and healthcare provision disruptions. This study suggests that intervention strategies can control the rapid spread of COVID-19 with hands-on crisis management measures, and the healthcare system will resume normal conditions quickly. Global economies will revitalize scientific contributions and collaborations, including social science and business industries, through government support.