COVID-19: End of the beginning?

Bergquist, Robert; Stengaard, Anna-Sofie

doi:10.4081/gh.2020.897

Cited by 12 publications

(4 citation statements)

References 18 publications

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“…The high frequency of Covid-19 infection in these age groups may be due to the low median age (30 years) of the Iranian population and/or due to inclusion of mainly working-age population in this age group [22]. It seems that the frequency of Covid-19 infections rises considerably from age group of 50-59 years [23, 24]. In contrast to findings in China and Italy [6, 25], we found a considerably higher proportion of fatalities in the age groups panning the range from 20-50 years.…”

Section: Discussionmentioning

confidence: 99%

Demographic and Clinical Characteristics of the Severe Covid-19 Infections: First Report from Mashhad University of Medical Sciences, Iran

Goshayeshi

Rad

Bergquist³

et al. 2020

Preprint

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Purpose. To report a comparison between fatalities and recovery of the severe Covid-19 infected patients based on demographic and clinical characteristics. Methods. Between 5 March and 12 May 2020, of 4,000 patients, 1,278 were laboratory confirmed to be Covid-19 infection in Mashhad, Iran. Finally, 334 deceased and 733 recovered cases were assessed in terms of demography, exposure history, health outcomes and clinical symptoms. Results. Mean (SD) age for confirmed patients, for deceased cases and for recovered cases. Mean (SD) age for all confirmed-patients was 56.9 (18.74), for the fatalities 67.26 (15.77) and for those recovered 52.82 (17.91) years. The rise of the mortality rate in relation to seniority was statistically significant. Despite a high frequency of Covid-19 infections accrued in the age groups of 30-39 and 40-49 years, most of these cases (88.2 and 85.8%, respectively) recovered. The median (IQR) duration of hospitalization was 9.0 (9) days. The most prevalent co-morbidities were cardiovascular disorders (21%) and diabetes (16.3%). Fever (63.8%), cough (68.1%), and dyspnoea (72.7%) were the most frequent clinical symptoms. 5.2% of infected-cases were healthcare workers that two (3%) of them died. Most patients (48%) received both antiviral and antibiotic therapy. The mortality rate of Kaletra combination prescribed for severe cases was 46.7%. Conclusions. The characteristics of Covid-19 varied from died to survived infected patients. There were a higher number of fatalities in younger patients than in international studies. Diabetes and cardiovascular disorders were most prevalent co-morbidities. The study could not address the case-fatality rate of Covid-19 infection that remains for future studies.

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Section: Discussionmentioning

confidence: 99%

Demographic and Clinical Characteristics of the Severe Covid-19 Infections: First Report from Mashhad University of Medical Sciences, Iran

Goshayeshi

Rad

Bergquist³

et al. 2020

Preprint

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“…The pathogen was diagnosed as one of the coronavirus family and is currently known as the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) [2]. Due to the highly contagious nature of this virus, it spread in a short time to almost all countries and eventually being declared a pandemic by the World Health Organization (WHO) on 11 March 2020 [3,4]. As of 19 July 2021, more than 191 million confirmed cases and approximately 2.15% mortality were identified worldwide.…”

Section: Objectivementioning

confidence: 99%

A spatial-epidemiological dataset of subjects infected by SARS-CoV-2 during the first wave of the pandemic in Mashhad, second-most populous city in Iran

et al. 2021

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Objective In March 2020, Iran tackled the first national wave of COVID-19 that was particularly felt in Mashhad, Iran’s second-most populous city. Accordingly, we performed a spatio-temporal study in this city to investigate the epidemiological aspects of the disease in an urban area and now wish to release a comprehensive dataset resulting from this study. Data description These data include two data files and a help file. Data file 1: “COVID-19_Patients_Data” contains the patient sex and age + time from symptoms onset to hospital admission; hospitalization time; co-morbidities; manifest symptoms; exposure up to 14 days before admission; disease severity; diagnosis (with or without RT-PCR assay); and outcome (recovery vs. death). The data covers 4000 COVID-19 patients diagnosed between 14 Feb 2020 and 11 May 2020 in Khorasan-Razavi Province. Data file 2: “COVID-19_Spatiotemporal_Data” is a digital map of census tract divisions of Mashhad, the capital of the province, and their population by gender along with the number of COVID-19 cases and deaths including the calculated rates per 100,000 persons. This dataset can be a valuable resource for epidemiologists and health policymakers to identify potential risk factors, control and prevent pandemics, and optimally allocate health resources.

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“…ijmp.jor.br v. 12, n. 8, November-December 2021ISSN: 2236-269X DOI: 10.14807/ijmp.v12i8.1510 were already millions of cases worldwide. Thus, the World Health Organization (WHO) declared a global pandemic of COVID 19 on 11 March 2020, going from more than three million cases worldwide to 27 April (Bergquist and Stensgaard, 2020).…”

Section: Independent Journal Of Management and Production (Ijmandp)mentioning

confidence: 99%

Health care logistics: mapping and optimization of patients logistics

Chiroli¹,

Coradazi²,

Branco³

et al. 2021

Ind. Jour. Manag. & Prod.

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Healthcare logistics play an important role in management, being attributed the activities of acquisition, distribution and movement of materials, professionals and patients. This work aims to develop a study, using the healthcare logistics in the movement of patients in the third health region of Paraná, proposing a linear programming problem that will pass through a computational simulation, considering the existing demands and constraints in the system, aiming to optimize the flow of patients from this region. The present study developed four mathematical models, based on demands and constraints followed by linear programming in order to find the best possible solution for the flow of patients from the third health region of the state of Paraná. The study developed reached its goal of optimization, generating an economy in the transportation of patients. Through the analysis of the results, it is concluded that the model that best suits the presented problem is the one of costs minimization, since the one of vehicles presented higher costs. Possibly the model that minimizes the vehicles would bring better results if the vehicles were not outsourced, but of the Ponta Grossa City Hall (PMPG). Was possible to verify the importance of the theme, especially when referring to the flow of patients in the health services due to the lack of studies with this specific approach. Even with the scarcity of data, it is possible to notice the potential for improvements on this patient transport system.

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COVID-19: End of the beginning?

Cited by 12 publications

References 18 publications

Demographic and Clinical Characteristics of the Severe Covid-19 Infections: First Report from Mashhad University of Medical Sciences, Iran

Demographic and Clinical Characteristics of the Severe Covid-19 Infections: First Report from Mashhad University of Medical Sciences, Iran

A spatial-epidemiological dataset of subjects infected by SARS-CoV-2 during the first wave of the pandemic in Mashhad, second-most populous city in Iran

Health care logistics: mapping and optimization of patients logistics

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