Phase- and epidemic region-adjusted estimation of the number of coronavirus disease 2019 cases in China

Impact of COVID-19 pandemic on the mental health status among general masses: An in-deep analysis of the worst "hitters" of COVID-19 pandemic

BACKGROUND

A plethora of studies have reported the adverse psychiatric outcomes among the general masses during the COVID-19 pandemic; however, not much data is available in relation to the Indian population from this perspective. The aim of the present study was to investigate the impact of the COVID-19 pandemic on the mental health status among the general masses in the Indian population.

MATERIALS AND METHODS

The present study was planned in a cross-sectional study design between July 2020 and October 2021 in which a well-structured questionnaire, consisting of questions assessing the sociodemographic profile, while, also, specific questions related to the stress and anxiety-related variables, was used. The questionnaire was validated through intra-class correlation with a strong correlation of 0.84. The Chi-square test was used for statistical analysis to test the association between the studied variables, while P < 0.05 was considered statistically significant.

RESULTS

On comparison between the male and female participants using stress and anxiety-related variables, 43.81% of males as against 56.19% of the female participants reported that they felt horrified due to the pandemic with the results being statistically highly significant (P = 0.0043). Similarly, 45.18% of male and 54.82% of female participants expressed apprehension due to the fear of the pandemic with the results being statistically significant (P = 0.0217).

CONCLUSION

The research findings of the present study indicated that men and women responded to stress differently, with women experiencing greater sadness and anxiety and were found to be at a relatively greater risk for developing anxiety and depression than men.

Evaluation of phase-adjusted interventions for COVID-19 using an improved SEIR model

A local COVID-19 outbreak with two community clusters occurred in a large industrial city, Shaoxing, China, in December 2021 after serial interventions were imposed. We aimed to understand the reason by analysing the characteristics of the outbreak and evaluating the effects of phase-adjusted interventions. Publicly available data from 7 December 2021 to 25 January 2022 were collected to analyse the epidemiological characteristics of this outbreak. The incubation period was estimated using Hamiltonian Monte Carlo method. A well-fitted extended susceptible-exposed-infectious-recovered model was used to simulate the impact of different interventions under various combination of scenarios. There were 387 SARS-CoV-2-infected cases identified, and 8.3% of them were initially diagnosed as asymptomatic cases. The estimated incubation period was 5.4 (95% CI 5.2-5.7) days for all patients. Strengthened measures of comprehensive quarantine based on tracing led to less infections and a shorter duration of epidemic. With a same period of incubation, comprehensive quarantine was more effective in containing the transmission than other interventions. Our findings reveal an important role of tracing and comprehensive quarantine in blocking community spread when a cluster occurred. Regions with tense resources can adopt home quarantine as a relatively affordable and low-impact intervention measure compared with centralized quarantine.

Roll to roll in situ preparation of recyclable, washable, antibacterial Ag loaded nonwoven fabric

Functional fabrics with antibacterial performance are more welcome nowadays. However, the fabrication of functional fabrics with durable, steady performance via a cost-effective way remains a challenge. Polypropylene (denoted as PP) nonwoven fabric was modified by polyvinyl alcohol (denoted as PVA), followed by the in-situ deposition of silver nanoparticles (denoted as Ag NPs) to afford PVA-modified and Ag NPs-loaded PP (denoted as Ag/PVA/PP) fabric. The encapsulation of PP fiber by PVA coating contributes to greatly enhancing the adhesion of the loaded Ag NPs to the PP fiber, and the Ag/PVA/PP nonwoven fabrics exhibit significantly improved mechanical properties as well as excellent antibacterial activity against Escherichia coli (coded as E. coli). Typically, the Ag/PVA/PP nonwoven fabric obtained at a silver ammonia concentration of 30 mM has the best mechanical properties and the antibacterial rate reaches 99.99% against E. coli. The fabric retains excellent antibacterial activity even after washing for 40 cycles, showing prospects in reuse. Moreover, the Ag/PVA/PP nonwoven fabric could find promising application in industry, thanks to its desired air-permeability and moisture-permeability. In addition, we developed a roll-to-roll production process and conducted preliminary exploration to verify the feasibility of this method.

Psychometric Properties of an Instrument to Assess the Fear of COVID-19 in a Sample in Argentina: a Mixed Approach

The FCV-19S was the first instrument designed to assess the severity of fear related to COVID-19 and has already been validated in many languages. The objective of this study was to evaluate the homogeneity and construct validity of the 5-item version of the FCV19S, using an online questionnaire in 599 people. The participants' age ranged from 18 to 65 years. Age, gender, marital status, educational level, employment status, and socioeconomic status were analyzed. In the evaluation process we assessed interitem correlation, item rest-correlation, confirmatory factor analysis: Root Mean Square Error of Approximation, Comparative Fix Index, Tucker-Lewis Index; internal consistency (Cronbach's alpha, McDonald's omega), and the Rasch model was assessed for learning more about the psychometric properties of the scale, which allows a detailed knowledge of the strengths and weaknesses of a scale. The FCV-5S has adequate psychometric indicators from the perspective of the Classical Theory of Items. The major limitations were using a self-reported measure and having a convenience sample not necessarily representative of the general population of Argentina.

Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.

COVID-19: breaking down a global health crisis

Coronavirus disease 2019 (COVID-19) is the second pandemic of the twenty-first century, with over one-hundred million infections and over two million deaths to date. It is a novel strain from the Coronaviridae family, named Severe Acute Respiratory Distress Syndrome Coronavirus-2 (SARS-CoV-2); the 7th known member of the coronavirus family to cause disease in humans, notably following the Middle East Respiratory syndrome (MERS), and Severe Acute Respiratory Distress Syndrome (SARS). The most characteristic feature of this single-stranded RNA molecule includes the spike glycoprotein on its surface. Most patients with COVID-19, of which the elderly and immunocompromised are most at risk, complain of flu-like symptoms, including dry cough and headache. The most common complications include pneumonia, acute respiratory distress syndrome, septic shock, and cardiovascular manifestations. Transmission of SARS-CoV-2 is mainly via respiratory droplets, either directly from the air when an infected patient coughs or sneezes, or in the form of fomites on surfaces. Maintaining hand-hygiene, social distancing, and personal protective equipment (i.e., masks) remain the most effective precautions. Patient management includes supportive care and anticoagulative measures, with a focus on maintaining respiratory function. Therapy with dexamethasone, remdesivir, and tocilizumab appear to be most promising to date, with hydroxychloroquine, lopinavir, ritonavir, and interferons falling out of favour. Additionally, accelerated vaccination efforts have taken place internationally, with several promising vaccinations being mass deployed. In response to the COVID-19 pandemic, countries and stakeholders have taken varying precautions to combat and contain the spread of the virus and dampen its collateral economic damage. This review paper aims to synthesize the impact of the virus on a global, micro to macro scale.

A city cluster risk-based approach for Sars-CoV-2 and isolation barriers based on anonymized mobile phone users' location data

Given the recent outbreak of Sars-CoV-2, several countries started to seek different strategies to control contamination and minimize fatalities, which are usually the primary objectives for all strategies. Secondary objectives are related to economic factors, therefore ensuring that society would be able is to keep its essential activities and avoid supply disruptions. This paper presents an application of anonymized mobile phone users' location data to estimate population flow amongst cities with an origin-destination matrix. The work includes a clustering analysis of cities, which may enable policymakers (and epidemiologists) to develop public policies giving the appropriate consideration for each set of cities within a Province or State. Risk measures are included to analyze the severity of the spread among the clusters, which can be ranked. Then, intelligence can be obtained from the analysis, and some clusters could be isolated to avoid contagion while keeping their economic activities. Therefore, this analysis is reproducible for other states of Brazil and other countries and can be adapted for districts within a city, especially considering the possibility of a second wave COVID-19 pandemic.

Space-Time Variation and Spatial Differentiation of COVID-19 Confirmed Cases in Hubei Province Based on Extended GWR

Clarifying the regional transmission mechanism of COVID-19 has practical significance for effective protection. Taking 103 county-level regions of Hubei Province as an example, and taking the fastest-spreading stage of COVID-19, which lasted from 29 January 2020, to 29 February 2020, as the research period, we systematically analyzed the population migration, spatio-temporal variation pattern of COVID-19, with emphasis on the spatio-temporal differences and scale effects of related factors by using the daily sliding, time-ordered data analysis method, combined with extended geographically weighted regression (GWR). The results state that: Population migration plays a two-way role in COVID-19 variation. The emigrants’ and immigrants’ population of Wuhan city accounted for 3.70% and 73.05% of the total migrants’ population respectively; the restriction measures were not only effective in controlling the emigrants, but also effective in preventing immigrants. COVID-19 has significant spatial autocorrelation, and spatio-temporal differentiation has an effect on COVID-19. Different factors have different degrees of effect on COVID-19, and similar factors show different scale effects. Generally, the pattern of spatial differentiation is a transitional pattern of parallel bands from east to west, and also an epitaxial radiation pattern centered in the Wuhan 1 + 8 urban circle. This paper is helpful to understand the spatio-temporal evolution of COVID-19 in Hubei Province, so as to provide a reference for similar epidemic prevention.

Estimation of reproduction numbers of COVID-19 in typical countries and epidemic trends under different prevention and control scenarios

The coronavirus disease 2019 (COVID-19) has become a life-threatening pandemic. The epidemic trends in different countries vary considerably due to different policy-making and resources mobilization. We calculated basic reproduction number (R₀) and the time-varying estimate of the effective reproductive number (R_t) of COVID-19 by using the maximum likelihood method and the sequential Bayesian method, respectively. European and North American countries possessed higher R₀ and unsteady R_t fluctuations, whereas some heavily affected Asian countries showed relatively low R₀ and declining R_t now. The numbers of patients in Africa and Latin America are still low, but the potential risk of huge outbreaks cannot be ignored. Three scenarios were then simulated, generating distinct outcomes by using SEIR (susceptible, exposed, infectious, and removed) model. First, evidence-based prompt responses yield lower transmission rate followed by decreasing R_t. Second, implementation of effective control policies at a relatively late stage, in spite of huge casualties at early phase, can still achieve containment and mitigation. Third, wisely taking advantage of the time-window for developing countries in Africa and Latin America to adopt adequate measures can save more people’s life. Our mathematical modeling provides evidence for international communities to develop sound design of containment and mitigation policies for COVID-19.