scholarly journals Early detection of COVID-19 pandemic: evidence from Baidu Index

2020 ◽  
Author(s):  
Bizhi Tu ◽  
Laifu Wei ◽  
Yaya Jia ◽  
Jun Qian
Keyword(s):  
Search Engine ◽  
Human Life ◽  
Systems Science ◽  
Shortness Of Breath ◽  
Daily Growth ◽  
Global Pandemic ◽  
Sputum Production ◽  
Precautionary Measures ◽  
Index Value ◽  
Search Index

Abstract Background: New coronavirus disease 2019 (COVID-19) poses a severe threat to human life, and causes a global pandemic. The purpose of current research is to explore the onset and progress of the pandemic with a novel perspective using Baidu Index.Methods: We collected the confirmed data of COVID-19 infection between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Based on known literature, we obtained the search index values of the most common symptoms of COVID-19, including fever, cough, fatigue, sputum production, and shortness of breath. Spearman's correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptoms and the number of confirmed cases. Regional differences among 34 provinces/ regions were also analyzed. Results: Daily growth of confirmed cases and Baidu index values for each symptoms presented a robust positive correlation during the outbreak (fever: rs=0.705, p=9.623×10-6; cough: rs=0.592, p=4.485×10-4; fatigue: rs=0.629, p=1.494×10-4; sputum production: rs=0.648, p=8.206×10-5; shortness of breath: rs=0.656, p=6.182×10-5). The average search-to-confirmed interval is 19.8 days in China (fever: 22 days, cough: 19 days, fatigue: 20 days, sputum production: 19 days, and shortness of breath: 19 days). We discovered similar results in the top 10 provinces/regions, which had the highest cumulative cases. Conclusion: Search terms of COVID-19- related symptoms on the Baidu search engine can be used to early warn the outbreak of the epidemic. Relevant departments need to pay more attention to areas with high search index and take precautionary measures to prevent these potentially infected persons from spreading further. Baidu search engine can reflect the public's attention to the pandemic and regional epidemics of viruses. Based on changes in the Baidu index value, we can predict the arrival of the peak confirmed cases. The clinical characteristics related to COVID-19- including fever, cough, fatigue, shortness of breath, deserve more attention during the pandemic.

scholarly journals Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu Index 

2020 ◽  
Author(s):  
Bizhi Tu ◽  
Laifu Wei ◽  
Yaya Jia ◽  
Jun Qian
Keyword(s):  
Search Engine ◽  
Human Life ◽  
Systems Science ◽  
Optimal Time ◽  
Time Lags ◽  
Shortness Of Breath ◽  
Daily Growth ◽  
Global Pandemic ◽  
Sputum Production ◽  
Precautionary Measures

Abstract Background: New coronavirus disease 2019 (COVID-19) poses a severe threat to human life and causes a global pandemic. The purpose of current research is to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19.Methods: We collected the number of COVID-19 confirmed cases between January 11, 2020, and c, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from Baidu Index. Spearman's correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed. Results: Daily growth of confirmed cases and Baidu index values for each COVID-19 related symptoms presented a robust positive correlation during the outbreak (fever: rs=0.705, p=9.623×10-6; cough: rs=0.592, p=4.485×10-4; fatigue: rs=0.629, p=1.494×10-4; sputum production: rs=0.648, p=8.206×10-5; shortness of breath: rs=0.656, p=6.182×10-5). The average search-to-confirmed interval is 19.8 days in China. The daily Baidu Index value's optimal time lags were the fourth day for cough, third day for fatigue, firth day for sputum production, firth day for shortness of breath, and 0 days for fever. Conclusion: Search terms of COVID-19-related symptoms on the Baidu search engine have significant correlations with confirmed cases. Since the Baidu search engine can reflect the Public's attention to the pandemic and regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.

scholarly journals Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China - evidence from Baidu Index

2021 ◽  
Author(s):  
Bizhi Tu ◽  
Laifu Wei ◽  
Yaya Jia ◽  
Jun Qian
Keyword(s):  
Search Engine ◽  
Human Life ◽  
Systems Science ◽  
Optimal Time ◽  
Time Lags ◽  
Shortness Of Breath ◽  
Daily Growth ◽  
Sputum Production ◽  
Related Symptom ◽  
Precautionary Measures

Abstract Background: New coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19.Methods: We collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman's correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed.Results: Daily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: rs=0.705, p=9.623×10-6; cough: rs=0.592, p=4.485×10-4; fatigue: rs=0.629, p=1.494×10-4; sputum production: rs=0.648, p=8.206×10-5; shortness of breath: rs=0.656, p=6.182×10-5). The average search-to-confirmed interval (STCI) was 19.8 days in China. The daily Baidu Index value's optimal time lags were the four days for cough, two days for fatigue, three days for sputum production, one day for shortness of breath, and 0 days for fever.Conclusion: The searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public's attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.

scholarly journals Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu index

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bizhi Tu ◽  
Laifu Wei ◽  
Yaya Jia ◽  
Jun Qian
Keyword(s):  
Search Engine ◽  
Human Life ◽  
Systems Science ◽  
Optimal Time ◽  
Time Lags ◽  
Shortness Of Breath ◽  
Daily Growth ◽  
Sputum Production ◽  
Related Symptom ◽  
Precautionary Measures

Abstract Background New coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19. Methods We collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman’s correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed. Results Daily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: rs=0.705, p=9.623× 10− 6; cough: rs=0.592, p=4.485× 10− 4; fatigue: rs=0.629, p=1.494× 10− 4; sputum production: rs=0.648, p=8.206× 10− 5; shortness of breath: rs=0.656, p=6.182× 10–5). The average search-to-confirmed interval (STCI) was 19.8 days in China. The daily Baidu Index value’s optimal time lags were the 4 days for cough, 2 days for fatigue, 3 days for sputum production, 1 day for shortness of breath, and 0 days for fever. Conclusion The searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public’s attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.

scholarly journals A snapshot on COVID-19: A review

2020 ◽  
Author(s):  
Mahfuz Al Mamun ◽  
Kaiissar Mannoor ◽  
Tahmina Shirin ◽  
Meerjady Sabrina Flora ◽  
Firdausi Qadri ◽  
...  
Keyword(s):  
Intermediate Host ◽  
Organ Failure ◽  
Clinical Manifestations ◽  
Case Fatality ◽  
Shortness Of Breath ◽  
Aged Patients ◽  
Treatment And Prevention ◽  
Global Pandemic ◽  
Sputum Production ◽  
Common Enemy

The emergence of novel SARS-CoV-2 virus in China in December 2019 has turned into a global pandemic through continued spread beyond borders. This review was aimed to extract up-to-date information on the evolution, transmission, clinical manifestations, diagnosis, treatment and prevention of COVID-19 to fight against this common enemy. PubMed, Scopus and Google Scholar were the sources of literature; whereas CDC, WHO and Worldometer provided updated information. Bats served as the reservoirs of this virus while pangolin is believed as an intermediate host to transmit the virus to humans. Direct human-to-human and indirect transmissions were involved. Major clinical manifestations included fever, cough, fatigue, sputum production and shortness of breath. Chest radiographs mostly showed bilateral ground-glass opacities. Aged patients and patients with comorbidities had higher case fatality ratios. Critical cases were vulnerable to develop pneumonia, multi-organ failure and deaths. Overall situation in China has improved substantially. The European region and region of the Americas were the worst hit out of six WHO global regions. PCR based methods are used for the diagnosis of COVID-19. Severe/critical cases essentially require supportive or intensive cares. Avoiding exposure to COVID-19 is the best way to prevent the disease. Thus, this review provides a snapshot on COVID-19.

scholarly journals SARS-COV-2 Pandemic in Syria: Symptoms and Treatments

2021 ◽  
Vol 8 (7) ◽  
Keyword(s):  
Human Life ◽  
Human Case ◽  
Immunomodulatory Drugs ◽  
Shortness Of Breath ◽  
Clinical Recovery ◽  
Global Pandemic ◽  
First Case

ABSTRACT SARS-CoV-2 has caused global pandemic that resulted in a dramatic loss of human life worldwide. The first human case was reported in December 2019 in China, and while the first case in Syria was confirmed on March 2020. By July 1st, 2021, 25551 cases were reported in Syria with 1879 deaths. The most common symptoms of covid-19 are fever, dry cough, and shortness of breath. Some patients may endure from musculoskeletal, gastrointestinal and dermatologic symptoms. Many drugs (including antibiotics, corticosteroids, antiviral and Immunomodulatory drugs) have been evaluated and recommended to be used in COVID-19 treatment, resulting in clinical recovery. Keywords: SARS-CoV-2, Syria, pandemic, remdesivir, corticosteroids, antiviral Keyword : SARS-CoV-2, Syria, pandemic, remdesivir, corticosteroids, antiviral

scholarly journals ‘Long COVID’ syndrome

2021 ◽  
Vol 14 (4) ◽  
pp. e241485
Author(s):  
Priyal Taribagil ◽  
Dean Creer ◽  
Hasan Tahir
Keyword(s):  
Public Health ◽  
Cognitive Deficits ◽  
Recent Literature ◽  
Complex Case ◽  
Shortness Of Breath ◽  
Gastrointestinal Dysfunction ◽  
Health Crisis ◽  
Public Health Crisis ◽  
Global Pandemic ◽  
Poor Memory

SARS-CoV-2 has resulted in a global pandemic and an unprecedented public health crisis. Recent literature suggests the emergence of a novel syndrome known as ‘long COVID’, a term used to describe a diverse set of symptoms that persist after a minimum of 4 weeks from the onset of a diagnosed COVID-19 infection. Common symptoms include persistent breathlessness, fatigue and cough. Other symptoms reported include chest pain, palpitations, neurological and cognitive deficits, rashes, and gastrointestinal dysfunction. We present a complex case of a previously well 28-year-old woman who was diagnosed with COVID-19. After resolution of her acute symptoms, she continued to experience retrosternal discomfort, shortness of breath, poor memory and severe myalgia. Investigations yielded no significant findings. Given no alternative diagnosis, she was diagnosed with ‘long COVID’.

Towards the New Post-2020 Global Biodiversity Framework

2021 ◽  
pp. 1-9
Author(s):  
Elizabeth Maruma Mrema
Keyword(s):  
Human Rights ◽  
Biological Diversity ◽  
Human Life ◽  
Global Pandemic ◽  
The World ◽  
Global Biodiversity

While 2020 –dubbed the “Super Year for Nature –has seen the world battling an unforeseen global pandemic, this article comes back on the Convention of Biological Diversity and its regime, studies the aim of the negotiations of the post-2020 global biodiversity framework and the relevance of this framework for the planet, considering that the protection of biological diversity impacts all aspects of human life, including the full enjoying of human rights and protection against future pandemics.

scholarly journals A Systematic Review of Characteristics Associated with COVID-19 in Children with Typical Presentation and with Multisystem Inflammatory Syndrome

2021 ◽  
Vol 18 (16) ◽  
pp. 8269
Author(s):  
Jeffrey Kornitzer ◽  
Jacklyn Johnson ◽  
Max Yang ◽  
Keith W. Pecor ◽  
Nicholas Cohen ◽  
...  
Keyword(s):  
Systematic Review ◽  
Sore Throat ◽  
Literature Search ◽  
Clinical Presentation ◽  
Pediatric Population ◽  
Chinese Language ◽  
Clinical Information ◽  
Shortness Of Breath ◽  
Global Pandemic ◽  
Inflammatory Syndrome

Setting off a global pandemic, coronavirus disease 2019 (COVID-19) has been marked by a heterogeneous clinical presentation that runs the gamut from asymptomatic to severe and fatal. Although less lethal in children than adults, COVID-19 has nonetheless afflicted the pediatric population. This systematic review used clinical information from published literature to assess the spectrum of COVID-19 presentation in children, with special emphasis on characteristics associated with multisystem inflammatory syndrome (MIS-C). An electronic literature search for English and Chinese language articles in COVIDSeer, MEDLINE, and PubMed from 1 January 2020 through 1 March 2021 returned 579 records, of which 54 were included for full evaluation. Out of the total 4811 patients, 543 (11.29%) exhibited MIS-C. The most common symptoms across all children were fever and sore throat. Children presenting with MIS-C were less likely to exhibit sore throat and respiratory symptoms (i.e., cough, shortness of breath) compared to children without MIS-C. Inflammatory (e.g., rash, fever, and weakness) and gastrointestinal (e.g., nausea/vomiting and diarrhea) symptoms were present to a greater extent in children with both COVID-19 and MIS-C, suggesting that children testing positive for COVID-19 and exhibiting such symptoms should be evaluated for MIS-C.

scholarly journals Risk Assessment for Cardiac Arrest : A Deep Learning Approach With Channel Selection

2020 ◽  
Author(s):  
Atınç Yılmaz
Keyword(s):  
Risk Assessment ◽  
Cardiac Arrest ◽  
Deep Learning ◽  
Heart Attack ◽  
Human Life ◽  
Numerical Data ◽  
Channel Selection ◽  
Heart Attacks ◽  
Precautionary Measures ◽  
Feature Filters

Abstract Background: Risk of developing cardiovascular diseases, in the world, is increasing day by day. Accordingly, the number of deaths due to heart attacks is quite remarkable. Early risk assessment and diagnosis of heart disease are vital to prevent heart attacks by providing effective treatment planning and evaluation of outcomes. When a patient with high risk of heart attack is not treated correctly, chances of survival may reduce dramatically. For this reason, artificial intelligence-assisted systems can support the decision of doctors and it can anticipate risk without fatal consequences.Methods: In this study, individuals who has heart attack risks are predicted by using a proposed CNNs method. A set of medical data from patients with heart attacks and healthy individuals are provided from the UCI database. Reinforced deep learning and ANFIS architectures are also applied to the same problem in order to compare the results and put forth the efficiency of proposed method. In addition, ROC analysis and measurements of processing times for the applied methods were performed to reveal the performance, accuracy and efficiency of the study.Results: The proposed CNNs method and other methods are tested and evaluated. The accuracy performance of the methods were 94.34% for the proposed CNNs method, 91.58% for the ANFIS, and 92.66% for the deep multilayer neural network. Highest accuracy has been obtained by using the proposed CNNs method, which is 94.34%. The reasons why the proposed CNNs method is better than other methods is the use of channel selection layer, the number of convolution and pooling layers, the filter size used in these layers, and the functions used in the loss and activation layers.Conclusions: In the study, the channel selection formula is introduced in the proposed CNNs model to select the most discriminatory feature filters. Besides, the applicability of proposed CNNs method with images obtained from numerical data has been demonstrated. With the early prediction system proposed, it is now possible to take precautionary measures against possible cardiac arrest. In this study; a new method based on CNNs is proposed for early detection of possible heart attack, which is a great risk for human life. Different from studies in the literature, the channel selection formula is presented in the proposed CNNs method to select the most selective feature filters. Besides differently, it was used in the proposed CNNs method by converting all numerical data from dataset into 2D images. Afterwards, to show whether this the proposed method is applicable or not, the dataset which is numerical form was applied to other methods and compared.

scholarly journals An Overview of Post-COVID-19 Patient’s Health Monitoring Systems

2021 ◽  
Vol 7 (10) ◽  
pp. 61-67
Author(s):  
Chirag Satapathy, Hrishikesh Gokhale, Ali Zoya Syed, Keerti Srivastava and Ruban Nersisson
Keyword(s):  
Health Monitoring ◽  
Human Life ◽  
Vital Role ◽  
Health Conditions ◽  
Smart Sensors ◽  
Monitoring Systems ◽  
Comprehensive Review ◽  
Global Pandemic ◽  
Health Monitoring Systems ◽  
Time Basis

COVID-19 is a global pandemic infecting human life. There are many patients who have recovered from this deadly virus and need to be monitored constantly even when they are at home. IoT plays a vital role in health systems that help to monitor patient’s health conditions. These healthcare frameworks consist of smart sensors to keep a track of patient’s vitals on a real-time basis. These systems will help bridge gaps between the patients and doctors during the pandemic situation. In order to make our system competitive against the already existing devices, we prepared a comprehensive review where we extensively studied other products and compared them to find what's best for the patients.

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