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During the COVID-19 pandemic, children under the age of 12 are the most vulnerable age group to health concerns. The goal of this study was to conduct a spatiotemporal analysis of the distribution of COVID-19 cases in Central Java children using the GWR (Geographically Weighted Regression) approach. The data source is the Central Java Provincial Health Office, and the study objects are 35 cities and districts in Central Java province. The data obtained are the number of COVID-19 cases in children aged 0-11 years, the total number of Covid-19 cases, the number of PCR tests per day, the number of vaccinations and the number of health care facilities per city and district per month from March 2020 to November 2021. Hotspot analysis and the GWR approach were used to examine data in semesters 1–4 (S1–S4). From S1 to S4, the number of COVID-19 cases in children increased. Areas that became hotspots for more than two semesters were Semarang City, Semarang Regency, Banyumas, Cilacap, Kendal, and Demak. According to the GWR analysis in S1-S4, the total number of COVID-19 cases, PCR tests per day, vaccinations, and health care facilities all affect the number of COVID-19 patients in children by more than 75%. The total number of COVID-19 cases has a significant impact on the number of COVID-19 cases in children but the number of health care facilities has no effect. The results of the GWR prediction of COVID-19 cases in children show that the cities of Semarang and Banyumas became areas with a larger number of COVID-19 cases in two semesters. According to the hotspot and GWR analysis, the cities of Semarang and Banyumas are regions to be on the lookout for in the spread of COVID-19 cases in S1-S4. © 2022 Praise Worthy Prize S.r.l.-All rights reserved.
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In order to comprehensively understand the research hotspots and development trends of Lonicera Japonica Flos in the past 20 years, and to provide intuitive data reference and objective opinions and suggestions for subsequent related research in this field, this study collected 8 871 Chinese literature and 311 English literature related to Lonicera Japonica Flos research in the core collection databases of Wanfang Data), CNKI and Web of Science (WOS) from 2002 to 2021, and conducted bibliometric and visual analysis using vosviewer. The results showed that the research on the active components of Lonicera Japonica Flos based on phenolic acid components, the research on the mechanism of novel coronavirus pneumonia based on data mining and molecular docking technology, and the pharmacological research on the anti-inflammatory and antiviral properties of Lonicera Japonica Flos are the three hot research directions in the may become the future research direction. In this paper, we analyze the research on Lonicera Japonica Flos from five aspects: active ingredients, research methods, formulation and preparation, pharmacological effects and clinical applications, aiming to reveal the research hotspots, frontiers and development trends in this field and provide predictions and references for future research.Copyright © Drug Evaluation Research 2022.
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Half of the world's population lives in cities, where usually there are few little green space and there are also high levels of air pollution. Moreover, the traditional urbanization of cities contributes to climate change, promotes the loss of global biodiversity and induces serious health problems for citizens. Both climate change and the loss of biodiversity affect negatively to the ecosystems and therefore human health, as they are responsible for providing clean air, food, fresh water, medicines, renewable resources. . . This deterioration increases significantly the risk of human-borne infectious diseases such as coronavirus or HIV. The ability we have to re-naturalize anthropogenic spaces and learn to generate spaces for coexistence will be key for the future of our society. The research presented in this paper aims to do a step forward to achieve that ability by working in three schools of the city of Barcelona and their surroundings. Among other actions, in this project, a diagnosis of neighborhood has been carried out. The diagnosis includes the identification and quantification of relevant indicators regarding neighborhood's biodiversity and also the quality of daily life and the analysis of pollutants (NO2 and PM10) near the schools during the 2021-2022 school year. All these information has been merged in a single geographic data base and relevant hotspots where to act have been identified. The information has been shared with city council and citizens. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda.
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According to the World Health Organization (WHO), the elderly and people with comorbidities are most vulnerable to COVID-19 infection. With this, the challenges and threats posed to the vulnerable population require targeted interventions. While public health surveillance methods had developed recent advances to meet users' information needs, the volume and complexity of infectious disease data had increased, resulting in increasing difficulty to facilitate risk communication with the public and for decision-makers to make informed measures to protect the public's health. Moreover, the implementation of COVID-19 spatiotemporal disease surveillance strategies specifically targeting the vulnerable population in the Davao Region had been previously unexplored. This paper investigated the COVID-19 incidence in the Davao Region from 03 Mar 2020, the earliest recorded date of onset, to 31 Aug 2021 using geospatial tools. The variables were visualized through choropleth maps and graduated symbols, and subsequently examined through spatial autocorrelation and hotspot analysis. Hotspots across the region were observed to be in high-density areas. These areas pose greater risks of infection due to the presence of a high concentration of cases. However, high case fatality rates were found in far-flung municipalities where access to COVID-19 healthcare facilities is a dilemma. In the COVID-19 setting and future disease outbreaks similar to COVID-19, results from this study may provide insights to government offices and other related agencies to improve healthcare systems and programs such as providing and initiating tailor-fitted isolation and consultation mechanisms appropriate to the vulnerable population in a community. [ FROM AUTHOR] Copyright of Philippine Journal of Science is the property of Science & Technology Information Institute 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.)
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For equatorial African countries such as Rwanda the power grid in some regions is either absent or highly unreliable even though these locations are blessed with reliable solar radiation most of the time. Designing and implementing solar power systems capable of supporting micro-computer systems such as Raspberry Pi devices that can be used in educational environments is a way to overcome grid challenges while at the same time imparting valuable lessons covering Engineering, Technology, and Computing. Using Learning Engineering Sciences best practices effectively mitigates how COVID-19 that has required standard face-to-face project and learning strategies to transition to virtual or hybrid strategies that utilize Open Educational Resources (OER). These strategies include video conferencing, file sharing platforms, and messaging applications to generate learning activities, create courses to construct the learning program for training teachers in the use of OER and Raspberry Pi desktop devices. © 2023 IEEE.
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[Background] The COVID-19 pandemic hints at the importance of modernizing disease control system. To understand the scientific research strength of our country's disease control system in recent years is conducive to formulating more targeted policies or measures to promote the modernization of the disease control system. [Objective] To understand the scientific research strength and research hotspots of China's provincial-level centers for disease control and prevention (CDCs) from 2011 to 2020, and provide evidence for the development of scientific research work, discipline construction, and talent team construction in CDCs in the future. [Methods] The Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) of the Web of Science Core Collection were used to retrieve SCI-indexed English papers published by 31 provincial CDCs (excluding Taiwan Province, Hong Kong and Macau Special Administrative Regions) in our country from 2011 to 2020, and to screen literature with provincial CDCs as the first affiliation for bibliometric analysis and visual analysis. Bibliometric analysis included the SCI-indexed publications of different provincial CDCs (as co-affiliation and the first affiliation), the number of SCI-indexed papers published by provincial CDCs (as the first affiliation) and funding rates by years, the high-frequency authors of SCI-indexed papers published by provincial CDCs (as the first affiliation) and their distribution, and the characteristics of the journals. Visual analysis software Citespace 5.8.R1 was used to draw keyword co-occurrence maps, cluster information tables, and emergence maps to provide information on research hotspots and their evolution. [Results] From 2011 to 2020, the number of SCI-indexed papers from 31 provincial CDCs was 8 420 (including co-affiliation), of which 2 060 papers listed provincial CDCs as the first affiliation. The provincial CDCs of Zhejiang, Jiangsu, Shanghai, Beijing, Shandong, and Guangdong were the leading six institutes in terms of the total number of SCI-indexed papers contributed as co-affiliation or the first affiliation. There was a large gap in the total number of SCI-indexed papers among the provincial CDCs. The highest total number of SCI-indexed papers contributed by provincial CDCs as the first affiliation was Zhejiang CDC (448 papers), while the lowest number was Xinjiang CDC (only 1 paper). From 2011 to 2020, the total number of SCI-indexed papers contributed by the 31 provincial CDCs as the first affiliation showed an overall increasing trend. Except for 2011, which was 63.1%, the funding rates in other years exceeded 70%. In terms of high-frequency authors, 13 first authors published >=10 SCI-indexed papers: Zhang Yingxiu from Shandong CDC had the highest number of SCI-indexed papers (47), followed by Hu Yu from Zhejiang CDC. Zhejiang, Jiangsu, Beijing, Guangdong, Shanghai, and Shandong still ranked the top six of >=4 first authored-SCI papers. In terms of journal characteristics, the top 20 journals with the highest number of SCI papers published a total of 862 papers, accounting for 41.8% (862/2 060), and PLOS ONE ranked the first (188 papers). The research hotspots were mainly concentrated in the fields of infection, child health, and epidemiology. The main keywords of the first three cluster categories were related to the research fields of adolescent overweight and obesity, HIV, and vaccine immunity. The results of keyword emergence showed that research hotspots shifted from overweight, obesity, and body mass index to antibodies, vaccines/vaccination, and cohorts. [Conclusion] The past ten years have witnessed increasing numbers of SCI-indexed papers published by provincial CDCs in our country and a stubbornly high funding rate. However, the gap among the provincial CDCs is still large seeing that economically developed eastern provincial CDCs published more SCI-indexed papers. Research hotspots have gradually shifted from overweight, obesity, and body mass index to antibodies, vaccines/vaccination, and cohorts.Copyright © 022 Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.
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BACKGROUND: A considerable amount of various types of data have been collected during the COVID-19 pandemic, the analysis and understanding of which have been indispensable for curbing the spread of the disease. As the pandemic moves to an endemic state, the data collected during the pandemic will continue to be rich sources for further studying and understanding the impacts of the pandemic on various aspects of our society. On the other hand, naïve release and sharing of the information can be associated with serious privacy concerns. METHODS: We use three common but distinct data types collected during the pandemic (case surveillance tabular data, case location data, and contact tracing networks) to illustrate the publication and sharing of granular information and individual-level pandemic data in a privacy-preserving manner. We leverage and build upon the concept of differential privacy to generate and release privacy-preserving data for each data type. We investigate the inferential utility of privacy-preserving information through simulation studies at different levels of privacy guarantees and demonstrate the approaches in real-life data. All the approaches employed in the study are straightforward to apply. RESULTS: The empirical studies in all three data cases suggest that privacy-preserving results based on the differentially privately sanitized data can be similar to the original results at a reasonably small privacy loss ([Formula: see text]). Statistical inferences based on sanitized data using the multiple synthesis technique also appear valid, with nominal coverage of 95% confidence intervals when there is no noticeable bias in point estimation. When [Formula: see text] and the sample size is not large enough, some privacy-preserving results are subject to bias, partially due to the bounding applied to sanitized data as a post-processing step to satisfy practical data constraints. CONCLUSIONS: Our study generates statistical evidence on the practical feasibility of sharing pandemic data with privacy guarantees and on how to balance the statistical utility of released information during this process.
Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Privacy , Pandemics , Computer Simulation , Contact Tracing/methodsABSTRACT
Background: Immune thrombocytopenia (ITP) is an autoimmune disease characterized by isolated thrombocytopenia. Recently, the pathophysiology and novel drugs of ITP have been the focus of researchers with plenty of publications emerging. Bibliometrics is the process of extracting measurable data through statistical analysis of published research studies to provide an insight into the trends and hotspots. Objective: This study aimed to provide an insight into developing trends and hotspots in the field of ITP by bibliometric analysis. Methods: By using three bibliometric mapping tools (bibliometrix R package, VOSviewer, CiteSpace), we summarized the overview information of retrieved publications, as well as the analysis of keyword co-occurrence and reference co-citation. Results: A total of 3299 publications with 78066 citations on ITP research were included in the analysis. The keyword co-occurrence network identified 4 clusters relating to the diagnosis, pathophysiology, and treatment of ITP respectively. Then the reference co-citation analysis produced 12 clusters with a well-structured and highly credible clustering model, and they can be divided into 5 trends: second-line treatment, chronic ITP, novel therapy and pathogenesis, COVID-19 vaccine. Treg cells, spleen tyrosine kinase, and mesenchymal stem cells were the latest hotspots with strong burstness. Conclusion: This bibliometric analysis provided a comprehensive insight into research hotspots and trends on ITP, which would enrich the review of the ITP research.
Subject(s)
COVID-19 , Purpura, Thrombocytopenic, Idiopathic , Thrombocytopenia , Humans , Purpura, Thrombocytopenic, Idiopathic/therapy , COVID-19 Vaccines , BibliometricsABSTRACT
This study aims to assess the situation of Italian hotspots for migrant reception during the COVID-19 pandemic, and specifically analyzing the situation of two hotspots located in the Sicily Region (Pozzallo harbor and Lampedusa Island), to identify critical issues. At the same time, we hypothesize solutions to guarantee the respect of human rights and suggest an operational protocol to be applied in similar situations, considering that the migration phenomenon is increasing and involving new geographical areas. Based on data obtained through the site inspections, the facilities of Pozzallo and Lampedusa exceeded their capacity to adequately contain the spread of the SARS-CoV-2 infection. Considering these findings, we suggest a practical workflow summarizing the main actions that should be applied to contain COVID-19, or other infectious disease, spreading in hotspots for migrants. The impact of the COVID-19 pandemic on migrants has received limited attention, although the migration phenomenon did not slow down during the pandemic period. Regarding the risk of spreading infectious diseases such as COVID-19, it is necessary that those countries who are most exposed to migration flows, such as Italy, plan dedicated strategies to minimize the possibility of transmission of SARS-CoV-2, using adequate protocols to monitor the possible insurgence of variants of interest (VOIs) or variants of concern (VOCs). Finally, it is important to state that these suggestions could be applied in any future pandemics.
Subject(s)
COVID-19 , Transients and Migrants , Humans , COVID-19/epidemiology , COVID-19/prevention & control , SARS-CoV-2 , Pandemics/prevention & control , Mediterranean Sea , Italy/epidemiologyABSTRACT
Background Primary healthcare is the first line of defense for the containment of COVID-19 pandemic. Primary healthcare has been studied extensively by academic circles in various countries during the pandemic，but the focuses vary across these studies due to differences in primary healthcare systems in different countries. Objective To understand the advances，hotspots，trends and differences of primary care-related research at home and abroad during the COVID-19 epidemic，and to provide a reference for further research in this field. Methods Primary healthcare-related studies published during the COVID-19 pandemic（between January 1，2020 and June 30，2022）were searched in databases of CNKI and Web of Science Core Collection on July 5，2022，and 282 from the former database and 1 755 from the latter were included. CiteSpace was used for visualization analysis to provide a graphic visualization of co-occurrence networks of authors，keywords and keywords clusters，to perform a timeline analysis，and to detect keywords with bursts. Results The number of publications in China grew fast at the beginning of the pandemic，then the growth gradually decreased，and tended to level off at the late stage. In contrast，relevant research started later in foreign countries，but the number of relevant publications maintained high-speed growth as of the study time. The major author cooperation forms were inter-small teams cooperation and inter-individual cooperation，and no large-scale inter-team cooperation was found. The hotspots of domestic research focus on the systems，the exploration of mechanisms and management practices related to pandemic prevention and control，while international research focuses on changes in healthcare-seeking patterns and the satisfaction of patients' medical needs under the influence of the pandemic. Psychological problems related to the pandemic were concerned by both domestic and international research. Conclusion Domestic and foreign studies have similarities and different focuses. To continuous refine and diversify domestic research，it is suggested to learn international experience，pay attention to the construction of relevant research forces，improve the knowledge system in this field，and actively use information technology to improve the primary care system amid the pandemic. © 2023 Chinese General Practice. All rights reserved.
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White-tailed deer (Odocoileus virginianus, WTD) spread communicable diseases such the zoonotic coronavirus SARS-CoV-2, which is a major public health concern, and chronic wasting disease (CWD), a fatal, highly contagious prion disease occurring in cervids. Currently, it is not well understood how WTD are spreading these diseases. In this paper, we speculate that "super-spreaders" mediate disease transmission via direct social interactions and indirectly via body fluids exchanged at scrape sites. Super-spreaders are infected individuals that infect more contacts than other infectious individuals within a population. In this study, we used network analysis from scrape visitation data to identify potential super-spreaders among multiple communities of a rural WTD herd. We combined local network communities to form a large region-wide social network consisting of 96 male WTD. Analysis of WTD bachelor groups and random network modeling demonstrated that scraping networks depict real social networks, allowing detection of direct and indirect contacts, which could spread diseases. Using this regional network, we model three major types of potential super-spreaders of communicable disease: in-degree, out-degree, and betweenness potential super-spreaders. We found out-degree and betweenness potential super-spreaders to be critical for disease transmission across multiple communities. Analysis of age structure revealed that potential super-spreaders were mostly young males, less than 2.5 years of age. We also used social network analysis to measure the outbreak potential across the landscape using a new technique to locate disease transmission hotspots. To model indirect transmission risk, we developed the first scrape-to-scrape network model demonstrating connectivity of scrape sites. Comparing scrape betweenness scores allowed us to locate high-risk transmission crossroads between communities. We also monitored predator activity, hunting activity, and hunter harvests to better understand how predation influences social networks and potential disease transmission. We found that predator activity significantly influenced the age structure of scraping communities. We assessed disease-management strategies by social-network modeling using hunter harvests or removal of potential super-spreaders, which fragmented WTD social networks reducing the potential spread of disease. Overall, this study demonstrates a model capable of predicting potential super-spreaders of diseases, outlines methods to locate transmission hotspots and community crossroads, and provides new insight for disease management and outbreak prevention strategies.
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In recent years, several bacterial strains have acquired significant antibiotic resistance and can, therefore, become difficult to contain. To counteract such trends, relational databases can be a powerful tool for supporting the decision-making process. The case of Klebsiella pneumoniae diffusion in a central region of Italy was analyzed as a case study. A specific relational database is shown to provide very detailed and timely information about the spatial-temporal diffusion of the contagion, together with a clear assessment of the multidrug resistance of the strains. The analysis is particularized for both internal and external patients. Tools such as the one proposed can, therefore, be considered important elements in the identification of infection hotspots, a key ingredient of any strategy to reduce the diffusion of an infectious disease at the community level and in hospitals. These types of tools are also very valuable in the decision-making process related to antibiotic prescription and to the management of stockpiles. The application of this processing technology to viral diseases such as COVID-19 is under investigation.
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The risk of severe illness or death from COVID-19 is associated with specific demographic characteristics or composition of the population within geographic areas, and the spatial relationship between these areas. The aim of this paper is to identify areas with a higher concentration of population vulnerable to COVID-19, relying on the concept of spatial dependence. Hence, we focus on the share of vulnerable populations using several salient proxy measures at municipality level data for Serbia. The degree of vulnerability at the municipality level was determined by hotspot analysis, specifically the Getis-Ord Gi* statistics. The results indicate heterogeneity in the spatial patterning and typologies of clusters across Serbia. This spatial heterogeneity reveals potentially differing degrees of risk across municipalities. The results can inform decision-makers in the fight against COVID-19 by helping to identify those areas with vulnerable populations that if exposed may stress the local health care system. © 2022 University of Craiova, Faculty of Social Sciences, Department of Geography. All rights reserved.
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The worldwide health crisis is caused by the widespread of the Covid-19 virus. The virus is transmitted through droplet infection and it causes the common cold, coughing, sneezing, and also respiratory distress in the infected person and sometimes becomes fatal causing death. As the world battles against covid-19, the proposed approach can help to contain the clustering of covid hotspot areas for the treatment of over a million affected patients. Drones/ Unmanned Aerial Vehicles (UAVs) offer a great deal of support in this pandemic. As suggested in this research, they can also be used to get to remote places more quickly and efficiently than with conventional means. In the hospital's control room, there would be a person in command of the ambulance drone. For hotspot area detection, the drone would be equipped with FLIR camera and for detection and recognition of face the video transmission is used by raspberry pi camera. The detection of face is done by Haar cascade Classifier and recognition of the face with LBPH algorithm. This is used for identify the each individual's medical history or can be verified by Aadhar Card. Face recognition between still and video photos was compared, and the average accuracy of still and video images was 99.8 percent and 99.57 percent, respectively. To find the hotspot area is to use the CNN Crowd counting algorithm. If the threshold value is less than equal to 0.5 than it is hotspot area , if it is greater than 0.5 and less than equal to 0.75 than it is semi-normal area , if it is greater than 0.75 and less than equal to 1 than it is normal area. © 2022 IEEE.
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The geophilosophical realness of risk, as introduced in this study, is composed of the risk hotspot or cold spot information which are stored and sorted in hexagonal bins representing the host environment within the 25-km radius from the crater of the Mayon Volcano. The z scores measured from these hexagonal bins mimic the risk realness or risk reality phenomenon happening in Albay Province, Philippines. The objective of the study is to assess risk reality phenomena that generate risk knowledge originated from applying the seven metatheorems based on the Schoen Golden Triangle and the Fibonacci Golden Ratio. Risk assessment in this study uses the stability site selection criteria and hexagonal binning technique to store, sort, and process risk hotspot and coldspot information. This approach led to the disclosure of risk phenomenon on the 14 out of 25 resettlement sites (host environment) that remained at risk and continuously increasing the risk trend. When people are continuously allowed to occupy risk hotspots areas it hints at ineffective risk governance to neutralize the passively exposed population. This study concluded that the risk reality phenomena assessment opens new avenues for scientifically informed land use, nil exposure, and 0-risk policy in addition to the existing 0-casualty goal to get prepared with the right direction, decision and action to sensitively utilize the stable host environments aligned to improve risk governance.
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BACKGROUND: Infectious diseases represent a major challenge for health systems worldwide. With the recent global pandemic of COVID-19, the need to research strategies to treat these health problems has become even more pressing. Although the literature on big data and data science in health has grown rapidly, few studies have synthesized these individual studies, and none has identified the utility of big data in infectious disease surveillance and modeling. OBJECTIVE: The aim of this study was to synthesize research and identify hotspots of big data in infectious disease epidemiology. METHODS: Bibliometric data from 3054 documents that satisfied the inclusion criteria retrieved from the Web of Science database over 22 years (2000-2022) were analyzed and reviewed. The search retrieval occurred on October 17, 2022. Bibliometric analysis was performed to illustrate the relationships between research constituents, topics, and key terms in the retrieved documents. RESULTS: The bibliometric analysis revealed internet searches and social media as the most utilized big data sources for infectious disease surveillance or modeling. The analysis also placed US and Chinese institutions as leaders in this research area. Disease monitoring and surveillance, utility of electronic health (or medical) records, methodology framework for infodemiology tools, and machine/deep learning were identified as the core research themes. CONCLUSIONS: Proposals for future studies are made based on these findings. This study will provide health care informatics scholars with a comprehensive understanding of big data research in infectious disease epidemiology.
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The analysis of the daily spatial patterns of near-surface Nitrogen dioxide (NO2) concentrations can assist decision makers mitigate this common air pollutant in urban areas. However, comparative analysis of NO2 estimates in different urban agglomerations of China is limited. In this study, a new linear mixed effect model (LME) with multi-source spatiotemporal data is proposed to estimate daily NO2 concentrations at high accuracy based on the land-use regression (LUR) model and Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) products. In addition, three models for NO2 concentration estimation were evaluated and compared in four Chinese urban agglomerations from 2018 to 2020, including the COVID-19 closed management period. Each model included a unique combination of methods and satellite NO2 products: ModelⅠ: LUR model with OMI products;Model Ⅱ: LUR model with TropOMI products;Model Ⅱ: LME model with TropOMI products. The results show that the LME model outperformed the LUR model in all four urban agglomerations as the average RMSE decreased by 16.09% due to the consideration of atmospheric dispersion random effects, and using TropOMI instead of OMI products can improve the accuracy. Based on our NO2 estimations, pollution hotspots were identified, and pollution anomalies during the COVID-19 period were explored for two periods;the lockdown and revenge pollution periods. The largest NO2 pollution difference between the hotspot and non-hotspot areas occurred in the second period, especially in the heavy industrial urban agglomerations. © 2022 Elsevier Ltd
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It is difficult to maintain social distancing in highly populated areas where people live in proximity. This study aimed to qualitatively explore experiences of COVID-19 recovered patients residing in one such area. We employed semi-structured face-to-face interviews. An interview guide was developed, validated, piloted, and minor changes were made. People living in this area, above 18 years of age, and recovered from COVID-19 were approached for the interviews, 11 of them were recruited to be interviewed, and their verbal informed consent was audio recorded. The interviews were conducted in the Arabic language in a semi-private area of the community center, audio-recorded, transcribed verbatim, and thematically analyzed later. Thematic analysis generated 30 subthemes, which were categorized into seven overarching themes: information about COVID-19;life during COVID-19 illness;spreading of COVID-19;precautionary measures;interventions that helped in recovery;impact of COVID-19 on life;support received during COVID-19 illness. Experiences of people from the hotspot who had recovered from COVID-19 highlighted what life had been like in the hotspot under lockdown, especially with having been afflicted with the infection, factors that facilitated their recovery, and the way their lives were and have been affected due to COVID-19.
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Restrictions on outdoor activities are required to suppress the COVID-19 pandemic. To monitor social risks and control the pandemic through sustainable restrictions, we focus on the relationship between the number of people going out and the effective reproduction number. The novelty of this study is that we have considered influx population instead of staying-population, as the data represent congestion. This enables us to apply our analysis method to all meshes because the influx population may always represent the congestion of specific areas, which include the residential areas as well. In this study, we report the correlation between the influx population in downtown areas and business districts in Tokyo during the pandemic considering the effective reproduction number and associated time delay. Moreover, we validate our method and the influx population data by confirming the consistency of the results with those of the previous research and epidemiological studies. As a result, it is confirmed that the social risk with regard to the spread of COVID-19 infection when people travel to downtown areas and business districts is high, and the risk when people visit only residential areas is low.
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Many social media users express concerns about vaccines and their side effects on Twitter. These concerns lead to a compromise of confidence which brings about vaccine hesitancy. In Africa, vaccine hesitancy is a major challenge faced by health policymakers in the fight against COVID-19. Given that most tweets are geotagged, clustering them according to their sentiments could help identify locations that may likely experience vaccine hesitancy for health policy and planning. In this study, we collected 70 000 geotagged vaccine-related tweets in nine African countries, from December 2020 to February 2022. The tweets were classified into three sentiment classes—positive, negative, and neutral. The quality of the classification outputs was achieved using Naíve Bayes (NB), logistic regression (LR), support vector machines (SVMs), decision tree (DT), and K-nearest neighbor (KNN) machine learning classifiers. The LR achieved the highest accuracy of 71% with an average area under the curve of 85%. The point-based location technique was used to calculate the hotspots based on the locations of the classified tweets. Locations with green, red, and gray backgrounds on the map signify a hotspot for positive, negative, and neutral sentiments. The outcome of this research shows that discussions on social media can be analyzed to identify hotspots during a disease outbreak, which could inform health policy in planning and management of vaccine hesitancy in Africa. Author