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1.
Journal of Psychosomatic Research ; : 111046, 2022.
Article in English | ScienceDirect | ID: covidwho-2041977

ABSTRACT

Objective Psychogenic non-epileptic seizures (PNES) resemble epileptic seizures but are not due to underlying epileptic activity and in some cases coexist alongside epilepsy. We described the clinical characteristics of patients with PNES as reported in the literature from the outbreak of the COVID-19 pandemic. We evaluated differences between patients with a diagnosis made immediately before the pandemic (pPNES) and those newly diagnosed during it (nPNES). Methods A systematic search with individual patient analysis of PNES cases published since the COVID-19 pandemic outbreak was performed. Differences between pPNES and nPNES were analyzed using Chi-square or Fisher exact test. Results Eleven articles were included, with 133 patients (106 pPNES and 27 nPNES). In the pPNES group, PNES frequency increased during the pandemic in 20/106 patients, whereas in 78/106, the frequency remained stable or decreased. nPNES was associated with higher risks of SARS-CoV-2 infection and epilepsy diagnosis, whereas psychiatric comorbidities were less frequent. Conclusions During the pandemic, most patients with pPNES remained stable or improved, whereas nPNES was associated with a lower burden of psychiatric comorbidities. These intriguing findings suggest that, at least in some patients, the COVID-19 pandemic may not necessarily lead to worsening in the frequency of PNES and quality of life.

2.
Epidemics ; : 100635, 2022.
Article in English | ScienceDirect | ID: covidwho-2041739

ABSTRACT

Background Social contact survey data forms a core component of modern epidemic models: however, there has been little assessment of the potential biases in such data. Methods We conducted focus groups with university students who had (n=13) and had never (n=14) completed a social contact survey during the COVID-19 pandemic. Qualitative findings were explored quantitatively by analysing participation data. Results The opportunity to contribute to COVID-19 research, to be heard and feel useful were frequently reported motivators for participating in the contact survey. Reductions in survey engagement following lifting of COVID-19 restrictions may have occurred because the research was perceived to be less critical and/or because the participants were busier and had more contacts. Having a high number of contacts to report, uncertainty around how to report each contact, and concerns around confidentiality were identified as factors leading to inaccurate reporting. Focus groups participants thought that financial incentives or provision of study results would encourage participation. Conclusions Incentives could improve engagement with social contact surveys. Qualitative research can inform the format, timing, and wording of surveys to optimise completion and accuracy.

3.
Kidney International Reports ; 7(9):S527, 2022.
Article in English | EMBASE | ID: covidwho-2041723

ABSTRACT

Introduction: Acute Interstitial Nephritis (AIN) is an important cause of Acute Kidney Injury (AKI), and infections are the second most common etiology, after the drugs. However, AIN following fungal infections is rare. We describe two cases of AIN, which on the investigation turn out to be candidemia following fungal infective endocarditis. Methods: CASE 1: A 65-year-old man with hypertension and diabetes without diabetic or hypertensive retinopathy and prior normal renal function, presented to us with vague abdominal pain with steadily creeping creatinine to 2mg/dl within 2 weeks, and urine showed no albuminuria and sediments. There was no history of any specific drug intake. His hematological and other parameters were normal. Blood and urine cultures were sterile. He underwent a renal biopsy which revealed acute interstitial nephritis (Figure 1). He was started on prednisolone at 1mg/kg/day for 1-week following which he had a rapidly worsening azotemia requiring hemodialysis. Steroids were stopped. Repeat blood cultures were sent which grew candida albicans resistant to flucytosine. Re-evaluation of the fundus revealed macular infarct in the right eye with vitreoretinitis in the left eye suggestive of endophthalmitis. PET CT showed increased FDG uptake in both kidneys suggestive of pyelonephritis. Trans-esophageal echocardiography (TEE) showed aortic valve vegetations. He was treated with antifungals for 3 months. He was dialysis-dependent for 2 weeks. He gradually regained normal renal function 3 weeks after starting anti-fungal agents. CASE 2: A 57-years-old man with diabetic, hypertensive, and no diabetic retinopathy had severe covid pneumonia in June 2021 requiring oxygen and tocilizumab 80 mg for 4 days, recovered with normal renal function. He presented to us 1 month later with unexplained non-oliguric severe AKI requiring dialysis, with bland urine sediments. Renal biopsy showed lymphocytic infiltrates in the interstitium suggestive of AIN (Figure 2). Blood cultures were sterile, but serum beta-D-glucan was elevated at 333 pg/ml. He was Initiated on 1mg/kg of prednisolone, on the presumption of drug-induced AIN. Simultaneously workup for systemic infection revealed mitral anterior leaflet endocarditis. He was initiated on anti-fungal therapy on the advice of an infectious disease specialist and the steroid was stopped. He continued to be dialysis-dependent after 6 weeks, despite anti-fungal agents. Results: [Formula presented] Conclusions: AIN contributes a significant proportion of cases in unexplained AKI. Prompt evaluation with a renal biopsy is warranted. Acute interstitial nephritis particularly due to candidemia can be oligosymptomatic as seen in our two cases. Since steroids have a significant role in treating early AIN, a dedicated search for underlying silent endocarditis and candidemia is advisable before initiating steroid therapy. Ophthalmic fundus evaluation, TEE, and repeat blood culture may be necessary to identify hidden candidemia. We recommend an evaluation to exclude fungal endocarditis in patients with AIN who present with minimal or no symptoms and no definitive cause for AIN is present. No conflict of interest

4.
Transportation Amid Pandemics ; : 15-23, 2023.
Article in English | ScienceDirect | ID: covidwho-2041409

ABSTRACT

In this chapter, we overview historical pandemic events that are closely associated with the evolution of transportation through case studies. The selected case studies are: (a) plague (14th, 17th, and 19th centuries), (b) Spanish flu (20th century), and (c) SARS (21st century). Historical pandemics reveal the connection between the spread of pandemics and development of transportation. Much faster and reliable means of mass transportation provide a greater capacity for people to travel across the world efficiently, but this advancement of transportation also carry infectious diseases to anywhere in the world. Transportation hubs (land, river, marine, and air) across the world should also play a large role in preventing and controlling the outbreak once the disease arrives. In this chapter, lessons from history that can be utilized to cope with the current global COVID-19 pandemic are illustrated and implications for transport policies and public health are discussed.

5.
Journal of Datta Meghe Institute of Medical Sciences University ; 17(5):S88-S93, 2022.
Article in English | Scopus | ID: covidwho-2040172

ABSTRACT

The impact that one health (OH) concept can have on the worldwide response to the COVID-19 pandemic is significant. We highlight four areas where the use of OH has the potential to greatly improve infectious disease governance in general, and COVID-19 governance in particular. For starters, a better-integrated surveillance infrastructure and monitoring of the occurrence of infectious diseases in humans and animals can make it easier to discover emerging infectious agents with comparable genotypes across species and track their spatiotemporal spread. This information can help public and animal health officials plan effective responses. Second, using the OH approach can help stakeholders representing seemingly conflicting domains coordinate and collaborate more effectively. Third, the OH approach emphasizes the importance of a strong institutional environment that allows for sufficient regulation of hotspots for infectious disease transmission between people and animals, such as live animal marketplaces. Finally, OH thinking emphasizes the need for equitable solutions to infectious disease challenges, implying that policy response mechanisms and interventions should take into account illness burdens faced disproportionately by vulnerable and marginalized people, as well as those helping sick people with health treatment and other important services. Within the 'One World - One Health' strategy, four major components can be identified as crucial elements: the geographical component, the ecological component, human activities, and food agriculture activities. We go over what we know about infections that emerge, the hosts they come from, and the circumstances that cause them to develop. We explore the obstacles to their control as well as innovative tactics for predicting pandemics, focusing surveillance on the most critical interfaces, and developing prevention strategies. © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

6.
Journal of Datta Meghe Institute of Medical Sciences University ; 17(5):S1-S4, 2022.
Article in English | Scopus | ID: covidwho-2040169

ABSTRACT

Context: COVID-19 is an emerging infectious disease and blood group has an influence on the susceptibility of infectious diseases including COVID-19. Aim: The present study was conducted with the aim to observe the association of ABO blood groups with COVID-19. Setting and Design: A nonexperimental hospital-based case-control research design was adopted to conduct the study with 200 COVID-19 patients who met the inclusion criteria. Subjects and Methods: Informed consent was obtained from the participants after explained the purpose of the study. Data were collected by interview method using a structured questionnaire and medical record was also utilized to collect the data. The collected data were prepared for analysis using Microsoft Excel. Statistical Analysis Used: Both descriptive and inferential statistical methods were used to analyze the data using the software SPSS 16 version. Results: The results of the study revealed that out of 200 participants, 83 (42.5%) belonged to A+, 68 (33%) belonged to B+, 7 (14%) belonged to O+, 18 (9%) belonged to AB +, and 4 (%) belonged to A-blood group. Conclusion: The findings of the current study concluded that the prevalence rate of COVID-19 was higher among non-O blood group than in the O blood group and the blood group is associated with the severity of illness. Despite further studies on the individuals with confirmed exposure to COVID-19 infection should be conducted with large samples to generalize the findings. © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

8.
ACM Transactions on Internet Technology ; 22(3), 2021.
Article in English | Scopus | ID: covidwho-2038355

ABSTRACT

Artificial intelligence-(AI) based fog/edge computing has become a promising paradigm for infectious disease. Various AI algorithms are embedded in cooperative fog/edge devices to construct medical Internet of Things environments, infectious disease forecast systems, smart health, and so on. However, these systems are usually done in isolation, which is called single-task learning. They do not consider the correlation and relationship between multiple/different tasks, so some common information in the model parameters or data characteristics is lost. In this study, each data center in fog/edge computing is considered as a task in the multi-task learning framework. In such a learning framework, a multi-task weighted Takagi-Sugeno-Kang (TSK) fuzzy system, called MW-TSKFS, is developed to forecast the trend of Coronavirus disease 2019 (COVID-19). MW-TSKFS provides a multi-task learning strategy for both antecedent and consequent parameters of fuzzy rules. First, a multi-task weighted fuzzy c-means clustering algorithm is developed for antecedent parameter learning, which extracts the public information among all tasks and the private information of each task. By sharing the public cluster centroid and public membership matrix, the differences of commonality and individuality can be further exploited. For consequent parameter learning of MW-TSKFS, a multi-task collaborative learning mechanism is developed based on ϵ-insensitive criterion and L2 norm penalty term, which can enhance the generalization and forecasting ability of the proposed fuzzy system. The experimental results on the real COVID-19 time series show that the forecasting tend model based on multi-task the weighted TSK fuzzy system has a high application value. © 2021 Association for Computing Machinery.

9.
BMJ Open ; 12(9):e065453, 2022.
Article in English | PubMed | ID: covidwho-2038322

ABSTRACT

Vaccination is critical to control the ongoing COVID-19 pandemic, but despite the availability of safe and effective vaccine in children over 5 years, vaccination rates remain low. There is paucity of data about vaccine acceptance and factors influencing parents' hesitancy about the COVID-19 vaccine for young children. AIMS AND OBJECTIVES: To estimate vaccine acceptance by parents of children 6 months through 4 years, and to evaluate the factors influencing vaccine hesitancy. METHODS: Electronic survey was sent to parents of children 6 months through 4 years through an online portal account at Mayo Clinic Health System, Northwest-Wisconsin. Data were captured via Research Electronic Data Capture secured data collection software. Bivariate and multivariate regression was used to determine most pertinent factors influencing parents' decisions against the outcome, 'Intent to Vaccinate'. RESULTS: 39.7% of the parents were 'very likely' or 'somewhat likely' to vaccinate their children once the vaccine became available, while 49.8% were not likely or highly unlikely to vaccinate. Routine childhood vaccination, receiving seasonal influenza vaccine, parents' perception of COVID-19 severity in children and safety and effectiveness of the vaccine were all associated with more vaccine acceptance. 71.4% of parents who will likely not vaccinate their children indicated that they are unlikely to change their decision. The need for more research on the vaccine and more information from the PCP office were the most common reasons behind the vaccine decision-making. CONCLUSIONS: Vaccine hesitancy remains a major issue regarding uptake of the upcoming COVID-19 vaccine. Strong and clear evidence-based recommendations from primary care provider and more information from trusted websites such as Centers for Disease Control and Prevention can decrease vaccine hesitancy in parents. Further research targeted at understanding beliefs and perspectives of parents from different demographics can assist policy-makers in implementing measures to improve vaccination rates in children and tailor our dialogue to match the needs of our patients and families.

10.
IEEE Transactions on Network Science and Engineering ; : 1-13, 2022.
Article in English | Scopus | ID: covidwho-2037845

ABSTRACT

Infectious diseases pose a severe threat to human health, especially the outbreak of COVID-19. After the infectious disease enters the stage of large-scale epidemics, vaccination is an effective way to control infectious diseases. However, when formulating a vaccination strategy, some restrictions still exist, such as insufficient vaccines or insufficient government funding to afford everyone's vaccination. Therefore, in this paper, we propose a vaccination optimization problem with the lowest total cost based on the susceptible-infected-recovered (SIR) model, which is called the Lowest Cost Of Vaccination Strategy (LCOVS) problem. We first establish a mathematical model of the LCOVS problem. Then we propose a practical Differential Evolution based Simulated Annealing (DESA) method to solve the mathematical optimization problem. We use the simulated annealing algorithm (SA) as a local optimizer for the results obtained by the differential evolution algorithm (DE) and optimized the mutation and crossover steps of DE. Finally, the experimental results on the six data sets demonstrate that our proposed DESA can achieve a more low-cost vaccination strategy than the baseline algorithms. IEEE

11.
23rd IEEE International Conference on Mobile Data Management, MDM 2022 ; 2022-June:222-229, 2022.
Article in English | Scopus | ID: covidwho-2037827

ABSTRACT

Since the onset of the COVID-19 pandemic, mil-lions of coronavirus sequences have been rapidly deposited in publicly available repositories. The sequences have been used primarily to monitor the evolution and transmission of the virus. In addition, the data can be combined with spatiotemporal information and mapped over space and time to understand transmission dynamics further. For example, the first COVID-19 cases in Australia were genetically related to the dominant strain in Wuhan, China, and spread via international travel. These data are currently available through the Global Initiative on Sharing Avian Influenza Data (GISAID) yet generally remains an untapped resource for data scientists to analyze such multi-dimensional data. Therefore, in this study, we demonstrate a system named Phyloview, a highly interactive visual environment that can be used to examine the spatiotemporal evolution of COVID-19 (from-to) over time using the case study of Louisiana, USA. PhyloView (powered by ArcGIsInsights) facilitates the visualization and exploration of the different dimensions of the phylogenetic data and can be layered with other types of spatiotemporal data for further investigation. Our system has the potential to be shared as a model to be used by health officials that can access relevant data through GISAID, visualize, and analyze it. Such data is essential for a better understanding, predicting, and responding to infectious diseases. © 2022 IEEE.

12.
The New England Journal of Medicine ; 387(11):964-965, 2022.
Article in English | ProQuest Central | ID: covidwho-2036977

ABSTRACT

Finding a New MantraDuring the frightening early months of the Covid pandemic, a palliative care physician finds herself silently mouthing a familiar mantra: I’m OK. We’re OK. We’ll be OK. But she is not OK.

13.
BMC Health Serv Res ; 22(1): 272, 2022 Mar 01.
Article in English | MEDLINE | ID: covidwho-2038736

ABSTRACT

BACKGROUND: Events such as the COVID-19 pandemic remind us of the heightened risk that healthcare workers (HCWs) have from acquiring infectious diseases at work. Reducing the risk requires a multimodal approach, ensuring that staff have the opportunity to undertake occupational infection prevention and control (OIPC) training. While studies have been done within countries to look at availability and delivery of OIPC training opportunities for HCWs, there has been less focus given to whether their infection prevention and control (IPC) guidelines adhere to recommended best practices. OBJECTIVES: To examine national IPC guidelines for the inclusion of key recommendations on OIPC training for HCWs to protect them from infectious diseases at work and to report on areas of inconsistencies and gaps. METHODS: We applied a scoping review method and reviewed guidelines published in the last twenty years (2000-2020) including the IPC guidelines of World Health Organization and the United States Centers for Disease Control and Prevention. These two guidelines were used as a baseline to compare the inclusion of key elements related to OIPC training with IPC guidelines of four high-income countries /regions i.e., Gulf Cooperation Council, Australia, Canada, United Kingdom and four low-, and middle-income countries (LMIC) i.e. India, Indonesia, Pakistan and, Philippines. RESULTS: Except for the Filipino IPC guideline, all the other guidelines were developed in the last five years. Only two guidelines discussed the need for delivery of OIPC training at undergraduate and/or post graduate level and at workplace induction. Only two acknowledged that training should be based on adult learning principles. None of the LMIC guidelines included recommendations about evaluating training programs. Lastly the mode of delivery and curriculum differed across the guidelines. CONCLUSIONS: Developing a culture of learning in healthcare organizations by incorporating and evaluating OIPC training at different stages of HCWs career path, along with incorporating adult learning principles into national IPC guidelines may help standardize guidance for the development of OIPC training programs. Sustainability of this discourse could be achieved by first updating the national IPC guidelines. Further work is needed to ensure that all relevant healthcare organisations are delivering a package of OIPC training that includes the identified best practice elements.


Subject(s)
COVID-19 , Communicable Diseases , Adult , COVID-19/prevention & control , Health Personnel , Humans , Infection Control/methods , Pandemics/prevention & control , SARS-CoV-2
14.
International Journal of Radiation Oncology, Biology, Physics ; 114(3):e466-e466, 2022.
Article in English | Academic Search Complete | ID: covidwho-2036119

ABSTRACT

To develop an analytic risk management method that uses mathematical models in Failure Modes and Effects Analysis (FMEA) to design mitigation efforts to control pandemic infection, while ensuring safe delivery of radiotherapy. A two-stage FMEA approach is proposed to modify radiotherapy workflow during a pandemic. In stage 1, an Infection Control FMEA (IC-FMEA) is conducted, where risks are evaluated based on environmental parameters, clinical interactions, and modeling of the pandemic infection risk. Occupancy' Risk Index (ORI) is defined as a metric of infection risk probability in each room, based on the degree of occupancy during clinical operations. ORI, in combination with ventilation rate per person (R p), is used to provide a broad infection risk assessment of workspaces. For detailed IC-FMEA of clinical processes, Infection containment failure mode (ICFM) is defined to be any instance of disease transmission within the clinic. Infection risk priority number (IRPN) has been formulated as a function of time, distance, and degree of protective measures. Infection control measures are then systematically integrated into the workflow. In stage 2, a conventional radiotherapy FMEA (RT-FMEA) can be performed on the adjusted workflow. A number of different clinical processes within radiotherapy workflow have been evaluated with this approach. The COVID-19 pandemic was used to illustrate stage 1 IC-FMEA. ORI and R p values were calculated for various workspaces within a radiotherapy clinic. A deep inspiration breath hold (DIBH) CT simulation was used as an example to demonstrate detailed IC-FMEA with ICFM identification and IRPN evaluation. A total of 90 ICFMs were identified in the DIBH process. For minimal protective measures the IRPN values ranged from 2 – 1200, while for increasing degrees of infection control the values decreased to 2-530 and 1-189 corresponding to moderate and enhanced measures respectively. The framework developed in this work provides tools for radiotherapy clinics to analytically assess risks and adjust workflows during a pandemic. [ FROM AUTHOR] Copyright of International Journal of Radiation Oncology, Biology, Physics is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

15.
Methods of Mathematical Modelling: Infectious Diseases ; : 189-216, 2022.
Article in English | Scopus | ID: covidwho-2035635

ABSTRACT

In this chapter, we develop the mathematical model of four compartments including classes of susceptible, infected, recovered, and death of infected ones for the recent outbreak of a coronavirus infectious disease (COVID-19). The model is investigated for both integer-order and fractional-order derivatives. The integer-order model is analyzed for an approximate solution using the Taylor's series method along with the numerical simulation showing the validity of the obtained scheme. The fractional-order model is evaluated numerically by Euler's iterative techniques and its results are compared to that of the Taylor's series scheme. The numerical simulation is drawn against the available data at different fractional orders. The fractional-order model is also investigated for qualitative analysis using the well-known theorems of fixed-point theory. The said model is also checked for feasibility and stability by using the techniques of basic reproduction number. © 2022 Elsevier Inc. All rights reserved.

16.
Big Data Analytics for Healthcare: Datasets, Techniques, Life Cycles, Management, and Applications ; : 153-163, 2022.
Article in English | Scopus | ID: covidwho-2035590

ABSTRACT

Infectious diseases threaten the lives of the entire global population. Some diseases such as SARS and COVID-19 trigger pandemics, as spread from country to country, with severe adverse effects on the medical system, such as shortages in medical professionals and equipment, financial burden, and death. Therefore, it is crucial to predict and respond to the spread of infectious diseases. In this chapter, we reviewed the research related to the prediction models of the spread of infectious diseases, based on various methodologies. Studies that adopt conventional mathematical models, such SIR, SEIR, and agent-based models are considered. In addition, an analysis centered on artificial intelligence, big data, and machine learning methodologies was carried out. Decision-makers should arrive at decisions by considering limitations of modeling infectious diseases. In particular, the internal structure of deep learning is a black box;hence, it difficult to interpret the results. Modelers should transparently provide data collection, coding, and modeling processes, as well as provide information on model uncertainty to help decision-makers create policy decisions. Furthermore, to make scientific and rational decisions based on evidence, considering the geographic information system interpersonal interactions, national, and social environments, decision-makers should refer to epidemiologic data and modeling results. © 2022 Elsevier Inc. All rights reserved.

17.
Lecture Notes on Data Engineering and Communications Technologies ; 142:273-282, 2023.
Article in English | Scopus | ID: covidwho-2035008

ABSTRACT

The coronavirus disease (COVID-19) is an infectious disease caused by coronavirus. The COVID-19 virus spreads mostly through droplets of saliva or discharge from the nose when an infected person coughs or sneezes, so it is important to practice respiratory etiquette. The COVID-19 is spreading our community in a faster manner, stay safe by taking some simple precautions, such as physical distancing, wearing a mask, keeping rooms well ventilated, avoiding crowds, and cleaning hands. The appropriate use of wearing a mask is a normal part of our life. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel severe acute respiratory syndrome coronavirus. Genetic variants of SARS-CoV-2 have been emerging and circulating around the world throughout the COVID-19 pandemic. To minimize the risk of transmissions, the use of face masks or coverings has been recommended in public settings. Many countries and local jurisdictions encourage or mandate the use of face masks by members of the public to limit the spread of the virus. Masks are also strongly recommended for those who may have been infected and those taking care of someone who may have the disease. In this paper, novel face mask detection on masked face data set is done by using pretrained Xception, deep learning with depth wise separable convolution. The proposed method classifies from the given face image, mask is worn or not. The proposed method is tested and validated using the face mask data set obtained from Kaggle. This data set contains about 503 face images with mask and 503 images without mask. The experimental results show that the proposed face mask detection method significantly dominates other compared pretrained models. The results of the receiver operating characteristic curve and area under curve justify the relevance of the better results in favor of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

18.
Lecture Notes on Data Engineering and Communications Technologies ; 147:419-431, 2023.
Article in English | Scopus | ID: covidwho-2035000

ABSTRACT

Over the past decade, the emergence of new infectious diseases in the world has become a serious problem requiring special attention. These days the COVID-19 epidemic is affecting not only the health sector but also the economy. Therefore, it is of great importance to build models that appropriately derive and preside over the spread of the epidemic to improve the control of epidemics. As well as to adopt appropriate strategies to avoid or at least mitigate its spread faster, different modeling methods have been proposed to build epidemiological models, we find the use of an agent-based model which makes it possible to reproduce the real behavior of the daily course of individuals already seen in the previous article [1], However this article presents in the same context stimulates the spread of covid using stochastic SIR model (Susceptible - Infected – Recovered) and its extension SVIRD (Susceptible - Vaccinated - Infected - Recovered – Death) which takes into consideration the vaccination parameter. Results: For a sample of 50 citizens network, we used a combination of simulations for the 4 parameters in SVIRD model, The result of the simulation shows that: The more connected a population is, the higher vaccination rates need to be to effectively protect the population. Also, the relationship between vaccination and infection rates looks more like an exponential decay and infection rates scale linearly with death rates for very low and very high numbers of connections. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

19.
Economics Letters ; : 110302, 2022.
Article in English | ScienceDirect | ID: covidwho-1647909

ABSTRACT

We investigate the effect of equity market volatility due to infectious disease on U.S. firms’ corporate activities from 1985 to 2020. Consistent with the theoretical framework, firms decrease their debt levels, debt maturity, corporate investments and dividend payout, and increase their cash holdings, research and development expenditure.

20.
Patient Saf Surg ; 16(1): 26, 2022 Aug 06.
Article in English | MEDLINE | ID: covidwho-2032619

ABSTRACT

BACKGROUND: Airborne transmission diseases can transfer long and short distances via sneezing, coughing, and breathing. These airborne repertory particles can convert to aerosol particles and travel with airflow. During the Coronavirus disease 2019 (COVID-19) pandemic, many surgeries have been delayed, increasing the demand for establishing a clean environment for both patient and surgical team in the operating room. METHODS: This study aims to investigate the hypothesis of implementing a protective curtain to reduce the transmission of infectious contamination in the surgical microenvironment of an operating room. In this regard, the spread of an airborne transmission disease from the patient was evaluated, consequently, the exposure level of the surgical team. In the first part of this study, a mock surgical experiment was established in the operating room of an academic medical center in Norway. In the second part, the computational fluid dynamic technique was performed to investigate the spread of airborne infectious diseases. Furthermore, the field measurement was used to validate the numerical model and guarantee the accuracy of the applied numerical models. RESULTS: The results showed that the airborne infectious agents reached the breathing zone of the surgeons. However, using a protective curtain to separate the microenvironment between the head and lower body of the patient resulted in a 75% reduction in the spread of the virus to the breathing zone of the surgeons. The experimental results showed a surface temperature of 40 ˚C, which was about a 20 ˚C increase in temperature, at the wound area using a high intensity of the LED surgical lamps. Consequently, this temperature increase can raise the patient's thermal injury risk. CONCLUSION: The novel method of using a protective curtain can increase the safety of the surgical team during the surgery with a COVID-19 patient in the operating room.

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