Exit strategies from lockdowns due to COVID-19: a scoping review

Is SARS-CoV-2 elimination or mitigation best? Regional and disease characteristics determine the recommended strategy

Public health responses to the COVID-19 pandemic varied across the world. Some countries (e.g. mainland China, New Zealand and Taiwan) implemented elimination strategies involving strict travel measures and periods of rigorous non-pharmaceutical interventions (NPIs) in the community, aiming to achieve periods with no disease spread; while others (e.g. many European countries and the USA) implemented mitigation strategies involving less strict NPIs for prolonged periods, aiming to limit community spread. Travel measures and community NPIs have high economic and social costs, and there is a need for guidelines that evaluate the appropriateness of an elimination or mitigation strategy in regional contexts. To guide decisions, we identify key criteria and provide indicators and visualizations to help answer each question. Considerations include determining whether disease elimination is: (1) necessary to ensure healthcare provision; (2) feasible from an epidemiological point of view and (3) cost-effective when considering, in particular, the economic costs of travel measures and treating infections. We discuss our recommendations by considering the regional and economic variability of Canadian provinces and territories, and the epidemiological characteristics of different SARS-CoV-2 variants. While elimination may be a preferable strategy for regions with limited healthcare capacity, low travel volumes, and few ports of entry, mitigation may be more feasible in large urban areas with dense infrastructure, strong economies, and with high connectivity to other regions.

Physical fitness level and the risk of severe COVID-19: A systematic review

To verify systematically the association between the status of physical fitness and the risk of severe Coronavirus disease 2019 (COVID-19). This systematic review is in accordance with the Preferred Reporting Items for Systematic Review and Meta Analyses (PRISMA) statement and the eligibility criteria followed the Population, Intervention, Comparison, Outcomes and Study (PICOS) recommendation. PubMed, Embase, SciELO and Cochrane electronic databases were searched. All studies that explored the relationship between the pattern of physical fitness and COVID-19 adverse outcomes (hospitalization, intensive care unit admission, intubation, or mortality), were selected. The quality of the studies was assessed by the specific scale of the Newcastle-Ottawa Scale. A total of seven observational studies were identified in this systematic review; 13 ​468 patients were included in one case-control study, two cohort studies, and four cross-sectional studies. All studies reported an inverse association between high physical fitness and severe COVID-19 (hospitalization, intensive care admission, or mortality). Only some studies reported comorbidities, especially obesity and cardiovascular disorders, but the results remained unchanged after controlling for comorbidities. The quality of the seven studies included was moderate according to the Newcastle-Ottawa Quality Assessment Scale. The methodological heterogeneity of the studies included did not allow a meta-analysis of the findings. In conclusion, higher physical fitness levels were associated with lower risk of hospitalization, intensive care admissions, and mortality rates among patients with COVID-19.

Multiple Concurrent Causal Relationships and Multiple Governance Pathways for Non-Pharmaceutical Intervention Policies in Pandemics: A Fuzzy Set Qualitative Comparative Analysis Based on 102 Countries and Regions

The global outbreak of COVID-19 has been wreaking havoc on all aspects of human societies. In addition to pharmaceutical interventions, non-pharmaceutical intervention policies have been proven to be crucial in slowing down the spread of the virus and reducing the impact of the outbreak on economic development, daily life, and social stability. However, no studies have focused on which non-pharmaceutical intervention policies are more effective; this is the focus of our study. We used data samples from 102 countries and regions around the world and selected seven categories of related policies, including work and school suspensions, assembly restrictions, movement restrictions, home isolation, international population movement restrictions, income subsidies, and testing and screening as the condition variables. A susceptible-exposed-infected-quarantined-recovered (SEIQR) model considering non-pharmaceutical intervention policies and latency with infectiousness was constructed to calculate the epidemic transmission rate as the outcome variable, and a fuzzy set qualitative comparative analysis (fsQCA) method was applied to explore the multiple concurrent causal relationships and multiple governance paths of non-pharmaceutical intervention policies for epidemics from the configuration perspective. We found a total of four non-pharmaceutical intervention policy pathways. Among them, L1 was highly suppressive, L2 was moderately suppressive, and L3 was externally suppressive. The results also showed that individual non-pharmaceutical intervention policy could not effectively suppress the spread of the pandemic. Moreover, three specific non-pharmaceutical intervention policies, including work stoppage and school closure, testing and screening, and economic subsidies, had a universal effect in the policies grouping for effective control of the pandemic transmission.

Design and Analysis of Hospital Throughput Maximization Algorithm under COVID-19 Pandemic

Under the global pandemic of COVID-19, public health facilities, such as hospitals, are required to readjust, design, and plan a safe movement flow of people to meet the social distance rules and quarantine COVID-19 and the non-COVID-19 patients to prevent cross-infection. However, readjustments to separate patients have significantly reduced the maximum throughput of public health facilities, worsening already scarce public health resources. Therefore, this paper proposes throughput maximization algorithms based on the one-way street problem which meets the requirements of social distance rules. First, the floor plan of a hospital is transformed into a graph, each node is traversed by breadth-first search. Then, this paper considers patients' node pair sets as different set unions, the direction of edges, and the color of links based on DFS-XOR algorithm are designed to distinguish the paths of COVID-19 and non-COVID-19 patients. Finally, this paper utilizes minimum shared link algorithms to determine the minimized sharing links between paths linking different set unions and components. The throughput is maximized by reducing the number of shared links and alternating links. The results indicate that compared with the brute force algorithms, the algorithms proposed in this paper significantly improve the maximum throughput.

A novel method to detect the early warning signal of COVID-19 transmission

BACKGROUND

Infectious illness outbreaks, particularly the corona-virus disease 2019 (COVID-19) pandemics in recent years, have wreaked havoc on human society, and the growing number of infected patients has put a strain on medical facilities. It's necessary to forecast early warning signals of potential outbreaks of COVID-19, which would facilitate the health ministry to take some suitable control measures timely to prevent or slow the spread of COVID-19. However, since the intricacy of COVID-19 transmission, which connects biological and social systems, it is a difficult task to predict outbreaks of COVID-19 epidemics timely.

RESULTS

In this work, we developed a new model-free approach, called, the landscape network entropy based on Auto-Reservoir Neural Network (ARNN-LNE), for quantitative analysis of COVID-19 propagation, by mining dynamic information from regional networks and short-term high-dimensional time-series data. Through this approach, we successfully identified the early warning signals in six nations or areas based on historical data of COVID-19 infections.

CONCLUSION

Based on the newly published data on new COVID-19 disease, the ARNN-LNE method can give early warning signals for the outbreak of COVID-19. It's worth noting that ARNN-LNE only relies on small samples data. Thus, it has great application potential for monitoring outbreaks of infectious diseases.

Molecular and Serologic Diagnostic Technologies for SARS-CoV-2

The COVID-19 pandemic has presented many challenges that have spurred biotechnological research to address specific problems. Diagnostics is one area where biotechnology has been critical. Diagnostic tests play a vital role in managing a viral threat by facilitating the detection of infected and/or recovered individuals. From the perspective of what information is provided, these tests fall into two major categories, molecular and serological. Molecular diagnostic techniques assay whether a virus is present in a biological sample, thus making it possible to identify individuals who are currently infected. Additionally, when the immune system is exposed to a virus, it responds by producing antibodies specific to the virus. Serological tests make it possible to identify individuals who have mounted an immune response to a virus of interest and therefore facilitate the identification of individuals who have previously encountered the virus. These two categories of tests provide different perspectives valuable to understanding the spread of SARS-CoV-2. Within these categories, different biotechnological approaches offer specific advantages and disadvantages. Here we review the categories of tests developed for the detection of the SARS-CoV-2 virus or antibodies against SARS-CoV-2 and discuss the role of diagnostics in the COVID-19 pandemic.