Access to the COVID-19 services during the pandemic - a scoping review.

Appropriate accessibility to coronavirus disease 2019 (COVID-19) services is essential in the efficient management of the pandemic. Different geospatial methods and approaches have been used to measure accessibility to COVID-19 health-related services. This scoping review aimed to summarize and synthesize the geospatial studies conducted to measure accessibility to COVID-19 healthcare services. Web of Science, Scopus, and PubMed were searched to find relevant studies. From 1113 retrieved unique citations, 26 articles were selected to be reviewed. Most of the studies were conducted in the USA and floating catchment area methods were mostly used to measure the spatial accessibility to COVID-19 services including vaccination centres, Intensive Care Unit beds, hospitals and test sites. More attention is needed to measure the accessibility of COVID-19 services to different types of users especially with combining different non-spatial factors which could lead to better allocation of resources especially in populations with limited resources.

Clinical informatics solutions in COVID-19 pandemic: Scoping literature review.

Background: The global outbreak of COVID-19 (coronavirus disease 2019) disease has highlighted the importance of disease monitoring, diagnosing, treating, and screening. Technology-based instruments could efficiently assist healthcare systems during pandemics by allowing rapid and widespread transfer of information, real-time tracking of data transfer, and virtualization of meetings and patient visits. Therefore, this study was conducted to investigate the applications of clinical informatics (CI) during the COVID-19 outbreak. Methods: A comprehensive search was performed on Medline and Scopus databases in September 2020. Eligible studies were selected based on the inclusion and exclusion criteria. The extracted data from the studies reviewed were about study sample, study type, objectives, clinical informatics domain, applied method, sample size, outcomes, findings, and conclusion. The risk of bias was evaluated in the studies using appropriate instruments based on the type of each study. The selected studies were then subjected to thematic synthesis. Results: In this review study, 72 out of 2716 retrieved articles met the inclusion criteria for full-text analysis. Most of the articles reviewed were done in China and the United States of America. The majority of the studies were conducted in the following CI domains: prediction models (60%), telehealth (36%), and mobile health (4%). Most of the studies in telehealth domain used synchronous methods, such as online and phone- or video-call consultations. Mobile applications were developed as self-triage, self-scheduling, and information delivery tools during the COVID-19 pandemic. The most common types of prediction models among the reviewed studies were neural network (49%), classification (42%), and linear models (4.5%). Conclusion: The present study showed clinical informatics applications during COVID-19 and identified current gaps in this field. Health information technology and clinical informatics seem to be useful in assisting clinicians and managers to combat COVID-19. The most common domains in clinical informatics for research on the COVID-19 crisis were prediction models and telehealth. It is suggested that future researchers conduct scoping reviews to describe and analyze other levels of medical informatics, including bioinformatics, imaging informatics, and public health informatics.

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.

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.

Epidemiological characteristics and initial spatiotemporal visualisation of COVID-19 in a major city in the Middle East.

BACKGROUND: The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) emerged initially in China in December 2019 causing the COVID-19 disease, which quickly spread worldwide. Iran was one of the first countries outside China to be affected in a major way and is now under the spell of a fourth wave. This study aims to investigate the epidemiological characteristics of COVID-19 cases in north-eastern Iran through mapping the spatiotemporal trend of the disease. METHODS: The study comprises data of 4000 patients diagnosed by laboratory assays or clinical investigation from the beginning of the disease on Feb 14, 2020, until May 11, 2020. Epidemiological features and spatiotemporal trends of the disease in the study area were explored by classical statistical approaches and Geographic Information Systems. RESULTS: Most common symptoms were dyspnoea (69.4%), cough (59.4%), fever (54.4%) and weakness (19.5%). Approximately 82% of those who did not survive suffered from dyspnoea. The highest Case Fatality Rate (CFR) was related to those with cardiovascular disease (27.9%) and/or diabetes (18.1%). Old age (≥60 years) was associated with an almost five-fold increased CFR. Odds Ratio (OR) showed malignancy (3.8), nervous diseases (2.2), and respiratory diseases (2.2) to be significantly associated with increased CFR with developments, such as hospitalization at the ICU (2.9) and LOS (1.1) also having high correlations. Furthermore, spatial analyses revealed a geographical pattern in terms of both incidence and mortality rates, with COVID-19 first being observed in suburban areas from where the disease swiftly spread into downtown reaching a peak between 25 February to 06 March (4 incidences per km2). Mortality peaked 3 weeks later after which the infection gradually decreased. Out of patients investigated by the spatiotemporal approach (n = 727), 205 (28.2%) did not survive and 66.8% of them were men. CONCLUSIONS: Older adults and people with severe co-morbidities were at higher risk for developing serious complications due to COVID-19. Applying spatiotemporal methods to identify the transmission trends and high-risk areas can rapidly be documented, thereby assisting policymakers in designing and implementing tailored interventions to control and prevent not only COVID-19 but also other rapidly spreading epidemics/pandemics.