scholarly journals SymptomID: A Framework for Rapid Symptom Identification in Pandemics Using News Reports

2021 ◽  
Vol 12 (4) ◽  
pp. 1-17
Author(s):  
Kang Gu ◽  
Soroush Vosoughi ◽  
Temiloluwa Prioleau
Keyword(s):  
Infectious Disease ◽  
Language Processing ◽  
Clinical Studies ◽  
Language Model ◽  
Rapid Identification ◽  
Training Data ◽  
The Novel ◽  
News Reports ◽  
Novel Coronavirus

The ability to quickly learn fundamentals about a new infectious disease, such as how it is transmitted, the incubation period, and related symptoms, is crucial in any novel pandemic. For instance, rapid identification of symptoms can enable interventions for dampening the spread of the disease. Traditionally, symptoms are learned from research publications associated with clinical studies. However, clinical studies are often slow and time intensive, and hence delays can have dire consequences in a rapidly spreading pandemic like we have seen with COVID-19. In this article, we introduce SymptomID, a modular artificial intelligence–based framework for rapid identification of symptoms associated with novel pandemics using publicly available news reports. SymptomID is built using the state-of-the-art natural language processing model (Bidirectional Encoder Representations for Transformers) to extract symptoms from publicly available news reports and cluster-related symptoms together to remove redundancy. Our proposed framework requires minimal training data, because it builds on a pre-trained language model. In this study, we present a case study of SymptomID using news articles about the current COVID-19 pandemic. Our COVID-19 symptom extraction module, trained on 225 articles, achieves an F1 score of over 0.8. SymptomID can correctly identify well-established symptoms (e.g., “fever” and “cough”) and less-prevalent symptoms (e.g., “rashes,” “hair loss,” “brain fog”) associated with the novel coronavirus. We believe this framework can be extended and easily adapted in future pandemics to quickly learn relevant insights that are fundamental for understanding and combating a new infectious disease.

scholarly journals Building Telemedicine Capacity for Trainees During the Novel Coronavirus Outbreak: a Case Study and Lessons Learned

2020 ◽  
Vol 35 (9) ◽  
pp. 2675-2679 ◽  
Author(s):  
Katharine Lawrence ◽  
Kathleen Hanley ◽  
Jennifer Adams ◽  
Daniel J Sartori ◽  
Richard Greene ◽  
...  
Keyword(s):  
Lessons Learned ◽  
The Novel ◽  
Novel Coronavirus

scholarly journals Vertical Transmission

2020 ◽  
Vol 41 (1) ◽  
pp. 2
Author(s):  
Dena Lyras
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Vertical Transmission ◽  
Racial Groups ◽  
The Novel ◽  
Local Part ◽  
Rapid Dissemination ◽  
Novel Coronavirus ◽  
The Impact

As we begin 2020, Microbiology is dominating the news with the emergence and rapid dissemination of the novel coronavirus COVID-19. The impact of COVID-19 on public health, with significant financial, logistical and social repercussions, has quickly become apparent. As microbiologists we have an important role to play during this time because we can use our knowledge, expertise and experience to educate the community around us, and to reduce the panic that results from fear and misinformation. It is also critical that we ensure that racial groups are not stigmatised because of an infectious disease. A co-ordinated global effort is required to tackle this new infectious threat, and we are an important local part of this effort. It is also important to develop strategies that can be deployed when the next threat emerges, as it surely will.

scholarly journals SARS2020: an integrated platform for identification of novel coronavirus by a consensus sequence-function model

2020 ◽  
Author(s):  
Dachuan Zhang ◽  
Tong Zhang ◽  
Sheng Liu ◽  
Dandan Sun ◽  
Shaozhen Ding ◽  
...  
Keyword(s):  
Biological Function ◽  
Consensus Sequence ◽  
Rapid Identification ◽  
Data Driven ◽  
Supplementary Information ◽  
Respiratory Syndrome Virus ◽  
The Novel ◽  
Function Model ◽  
Catalytic Function ◽  
Novel Coronavirus

Abstract Motivation The 2019 novel coronavirus outbreak has significantly affected global health and society. Thus, predicting biological function from pathogen sequence is crucial and urgently needed. However, little work has been conducted to identify viruses by the enzymes that they encode, and which are key to pathogen propagation. Results We built a comprehensive scientific resource, SARS2020, which integrates coronavirus-related research, genomic sequences and results of anti-viral drug trials. In addition, we built a consensus sequence-catalytic function model from which we identified the novel coronavirus as encoding the same proteinase as the severe acute respiratory syndrome virus. This data-driven sequence-based strategy will enable rapid identification of agents responsible for future epidemics. Availabilityand implementation SARS2020 is available at http://design.rxnfinder.org/sars2020/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili

2019 ◽  
Vol 9 (18) ◽  
pp. 3648
Author(s):  
Casper S. Shikali ◽  
Zhou Sijie ◽  
Liu Qihe ◽  
Refuoe Mokhosi
Keyword(s):  
Language Processing ◽  
Critical Role ◽  
Language Model ◽  
Central Africa ◽  
Spoken Language ◽  
Language Models ◽  
Word Embeddings ◽  
Word Representation

Deep learning has extensively been used in natural language processing with sub-word representation vectors playing a critical role. However, this cannot be said of Swahili, which is a low resource and widely spoken language in East and Central Africa. This study proposed novel word embeddings from syllable embeddings (WEFSE) for Swahili to address the concern of word representation for agglutinative and syllabic-based languages. Inspired by the learning methodology of Swahili in beginner classes, we encoded respective syllables instead of characters, character n-grams or morphemes of words and generated quality word embeddings using a convolutional neural network. The quality of WEFSE was demonstrated by the state-of-art results in the syllable-aware language model on both the small dataset (31.229 perplexity value) and the medium dataset (45.859 perplexity value), outperforming character-aware language models. We further evaluated the word embeddings using word analogy task. To the best of our knowledge, syllabic alphabets have not been used to compose the word representation vectors. Therefore, the main contributions of the study are a syllabic alphabet, WEFSE, a syllabic-aware language model and a word analogy dataset for Swahili.

scholarly journals Learning Semantic Representations for Novel Words: Leveraging Both Form and Context

2019 ◽  
Vol 33 ◽  
pp. 6965-6973 ◽  
Author(s):  
Timo Schick ◽  
Hinrich Schütze
Keyword(s):  
Language Processing ◽  
Training Data ◽  
The Novel ◽  
Word Embeddings ◽  
Surface Form ◽  
Sources Of Information ◽  
Training Corpus ◽  
High Performing ◽  
Problem Setting ◽  
Novel Words

Word embeddings are a key component of high-performing natural language processing (NLP) systems, but it remains a challenge to learn good representations for novel words on the fly, i.e., for words that did not occur in the training data. The general problem setting is that word embeddings are induced on an unlabeled training corpus and then a model is trained that embeds novel words into this induced embedding space. Currently, two approaches for learning embeddings of novel words exist: (i) learning an embedding from the novel word’s surface-form (e.g., subword n-grams) and (ii) learning an embedding from the context in which it occurs. In this paper, we propose an architecture that leverages both sources of information – surface-form and context – and show that it results in large increases in embedding quality. Our architecture obtains state-of-the-art results on the Definitional Nonce and Contextual Rare Words datasets. As input, we only require an embedding set and an unlabeled corpus for training our architecture to produce embeddings appropriate for the induced embedding space. Thus, our model can easily be integrated into any existing NLP system and enhance its capability to handle novel words.

scholarly journals The Importance of Building Community Trust for Sustained Malaria Elimination Interventions During Unforeseen Major Disruptive Events Such as COVID-19: A Cambodia Case Study

2020 ◽  
Author(s):  
Mitra Feldman ◽  
Lieven Vernaeve ◽  
James Tibenderana ◽  
Leo Braack ◽  
Mark Debackere ◽  
...  
Keyword(s):  
Rural Communities ◽  
Malaria Elimination ◽  
The Novel ◽  
National Guidelines ◽  
Patients At Risk ◽  
Building Community ◽  
Novel Coronavirus ◽  
High Level ◽  
First Time

Abstract Background Impressive progress in reducing malaria trends combined with the 2018 report of no malaria related deaths for the first time, puts Cambodia well on track to reaching its malaria elimination goals. However, the novel coronavirus SARS-CoV-2 (COVID-19) pandemic presents a potential challenge to this goal. The path towards malaria elimination is dependent on sustained interventions to prevent rapid resurgence, which can quickly set back any gains achieved. Methods Mobile Malaria Workers (MMWs) need to have a strong understanding of the local geography and, most importantly, build and maintain trust among the communities they serve. To achieve this, Malaria Consortium uses a peer-to-peer approach for the MMWs and ensures the same level of trust operates between the MMWs and Malaria Consortium. Malaria Consortium’s policy during COVID-19 has been to follow national guidelines while continuing to support community-based malaria services via the MMWs / mobile malaria posts (MPs) with as minimal disruption as possible. A risk assessment was carried out by Malaria Consortium, with a mitigation plan quickly developed and implemented, to ensure MMWs were able to continue providing services without putting themselves or their patients at risk. Results Malaria Consortium ensured the MMW/ mobile MP program is built on trust, relevance to, and connection with the communities being served. An overall decline in malaria testing was reported from Health Centres and VMWs among all three provinces in March and April, not seen in previous years and possibly attributable to fear of COVID-19. However, Malaria Consortium supported MMWs have not reported any such decline in the utilization of their services and attribute this to the trust they have among the communities. Conclusion Malaria Consortium has effectively demonstrated care and solidarity with and among the MMWs and communities being served. This has ensured a high level of trust, and therefore willingness among the MMWs and communities to continue providing and utilising malaria services as usual despite the fear of COVID-19. Building trust among rural communities builds resilience and ensures uninterrupted and effective malaria elimination activities can continue even during a potential extraneous disruptive force, such as the Covid-19 pandemic.

scholarly journals Digital phenotyping of complex psychological responses to the COVID-19 pandemic (Preprint)

2020 ◽  
Author(s):  
Thomas D. Hull ◽  
Jacob Levine ◽  
Niels Bantilan ◽  
Angel N. Desai ◽  
Maimuna S. Majumder
Keyword(s):  
Mental Health ◽  
Language Processing ◽  
Anxiety Symptoms ◽  
Depression Symptoms ◽  
The Novel ◽  
Depression And Anxiety ◽  
Clinical Assessments ◽  
Digital Phenotyping ◽  
Novel Coronavirus ◽  
Therapy Service

BACKGROUND The novel coronavirus disease 2019 (COVID-19) has negatively impacted mortality, economic conditions, and mental health and these impacts are likely to continue after the pandemic comes to an end. OBJECTIVE At present, no method has characterized the mental health burden of the pandemic distinct from pre-COVID-19 levels. Accurate detection of illness is critical to facilitate pandemic-related treatment to prevent worsening symptoms. METHODS An algorithm for the isolation of pandemic-related concerns on a large digital mental health service is reported that utilized natural language processing (NLP) on unstructured therapy transcript data, in parallel with brief clinical assessments of depression and anxiety symptoms. RESULTS Results demonstrate a significant increase in COVID-related intake anxiety symptoms, but no detectable difference in intake depression symptoms. Transcript analyses identified terms classifiable into 24 symptoms in excess of those included in the diagnostic criteria for anxiety and depression. CONCLUSIONS Findings for this large digital therapy service suggest that treatment seekers are presenting with more severe intake anxiety levels than before the COVID-19 outbreak. Importantly, monitoring additional symptoms as part of a new COVID-19 Syndrome category could be advised to fully capture the effects of COVID019 on mental health.

scholarly journals Nanotechnology against the novel coronavirus (severe acute respiratory syndrome coronavirus 2): diagnosis, treatment, therapy and future perspectives

Nanomedicine ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. 497-516
Author(s):  
Hamid Rashidzadeh ◽  
Hossein Danafar ◽  
Hossein Rahimi ◽  
Faezeh Mozafari ◽  
Marziyeh Salehiabar ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Therapeutic Agents ◽  
Rapid Diagnosis ◽  
Socioeconomic Impact ◽  
The Novel ◽  
Future Perspectives ◽  
Mortality And Morbidity ◽  
Self Assembling ◽  
Novel Coronavirus ◽  
Significant Mortality

COVID-19, as an emerging infectious disease, has caused significant mortality and morbidity along with socioeconomic impact. No effective treatment or vaccine has been approved yet for this pandemic disease. Cutting-edge tools, especially nanotechnology, should be strongly considered to tackle this virus. This review aims to propose several strategies to design and fabricate effective diagnostic and therapeutic agents against COVID-19 by the aid of nanotechnology. Polymeric, inorganic self-assembling materials and peptide-based nanoparticles are promising tools for battling COVID-19 as well as its rapid diagnosis. This review summarizes all of the exciting advances nanomaterials are making toward COVID-19 prevention, diagnosis and therapy.

scholarly journals Thinking Too Much About the Novel Coronavirus. The Link Between Persistent Thinking About COVID-19, SARS-CoV-2 Anxiety and Trauma Effects

2020 ◽  
Author(s):  
Sebastian Skalski ◽  
Patrycja Uram ◽  
Paweł Dobrakowski ◽  
Anna Kwiatkowska
Keyword(s):  
Mental Health ◽  
Infectious Disease ◽  
Regression Analysis ◽  
Psychometric Properties ◽  
Short Form ◽  
Anxiety Scale ◽  
The Novel ◽  
Mental Functioning ◽  
Novel Coronavirus ◽  
Polish Adaptation

Background. Earlier reports have shown that anxiety over the novel coronavirus may predict mental functioning during the pandemic. The objective of this study was to assess the links between persistent thinking about COVID-19, anxiety over SARS-CoV-2 and trauma effects. For the purpose of this study, the Polish adaptation of the Obsession with COVID-19 Scale (OCS) was implemented. Participants and procedure. The study involved 356 individuals aged 18–78 (58% females). In addition to OCS, the participants completed the following questionnaires: the Coronavirus Anxiety Scale and the Short Form of the Changes in Outlook Questionnaire. Results. OCS was characterized by satisfactory psychometric properties (α = .82). Regression analysis indicated that persistent thinking about COVID-19 was associated with increased coronavirus anxiety and negative trauma effects. In addition, anxiety served as a partial mediator in the link between persistent thinking about COVID-19 and negative trauma effects. Conclusions. The data obtained suggest that persistent thinking about the pandemic may be dysfunctional for mental health during the spread of the infectious disease.

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