Following stay-at-home (SAH) orders issued for coronavirus disease (COVID-19), state-level economic concerns increased and many let these orders expire. As a method to measure public preparedness, we sought to explore the association between public interest in preventive measures and the easing of SAH orders – specifically the increases in COVID-19 cases and fatalities after the orders expired.

Search volume was collected from Google Trends for “hand sanitizer,” “social distancing,” “COVID testing,” and “contact tracing” for each state. Bivariate correlations were computed to analyze associations between public interest in preventive measures, changes in confirmed COVID-19 cases after SAH expirations, COVID-19 case-fatality rates, and by-state presidential voting percentages.

A higher interest in preventive measures was associated with lower rates of confirmed cases after SAH orders had expired (r = −0.33), higher state-wide deaths per capita (r = 0.42), and case-fatality rates (r = 0.60). Moderate to strong negative correlations were found between states’ percentage of voters supporting the Republican nominee in 2016 and proportion of queries for average preventive measures (r = −0.77).

Our investigation shows that increased public interest in COVID-19 prevention was associated with longer SAH orders and less COVID-19 cases after the SAH orders’ expiration; however, it was also associated with higher case-fatality rates.

There is a paucity of scientific analysis that has examined spatial heterogeneities in the socioeconomic vulnerabilities related to coronavirus disease 2019 (COVID-19) risk and potential mitigation strategies at the sub-national level in India. The present study examined the demographic, socioeconomic, and health system-related vulnerabilities shaping COVID-19 risk across 36 states and union territories in India.

Using secondary data from the Ministry of Health and Family Welfare (MoHFW), Government of India; Census of India, 2011; National Family Health Survey, 2015-16; and various rounds of the National Sample Survey, we examined socioeconomic vulnerabilities associated with COVID-19 risk at the sub-national level in India from March 16, 2020, to May 3, 2020. Descriptive statistics, principal component analysis, and the negative binomial regression model were used to examine the predictors of COVID-19 risk in India.

There persist substantial heterogeneities in the COVID-19 risk across states and union territories in India. The underlying demographic, socioeconomic, and health infrastructure characteristics drive the vulnerabilities related to COVID-19 in India.

This study emphasizes that concerted socially inclusive policy action and sustained livelihood/economic support for the most vulnerable population groups is critical to mitigate the impact of the COVID-19 pandemic in India.

This study explores the mental well-being of pregnant women in Japan during the coronavirus disease (COVID-19) pandemic.

We collected 1777 responses from pregnant women through an online survey. Using the Japanese version of the Edinburgh Postnatal Depression Scale (EPDS), we calculated the percentage of pregnant women above the cutoff (≥ 13), and the factor scores of anhedonia, anxiety, and depression. Regression analyses were performed to identify factors and socioeconomic characteristics correlated with depressive symptoms.

The point prevalence of pregnant women with an EPDS score of ≥ 13 was 17%. The mean scores were 0.73, 3.68, and 1.82 for anhedonia, anxiety, and depression, respectively. The probability of becoming above the cutoff score positively correlated with the cancellation of planned informal support, higher perceived risk for infection of COVID-19, difficulties in household finances, and lack of social support. Moreover, being younger, less wealthy, unemployed, and without a partner showed a significantly higher possibility of having a score above the cutoff.

The present study found a high percentage of pregnant women with depressive symptoms. Notably, COVID-19-related variables, including perceived risk for the infection, fear of decreasing economic wealth, and social support, were significantly associated with depressive symptoms.

Health system preparedness for coronavirus disease (COVID-19) includes projecting the number and timing of cases requiring various types of treatment. Several tools were developed to assist in this planning process. This review highlights models that project both caseload and hospital capacity requirements over time.

We systematically reviewed the medical and engineering literature according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We completed searches using PubMed, EMBASE, ISI Web of Science, Google Scholar, and the Google search engine.

The search strategy identified 690 articles. For a detailed review, we selected 6 models that met our predefined criteria. Half of the models did not include age-stratified parameters, and only 1 included the option to represent a second wave. Hospital patient flow was simplified in all models; however, some considered more complex patient pathways. One model included fatality ratios with length of stay (LOS) adjustments for survivors versus those who die, and accommodated different LOS for critical care patients with or without a ventilator.

The results of our study provide information to physicians, hospital administrators, emergency response personnel, and governmental agencies on available models for preparing scenario-based plans for responding to the COVID-19 or similar type of outbreak.

The purpose of this study was to demonstrate the use of social network analysis to understand public discourse on Twitter around the novel coronavirus disease 2019 (COVID-19) pandemic. We examined different network properties that might affect the successful dissemination by and adoption of public health messages from public health officials and health agencies.

We focused on conversations on Twitter during 3 key communication events from late January to early June of 2020. We used Netlytic, a Web-based software that collects publicly available data from social media sites such as Twitter.

We found that the network of conversations around COVID-19 is highly decentralized, fragmented, and loosely connected; these characteristics can hinder the successful dissemination of public health messages in a network. Competing conversations and misinformation can hamper risk communication efforts in a way that imperil public health.

Looking at basic metrics might create a misleading picture of the effectiveness of risk communication efforts on social media if not analyzed within the context of the larger network. Social network analysis of conversations on social media should be an integral part of how public health officials and agencies plan, monitor, and evaluate risk communication efforts.

Qualitative fit testing is a popular method of ensuring the fit of sealing face masks such as N95 and FFP3 masks. Increased demand due to the coronavirus disease 2019 (COVID-19) pandemic has led to shortages in testing equipment and has forced many institutions to abandon fit testing. Three key materials are required for qualitative fit testing: the test solution, nebulizer, and testing hood. Accessible alternatives to the testing solution have been studied. This exploratory qualitative study evaluates alternatives to the nebulizer and hoods for performing qualitative fit testing.

Four devices were trialed to replace the test kit nebulizer. Two enclosures were tested for their ability to replace the test hood. Three researchers evaluated promising replacements under multiple mask fit conditions to assess functionality and accuracy.

The aroma diffuser and smaller enclosures allowed participants to perform qualitative fit tests quickly and with high accuracy.

Aroma diffusers show significant promise in their ability to allow individuals to quickly, easily, and inexpensively perform qualitative fit testing. Our findings indicate that aroma diffusers and homemade testing hoods may allow for qualitative fit testing when conventional apparatus is unavailable. Additional research is needed to evaluate the safety and reliability of these devices.

The objectives of this study is to predict the possible trajectory of coronavirus disease 2019 (COVID-19) spread in the United States. Prediction and severity ratings of COVID-19 are essential for pandemic control and economic reopening in the United States.

In this study, we apply the logistic and Gompertz model to evaluate possible turning points of the COVID-19 pandemic in different regions. By combining uncertainty and severity factors, this study constructed an indicator to assess the severity of the coronavirus outbreak in various states.

Based on the index of severity ratings, different regions of the United States are classified into 4 categories. The result shows that it is possible to identify the first turning point in Montana and Hawaii. It is unclear when the rest of the states will reach the first peak. However, it can be inferred that 75% of regions will not reach the first peak of coronavirus before August 2, 2020.

It is still essential for the majority of states to take proactive steps to fight against COVID-19 before August 2, 2020.

The aim of this study was to evaluate hospital and emergency department (ED) preparedness in France facing the coronavirus disease 2019 (COVID-19) rapid growth epidemic-phase, and to determine the link between preparedness and responsiveness.

In this cross-sectional study, from March 7 to March 11, 2020, all heads of ED departments in France were contacted to answer an electronic survey, including 23 questions. Quality, Organization, Training, Resources, Management, Interoperability, and Responsiveness were evaluated by calculating scores (10 points). Multivariate analysis of variance was used to compare scores. Spearman’s correlation coefficient and multifaceted regression analysis were performed between Responsiveness and dimensions scores.

A total of 287 of 636 French EDs were included (45.1%). Calculated scores showed (median): Quality 5.38; Organization 6.4; Training 4.6; Resources 4.13; Management 2.38; Interoperability 4.0; Responsiveness 6.25; seasonal influenza score was 5. Significant differences between scores as a function of hospital and ED main characteristics were found. Furthermore, we found significant correlations (P < 0.01) between Responsiveness and all preparedness dimensions. Organization (adjusted-R2 0.2897), Management (aR2 0.321), and Interoperability (aR2 0.422) were significantly associated with Responsiveness.

Preparedness in all its dimensions is low, indicating vulnerability. Preparedness and responsiveness face a certain and ongoing risk are close linked, and that Organizational, Management, and Interoperability dimensions are main determinants.

Evaluate the relevance of the coronavirus disease 2019 (COVID-19) positive case detection policy or model implemented by the Ministry of Public Health (MPH) of Ecuador and to compare it with the experiences of other countries.

Data contained the daily reports publicized by the MPH. The formulations were carried out under the Conditioned Probability modality applying Bayes’ Theorem. All the COVID-19 tests applied in relation to the confirmed cases per million inhabitants were considered to obtain their level of positivity, and compared with the experience of Iceland and South Korea.

The probability of detecting positive cases of COVID-19 in Ecuador was higher than Iceland and South Korea, because the diagnostic tests were aimed at symptomatic patients, without identifying asymptomatic or mild symptomatic, who play an important role in the transmission of the disease. In addition, many symptomatic patients were examined but will remain undiagnosed due to the unavailability of tests and the low quality of many of them.

The daily reports on the behavior of the COVID-19 issued by the Ecuadorian government do not adequately represent the growth in the number of those infected each day, nor the actual behavior of the epidemic, affecting possible control measures.

To characterize associations between exposures within and outside the medical workplace with healthcare personnel (HCP) SARS-CoV-2 infection, including the effect of various forms of respiratory protection.

Case–control study.

We collected data from international participants via an online survey.

In total, 1,130 HCP (244 cases with laboratory-confirmed COVID-19, and 886 controls healthy throughout the pandemic) from 67 countries not meeting prespecified exclusion (ie, healthy but not working, missing workplace exposure data, COVID symptoms without lab confirmation) were included in this study.

Respondents were queried regarding workplace exposures, respiratory protection, and extra-occupational activities. Odds ratios for HCP infection were calculated using multivariable logistic regression and sensitivity analyses controlling for confounders and known biases.

HCP infection was associated with non–aerosol-generating contact with COVID-19 patients (adjusted OR, 1.4; 95% CI, 1.04–1.9; P = .03) and extra-occupational exposures including gatherings of ≥10 people, patronizing restaurants or bars, and public transportation (adjusted OR range, 3.1–16.2). Respirator use during aerosol-generating procedures (AGPs) was associated with lower odds of HCP infection (adjusted OR, 0.4; 95% CI, 0.2–0.8, P = .005), as was exposure to intensive care and dedicated COVID units, negative pressure rooms, and personal protective equipment (PPE) observers (adjusted OR range, 0.4–0.7).

COVID-19 transmission to HCP was associated with medical exposures currently considered lower-risk and multiple extra-occupational exposures, and exposures associated with proper use of appropriate PPE were protective. Closer scrutiny of infection control measures surrounding healthcare activities and medical settings considered lower risk, and continued awareness of the risks of public congregation, may reduce the incidence of HCP infection.