A survey of preparedness against coronavirus disease 2019 (COVID-19) in hospitals in Tokyo, Japan, with healthcare personnel with COVID-19 and in-facility transmission

Tagashira, Yasuaki; Takamatsu, Akane; Hasegawa, Shinya; Uenoyama, Yuki; Honda, Hitoshi

doi:10.1017/ice.2020.1304

Cited by 6 publications

(8 citation statements)

References 9 publications

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“…Cases of COVID-19 related to nosocomial transmission involved 66 inpatients (15%) in London in March and April 2020 and patients at several hospitals in Tokyo (Japan) in May 2020 [ 41 , 42 ]. In hospital-acquired cases, the possible routes of infection are direct contact with patients residing in the same bay and indirect contact via facility sharing or staff movement, and a high rate of mortality has been observed [ 41 , 42 ].…”

Section: Resultsmentioning

confidence: 99%

Analyzing the Effect of Social Distancing Policies on Traffic at Sinchon Station, South Korea, during the COVID-19 Pandemic in 2020 and 2021

Kim

Jang

Noh

et al. 2022

IJERPH

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The COVID-19 pandemic is recognized as one of the most serious global health problems, and many countries implemented lockdown measures to mitigate the effects of the crisis caused by this respiratory infectious disease. In this study, we investigated the relationship between social distancing policies and changes in traffic volume in Sinchon Station, South Korea. We used an official COVID-19 report provided by the Korea Disease Control and Prevention Agency (KCDA) and Seoul Metropolitan Government (SMG) to review social distancing policies, and the changes in traffic patterns before and during the COVID-19 pandemic between January 2020 and November 2021 were analyzed. Our study reveals that the changes in the overall traffic patterns from acceleration phases to deceleration phases of COVID-19 were related to the alert levels of social distancing policies implemented to tackle the situation resulting from the COVID-19 pandemic. Herein, we found that a significant decline in traffic volume took place from August to September 2020 (13.5–19.7%, weekday; 19.4–31.7%, weekend), from December 2020 to January 2021 (20.0%−26.6%, weekday; 26.8–34.0%, weekend), and from July to September 2021 (3.2–13.1%, weekday; 38.3–44.7%, weekend) when compared to the corresponding periods in 2019 (paired t-test; p < 0.001). The results of this study provide strong support for the effectiveness of Seoul’s preemptive measures, namely, the central government’s intensive social distancing campaign, in managing and reducing the impact of the pandemic situation based on the precise analysis of 10 types of facilities.

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Section: Resultsmentioning

confidence: 99%

Analyzing the Effect of Social Distancing Policies on Traffic at Sinchon Station, South Korea, during the COVID-19 Pandemic in 2020 and 2021

Kim

Jang

Noh

et al. 2022

IJERPH

View full text Add to dashboard Cite

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Section: Discussionmentioning

confidence: 99%

“…42 Tagashira, Takamatsu, Hasegawa, Uenoyama and Honda in their study on preparedness against COVID-19 in Japan, reported that hospital preparedness in the early phase of the pandemic was linked with nosocomial transmission in healthcare personnel. 43 With respect to personal preparedness, almost 90% of the sample did not feel professionally prepared for this pandemic. Additionally, more than 80% of the respondents considered inadequate the training concerning COVID-19 and the use of PPE.…”

Section: Discussionmentioning

confidence: 99%

Concerns, Perceived Impact, Preparedness in Coronavirus Disease (COVID-19) Pandemic and Health Outcomes among Italian Physicians: A Cross-Sectional Study

Portoghese

Meloni

Piras

et al. 2021

J Prim Care Community Health

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Background: Health care workers (HCWs) are among the professionals at serious risk for the impact of the COVID-19 pandemic on their mental health. In this sense, the next public health challenge globally will be to preserving healthy HCWs during this pandemic. Aim: The present study has the aim of investigating the relationship among concerns, perceived impact, preparedness for the COVID-19 pandemic and the mental health of Italian physicians. Methods: From March 29th to April 15th 2020, we conducted an online survey using snowball sampling techniques through Limesurvey platform. Data were analyzed using descriptive statistics and multiple binary logistic regressions. Results: Multivariate analysis showed that the risk factors for perceived job stress were concerns about catching COVID-19 (OR = 3.18 [95% CI = 2.00-5.05] P < .001), perceived impact on job demands (OR = 1.63 [95% CI = 1.05-2.52] P < .05), perceived impact on job role (OR = 2.50 [95% CI = 1.60-3.90] P < .001), and non-working concerns (OR = 1.86 [95% CI = 1.15-3.03] P < .05). With respect to the risk factors for rumination about the pandemic emerged concerns about catching COVID-19 (OR 1.74, [95% CI = 1.12-2.71] P < .05), perceived impact on job role (OR = 1.68 [95% CI = 1.12-2.52] P < .05), and impact on personal life (OR = 2.04 [95% CI = 1.08-3.86] P < .05). Finally, the risk factors for crying at work were perceived impact on job role (OR = 2.47, [95% CI = 1.20-5.09] P < .05), rumination about the pandemic (OR = 3.027 [95% CI = 1.27-7.19] P < .01), watching colleagues crying at work (OR = 3.82 [95% CI = 1.88-7.77] P < .01), and perceived job stress (OR = 3.53 [95% CI = 1.24-10.07] P < .05). Conclusion: In general, our results highlighted that being concerned about being infected/infecting other people, carrying out new and unusual tasks, and witnessing colleagues crying at work were important risk factors for physicians’ well-being. Additional data are necessary to advance understanding of these risk factors in a long-term perspective.

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“…The sudden increase in COVID-19 cases is imparting high pressure on healthcare services worldwide [ 92 , 93 ]. Precise diagnosis of COVID-19 infected patients is fundamental in the process of providing proper treatment and avoiding the overburdening of the healthcare system.…”

Section: Role Of Ai In the Screening Of Covid-19 Infected Patients And Diagnosismentioning

confidence: 99%

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

et al. 2021

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As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning(ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

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A survey of preparedness against coronavirus disease 2019 (COVID-19) in hospitals in Tokyo, Japan, with healthcare personnel with COVID-19 and in-facility transmission

Cited by 6 publications

References 9 publications

Analyzing the Effect of Social Distancing Policies on Traffic at Sinchon Station, South Korea, during the COVID-19 Pandemic in 2020 and 2021

Analyzing the Effect of Social Distancing Policies on Traffic at Sinchon Station, South Korea, during the COVID-19 Pandemic in 2020 and 2021

Concerns, Perceived Impact, Preparedness in Coronavirus Disease (COVID-19) Pandemic and Health Outcomes among Italian Physicians: A Cross-Sectional Study

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

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