Search

Quantitative geographical methods have played an important role in COVID-19 research. To complement and extend previous review studies, we conduct a scoping review of COVID-19 studies employing quantitative geographical approaches by focusing on 331 papers published in 45 journals in geography, urban studies, and planning. We identify four major research themes (clusters): (1) how SARS-CoV-2 viruses spread in cities, (2) the COVID-19 mortality (death) rates and their association with socioeconomic variables, (3) how the COVID-19 pandemic changed people's mobilities, and (4) how the COVID-19 pandemic affects air pollution. We conclude that spatial models play a key role in COVID-19 quantitative geographical approaches, and human mobility is an important and widely studied topic. We also reveal a lack of research focusing on environmental pollution (other than air pollution) that potentially worsened during the pandemic. [ABSTRACT FROM AUTHOR]

The 2019 new coronavirus illness (COVID-19) is an international public health emergency. Our social and healthcare systems are under a great deal of strain as a result of the daily increase in infection rates and fatalities. Doctors typically perform a chest Xray to identify the diseased areas of the lungs since pneumonia is a common type of infection that spreads in the lungs. In this paper, we propose a Convolution Neural Network via the li regularization model to detect COVID-19 patients using chest X-Ray images. Due to the lack of the COVID-19 benchmark dataset, we use deep learning techniques to identify the best pre-trained CNN model for this job by comparing 15 models. The suggested algorithm was tested on 1316 photos (116 COVID-19 cases, 328 healthy controls, and 872 pneumonia cases), with 66% for training, 17% for validation, and 17% for testing. The classification accuracy, loss, valueaccuracy, and value-loss values obtained by the suggested technique are 0.9912, 0.0187, 0.1119, and 0.9506 respectively. Additionally, the model effectively decreases training loss while boosting accuracy. The results show that proposed procedures are more effective than existing ones at identifying COVID-19 cases from chest X-ray pictures. [ABSTRACT FROM AUTHOR]

Worldwide, the recent SARS-CoV-2 virus disease outbreak has infected more than 691,000,000 people and killed more than 6,900,000. Surprisingly, Sub-Saharan Africa has suffered the least from the SARS-CoV-2 pandemic. Factors that are inherent to developing countries and that contrast with their counterparts in developed countries have been associated with these disease burden differences. In this paper, we developed data-driven COVID-19 mathematical models of two 'extreme': Cameroon, a developing country, and New York State (NYS) located in a developed country. We then identified critical parameters that could be used to explain the lower-than-expected COVID-19 disease burden in Cameroon versus NYS and to help mitigate future major disease outbreaks. Through the introduction of a 'disease burden' function, we found that COVID-19 could have been much more severe in Cameroon than in NYS if the vaccination rate had remained very low in Cameroon and the pandemic had not ended. [ABSTRACT FROM AUTHOR]

In this paper, we explored how vulnerable, immunocompromised groups and caregivers of the elderly experienced and perceived the onset of the Covid-19 pandemic in South Africa. Semi-structured interviews were conducted remotely between the 5th andthe 18th of April 2020 in the three South African provinces hardest hit by Covid-19, namely Gauteng, KwaZulu-Natal and the Western Cape. In total, 60 qualitative key informant interviews and one focus group discussion were conducted. Study participants expressed concerns for elderly people and people with underlying health conditions because of their increased vulnerability to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). People living with HIV expressed an increased fear of infection following the advent of the Covid-19 pandemic in South Africa. The sidelining of healthcare services and stock-outs of medication proved to be an added concern in particular for vulnerable and immunocompromised groups. Overall, the data suggest that the fear of infection is ubiquitous for people who live in unstable environments such as overcrowded townships and informal settlements. Given the increased fears of infection brought on by the Covid-19 pandemic, the mental health of vulnerable communities and those caring for them becomes an added burden for people living in unstable environments. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has spread seriously throughout the world. Predicting the spread, or the number of cases, in the future can facilitate preparation for, and prevention of, a worst-case scenario. To achieve these purposes, statistical modeling using past data is one feasible approach. This paper describes spatio-temporal modeling of COVID-19 case counts in 47 prefectures of Japan using a nonlinear random effects model, where random effects are introduced to capture the heterogeneity of a number of model parameters associated with the prefectures. The negative binomial distribution is frequently used with the Paul-Held random effects model to account for overdispersion in count data; however, the negative binomial distribution is known to be incapable of accommodating extreme observations such as those found in the COVID-19 case count data. We therefore propose use of the beta-negative binomial distribution with the Paul-Held model. This distribution is a generalization of the negative binomial distribution that has attracted much attention in recent years because it can model extreme observations with analytical tractability. The proposed beta-negative binomial model was applied to multivariate count time series data of COVID-19 cases in the 47 prefectures of Japan. Evaluation by one-step-ahead prediction showed that the proposed model can accommodate extreme observations without sacrificing predictive performance. [ABSTRACT FROM AUTHOR]

This paper intends to contribute to the collaborative efforts of the scientific community by providing a literature review of the status of the coronavirus research by adding a multi-disciplinary perspective and looking into a broad spectrum of SARS-CoV-2 studies on virus molecular structure, biophysical approach, electrostatic interaction and UVC rays. The paper identifies future research directions for each group of studies and points out remaining questions on the way related to COVID-19. The summary of the literature review will intend to assist future studies; provide a biophysical understanding of the virus interaction with host cells and help better identify antiviral therapy and the development of new vaccines/drugs to tackle COVID-19 and any virus outbreak. In addition to the electrostatic interactions of SARS-CoV-2, this paper also discusses whether UVC rays are a safe alternative to many chemical sterilization methods which are frequently used in our daily life since the beginning of the COVID-19 outbreak and whose health effects are controversial. This article also briefly discusses the relationship of some trace elements with COVID-19 infection. In conclusion, focusing on biophysical mechanisms of virus–cell interactions with a broad perspective has potential to give a different approach to the reader for future treatment methods. [ABSTRACT FROM AUTHOR]

Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2) known as Coronavirus surfaced in late 2019. It turned out to be a life-threatening disease and is causing chaos all around the world. The World Health Organisation (WHO) declared it a pandemic in March 2020. To handle COVID-19 related problems, research in many areas of science was introduced. Machine learning (ML), being one of the most successful stories in recent times is widely used to solve a variety of problems in our everyday life. Here, an overview of machine learning that tackles the pandemic is discussed in the beginning. Various datasets related to COVID-19 are also explored. Diagnosis of this viral disease using CT-Scans, X-ray images, sound analysis and blood tests using machine learning are presented in-depth. Drug and vaccine development using machine learning for COVID-19 are also discussed. Pandemic management and control were also examined. The main objective of this paper is to conduct a systematic review of machine learning applications that fight the deadly virus. This paper helps the researchers to understand and analyse the data trends related to COVID-19 and also prepare for a future outbreak which might happen due to new strains of COVID-19. Challenges and directions for the future are also provided. [ABSTRACT FROM AUTHOR]

Public health lessons from one pandemic become the planning assumptions for the next one. Aotearoa New Zealand's 2017 pandemic plan was derived from past experience of influenza. When Covid-19 emerged as a major global health threat, it took time for the realisation to crystallise that this infection was so different from influenza that it required a completely new pandemic response strategy. In this paper we describe how early evidence about SARS-CoV-2 transmission from China led to the adoption of an elimination strategy in Aotearoa New Zealand, making it the first country to choose elimination as a specific policy response. We discuss how further evidence has shaped the selection and design of Covid-19 pandemic control measures such as border restrictions, case and contact management, hygiene practices and use of face masks, physical distancing, and vaccines. This experience demonstrates the need for a different approach to the design of the next national pandemic plan. We identify key early evidence that will be required to develop a flexible and appropriate public health response to a new pandemic threat. We present a framework for a new pandemic plan that aims to learn from the Covid-19 experience by making as few limiting assumptions as possible. [ABSTRACT FROM AUTHOR]

Given the rapid spread of the COVID-19 pandemic and the huge negative impacts it is causing, researching on COVID-19-related issues is very important for designing proactive and comprehensive public health interventions to fight against the pandemic. We describe the characteristics of COVID-19 patients detected in the two phases of the epidemic in Vietnam. Data used in this paper were mainly obtained from the official database of the Ministry of Health of Vietnam. Descriptive statistics were carried out using Stata 16 software. As of 18 May 2020, the cumulative number of COVID-19 cases detected in Vietnam was 324, 16 cases from 4 cities and provinces in the first phase (during 20 days, 0.8 cases detected per day) and 308 cases from 35 cities, provinces in the second phase (during 76 days, 4.1 cases detected per day). Vietnam has mobilized its entire political system to fight the COVID-19 and achieved some initial successes. We found both similarities and differences between the two phases of the COVID-19 epidemic in Vietnam. We demonstrated that the situation of the COVID-19 epidemic in Vietnam is getting more complicated and unpredictable. [ABSTRACT FROM AUTHOR]