scholarly journals Whether the Weather Will Help Us Weather the COVID-19 Pandemic: Using Machine Learning to Measure Twitter Users' Perceptions

2020 ◽  
Author(s):  
Marichi Gupta ◽  
Adity Bansal ◽  
Bhav Jain ◽  
Jillian Rochelle ◽  
Atharv Oak ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Language Processing ◽  
Scientific Evidence ◽  
The Public ◽  
Potential Impact ◽  
Twitter Users ◽  
Processing Techniques ◽  
The Impact ◽  
Weather’S Impact

Objective: The potential ability for weather to affect SARS-CoV-2 transmission has been an area of controversial discussion during the COVID-19 pandemic. Individuals' perceptions of the impact of weather can inform their adherence to public health guidelines; however, there is no measure of their perceptions. We quantified Twitter users' perceptions of the effect of weather and analyzed how they evolved with respect to real-world events and time. Materials and Methods: We collected 166,005 tweets posted between January 23 and June 22, 2020 and employed machine learning/natural language processing techniques to filter for relevant tweets, classify them by the type of effect they claimed, and identify topics of discussion. Results: We identified 28,555 relevant tweets and estimate that 40.4% indicate uncertainty about weather's impact, 33.5% indicate no effect, and 26.1% indicate some effect. We tracked changes in these proportions over time. Topic modeling revealed major latent areas of discussion. Discussion: There is no consensus among the public for weather's potential impact. Earlier months were characterized by tweets that were uncertain of weather's effect or claimed no effect; later, the portion of tweets claiming some effect of weather increased. Tweets claiming no effect of weather comprised the largest class by June. Major topics of discussion included comparisons to influenza's seasonality, President Trump's comments on weather's effect, and social distancing. Conclusion: There is a major gap between scientific evidence and public opinion of weather's impacts on COVID-19. We provide evidence of public's misconceptions and topics of discussion, which can inform public health communications.

scholarly journals Fake News Detection with Machine Learning

2020 ◽  
Vol 10 (1) ◽  
pp. 124-127
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Online News ◽  
The Internet ◽  
Daily Routine ◽  
Fake News ◽  
Criminal Records ◽  
The Public ◽  
Processing Techniques

As the internet is becoming part of our daily routine there is sudden growth and popularity of online news reading. This news can become a major issue to the public and government bodies (especially politically) if its fake hence authentication is necessary. It is essential to flag the fake news before it goes viral and misleads the society. In this paper, various Natural Language Processing techniques along with the number of classifiers are used to identify news content for its credibility.Further this technique can be used for various applications like plagiarismcheck , checking for criminal records.

scholarly journals Understanding the Potential Impact of Different Drug Properties On SARS-CoV-2 Transmission and Disease Burden: A Modelling Analysis

2021 ◽  
Author(s):  
Charles Whittaker ◽  
Oliver J Watson ◽  
Carlos Alvarez-Moreno ◽  
Nasikarn Angkasekwinai ◽  
Adhiratha Boonyasiri ◽  
...  
Keyword(s):  
Public Health ◽  
Supportive Care ◽  
Low Income ◽  
Health Impact ◽  
Public Health Impact ◽  
The Public ◽  
Hospitalised Patients ◽  
Potential Impact ◽  
The Impact ◽  
Potential Therapeutics

Abstract Background The public health impact of the COVID-19 pandemic has motivated a rapid search for potential therapeutics, with some key successes. However, the potential impact of different treatments, and consequently research and procurement priorities, have not been clear. Methods Using a mathematical model of SARS-CoV-2 transmission, COVID-19 disease and clinical care, we explore the public-health impact of different potential therapeutics, under a range of scenarios varying healthcare capacity, epidemic trajectories; and drug efficacy in the absence of supportive care. Results The impact of drugs like dexamethasone (delivered to the most critically-ill in hospital and whose therapeutic benefit is expected to depend on the availability of supportive care such as oxygen and mechanical ventilation) is likely to be limited in settings where healthcare capacity is lowest or where uncontrolled epidemics result in hospitals being overwhelmed. As such, it may avert 22% of deaths in high-income countries but only 8% in low-income countries (assuming R=1.35). Therapeutics for different patient populations (those not in hospital, early in the course of infection) and types of benefit (reducing disease severity or infectiousness, preventing hospitalisation) could have much greater benefits, particularly in resource-poor settings facing large epidemics. Conclusions Advances in the treatment of COVID-19 to date have been focussed on hospitalised-patients and predicated on an assumption of adequate access to supportive care. Therapeutics delivered earlier in the course of infection that reduce the need for healthcare or reduce infectiousness could have significant impact, and research into their efficacy and means of delivery should be a priority.

scholarly journals THE IMPACT OF COVID -19 PANDEMIC ON THE ECONOMIC GROWTH IN BANGLADESH: A CONCEPTUAL REVIEW

2020 ◽  
Vol 6 (2) ◽  
pp. 1-12
Author(s):  
Md. Shahbub Alam ◽  
Md. Jafor Ali ◽  
Abul Bashar Bhuiyan ◽  
Mohammad Solaiman ◽  
Mohammad Abdur Rahman
Keyword(s):  
Public Health ◽  
Government Revenue ◽  
Sustainable Development Goal ◽  
Health Crisis ◽  
The Public ◽  
Public Health Crisis ◽  
The World ◽  
Potential Impact ◽  
The Government ◽  
The Impact

Since the outburst of Covid-19 in China, the world economy is passing in a turmoil situation. Undeniably the economy of Bangladesh is also grappled by the severe public health crisis of the Covid-19. As the public health emergency is heavily interconnected with economic affairs, it has impacted each of the pillars of the economy of Bangladesh. The main purpose of this paper is to make evaluations of the potential impact of the COVID-19 pandemic on the economy of Bangladesh.  This study is based on an empirical review of the recent study works, reports, working papers of home, and abroad regarding economic crisis. The review findings of the paper revealed that the COVID-19 pandemic have significant impacts on the different indicators of the economy of Bangladesh especially, Readymade Garments Sector, Foreign Remittance, Bank and Financial Institutions, Food and Agricultures, Local Trade, Foreign Trade (Export and Import), GDP (Gross Domestic Product), SDGs (Sustainable Development Goal), Government Revenue and Employment etc. This study suggested that as Covid-19 still surfacing all over the world so some steps should be ensured by the government agencies of Bangladesh to mitigate possible threats for the economy.

scholarly journals Analysis of Harassment Complaints to Detect Witness Intervention by Machine Learning and Soft Computing Techniques

2021 ◽  
Vol 11 (17) ◽  
pp. 8007
Author(s):  
Marina Alonso-Parra ◽  
Cristina Puente ◽  
Ana Laguna ◽  
Rafael Palacios
Keyword(s):  
Machine Learning ◽  
Soft Computing ◽  
Language Processing ◽  
Public Awareness ◽  
Free Text ◽  
Soft Computing Techniques ◽  
Awareness Raising ◽  
Processing Techniques ◽  
The Impact ◽  
The City

This research is aimed to analyze textual descriptions of harassment situations collected anonymously by the Hollaback! project. Hollaback! is an international movement created to end harassment in all of its forms. Its goal is to collect stories of harassment through the web and a free app all around the world to elevate victims’ individual voices to find a societal solution. Hollaback! pretends to analyze the impact of a bystander during a harassment in order to launch a public awareness-raising campaign to equip everyday people with tools to undo harassment. Thus, the analysis presented in this paper is a first step in Hollaback!’s purpose: the automatic detection of a witness intervention inferred from the victim’s own report. In a first step, natural language processing techniques were used to analyze the victim’s free-text descriptions. For this part, we used the whole dataset with all its countries and locations. In addition, classification models, based on machine learning and soft computing techniques, were developed in the second part of this study to classify the descriptions into those that have bystander presence and those that do not. For this machine learning part, we selected the city of Madrid as an example, in order to establish a criterion of the witness behavior procedure.

scholarly journals Text pre-processing of multilingual for sentiment analysis based on social network data

2022 ◽  
Vol 12 (1) ◽  
pp. 776
Author(s):  
Neha Garg ◽  
Kamlesh Sharma
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Text Processing ◽  
Problem Definition ◽  
Domain Specific ◽  
Twitter Data ◽  
Stop Word ◽  
Processing Techniques ◽  
The Impact

<span>Sentiment analysis (SA) is an enduring area for research especially in the field of text analysis. Text pre-processing is an important aspect to perform SA accurately. This paper presents a text processing model for SA, using natural language processing techniques for twitter data. The basic phases for machine learning are text collection, text cleaning, pre-processing, feature extractions in a text and then categorize the data according to the SA techniques. Keeping the focus on twitter data, the data is extracted in domain specific manner. In data cleaning phase, noisy data, missing data, punctuation, tags and emoticons have been considered. For pre-processing, tokenization is performed which is followed by stop word removal (SWR). The proposed article provides an insight of the techniques, that are used for text pre-processing, the impact of their presence on the dataset. The accuracy of classification techniques has been improved after applying text pre-processing and dimensionality has been reduced. The proposed corpus can be utilized in the area of market analysis, customer behaviour, polling analysis, and brand monitoring. The text pre-processing process can serve as the baseline to apply predictive analysis, machine learning and deep learning algorithms which can be extended according to problem definition.</span>

scholarly journals Predicting the Impact of COVID-19 and the Potential Impact of the Public Health Response on Disease Burden in Uganda

2020 ◽  
Vol 103 (3) ◽  
pp. 1191-1197 ◽  
Author(s):  
David Bell ◽  
Kristian Schultz Hansen ◽  
Agnes N. Kiragga ◽  
Andrew Kambugu ◽  
John Kissa ◽  
...  
Keyword(s):  
Public Health ◽  
Disease Burden ◽  
Public Health Response ◽  
The Public ◽  
Health Response ◽  
Potential Impact ◽  
The Impact

scholarly journals Green Deal as a Climate Changes Dam on the Road to Health

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
M Jevtic ◽  
C Bouland
Keyword(s):  
Public Health ◽  
Climate Change ◽  
Circular Economy ◽  
Clinical Medicine ◽  
Health Sector ◽  
The Public ◽  
The Road ◽  
National Public Health ◽  
On The Road ◽  
The Impact

Abstract Public health professionals (PHP) have a dual task in climate change. They should persuade their colleagues in clinical medicine of the importance of all the issues covered by the GD. The fact that the health sector contributes to the overall emissions of 4.4% speaks to the lack of awareness within the health sector itself. The issue of providing adequate infrastructure for the health sector is essential. Strengthening the opportunities and development of the circular economy within healthcare is more than just a current issue. The second task of PHP is targeting the broader population. The public health mission is being implemented, inter alia, through numerous activities related to environmental monitoring and assessment of the impact on health. GD should be a roadmap for priorities and actions in public health, bearing in mind: an ambitious goal of climate neutrality, an insistence on clean, affordable and safe energy, a strategy for a clean and circular economy. GD provides a framework for the development of sustainable and smart transport, the development of green agriculture and policies from field to table. It also insists on biodiversity conservation and protection actions. The pursuit of zero pollution and an environment free of toxic chemicals, as well as incorporating sustainability into all policies, is also an indispensable part of GD. GD represents a leadership step in the global framework towards a healthier future and comprises all the non-EU members as well. The public health sector should consider the GD as an argument for achieving goals at national levels, and align national public health policies with the goals of this document. There is a need for stronger advocacy of health and public-health interests along with incorporating sustainability into all policies. Achieving goals requires the education process for healthcare professionals covering all of topics of climate change, energy and air pollution to a much greater extent than before.

scholarly journals A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842110286
Author(s):  
Kylie L. Anglin ◽  
Vivian C. Wong ◽  
Arielle Boguslav
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Intervention Implementation ◽  
Proof Of Concept ◽  
Coaching Intervention ◽  
Processing Techniques ◽  
Teacher Coaching ◽  
The Impact

Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention.

scholarly journals Effect of COVID-19 on childhood Mycoplasma pneumoniae infection in Chengdu, China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ying Zhang ◽  
Yijie Huang ◽  
Tao Ai ◽  
Jun Luo ◽  
Hanmin Liu
Keyword(s):  
Public Health ◽  
Mycoplasma Pneumoniae ◽  
Close Contact ◽  
Serum Antibody ◽  
Pathogen Transmission ◽  
Western China ◽  
Public Health Response ◽  
The Public ◽  
Health Response ◽  
The Impact

Abstract Background Following the outbreak of the COVID-19 pandemic, a change in the incidence and transmission of respiratory pathogens was observed. Here, we retrospectively analyzed the impact of COVID-19 on the epidemiologic characteristics of Mycoplasma pneumoniae infection among children in Chengdu, one of the largest cities of western China. Method M. pneumoniae infection was diagnosed in 33,345 pediatric patients with respiratory symptoms at the Chengdu Women’s & Children’s Central Hospital between January 2017 and December 2020, based on a serum antibody titer of ≥1:160 measured by the passive agglutination assay. Differences in infection rates were examined by sex, age, and temporal distribution. Results Two epidemic outbreaks occurred between October-December 2017 and April-December 2019, and two infection peaks were detected in the second and fourth quarters of 2017, 2018, and 2019. Due to the public health response to COVID-19, the number of positive M. pneumoniae cases significantly decreased in the second quarter of 2020. The number of M. pneumoniae infection among children aged 3–6 years was higher than that in other age groups. Conclusions Preschool children are more susceptible to M. pneumoniae infection and close contact appears to be the predominant factor favoring pathogen transmission. The public health response to COVID-19 can effectively control the transmission of M. pneumoniae.

scholarly journals Impact of the Affordable Care Act’s Dependent Coverage Expansion on the Health Care and Health Status of Young Adults: What Do We Know So Far?

2017 ◽  
Vol 75 (2) ◽  
pp. 131-152 ◽  
Author(s):  
Joshua Breslau ◽  
Bradley D. Stein ◽  
Bing Han ◽  
Shoshanna Shelton ◽  
Hao Yu
Keyword(s):  
Public Health ◽  
Health Status ◽  
Insurance Coverage ◽  
Health Impact ◽  
Care Utilization ◽  
The Public ◽  
Behavioral Health Treatment ◽  
Coverage Expansion ◽  
Dependent Coverage ◽  
The Impact

The dependent coverage expansion (DCE), a component of the Affordable Care Act, required private health insurance policies that cover dependents to offer coverage for policyholders’ children through age 25. This review summarizes peer-reviewed research on the impact of the DCE on the chain of consequences through which it could affect public health. Specifically, we examine the impact of the DCE on insurance coverage, access to care, utilization of care, and health status. All studies find that the DCE increased insurance coverage, but evidence regarding downstream impacts is inconsistent. There is evidence that the DCE reduced high out-of-pocket expenditures and frequent emergency room visits and increased behavioral health treatment. Evidence regarding the impact of the DCE on health is sparse but suggestive of positive impacts on self-rated health and health behavior. Inferences regarding the public health impact of the DCE await studies with greater methodological diversity and longer follow-up periods.

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