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This paper is in the following e-collection/theme issue:

Published on 4.4.2024 in Vol 26 (2024)

Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study

Authors of this article:

Original Paper

• Andrew J McMurry 1, 2 , PhD   ; 
• Amy R Zipursky 1, 3 , MD, MBI   ; 
• Alon Geva 1, 4, 5 , MD, MPH   ; 
• Karen L Olson 1, 2 , PhD   ; 
• James R Jones 1 , MPhil   ; 
• Vladimir Ignatov 1 , MFA   ; 
• Timothy A Miller 1, 2 , PhD   ; 
• Kenneth D Mandl 1, 2, 6 , MD, MPH  

1 Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States

2 Department of Pediatrics, Harvard Medical School, Boston, MA, United States

3 Division of Pediatric Emergency Medicine, Department of Pediatrics, The Hospital for Sick Children, Toronto, ON, Canada

4 Division of Critical Care Medicine, Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA, United States

5 Department of Anaesthesia, Harvard Medical School, Boston, MA, United States

6 Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States

Corresponding Author:

Kenneth D Mandl, MD, MPH

Computational Health Informatics Program

Boston Children's Hospital

Landmark 5506 Mail Stop BCH3187, 401 Park Drive

Boston, MA, 02215

United States

Phone: 1 6173554145

Email: [email protected]

Background: Real-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records.

Objective: This study sought to validate and test an artificial intelligence (AI)–based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak.

Methods: Subjects in this retrospective cohort study are patients who are 21 years of age and younger, who presented to a pediatric ED at a large academic children’s hospital between March 1, 2020, and May 31, 2022. The ED notes for all patients were processed with an NLP pipeline tuned to detect the mention of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria. For a gold standard, 3 subject matter experts labeled 226 ED notes and had strong agreement ( F 1 -score=0.986; positive predictive value [PPV]=0.972; and sensitivity=1.0). F 1 -score, PPV, and sensitivity were used to compare the performance of both NLP and the International Classification of Diseases, 10th Revision (ICD-10) coding to the gold standard chart review. As a formative use case, variations in symptom patterns were measured across SARS-CoV-2 variant eras.

Results: There were 85,678 ED encounters during the study period, including 4% (n=3420) with patients with COVID-19. NLP was more accurate at identifying encounters with patients that had any of the COVID-19 symptoms ( F 1 -score=0.796) than ICD-10 codes ( F 1 -score =0.451). NLP accuracy was higher for positive symptoms (sensitivity=0.930) than ICD-10 (sensitivity=0.300). However, ICD-10 accuracy was higher for negative symptoms (specificity=0.994) than NLP (specificity=0.917). Congestion or runny nose showed the highest accuracy difference (NLP: F 1 -score=0.828 and ICD-10: F 1 -score=0.042). For encounters with patients with COVID-19, prevalence estimates of each NLP symptom differed across variant eras. Patients with COVID-19 were more likely to have each NLP symptom detected than patients without this disease. Effect sizes (odds ratios) varied across pandemic eras.

Conclusions: This study establishes the value of AI-based NLP as a highly effective tool for real-time COVID-19 symptom detection in pediatric patients, outperforming traditional ICD-10 methods. It also reveals the evolving nature of symptom prevalence across different virus variants, underscoring the need for dynamic, technology-driven approaches in infectious disease surveillance.

Introduction

Real-time emerging infection surveillance requires a case definition that often involves symptomatology. To detect symptoms, population health monitoring systems and research studies tend to largely rely on structured data from electronic health records, including the International Classification of Diseases, 10th Revision (ICD-10) codes [ 1 ]. However, symptoms are not diagnoses and, therefore, may not be consistently coded, leading to incorrect estimates of the prevalence of COVID-19 symptoms [ 2 ]. Natural language processing (NLP) of unstructured data from electronic health records has proven useful in recognizing COVID-19 symptoms and identifying additional signs and symptoms compared to structured data alone [ 3 , 4 ]. However, surveillance of COVID-19 symptoms is nuanced as symptoms have been shown to differ by variant eras [ 5 , 6 ] and by age, with pediatric patients generally experiencing milder symptoms [ 7 ]. For example, while loss of taste or smell was reported with early COVID-19 variants, it was less commonly reported during the Omicron wave and in younger patients who more frequently experience fever and cough [ 8 - 11 ]. Understanding symptom patterns in children during different COVID-19 variant eras is important. Early in the pandemic, the availability of molecular testing was extremely limited. The less severe course of infection and varying presentations may lead to under testing due to mild symptoms [ 12 ], potentially underestimating pediatric COVID-19 cases. Additionally, relatively asymptomatic children can still transmit the virus. Tailoring interventions based on age-specific manifestations contribute to effective control of virus transmission within communities.

We sought to validate and test an open-source artificial intelligence (AI)–based NLP pipeline that includes a large language model (LLM) to detect COVID-19 symptoms from physician notes. As a formative use case, we sought to illustrate how this pipeline could detect COVID-19 symptoms and differentiate symptom patterns across SARS-CoV-2 variant eras in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak.

Study Design and Setting

This was a retrospective cohort study of all patients up to 21 years of age presenting to the ED of a large, free-standing, university-affiliated, pediatric hospital between March 1, 2020, and May 31, 2022.

Ethical Considerations

The Boston Children’s Hospital Committee on Clinical Investigation performed ethical, privacy, and confidentiality reviews of the study and found it to be exempt from human subjects oversight. A waiver of consent was obtained to cover the targeted extraction and secure review of clinical notes by approved study personnel in protected environments within the hospital firewall.

Study Variables

The main dependent variables were a set of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria [ 13 ]—fever or chills, cough, shortness of breath or difficulty breathing, fatigue, muscle or body aches, headache, new loss of taste or smell, sore throat, congestion or runny nose, nausea or vomiting, and diarrhea. We identified these symptoms by both NLP and ICD-10 codes. For the formative use case, the study period was divided into 3 variant eras defined using Massachusetts COVID-19 data from Covariant [ 14 ]. The pre-Delta era was from March 1, 2020, to June 20, 2021; the Delta era was from June 21, 2021, to December 19, 2021; and the Omicron era was from December 20, 2021, onward. A diagnosis of COVID-19 was defined as a positive SARS-CoV-2 polymerase chain reaction (PCR) test or the presence of ICD-10 code U07.1 for COVID-19 during the same ED encounter in which symptoms were evaluated.

AI/NLP Pipeline Development

A total of 3 reviewers reached a consensus on a symptom concept dictionary [ 15 ] to capture each of the 11 COVID-19 symptoms. They relied on the Unified Medical Language System [ 16 ], which has a nearly comprehensive list of symptom descriptors [ 17 ], including SNOMED (SNOMED International) coded clinical terms [ 18 ], ICD-10 codes for administrative billing, abbreviations, and common language for patients [ 19 ]. The open-source and free Apache cTAKES (Apache Software Foundation) NLP pipeline was tuned to recognize and extract coded concepts for positive symptom mentions (based on the dictionary) from physician notes [ 20 ]. Apache cTAKES uses a NegEx algorithm which can help address negation [ 20 - 23 ]. To further address negation, we incorporated an LLM, Bidirectional Encoder Representations from Transformers, that was fine-tuned for negation classification on clinical text [ 24 , 25 ].

Gold Standard

A total of 2 reviewers established a gold standard by manually reviewing physician ED notes. After all notes were labeled by the cTAKES pipeline, a test set of 226 ED notes was loaded into Label Studio [ 26 ], an open-source application for ground truth labeling. These notes were from patients both with and without COVID-19 and were selected to ensure that each of the 11 symptoms was mentioned in at least 30 ED notes. Some notes mentioned more than 1 symptom. Using an annotation guide ( Multimedia Appendix 1 ), 2 reviewers, who were masked from the terms identified by the NLP pipeline for note selection, each labeled 113 notes for mention of the 11 COVID-19 symptoms. As per the guide, only symptoms relevant to the present illness were considered positive mentions. Symptoms were not considered positive mentions if stated as past medical history, family history, social history, or an indication for a medication unrelated to the encounter.

Interrater Reliability

The F 1 -score was used to assess consistency in manual chart review. The F 1 -score is the balance of sensitivity and positive predictive value (PPV) [ 27 ]. It was computed by comparing the annotations of each of the 2 initial reviewers to those of a third reviewer, who independently labeled a subset (56/226, 25%) of notes annotated by the other reviewers. The choice of F 1 -score as the metric for agreement was informed by the observed high frequency of true negative annotations when they were assigned by chance [ 20 , 27 , 28 ]. Reliability analyses used Python (version 3.10; Python Software Foundation).

AI/NLP and ICD-10 Accuracy

Accuracy measures of the true symptom percentages in the test set for each symptom included F 1 -score, PPV, sensitivity, and specificity [ 29 , 30 ].

Formative Use Case

The impact of pandemic variant era on COVID-19 symptomatology was examined. Descriptive statistics were used to characterize patients presenting to the ED during each pandemic era. The percentage of patients in the ED with symptoms of COVID-19 was assessed in separate analyses for each symptom using chi-square analyses of 3×2 tables (pandemic era × symptom presence or absence) with α set at .05. Post hoc chi-square tests were used to compare each pandemic era with all others using a Bonferroni adjusted α of .017. To assess the effect of pandemic era, COVID-19 status, and the interaction of these variables on whether or not a patient had each symptom, logistic regression was used in separate analyses for each symptom. Bonferroni adjusted confidence limits were used for post hoc analyses. If the interaction term was not significant, the main effects of COVID-19 and variant era were reported. Data were analyzed using SAS (version 9.4; SAS Institute Inc).

Study Population

There were 59,173 unique patients with 85,678 ED encounters during the study period. For each ED encounter, there was 1 final physician ED note that aggregated all ED physician documentation. Characteristics of the entire study cohort and variant-specific cohorts are summarized in Table 1 . A patient could appear in the cohort more than once if they had multiple ED encounters.

a PCR: polymerase chain reaction.

b ICD-10: International Classification of Diseases, 10th Revision.

High consistency was demonstrated between reviewer 3, who labeled a subset of notes, and both reviewers 1 and 2, who each labeled half of the notes chosen to establish the gold standard. The F 1 -scores for the 2 reviewers were 0.988 and 0.984, respectively. The PPV was 0.976 and 0.968 and sensitivity was 1.0 for both.

AI or NLP ICD-10 Accuracy

As shown in Table 2 , the F 1 -score for NLP was higher and thus more accurate at identifying encounters in the test set with patients that had any of the COVID-19 symptoms than ICD-10. NLP also had higher F 1 -score for each individual symptom. In addition, NLP sensitivity of true positive symptoms was higher than ICD-10. However, NLP accuracy of true negative symptoms (specificity) was somewhat lower compared to ICD-10.

a NLP: natural language processing.

c F 1 -score: accuracy measure balancing PPV and sensitivity.

d PPV: positive predictive value.

The 2 most prevalent symptoms, cough and fever, had sensitivity scores for NLP that were among the highest of the symptoms, and much higher than those for ICD-10 codes. The greatest discrepancy between NLP and ICD-10 F 1 -scores was for congestion or runny nose. The smallest difference was for diarrhea.

Prevalence of Symptoms Over Time

The percentage of ED encounters with patients with COVID-19 who had symptoms was estimated using the NLP pipeline and ICD-10 codes. As shown in Figure 1 , during each month of the study, the percentage of encounters with no symptoms detected was much lower using NLP compared to ICD-10. Using NLP, the range was from 0% to 19% of encounters (mean 6%, SD 4%), while with ICD-10, the range was 22% to 52% (mean 38%, SD 7%).

The percentage of encounters with patients with COVID-19 who presented with each symptom by month was higher using NLP than ICD-10 ( Multimedia Appendix 2 ). The 2 most common symptoms, cough and fever, are shown in Figures 2 and 3 . On average, cough was identified during 52% (SD 13%) of the encounters each month using NLP, but only 15% (SD 5%) using ICD-10. On average, fever characterized 70% (SD 11%) of encounters using NLP, but 41% (SD 9%) using ICD-10.

Using ICD-10, there were many months where individual symptoms were not detected. Of the 27 study months, loss of taste or smell was not detected using ICD-10 during 24 months, nor were muscle or body aches during 13 months. A total of 3 more symptoms had at least 3 consecutive months where each was not detected using ICD-10. These were congestion or runny nose (9 total months, not all consecutive), sore throat (8 months), and fatigue (7 months). Sporadic months without detection using ICD-10 were observed for headache (5 months), diarrhea (2 months), cough (1 month), and nausea or vomiting (1 month). Using NLP, sporadic months without detection were observed for just 2 symptoms, loss of taste or smell (6 months) and sore throat (2 months).

Prevalence of Symptoms Across Variant Eras

The prevalence estimates of symptoms across variant eras for encounters with patients with COVID-19 differed for each symptom identified by NLP, except for nausea or vomiting and sore throat ( Table 3 ). Post hoc analyses revealed several patterns. New loss of taste or smell was the only symptom that varied across all 3 eras. It was most common in the pre-Delta era, followed by the Delta era, and then the Omicron era. Congestion or runny nose, cough, and fever or chills were more common during the Delta and Omicron era than during the pre-Delta era, but the Delta era did not differ from the Omicron era. Muscle or body aches were more common during the pre-Delta era than both the Delta and Omicron eras, but the Delta era did not differ from the Omicron era. Diarrhea, fatigue, headache, and shortness of breath were more common during the pre-Delta era than the Omicron era but were not different than the Delta era, and the Delta era did not differ from the Omicron era. Nausea or vomiting and sore throat did not differ by variant era. The chi-square results are in Multimedia Appendix 3 .

a,b,c Variant eras with the same superscript across a row did not differ in post hoc analyses.

Symptoms by COVID-19 Status and Variant Era

The interaction of COVID-19 status and variant era on the presence of each symptom is shown in Table 4 . However, because the interaction was not significant for 2 symptoms, fever and chills, and sore throat, the main effects for COVID-19 status are shown for both ( P <.001). The odds ratios (ORs) indicate that patients with COVID-19 were more likely to have each of these 2 symptoms than patients without this disease. These symptoms were also more likely to occur during the Delta and Omicron era than during the pre-Delta era. For the remaining symptoms, the interaction term was significant and the ORs in each variant era are shown in the table. The ORs comparing patients with COVID-19 to those without the disease differed among the variant eras. Several patterns were observed. Patients with COVID-19 were more likely to exhibit each of the symptoms of congestion or runny nose, cough, fatigue, headache, muscle or body aches, new loss of taste or smell, or shortness of breath or difficulty breathing. However, effect sizes (ORs) differed among pandemic eras. For diarrhea, this symptom was more likely for patients with COVID-19 in the pre-Delta and Delta eras, but not during the Omicron era. And nausea was more likely only in the pre-Delta era. Significant ORs ranged in size from 1.3 to 26.7 (mean 4.6, SD 5.3). The logistic regression results are in Multimedia Appendix 4 .

a Odds ratios compare patients with COVID-19 at an ED encounter to patients without the disease.

b CL: Bonferroni adjusted confidence limits in post hoc analyses.

c If the interaction term was significant, the effect of COVID-19 during each variant era is shown. Otherwise, the effect for COVID-19 is shown.

d Type 3 test of the interaction term (variant era × COVID-19) in a logistic regression analysis.

Principal Findings

We find evidence that AI-based NLP of physician notes is a superior method for capturing patient symptoms for real-time biosurveillance than reliance on traditional approaches using ICD-10. NLP was more sensitive than ICD-10 codes in identifying symptoms and some symptoms could only be detected using NLP. As a form of internal validation, the symptoms identified by the CDC as associated with COVID-19 were more common in patients with than without this disease.

Comparison With Prior Work

The study was also able to capture a nuanced picture of symptom prevalence and odds across different SARS-CoV-2 variant eras. Consistent with previous literature, symptom patterns changed over time as new variants emerged. Variants may present with differences in symptomatology as a result of a number of factors including differences in mutations in spike proteins, receptor binding, and ability to escape host antibodies [ 31 ]. As has been previously reported [ 11 , 32 - 35 ], we found that fever or chills were the most common COVID-19 symptom across the variants. In our cohort, shortness of breath was less common during the Omicron era than during the pre-Delta era. The Omicron variant has less of an ability to replicate in the lungs compared to the bronchi, which may explain why this symptom became less common [ 36 ]. Studies have reported sore throat as a common symptom in the Omicron era, but we did not observe a significant difference across eras [ 8 , 9 ]. It is possible that we did not see a higher percentage of sore throats in the Omicron era because it may be more challenging for pediatric patients to describe this symptom. One study found that sore throat was observed more often in those of 5-20 years of age compared to those of 0-4 years of age [ 8 ]. Similarly, a study reported that sore throat was more common in those greater than or equal to 13 years of age in the Omicron era compared to the Delta era [ 37 ]. In our study cohort, approximately half of the patients were younger than 5 years of age. As children this age may not be able to describe their symptoms well, symptoms that are also signs, such as fever or cough, might be more commonly documented in physician notes than symptoms such as sore throat. New loss of taste or smell was most common in the pre-Delta era, followed by the Delta era and then the Omicron era in this study. This symptom has been reported less commonly in the Omicron era [ 8 , 9 ]. Studies have postulated that patients with the Omicron variant are less likely to present with loss of taste or smell as this variant has less penetration of the mucus layer and therefore, may be less likely to infect the olfactory epithelium [ 38 ].

Limitations

There were important limitations in our use of NLP. The NLP pipeline was tested with a set of notes where some symptoms were more frequent in the test set (eg, loss of taste or smell) than in the formative use case. This was done to have sufficient data to evaluate the symptom pipeline. The NLP pipeline does not account for vital signs and so fever may not have been detected with the pipeline if it was documented in a patient’s vital signs rather than the clinical text. The cTAKES tool in the pipeline lacks the temporal context to ascertain if the mention of a symptom in a note is a new symptom or a prior symptom. We modified our technique because of this but nevertheless may have overestimated the prevalence of symptoms in our study. Future work will involve filtering by note section so that certain components of a note like past medical history are not included. We used 2 techniques to recognize negation, but some negated symptoms (eg, “patient had no cough”) were still captured as positive symptom mentions leading to a possible overestimation of symptom prevalence. Finally, this NLP pipeline did involve substantial preprocessing. We plan to evaluate the implementation of Generative Pre-trained Transformer (GPT) for this task. GPT-4 was able to extract COVID-19 symptoms in a recent study [ 39 ] and it may limit the need for preprocessing.

Our formative study had some limitations. First, we examined COVID-19 symptoms in patients presenting to a single urban pediatric ED. Patients presenting to outpatient settings, who likely had milder symptoms, were not included and our results may reflect patients with more severe symptoms. And because the setting was a single site, results may not generalize to other EDs. Second, we defined COVID-19 status as positive if a patient had a PCR positive test for COVID-19 or an appropriate ICD-10 code at the ED encounter. Patients who were COVID-19 positive on a test at home or at an outside center may not have been captured by this definition even if they presented to the ED with COVID-19 [ 40 ]. Additionally, symptoms may have differed across variant eras as a result of COVID-19 vaccinations or previous infections rather than variant differences. Literature in adults shows that vaccination is associated with a decrease in systemic symptoms [ 41 ]. The United States Food and Drug Administration authorized the use of the COVID-19 vaccine in October 2021, during the Delta era and prior to the Omicron era, for children 5-11 years of age [ 42 ]. Vaccination rates for pediatric patients vary by age group in Massachusetts, as of April 3, 2023, of those 0-19 years of age, 3% to 57% have received a primary series but have not been boosted, and 3% to 18% have been boosted since September 1, 2022 [ 43 ]. As such, some patients in the Delta and Omicron eras may have been vaccinated or had previous COVID-19 infections [ 44 ].

Conclusions

In an era where rapid and accurate infectious disease surveillance is crucial, this study underscores the transformative potential of AI-based NLP for real-time symptom detection, significantly outperforming traditional methods such as ICD-10 coding. The dynamic adaptability of NLP technology allows for the nuanced capture of evolving symptomatology across different virus variants, offering a more responsive and precise tool kit for biosurveillance efforts. Its integration into existing health care infrastructure could be a game changer, elevating our capabilities to monitor, understand, and ultimately control the spread of emerging infectious diseases.

Acknowledgments

This study was supported by the Centers for Disease Control and Prevention (CDC) of the US Department of Health and Human Services (HHS) as part of a financial assistance award. The contents are those of the authors and do not necessarily represent the official views of, nor an endorsement by CDC, HHS, or the US Government. Support was also obtained from the National Center for Advancing Translational Sciences, National Institutes of Health Cooperative Agreement (U01TR002623). ARZ was supported by a training grant from the National Institute of Child Health and Human Development (T32HD040128). Generative artificial intelligence (AI) was not used to design or conduct this study.

Data Availability

All data analyzed during this study for the formative use case are in Multimedia Appendix 5 of this published article.

Authors' Contributions

KDM, AJM, and TAM contributed to the conceptualization. KDM contributed to the funding. AJM, ARZ, AG, and KLO performed the formal analysis. AJM, JRJ, and VI contributed to the software. AJM, ARZ, and KDM contributed to writing original drafts. KLO and AG contributed to writing review and edits.

Conflicts of Interest

TAM is a member of the advisory council for Lavita AI. Others declare no conflicts of interest.

COVID-19 symptoms annotation guide.

Detection of COVID-19 symptoms using NLP and ICD-10 by month for emergency department encounters with patients with COVID-19. ICD-10: International Classification of Diseases, 10th Revision; NLP: natural language processing.

The chi-square analysis of COVID-19 symptom prevalence by pandemic variant era for emergency department encounters with patients with COVID-19, symptoms were detected using NLP. NLP: natural language processing.

Logistic regression analysis of the effect of COVID-19 status, pandemic variant era, and their interaction on symptom status for ED encounters, symptoms were detected using NLP. ED: emergency department; NLP: natural language processing.

Data files for the time series figures, the chi-square analysis of symptom prevalence, and the logistic regression analysis of the effects of COVID-19 status and pandemic variant era on symptom status.

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• Götzinger F, Santiago-García B, Noguera-Julián A, Lanaspa M, Lancella L, Carducci FIC, et al. COVID-19 in children and adolescents in Europe: a multinational, multicentre cohort study. Lancet Child Adolesc Health. 2020;4(9):653-661. [ FREE Full text ] [ CrossRef ] [ Medline ]
• King JA, Whitten TA, Bakal JA, McAlister FA. Symptoms associated with a positive result for a swab for SARS-CoV-2 infection among children in Alberta. CMAJ. 2021;193(1):E1-E9. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Takács AT, Bukva M, Gavallér G, Kapus K, Rózsa M, Bán-Gagyi B, et al. Epidemiology and clinical features of SARS-CoV-2 infection in hospitalized children across four waves in Hungary: a retrospective, comparative study from march 2020 to december 2021. Health Sci Rep. 2022;5(6):e937. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kenney PO, Chang AJ, Krabill L, Hicar MD. Decreased clinical severity of pediatric acute COVID-19 and MIS-C and increase of incidental cases during the Omicron wave in comparison to the Delta wave. Viruses. 2023;15(1):180. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hui KPY, Ho JCW, Cheung MC, Ng KC, Ching RHH, Lai KL, et al. SARS-CoV-2 Omicron variant replication in human bronchus and lung ex vivo. Nature. 2022;603(7902):715-720. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Shoji K, Akiyama T, Tsuzuki S, Matsunaga N, Asai Y, Suzuki S, et al. Clinical characteristics of COVID-19 in hospitalized children during the Omicron variant predominant period. J Infect Chemother. 2022;28(11):1531-1535. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Butowt R, Bilińska K, von Bartheld C. Why does the Omicron variant largely spare olfactory function? implications for the pathogenesis of anosmia in coronavirus disease 2019. J Infect Dis. 2022;226(8):1304-1308. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Wei WI, Leung CLK, Tang A, McNeil EB, Wong SYS, Kwok KO. Extracting symptoms from free-text responses using ChatGPT among COVID-19 cases in Hong Kong. Clin Microbiol Infect. 2024;30(1):142.e1-142.e3. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Wang L, Zipursky AR, Geva A, McMurry AJ, Mandl KD, Miller TA. A computable case definition for patients with SARS-CoV2 testing that occurred outside the hospital. JAMIA Open. 2023;6(3):ooad047. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bramante CT, Proper JL, Boulware DR, Karger AB, Murray T, Rao V, et al. Vaccination against SARS-CoV-2 is associated with a lower viral load and likelihood of systemic symptoms. Open Forum Infect Dis. 2022;9(5):ofac066. [ FREE Full text ] [ CrossRef ] [ Medline ]
• FDA authorizes Pfizer-BioNTech COVID-19 vaccine for emergency use in children 5 through 11 years of age. U.S. Food and Drug Administration. 2021. URL: https:/​/www.​fda.gov/​news-events/​press-announcements/​fda-authorizes-pfizer-biontech-covid-19-vaccine-emergency-use-children-5-through-11-years-age [accessed 2024-02-28]
• Weekly COVID-19 vaccination report (as of April 3, 2023). Massachusetts Department of Public Health. URL: https://www.mass.gov/doc/weekly-covid-19-vaccination-report-april-5-2023/download [accessed 2024-02-28]
• Bhattacharyya RP, Hanage WP. Challenges in inferring intrinsic severity of the SARS-CoV-2 Omicron variant. N Engl J Med. 2022;386(7):e14. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by T de Azevedo Cardoso; submitted 06.10.23; peer-reviewed by D Liebovitz; comments to author 09.11.23; revised version received 30.11.23; accepted 27.02.24; published 04.04.24.

©Andrew J McMurry, Amy R Zipursky, Alon Geva, Karen L Olson, James R Jones, Vladimir Ignatov, Timothy A Miller, Kenneth D Mandl. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

LESSON PLAN FOR ENGLISH TEACHERS

A case study for negotiations.

Level: Proficiency (C2)

Type of English: Business English

Tags: sales and negotiation contracts and agreements analysing case studies 18+ years old Proficiency Business course Article based Vocabulary lesson Situation based Video talk

Publication date: 03/11/2024

This lesson takes as a case study for negotiations in one of the most important deals in the entertainment industry: the acquisition of big chunk of the Fox empire by Disney. Students will compare what they know about the deal, then watch a short video about it. They then go on to read an article with more details about the timeline of the acquisition, extracting from it valuable lessons about the tactics of negotiation. They also see some business idioms that come from sports, and end by roleplaying the meeting that closed the famous deal.

by Edward Alden

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Type of English: Business English Level: Proficiency (C2)

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U.s. department of energy - energy efficiency and renewable energy, alternative fuels data center.

• Printable Version
• Electricity Basics
• Benefits & Considerations
• Availability
• Conversions
• Maintenance & Safety
• School Bus Education
• For Consumers
• Laws & Incentives

Emissions from Electric Vehicles

All-electric vehicles, plug-in hybrid electric vehicles (PHEVs), and hybrid electric vehicles (HEVs) typically produce lower tailpipe emissions than conventional vehicles do, and zero tailpipe emissions when running only on electricity. Tailpipe emissions are only one factor in considering a vehicle's life cycle emissions; gasoline and electricity fuel pathways also have upstream emissions to consider, which include extracting, refining, producing, and transporting the fuel. Estimating cradle-to-grave emissions must account for both fuel-cycle emissions (also known as "well to wheels") and vehicle-cycle emissions (material and vehicle production as well as end of life). The combined emissions from vehicle and fuel production through vehicle decommissioning (i.e., recycling or scrapping) are referred to as life cycle or cradle-to-grave emissions.

Electricity Sources and Fuel-Cycle Emissions

All-electric vehicles and PHEVs running only on electricity have zero tailpipe emissions, but electricity production, such as power plants, may generate emissions. In geographic areas that use relatively low-polluting energy sources for electricity generation, all-electric vehicles and PHEVs typically have an especially large life cycle emissions advantage over similar conventional vehicles running on gasoline or diesel. In areas with higher-emissions electricity, all-electric vehicles and PHEVs may not demonstrate as strong a life cycle emissions benefit.

Direct, Well-to-Wheel, and Cradle-to-Grave Emissions

Vehicle emissions can be divided into two general categories: air pollutants, which contribute to smog, haze, and health problems; and greenhouse gases (GHGs), such as carbon dioxide and methane. Both categories of emissions can be evaluated on a tailpipe basis, a well-to-wheel basis, and a cradle-to-grave basis.

Conventional vehicles with an internal combustion engine (ICE) produce direct emissions through the tailpipe, as well as through evaporation from the vehicle's fuel system and during the fueling process. Conversely, all-electric vehicles produce zero direct emissions. PHEVs produce zero direct emissions when they are in all-electric mode, but they can produce evaporative emissions. When using the ICE, PHEVs produce tailpipe emissions. However, their direct emissions are typically lower than those of comparable conventional vehicles.

Well-to-wheel emissions include all emissions related to fuel production, processing, distribution, and use. In the case of gasoline, emissions are produced while extracting petroleum from the earth, refining it, distributing the fuel to stations, and burning it in vehicles. In the case of electricity, most electric power plants produce emissions, and there are additional emissions associated with the extraction, processing, and distribution of the primary energy sources they use for electricity production.

Cradle-to-grave emissions include all emissions considered on a well-to-wheel basis as well as vehicle-cycle emissions associated with vehicle and battery manufacturing, recycling , and disposal.

Related Reports

Learn more about electric-drive vehicle emissions in two reports:

• Cradle-to-Grave Lifecycle Analysis of U.S. Light-Duty Vehicle-Fuel Pathways: A Greenhouse Gas Emissions and Economic Assessment of Current (2020) and Future (2030-2035) Technologies
• Emissions Associated with Electric Vehicle Charging: Impact of Electricity Generation Mix, Charging Infrastructure Availability, and Vehicle Type

This study delves into the intricacies of power relations and social capital within the Red Cross Village in Daanbantayan, Cebu, in the aftermath of Super Typhoon Haiyan in 2013. Applying an ethnographic approach and employing Foucault's theory of diffuse power relations, the research explores the complex interplay between diverse stakeholders in shaping housing insecurity and community resilience. Key findings reveal the destabilizing effect of a tenuous usufruct agreement on land conflict and disaster governance, highlighting the criticality of secure land tenure to resilience. Shifting political affiliations, everyday marginalization, and an uneven distribution of authority expose strains on community cohesion and disparities in resource access. Despite these challenges, villagers demonstrate agency through everyday acts of resistance, challenging dominant narratives and redefining perceptions of disaster victims. These insights point to the necessity of addressing the interplay between power relations in conjunction with fostering social capital, secure land tenure, fair resource access, and meaningful community participation to effectively build resilient post-disaster communities. The study also reveals the inadequacy of merely providing physical housing without considering the socio-political dynamics that underpin community cohesion and resilience. Future research directions recommended include comparative case studies across various post-disaster communities, large-scale ethnographic projects, participatory methods, and detailed social network analysis. Such efforts would provide a comprehensive understanding of the role of power relations and social capital in shaping post-disaster resilience and offer valuable insights for policymakers to balance urgency with empowerment in their approach to 'building back better.'

Examining Power Relations and Social Capital in Post-Disaster Housing: An Ethnographic Case Study of Red Cross Village

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Hyper-V vs VMware: What’s the difference and which should you choose?

October 10, 2023

Hyper-V and VMware are both technologies that allow businesses and organizations to use a single computer or server for multiple applications and use cases. From the time it was released to the public till now, it has been in heavy use by many companies and individuals.

These technologies are both called server virtualization technologies and are a major part of IT and networking.

So, if you don’t know anything about these technologies or are confused about choosing the best one for your business, don’t worry. In this tutorial, we will cover what they are both used for and which one is better for your specific use case.

What is Server Virtualization?

If VMware and Hyper-V don’t ring a bell in your mind, you might be wondering what is server virtualization technology. But if you already have prior knowledge then you are free to skip this part.

Server virtualization is the practice of using virtualization software to partition a physical server into multiple isolated virtual environments, often referred to as a container. It allows you to run multiple virtual machines at a time.

Each container operates with its own distinct operating system also known as guest OS. These containers are completely isolated from each other, meaning that no virtual server can access the data from the other server. This is possible by making virtual disks for each VM (virtual machine).

A Hypervisor is the real technology that enables the creation of a virtual machine and it sits between the hardware and the virtual machines.

Server virtualization is just a term used to describe the process of making virtual machines. However, the hypervisor is the real technology that is behind this process.

In the picture below you see a diagram of a hypervisor. Some hypervisors are installed on a host OS, which is called a Type-2 hypervisor. Others are installed directly on the hardware, which is called Type-1.

Benefits of Server Virtualization

The benefits of server virtualization are that it:

• Gets rid of extra IT costs and energy consumption
• Decreases the number of physical servers your company must have on its premises
• Creates an independent user environment
• Improves resource utilization
• Allows for dynamic resource allocation
• Offers improved resource prioritization

Learn more about Hyperconverged IaaS on the ServerMania Cloud .

This is the first type of server virtualization solution that is popular in the industry. VMware offers a range of virtualization solutions, with VMware-vSphere being their flagship product.

VMware VSphere is a cloud virtualization platform that is available for on-premise and cloud use. It uses VMware ESXi as its hypervisor.

VMware VSphere includes several components:

• VMware ESXi: VMware ESXi (formerly known as VMware ESX) is a Type-1 hypervisor that runs directly on bare metal (hardware) and does not require any Operating System. It is used in VSphere as a hypervisor and is responsible for running virtual machines and managing hardware such as memory, CPU, etc.
• VMware vCenter Server: This is a centralized management tool. It allows you to manage virtual machines or multiple ESXi hypervisors from a single interface.

Note! A complete guide to the VMware features comparison can be found here .

VMware VSphere System Requirements

Below are the system requirements of VMware ESXi hypervisor version 7.0.

• At least two CPU cores.
• Multi-core of 64-bit x86 processors.
• Requires the NX/XD bit to be enabled for the CPU in the BIOS.
• VMware VSphere requires a minimum of 8 GB of physical RAM to run virtual machines. But it is always recommended to increase it for better performance.
• VMware ESXi could run almost all the major operating systems including Windows server, Mac OS server, and various kinds of Linux distributions.
• Requires a boot disk of at least 32 GB of persistent storage such as HDD, SSD, or NVMe

Note! VMware also offers a  hardware compatibility tool , which will help determine if your hardware can run VMware.

VMware Pricing

VMware pricing depends upon many different factors including license type, edition, and resources.

• Pricing typically depends on the number of CPUs or sockets in your physical servers. You’ll need to purchase licenses for each CPU.
• VMware also offers subscription-based licensing for vSphere, which provides more flexibility and may include access to additional features and services.

Below you can see a picture of VSphere editions:

Hyper-V is a Type-1 Microsoft virtualization solution, designed for both Windows Server environments and Windows 10. In the new versions, it has also added support for the Linux operating system as well. Hyper-V could also be used on-premises or in the cloud using Microsoft Azure services.

Hyper-V System Requirements

Regardless of the specific Hyper-V features you plan to use, your system must meet these general requirements:

• A 64-bit processor with second-level address translation (SLAT) is necessary. This is needed to install the Hyper-V virtualization components, like the Windows hypervisor.
• VM Monitor Mode extensions are required.
• You should plan for at least 4 GB of RAM, although more memory is recommended.
• Virtualization support must be turned on in the BIOS or UEFI, including hardware-assisted virtualization (available in processors with Intel VT or AMD-V technology) and hardware-enforced Data Execution Prevention (DEP).

Hyper-V Pricing

Microsoft Hyper-V offers a range of editions with different pricing, spanning from $24.95 to $199 USD.

To determine the most suitable edition for your needs, explore the details and additional information about the product on their official website.

This will help you make an informed decision regarding which edition aligns best with your requirements. The available packages are mentioned below with their referring prices:

• Developer: $24.95 USD per month
• Bronze: $49 USD per month
• Silver: $89 USD per month
• Gold: $135 USD per month
• Platinum: $199 USD per month

Both VMware and Hyper-V have their pros and cons, so we’ve compared them below to help highlight the key differences.

Comparison in Pricing

The pricing for VMware and Hyper-V both depend on the resources you tend to use and the requirements of your business.

In essence, Hyper-V is considered to be an affordable alternative to VMware because VMware has various services that add to the overall cost of this solution.

Feature Comparison

To simplify the comparison of Hyper-V vs VMware, we have put together the following table:

In Summary: Should You Choose Hyper-V or VMware?

When faced with the decision of choosing between Hyper-V and VMware, it becomes evident that your selection should be intricately tied to your specific hardware requirements and the overarching objectives of your business. The choice between these virtualization solutions is not a one-size-fits-all scenario; rather, it’s a nuanced decision that necessitates a thorough evaluation of several critical factors.

First and foremost, affordability plays a pivotal role in your decision-making process. Assessing your budget constraints and aligning them with the cost of licensing, hardware compatibility, and ongoing maintenance is imperative. It’s essential to strike a balance between your financial resources and the capabilities offered by each platform.

Furthermore, consider the essential features required to meet your operational needs. Does your business demand advanced features like seamless container migration, memory management tools for example VMware memory management techniques, or superior support for specific guest operating systems?

By carefully evaluating your essential feature set, you can ensure that the chosen virtualization solution aligns seamlessly with your business requirements. Additionally, take scalability into account, as your virtualization platform should have the capacity to grow alongside your evolving needs. Ultimately, the decision between Hyper-V and VMware should be a well-informed one, driven by a comprehensive understanding of your organization’s unique hardware, budget, feature, and scalability demands.

In this guide, we’ve explained the difference between Hyper-V and VMware and have given you a better understanding of what they are used for.

If you would like to learn more about Hyper-V, take a look at Hyper-V Networking: what are the different switches used for? We also recommend taking a look at our Knowledge Base  to see our latest articles and tutorial videos to help you with cloud server hosting and setup.

Need even more advice or would you like a custom quote? Book a server consultation with one of our experts. We will assist you in identifying the most suitable solutions for you or your business.

About the author

Milad Karimyar

Crafting Digital Excellence

Milad is a seasoned Software Architect with a passion for crafting digital solutions that push the boundaries of innovation. With a keen eye for detail and a wealth of experience, Milad has established himself as a trailblazer in the world of software architecture.

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Leverage built-in backup and disaster recovery to ensure business continuity. HPE SimpliVity’s speed and efficiency enable recovery of a 1 TB VM in 60 seconds. Now you can deploy and consolidate workloads on HPE SimpliVity.

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HPE SimpliVity 380 gives IT organizations the agility and economics of the cloud with the control and governance of on-premises IT and HPE GreenLake for Private Cloud Business Edition integration. It delivers a powerhouse hyperconverged solution optimized to support the world’s most efficient and resilient data centers. This solution dramatically simplifies IT by combining infrastructure and advanced data services for virtualized workloads on a single device.

Do you need a dense platform with built-in security and flexibility that addresses key applications which need performance, availability along with ease of management? HPE SimpliVity 325 provides HCI choice with our 3rd Generation  AMD EPYC™ single CPU processor platform including all-flash storage. Highly dense, the solution is a 1U enclosure that scales in 1U increments and is ideal for remote office or space-constrained locations.

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