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1.
Pandemic Risk, Response, and Resilience: COVID-19 Responses in Cities around the World ; : 13-28, 2022.
Article in English | Scopus | ID: covidwho-2035622

ABSTRACT

The world continues to be gripped by COVID-19, though the pandemic's impact varies across countries and regions. The South Asian case is illustrative. The region is marked by inherent socioeconomic and other vulnerabilities, including high population density, relatively poor health care, and limited water sanitation facilities. South Asian countries also evince varied levels of damage from the pandemic. This chapter examines the region's circumstances as of November 2020, using macroeconomic data to explore varied pandemic impacts and fiscal policy responses. We also discuss the COVID-19 fund formed at the South Asian regional level with contributions from all eight South Asian countries. Our analysis includes each country's external and internal share of fiscal stimulus, and the implications for sustainable development goals. In an argument for integrating resilience and development frameworks, the chapter analyzes Japan's example of national resilience planning and related sustainable development frameworks. Our research indicates that a sustainable recovery is advantaged by fiscal stimulus that can be linked to extant developmental frameworks. © 2022 Elsevier Inc. All rights reserved.

2.
Studies in Big Data ; 109:433-457, 2022.
Article in English | Scopus | ID: covidwho-1941433

ABSTRACT

Pandemic COVID-19 ranked as one of the world’s worst pandemics ever witnessed in history. It has affected every country by spreading this disease with an increase in mortality at alarming rates despite the technologically advanced era of medicine. AI/ML is one of the strong wings in the medical field so while fighting the battle to control and diagnose the best medicine for the outbreak COVID-19 disease. Automated and AI-based prediction models for COVID-19 are the main attraction for the scientist hoping to support some good medical decisions at this difficult time. However, mostly classical image processing methods have been implemented to detect COVID-19 cases resultant in low accuracy. In this chapter, multiple naïve machine and deep learning architectures are implied to evaluate the performance of the models for the classification of COVID-19 using a dataset comprising of chest x-ray images of, i.e., COVID-19 patients and normal (non-infected) individuals. The analysis looks at three machine learning architectures including Logistic Regression, Decision Tree (DT) Classifier, and support vector machine (SVM), and four deep learning architectures, namely: Convolutional neural networks (CNNs), VGG19, ResNet50, and AlexNet. The dataset has been divided into train, test and validation set and the same data have been used for the training, testing, and validation of all the architectures. The result analysis shows that AlexNet provides the best performance out of all the architectures. It can be seen that the AlexNet model achieved 98.05% accuracy (ACC), 97.40% recall, 98.03% F1-score, 98.68% precision, and 98.05% area under the curve (AUC) score. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

3.
Journal of Applied and Natural Science ; 14(2):469-476, 2022.
Article in English | ProQuest Central | ID: covidwho-1912652

ABSTRACT

In the middle of December 2019, a virus known as coronavirus (COVID-19) generated by severe acute respiratory syndrome corona virus 2 (SARC-CoV-2) was first detected in Wuhan, Hubei Province, China. As of the 9th of March, 2022, spread to over 212 countries, causing 429 million confirmed cases and 6 million people to lose their lives worldwide. In developing countries like the South Asian area, alarming dynamic variations in the pattern of confirmed cases and death tolls were displayed. During epidemics, accurate assessment of the characteristics that characterize infectious disease transmission is critical for optimizing control actions, planning, and adapting public health interventions. The reproductive number, or the typical number of secondary cases caused by an infected individual, can be employed to determine transmissibility. Several statistical and mathematical techniques have been presented to calculate across the duration of an epidemic. A technique is provided for calculating epidemic reproduction numbers. It is a MATLAB version of the EpiEstim package's R function estimate R, version 2.2-3. in the South Asian Association for Regional Cooperation (SAARC) countries. The three methodologies supported are 'parametric SI,' 'non-parametric SI,' and 'uncertain SI.' The present study indicated that the highest reproduction number was 12.123 and 11.861 on 5th and 14th March 2020 in India and Sri_Lanka, whereas the lowest reproduction number was the lowest was 0.300 and 0.315 in Sri_Lanka and India. The Maximum and minimum reproductive number of Bangladesh was 3.752 and 0.725. In this study, we have tried to point out the worst, best and current situation of SAARC countries.

4.
Biophysical Journal ; 121(3):538A-538A, 2022.
Article in English | Web of Science | ID: covidwho-1755846
5.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-329714

ABSTRACT

Background: Coronavirus disease 2019 (COVID-19) is a pandemic that has affected the daily life, governments and economies of many countries all over the globe. Ghana is currently experiencing a surge in the number of cases with a corresponding increase in the cumulative confirmed cases and deaths. The surge in cases and deaths clearly shows that the preventive and management measures are ineffective and that policy makers lack a complete understanding of the dynamics of the disease. Most of the deaths in Ghana are due to lack of adequate health equipment and facilities for managing the disease. Knowledge of the number of cases in advance would aid policy makers in allocating sufficient resources for the effective management of the cases. Methods: : A predictive tool is necessary for the effective management and prevention of cases. This study presents a predictive tool that has the ability to accurately forecast the number of cumulative cases. The study applied polynomial and spline models on the COVID-19 data for Ghana, to develop a generalized additive model (GAM) that accurately captures the growth pattern of the cumulative cases. Results: : The spline model and the GAM provide accurate forecast values. Conclusion: Cumulative cases of COVID-19 in Ghana are expected to continue to increase if appropriate preventive measures are not enforced. Vaccination against the virus is ongoing in Ghana, thus, future research would consider evaluating the impact of the vaccine.

6.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-312910

ABSTRACT

Background: Coronavirus disease 2019 (COVID-19) is a pandemic that has affected the daily life, governments and economies of many countries all over the globe. Ghana is currently experiencing a surge in the number of cases with a corresponding increase in the cumulative confirmed cases and deaths. The surge in cases and deaths clearly shows that the preventive and management measures are ineffective and that policy makers lack a complete understanding of the dynamics of the disease. Most of the deaths in Ghana are due to lack of adequate health equipment and facilities for managing the disease. Knowledge of the number of cases in advance would aid policy makers in allocating sufficient resources for the effective management of the cases. Methods: A predictive tool is necessary for the effective management and prevention of cases. This study presents a predictive tool that has the ability to accurately forecast the number of cumulative cases. The study applied polynomial and spline models on the COVID-19 data for Ghana, to develop a generalized additive model (GAM) that accurately captures the growth pattern of the cumulative cases. Results: : The spline model and the GAM provide accurate forecast values. Conclusion: Cumulative cases of COVID-19 in Ghana are expected to continue to increase if appropriate preventive measures are not enforced. Vaccination against the virus is ongoing in Ghana, thus, future research would consider evaluating the impact of the vaccine.

7.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-305123

ABSTRACT

Emerging evidence suggests that patients with cancer are at increased risk of detrimental Covid-19 outcome. The relationship between SARS-CoV-2 viral load and risk factors and outcome of SARS-CoV-2 positive cancer patients remains largely unexplored. We assessed the outcomes of Covid-19 infection in 64 cancer patients and 120 non-cancer and measured SARS-CoV-2 viral load from nasopharyngeal swab samples using cycle threshold (Ct) values who were admitted to two geographically distinct hospitals. We also assessed the incubation period and serial interval time differences between the non-cancer and cancer groups. Our results indicated that the overall mortality rate was higher among cancer patients with a high SARS-CoV-2 viral load. Covid-19 positive cancer patients with higher viral load are more prone to severe outcomes compared to non-cancer and low viral load patients. In addition, patients with lung and hematologic cancer have higher tendencies of severe events in proportion to high viral load. Higher attributable mortality and severity were directly proportional to high viral load particularly patients who are receiving anticancer treatment. Importantly, we found that the incubation period and serial interval time is fairly shorter in cancer patients compared with non-cancer cases. Our report suggests that high SARS-CoV-2 viral loads may play significant role in the overall mortality and severity of Covid-19 positive cancer patients and warranted further study to explore the disease pathogenesis and their use as prognostic tools.

8.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-318072

ABSTRACT

Background: Transmission risk of coronavirus disease 2019 (COVID-19) to close contacts and at different exposure settings are yet to be fully understood for the evaluation of effective control measures. Methods: : We traced 1171 close contact cases who were linked to 291 index cases between July 3, 2020 and September 3, 2020. Clinical and epidemiological characteristics of all index cases, close contacts, and secondary contact cases were collected and analyzed the secondary attack rate and risk of transmission at different exposure settings. Results: : Median age of 291 index cases were 43.0 years (range 18.5-82.3) including 213 male and 78 females. Among all 1171 close contact cases, 39(3.3%) cases were identified as secondary infected cases. Among 39 secondary cases, 33(84.62%) cases were symptomatic and 3 (7.69%) cases were asymptomatic. Of the 33 symptomatic cases, 31(86.1%) male and 5(13.9%) female. Of these 36 symptomatic cases, 24(66.7%) cases between age 20-59 and remaining 12(33.3%) cases were age 60 and over. Of the 36 symptomatic cases, 11(30.6%) cases were identified as severe, 19(52.8%) as moderate and 6(16.7%) as mild. The overall secondary clinical attack rate was 3.07% (95% CI 2.49-3.64). The attack rate was higher among those aged between 50 to 69 years and shows higher risk of transmission than age below 50 years. The attack rate was higher among household contact (6.17%(95%CI 4.7-7.6;risk ratio 2.44[95%CI1.5-3.4]), and lower in hospital facility (2.29%,95%CI0.58-3.40;[risk ratio 0.91,95%CI 0.17-1.9]), funeral ceremony (2.53%,95%CI 0.32-4.73), work places (3.95%,95% CI2.5-5.42 [risk ratio 1.56,95%CI 0.63-2.5]), family contacts (3.87%,95%CI 2.4-5.3;risk ratio 1.53,95%CI 0.61-2.45]). Conclusions: Among all exposure settings analyzed, household contact exposure setting remained the highest transmission probability and risk of transmission of COVID-19 with the increase of age and disease severity.

9.
Journal of Applied and Natural Science ; 12(4):628-634, 2020.
Article in English | Scopus | ID: covidwho-1575798

ABSTRACT

Novel coronavirus disease-2019 (COVID-19) was acknowledged as a global pandemic by WHO, which was first observed at the end of December 2019 in Wuhan city, China, caused by extreme acute respiratory syndrome coronavirus2 (SARS-CoV-2). According to the Weekly operation Update on COVID-19 (November 13, 2020) of the World Health Organization, more than 53 million confirmed cases are reported, including 1.3 million deaths. Various precautionary measures have been taken worldwide to reduce its transmission, and extensive researches are going on. The purpose of this analysis was to determine the initial number of reproductions (Ro) of the coronavirus of SAARC countries named Afghanistan, Bangladesh, India, Pakistan, Bhutan, Nepal, the Maldives, and Sri-Lanka for the first 60 days as the growth is exponential in the early 60 days. The reproduction numbers of coronavirus for Afghanistan, Bangladesh, India, Pakistan, Bhutan, the Maldives, Nepal, and Sri Lanka are 1.47, 3.86, 2.07, 1.43, 1.31, 3.22, 1.75, and 2.39 respectively. The basic reproduction number (R0) 3.86 for Bangladesh and 1.31 for Bhutan indicated that up to 60-days of the outbreak COVID-19, the epidemic was more severe in Bangladesh and less severe in Bhutan among all the SAARC countries. Our predictions can be helpful in planning alertness and taking the appropriate measures to monitor it. ©: Author (s).

10.
J Cardiovasc Med (Hagerstown) ; 23(4): 264-271, 2022 04 01.
Article in English | MEDLINE | ID: covidwho-1562166

ABSTRACT

AIMS: To estimate if chronic anticoagulant (CAC) treatment is associated with morbidity and mortality outcomes of patients hospitalized for SARS-CoV-2 infection. METHODS: In this European multicentric cohort study, we included 1186 patients of whom 144 were on CAC (12.1%) with positive coronavirus disease 2019 testing between 1 February and 30 July 2020. The average treatment effect (ATE) analysis with a propensity score-matching (PSM) algorithm was used to estimate the impact of CAC on the primary outcomes defined as in-hospital death, major and minor bleeding events, cardiovascular complications (CCI), and acute kidney injury (AKI). We also investigated if different dosages of in-hospital heparin were associated with in-hospital survival. RESULTS: In unadjusted populations, primary outcomes were significantly higher among CAC patients compared with non-CAC patients: all-cause death (35% vs. 18% P < 0.001), major and minor bleeding (14% vs. 8% P = 0.026; 25% vs. 17% P = 0.014), CCI (27% vs. 14% P < 0.001), and AKI (42% vs. 19% P < 0.001). In ATE analysis with PSM, there was no significant association between CAC and primary outcomes except for an increased incidence of AKI (ATE +10.2%, 95% confidence interval 0.3-20.1%, P = 0.044). Conversely, in-hospital heparin, regardless of dose, was associated with a significantly higher survival compared with no anticoagulation. CONCLUSIONS: The use of CAC was not associated with the primary outcomes except for the increase in AKI. However, in the adjusted survival analysis, any dose of in-hospital anticoagulation was associated with significantly higher survival compared with no anticoagulation.


Subject(s)
Acute Kidney Injury , COVID-19 , Acute Kidney Injury/chemically induced , Acute Kidney Injury/epidemiology , Anticoagulants/adverse effects , COVID-19/complications , COVID-19 Testing , Cohort Studies , Hospital Mortality , Hospitals , Humans , Retrospective Studies , Risk Factors , SARS-CoV-2
11.
EAI/Springer Innovations in Communication and Computing ; : 1-10, 2022.
Article in English | Scopus | ID: covidwho-1404617

ABSTRACT

The new coronavirus has been declared as a global emergency. The first case was officially declared in Wuhan, China, during the end of 2019. Since then, the virus has spread to nearly every continent, and case numbers continue to rise. The scientists and engineers immediately responded to the virus and presented techniques, devices and treatment approaches to fight back and eliminate the virus. Machine learning is a popular scientific tool and is applied to several medical image recognition problems, involving tumour recognition, cancer detection, organ transplantation and COVID-19 diagnosis. It is proved that machine learning presents robust, fast and accurate results in various medical image recognition problems. Generally, machine learning-based frameworks consist of two stages: feature extraction and classification. In the feature extraction, overwhelmingly unsupervised learning techniques are applied to reduce the input data’s size. This step extracts appropriate features by reducing the computational time and increasing the performance of the classifiers. A classifier is the second step that aims to categorise the input. Within the proposed step, the unsupervised part relies on the feature extraction by using local binary patterns (LBP), followed by feature selection relying on factor analysis technique. The LBP is a kind of visual descriptor, mainly applied for image recognition problem. The aim of using LBP is to analyse the input COVID-19 image and extract salient features. Furthermore, factor analysis is a statistical technique applied to define variability among observed variables in less unnoticed variables named factors. The factor analysis applied to the LBP wavelet aims to select sensitive features from input data (LBP output) and reduce the size input. In the last stage, conic functions classifier is applied to classify two sets of data, categorising the extracted features by using LBP and factor analysis as positive or negative COVID-19 cases. The proposed solution aims to diagnose COVID-19 by using LBP and factor analysis, based on conic functions classifier. The conic functions classifier presents remarkable results compared with these popular classifiers and state-of-the-art studies presented in the literature. © 2022, Springer Nature Switzerland AG.

12.
Front Oncol ; 11: 715794, 2021.
Article in English | MEDLINE | ID: covidwho-1399159

ABSTRACT

The correlation between severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) viral load and risk of disease severity in cancer patients is poorly understood. Given the fact that cancer patients are at increased risk of severe coronavirus disease 2019 (COVID-19), analysis of viral load and disease outcome in COVID-19-infected cancer patients is needed. Here, we measured the SARS-CoV-2 viral load using qPCR cycle threshold (Ct) values collected from 120 noncancer and 64 cancer patients' nasopharyngeal swab samples who are admitted to hospitals. Our results showed that the in-hospital mortality for high viral load cancer patients was 41.38%, 23.81% for medium viral load and 14.29% for low viral load patients (p < -0.01). On the other hand, the mortality rate for noncancer patients was lower: 22.22% among patients with high viral load, 5.13% among patients with medium viral load, and 1.85% among patients with low viral load (p < 0.05). In addition, patients with lung and hematologic cancer showed higher possibilities of severe events in proportion to high viral load. Higher attributable mortality and severity were directly proportional to high viral load particularly in patients who are receiving anticancer treatment. Importantly, we found that the incubation period and serial interval time is shorter in cancer patients compared with noncancer cases. Our report suggests that high SARS-CoV-2 viral loads may play a significant role in the overall mortality and severity of COVID-19-positive cancer patients, and this warrants further study to explore the disease pathogenesis and their use as prognostic tools.

13.
US Cardiology Review ; 15, 2021.
Article in English | EMBASE | ID: covidwho-1344576

ABSTRACT

In patients presenting with ST-elevation MI, prompt primary coronary intervention is the preferred treatment modality. Several studies have described improved outcomes in patients with door-to-balloon (D2B) and symptom onset-to-balloon (OTB) times of less than 2 hours, but the specific implications of the coronavirus disease 2019 (COVID-19) pandemic on D2B and OTB times are not well-known. This review aims to evaluate the impact of COVID-19 on D2B time and elucidate both the factors that delay D2B time and strategies to improve D2B time in the contemporary era. The search was directed to identify articles discussing the significance of D2B times before and during COVID-19, from the initialization of the database to December 1, 2020. The majority of studies found that onset-of-symptom to hospital arrival time increased in the COVID-19 era, whereas D2B time and mortality were unchanged in some studies and increased in others.

14.
Current Respiratory Medicine Reviews ; 16(3):156-164, 2020.
Article in English | Scopus | ID: covidwho-1058347

ABSTRACT

Novel coronavirus-2019 (nCoV-2019) emerged as a potentially infectious respiratory disease caused by newly discovered β-coronavirus. nCoV-19 has emerged as a global pandemic due to the rapid transmission and high infection rate commonly involved in acute respiratory ill-ness. Literature search includes various databases like Google Scholar, PubMed, ScienceDirect, and Scopus for studies published using a different combination of keywords “coronavius”, “COVID-19”, “SARS”, “MERS”, “antiviral drugs”, “vaccines”, and “immunity”. We collected epidemiology data from the Worldometer portal (data available till 9 October, 2020). Fever, dry cough, dyspnea, sore throat, or fatigue are common clinical symptoms of the infection. Cytotoxic T-cells and T-helper cells plus Cytotoxic T cells (CD8+) account for maximum (approximately 80%) of total infiltrate in the pulmonary region of the affected nCoV individuals and act as a significant contributor to the clearance of the infection. This review intends to outline the literature con-cerning the mode of actual transmission, immune response, and possible therapeutic approach against the virus. © 2020 Bentham Science Publishers.

15.
Journal of Applied & Natural Science ; 12(4):628-634, 2020.
Article in English | Academic Search Complete | ID: covidwho-995120

ABSTRACT

Novel coronavirus disease-2019 (COVID-19) was acknowledged as a global pandemic by WHO, which was first observed at the end of December 2019 in Wuhan city, China, caused by extreme acute respiratory syndrome coronavirus2 (SARS-CoV-2). According to the Weekly operation Update on COVID-19 (November 13, 2020) of the World Health Organization, more than 53 million confirmed cases are reported, including 1.3 million deaths. Various precautionary measures have been taken worldwide to reduce its transmission, and extensive researches are going on. The purpose of this analysis was to determine the initial number of reproductions (Ro) of the coronavirus of SAARC countries named Afghanistan, Bangladesh, India, Pakistan, Bhutan, Nepal, the Maldives, and Sri-Lanka for the first 60 days as the growth is exponential in the early 60 days. The reproduction numbers of coronavirus for Afghanistan, Bangladesh, India, Pakistan, Bhutan, the Maldives, Nepal, and Sri Lanka are 1.47, 3.86, 2.07, 1.43, 1.31, 3.22, 1.75, and 2.39 respectively. The basic reproduction number (R0) 3.86 for Bangladesh and 1.31 for Bhutan indicated that up to 60-days of the outbreak COVID-19, the epidemic was more severe in Bangladesh and less severe in Bhutan among all the SAARC countries. Our predictions can be helpful in planning alertness and taking the appropriate measures to monitor it. [ABSTRACT FROM AUTHOR] Copyright of Journal of Applied & Natural Science is the property of Applied & Natural Science Foundation 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

16.
European Journal of Molecular and Clinical Medicine ; 7(3):526-534, 2020.
Article in English | EMBASE | ID: covidwho-956277

ABSTRACT

The Gulf Cooperation Council (GCC) countries) face the dual shock of a pandemic caused by the novel coronavirus (COIVD 19) and a collapse in oil prices. GCC countries many times experienced fluctuations in oil price and learnt how to deal the situation. However, the COVID-19 outbreak, being a new one, has created a lot of concern among GCC countries. This pandemic is causing turbulence to the economies of the GCC countries. Major industries that have been impacted in GCC countries due to COVID-19 pandemic include Energy, Aviation, Food & Beverage, Chemical, Retail & E-commerce, Travel & Tourism among others. Besides a major downfall in oil demand has been reported across the globe due to the effect of COVID-19. Due to this, many oil productions sites have been shut down or has to decrease production in the region. The closure of the industrial and commercial activities because of the pandemic would certainly affect their economies. Facility management (FM) constitutes a branch, jointly representing real estate market with property management and asset management. It plays a crucial role in economic activities in region as FM services are involved in all industrial and commercial activities. The Facility Management (FM) market in GCC countries has witnessed robust growth during the last few decades due to rapid economic activities in this region. It is an established fact in FM services manpower cost dominates the total cost whereas material cost plays vital role in construction industries. Majority of work forces in GCC countries in FM sector is migrant people from the Globe. CCC countries are showing actions that they are capable of acting effectively to contain the health and economic impacts of the pandemic within their own borders, albeit with marked shortcomings when it comes to protecting migrant workers. It is estimated that approximately 23 million migrant workers are living GCC countries . These millions of migrant workers across the Gulf face uncertainty as host countries lock down, employers withhold wages or mull redundancies, and strict coronavirus containment measures lead to deportations and confinement. This will have series impact on FM sector. In this paper a detail study the impact of COVID 19 on FM sector in GCC countries is reported. Strategies to overcome the crisis are listed along with the means and recommendation to implement the strategy.

17.
European Journal of Molecular and Clinical Medicine ; 7(3):506-510, 2020.
Article in English | EMBASE | ID: covidwho-956276

ABSTRACT

Globally, the COVID-19 pandemic has been the headline over the past few months and forced the institution and individuals to work remotely and practices such as social distancing. Consequently, the cybercriminals urgent implementation of technology to enable the organization to work remotely by conducting cyber-attack targeting critical organization within countries. This article discusses the different type of cyber threats and its impact to the organizations during COVID-19 pandemic by exploiting the digital and technology.

18.
Ann R Coll Surg Engl ; : 1-6, 2020 Jun 27.
Article in English | MEDLINE | ID: covidwho-620461

ABSTRACT

INTRODUCTION: The COVID-19 pandemic has put significant stress on healthcare systems globally. This study focuses on emergency general surgery services at a major trauma centre and teaching hospital. We aimed to identify whether the number of patients and the severity of their presentation has significantly changed since the implementation of a national lockdown. MATERIALS AND METHODS: This study is a retrospective review of acute referrals (from general practice and accident and emergency) to the emergency general surgery team over a 14-day period before (group 1) and during (group 2) lockdown. RESULTS: A total of 151 patients were reviewed by the general surgical team in group 1 and 75 in group 2 (a 50.3% reduction). The number of days with symptoms prior to presentation was significantly shorter in group 1 compared with group 2 (3 vs 4, p = 0.04). There was no significant difference in the National Early Warning Score, white blood cell count, lymphocytes and C-reactive protein on admission between the two groups of patients. There were significantly fewer patients admitted after lockdown compared with pre-lockdown (66% vs 48%, p = 0.01). Length of hospital stay was significantly shorter during lockdown compared with pre-lockdown (5 days vs 4 days, p = 0.04). CONCLUSION: Fewer patients were referred and admitted during lockdown compared with pre-lockdown, and the length of stay was also significantly reduced. There was also a delay in presentation to hospital, although these patients were not more unwell based on the scoring criteria used within this study.

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