The focus of this study is to monitor the effect of lockdown on the various air pollutants due to the coronavirus disease (COVID-19) pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced.

Various machine learning techniques: Decision Trees, Linear Regression, and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during the lockdown period and last two years, 2018 and 2019, has been presented.

From the experimental work, it has been observed that the pollutants ozone and toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are ozone, NH3, NO2, and PM10.

The novel coronavirus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control ozone pollution, as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

The present study examined the psychological effects and identify factors associated with worse outcomes, during the coronavirus disease 2019 (COVID-19) outbreak in Mexican nurses involved in fighting against COVID-19.

An anonymous online questionnaire was applied through an online survey, which collected information regarding basic information, traumatic distress response (IES-R scale), emotional exhaustion (MBI-EE), and psychological distress (K10 scale).

Results showed that 46.72% of nurses reported moderate-severe traumatic distress response, 42.40% of nurses evidenced a high level of emotional exhaustion, and 41.78% showed moderate-severe psychological distress. Nurses who have >2 children, an increase in working hours due to COVID-19, increase in tobacco and alcohol consumption, and presence of a confirmed and suspected case of COVID-19 in their workplace showed worse outcomes.

These findings demonstrate that a large portion of nurses in Mexico is suffering from psychological disturbances due to the COVID-19 outbreak. In the face of a health crisis, not seen in several years in Mexico, the proper psychological well-being of the nursing staff at this vulnerable time is essential.

Identification of hospital preparedness challenges against biological events such as coronavirus disease 2019 (COVID-19) is essential to improve dynamics, quality, and business continuity confidence in the health system. Accordingly, the purpose of the present study is to evaluate the challenges of hospital preparedness in biological events.

This study used a qualitative method using content analysis in which 20 health-care managers and experts who are experienced in biological events were selected through purposeful sampling. The data collection was done through semi-structured interviews, which continued until data saturation. The data were analyzed using qualitative content analysis as well as the Landman and Graneheim Approach.

Six main concepts (training and practice, resource management, safety and health, patient management, risk communication, and laboratory and surveillance) and 14 subconcepts were extracted on hospital preparedness challenges in biological events through analyzing interviews.

The present study indicated that the health system of the country faces many challenges in response to biological events and threats. Moreover, study participants indicated that Iranian hospitals were not prepared for biological events. It is recommended to design preparedness plans of hospitals based on preparedness standards for biological events. In addition, comprehensive measures are required to enhance their capacity to respond to biological emergencies.

This study aims to clarify the association between prosperity and the coronavirus disease (COVID-19) outcomes and its impact on the future management of pandemics.

This is an observational study using information from 2 online registries. The numbers of infected individuals and deaths and the prosperity rank of each country were obtained from worldometer.info and the Legatum Institute’s Prosperity Index, respectively.

There is a combination of countries with high and low prosperity on the list of COVID-19-infected countries. The risk of the virus pandemic seems to be more extensive in countries with high prosperity. A Spearman’s rho test confirmed a significant correlation between prosperity, the number of COVID-19 cases, and the number of deaths at the 99% level.

New emerging pandemics affect all nations. In order to increase the likelihood of successfully managing future events, it is important to consider preexisting health security, valid population-based management approaches, medical decision-making, communication, continuous assessment, triage, treatment, early and complete physical distancing strategies, and logistics. These elements cannot be taught on-site and on occasion. There is a need for innovative and regular educational activities for all stakeholders committed to safeguarding our future defense systems concerning diagnostic, protection, treatment, and rehabilitation in pandemics, as well as other emergencies.

Our purpose was to determine the intensive care units’ (ICU’s) medical staff surge capacity during the coronavirus disease 2019 (COVID-19) outbreak in Spring 2020 in Spain.

A multicenter retrospective survey was performed addressing the medical specialties present in the ICUs and the increase in bed capacity during this period.

Sixty-seven centers (62.04%) answered the questionnaire. The ICU bed capacity during the pandemic outbreak increased by 160% (95% confidence interval [CI], 128.97-191.03%). The average number of beds per intensive care medicine (ICM) specialist was 1.5 ± 0.60 and 3.71 ± 2.44 beds/specialist before and during the COVID-19 outbreak, respectively. Non-ICM specialists and residents were present in 50 (74.63%) and 23 (34.3%) ICUs during the outbreak, respectively. The number of physicians (ICM and non-ICM residents and specialists) in the ICU increased by 89.40% (95% CI, 64.26114.53%). The increase in ICM specialists was, however, 4.94% (95% CI, −1.35-11.23%). Most non-ICM physicians were anesthetists, followed by pediatricians and cardiologists.

The majority of ICUs in our study were able to rapidly expand critical care capacity by adapting areas outside of the normal ICU to manage critically ill patients, and by extending the critical care staff with noncritical care physicians working as force multipliers.

This study explores how social networks for coronavirus disease (COVID-19) are differentiated by regions.

This study employs a social network analysis for Twitter in New York and California.

National key players play an important role in New York, whereas regional key players exert a significant impact on California. Some key players, such as the US President, play an essential role in both New York and California. Hispanic key players play a crucial role in California. Each group is more likely to show communication networks within groups in New York, whereas it is more apt to exhibit communication networks across groups in California. Government players play a different role in social networks according to regions.

Governments should understand how social networks for COVID-19 are differentiated by regions to control the ongoing pandemic effectively.

Local characteristics of populations have been associated with coronavirus disease 2019 (COVID-19) outcomes. We analyze the municipality-level factors associated with a high COVID-19 mortality rate (MR) of in Mexico.

We retrieved information from cumulative confirmed symptomatic cases and deaths from COVID-19 as of June 20, 2020, and data from most recent census and surveys of Mexico. A negative binomial regression model was adjusted, the dependent variable was the number of COVID-19 deaths, and the independent variables were the quintiles of the distribution of sociodemographic and health characteristics among the 2457 municipalities of Mexico.

Factors associated with high MRs from COVID-19, relative to quintile 1, were diabetes and obesity prevalence, diabetes mortality rate, indigenous population, economically active population, density of economic units that operate essential activities, and population density. Among factors inversely associated with lower MRs from COVID-19 were high hypertension prevalence and houses without sewage drainage. We identified 1351 municipalities without confirmed COVID-19 deaths, of which, 202 had high and 82 very high expected COVID-19 mortality (mean = 8 and 13.8 deaths per 100,000, respectively).

This study identified municipalities of Mexico that could lead to a high mortality scenario later in the epidemic and warns against premature easing of mobility restrictions and to reinforce strategies of prevention and control of outbreaks in communities vulnerable to COVID-19.

This study aims to assess the knowledge and awareness, and to identify the practice reflection of knowledge concerning Middle East Respiratory Syndrome (MERS) on hospital visitor’s daily life.

A cross-sectional study, conducted in 2 tertiary referral hospitals in Riyadh Saudi Arabia, from February 2015 to February 2016. A total random sample of 305 hospital visitors consented to participate. Data were collected through a self-administered questionnaire consisting of questions regarding awareness and practice of measures to prevent the spread of infection.

Study showed that participants have a fair knowledge regarding the cause of MERS (N = 228; 74.8%). Nearly half of them (47%) stated that camels are the source of the spread of MERS. Approximately 70% of the participants preferred both sanitization and wearing facemasks as preventive measures for MERS. However, only 3.95% practiced not eating camel products, such as milk and meat.

Although hospital visitors showed some knowledge and positive awareness in several aspects of MERS awareness, there are weak areas where knowledge and awareness were not up to recommended guidelines. Continued educational programs are needed to improve awareness and knowledge of all the public toward MERS-coronavirus infection. This study may assist in the development of future strategies on preventive measures of the disease.

The UK is one of the epicenters of coronavirus disease (COVID-19) in the world. As of April 14, there have been 93 873 confirmed patients of COVID-19 in the UK and 12 107 deaths with confirmed infection. On April 14, it was reported that COVID-19 was the cause of more than half of the deaths in London.

The present paper addresses the modeling and forecasting of the outbreak of COVID-19 in the UK. This modeling must be accomplished through a 2-part time series model to study the number of confirmed cases and deaths. The period we aimed at a forecast was 46 days from April 15 to May 30, 2020. All the computations and simulations were conducted on Matlab R2015b, and the average curves and confidence intervals were calculated based on 100 simulations of the fitted models.

According to the obtained model, we expect that the cumulative number of confirmed cases will reach 282 000 with an 80% confidence interval (242 000 to 316 500) on May 30, from 93 873 on April 14. In addition, it is expected that, over this period, the number of daily new confirmed cases will fall to the interval 1330 to 6450 with the probability of 0.80 by the point estimation around 3100. Regarding death, our model establishes that the real case fatality rate of the pandemic in the UK approaches 11% (80% confidence interval: 8%–15%). Accordingly, we forecast that the total death in the UK will rise to 35 000 (28 000–50 000 with the probability of 80%).

The drawback of this study is the shortage of observations. Also, to conduct a more exact study, it is possible to take the number of the tests into account as an explanatory variable besides time.

The authors aim to demonstrate that the current drive-through testing model at a health district was improved in certain parameters compared with a previous testing protocol, and to provide the methodology of the current model for other coronavirus disease (COVID-19) testing sites to potentially emulate.

Initially, a small drive-through site was constructed at a converted tuberculosis clinic, but due to an increase in testing needs, an expanded point of screening and testing (POST) system was developed in an event center parking lot to administer tests to a higher volume of patients.

An average of 51.1 patients was tested each day (2.0 tests per personnel in personal protective equipment [PPE] per hour) at the initial tuberculosis clinic drive-through site, which increased to 217.8 patients tested each day (5.9 tests per personnel in PPE per hour) with the new drive-through POST system (P < 0.001). Mean testing time was 3.4 minutes and the total time on-site averaged 14.4 minutes.

This POST drive-through system serves as an efficient, safe, and adaptable model for high volume COVID-19 nasopharyngeal swabbing that the authors recommend other COVID-19 testing sites nationwide consider adopting for their own use.

The aim was to evaluate nurses’ self-efficacy, confidence, and nurse-patient interaction during caring of patients with coronavirus disease 2019 (COVID-19).

A cross-sectional design with online survey was used with a Self-efficacy scale, Self-confidence scale, and Caring nurse-patient interaction scale: 23-item Version-Nurse (CNPI-23 N).

A sample of 120 nurses participated in the current study. The results showed that the participants had a moderate level of self-efficacy, self-confidence and interaction (M = 28.84 (SD = 7.7), M = 47.41 (SD = 9.0), and M = 93.59 (SD = 16.3), respectively). Positive relationships were found between nurse’ self-efficacy, self-confidence, and nurse-patient interaction (r = 0.81; P < 0.0001 and 0.79; P < 0.0001, respectively). Significant differences were found in self-efficacy according to years of experience, academic qualifications and position (F = 2.10; P = 0.003; F = 3.60; P = 0.002, and F = 2.60; P =0.007, respectively). Furthermore, the results indicated that there was a significant difference in self-confidence and nurse-patient interaction also.

Nurse educators and administrators should develop and implement further strategies, such as continuing education and training, compensatory payment, organizational support, and availability of protective measures to increase their self-efficacy, self-confidence, and interaction with COVID-19 patients.

Coronavirus disease (COVID-19) has been identified as an acute respiratory illness leading to severe acute respiratory distress syndrome. As the disease spread, demands on health care systems increased, specifically the need to expand hospital capacity. Alternative care hospitals (ACHs) have been used to mitigate these issues; however, establishing an ACH has many challenges. The goal of this session was to perform systems testing, using a simulation-based evaluation to identify areas in need of improvement.

Four simulation cases were designed to depict common and high acuity situations encountered in the ACH, using a high technology simulator and standardized patient. A multidisciplinary observer group was given debriefing forms listing the objectives, critical actions, and specific areas to focus their attention. These forms were compiled for data collection.

Logistical, operational, and patient safety issues were identified during the simulation and compiled into a simulation event report. Proposed solutions and protocol changes were made in response to the identified issues.

Simulation was successfully used for systems testing, supporting efforts to maximize patient care and provider safety in a rapidly developed ACH. The simulation event report identified operational deficiencies and safety concerns directly resulting in equipment modifications and protocol changes.

This study was conducted to assess the feasibility of extended use of N95 masks in our hospital during the coronavirus disease 2019 (COVID-19) pandemic. We also studied the use pattern, user satisfaction, and issues faced during extended use of the mask.

This cross-sectional study was conducted among health-care providers in a large tertiary care teaching hospital in northern India from April 1 to May 31, 2020. A list was prepared from the institute’s register, and participants were chosen by random sampling. The data collected from the physical forms were transferred to excel sheets.

A total of 1121 responses were received. The most common problem stated with reuse of N95 masks was loss of fit followed by damage to the slings, highlighted by 44.6% and 44.4% of the participants, respectively. A total of 476 (42.5%) participants responded that they would prefer “cup-shaped N95 mask with respirator”. The median scores regarding the satisfaction with the quality of masks and their fit was also 4 each.

It was concluded that the extended use of N95 masks was acceptable, with more than 96% of the participants using these masks.

Early identification of patients with novel corona virus disease 2019 (COVID-19) who may be at high mortality risk is of great importance.

In this retrospective study, we included all patients with COVID-19 at Huanggang Central Hospital from January 23 to March 5, 2020. Data on clinical characteristics and outcomes were compared between survivors and nonsurvivors. Univariable and multivariable logistic regression were used to explore risk factors associated with in-hospital death. A nomogram was established based on the risk factors selected by multivariable analysis.

A total of 150 patients were enrolled, including 31 nonsurvivors and 119 survivors. The multivariable logistic analysis indicated that increasing the odds of in-hospital death associated with higher Sequential Organ Failure Assessment score (odds ratio [OR], 3.077; 95% confidence interval [CI]: 1.848-5.122; P < 0.001), diabetes (OR, 10.474; 95% CI: 1.554-70.617; P = 0.016), and lactate dehydrogenase greater than 245 U/L (OR, 13.169; 95% CI: 2.934-59.105; P = 0.001) on admission. A nomogram was established based on the results of the multivariable analysis. The AUC of the nomogram was 0.970 (95% CI: 0.947-0.992), showing good accuracy in predicting the risk of in-hospital death.

This finding would facilitate the early identification of patients with COVID-19 who have a high-risk for fatal outcome.

Since the first case of severe acute respiratory syndrome coronavirus-2, identified in December 2019, in Wuhan in China, the number of cases rapidly increased into a pandemic. Governments worldwide have adopted different strategies and measures to interrupt the transmission of coronavirus disease 2019 (COVID-19). The main objective was to report and evaluate the effectiveness of the adopted measures in North Africa countries.

In these countries, the effective reproductive number R(t), the naïve case fatality rate, and the adjusted case fatality rate were estimated and compared on different dates.

The obtained results show that the early strict application of containment measures and confinement could help contain the spread of the epidemic and maintain the number of deaths low.

These measures might be useful for other countries that are experiencing the start of local COVID-19 outbreaks. They could also serve to halt the spread of new epidemics or pandemics.