Acoustic epidemiology

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Acoustic epidemiology refers to the study of the determinants and distribution of disease. It also refers to the analysis of sounds produced by the body (coughs, sneezes, wheezing, etc.) through a single tool or a combination of diagnostic tools. [1]

Contents

In many cases, epidemiologists have worked across multiple disciplines and used different technologies in order to find answers pertaining to disease distribution. For example, in the 1800s, John Snow determined that cholera was plaguing Europe through contaminated water. This led to the decision to remove a pump that was the cause of this contamination, thus effectively ending the epidemic. More broadly, Snow's epidemiological efforts led to the development of sewage drainage and water purifying systems in other areas. [2]

As COVID-19 developed, genomic epidemiologists began using whole genomes to study the disease. On the CDC's website, they have posted a “COVID-19 Genomic Epidemiology Toolkit”, which provides a means to expand the field of genomic epidemiology with regards to COVID-19 within state and local populations. [3]

Acoustic epidemiology is a field that studies bodily sounds, such as coughs and breath sounds, in order to better identify determinants and distribution of disease. Following in the footsteps of epidemiological tools and efforts such as those outlined above, acoustic epidemiology is concerned with using body sound data to improve disease surveillance capabilities for COVID-19 and any other applicable diseases of the future. [4] [5]

Clinical relevance

Being that epidemiology is a population-based area of study, findings from acoustic disease surveillance are important on a large scale, and have far-reaching implications for society as a whole. Cough and breath sounds provide rich epidemiological data. [6]

Baseline Measurements and Deviations

Studying respiratory sounds and identifying deviations from baseline is an invaluable epidemiologic tool. [7] On a community and population level, this can help to determine to what extent a disease may be spreading or changing. One of the major themes of concern throughout the COVID-19 pandemic has been travel safety, hotspots, and outbreaks in certain areas. [8]

Acoustic Epidemiology Through Use of Smartphone Apps

As a means to overcome some of the restrictions imposed by the COVID-19 pandemic, smartphone apps were developed to capture and analyze respiratory health data safely. [9]

In a 2020-2021 study of acoustic epidemiology, in Navarra, Spain, the Hyfe app was used to track respiratory sounds in over 800 study participants. [10] [11]

Syndromic Surveillance

Syndromic surveillance is a complementary, and potentially faster method of health data collection and analysis as compared to standard methods of public health monitoring. [12]

Examples of Syndromic Surveillance

Instances of syndromic surveillance are easy to find. Examples include: [13]

Bias in Syndromic Surveillance

Sources for syndromic surveillance may be biased, as they vary based on healthcare access in a given area. Therefore, some have questioned whether certain common methods of syndromic surveillance are truly representative of the larger population. [16] [17]

The future of acoustic epidemiology

The value of being able to track signs of deviations from baseline with regards to respiratory sounds at a population level is becoming clear through research. [18] [19] Epidemiologists predict that respiratory viruses could continue to be a problem in the future. Therefore, effective monitoring of acoustic data will need to be easy, affordable, and available on a wide scale. [20] [18] [19]

See also

Related Research Articles

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Whooping cough, also known as pertussis or the 100-day cough, is a highly contagious, vaccine-preventable bacterial disease. Initial symptoms are usually similar to those of the common cold with a runny nose, fever, and mild cough, but these are followed by two or three months of severe coughing fits. Following a fit of coughing, a high-pitched whoop sound or gasp may occur as the person breathes in. The violent coughing may last for 10 or more weeks, hence the phrase "100-day cough". The cough may be so hard that it causes vomiting, rib fractures, and fatigue. Children less than one year old may have little or no cough and instead have periods when they cannot breathe. The incubation period is usually seven to ten days. Disease may occur in those who have been vaccinated, but symptoms are typically milder.

<span class="mw-page-title-main">SARS</span> Disease caused by severe acute respiratory syndrome coronavirus

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<span class="mw-page-title-main">Basic reproduction number</span> Metric in epidemiology

In epidemiology, the basic reproduction number, or basic reproductive number, denoted , of an infection is the expected number of cases directly generated by one case in a population where all individuals are susceptible to infection. The definition assumes that no other individuals are infected or immunized. Some definitions, such as that of the Australian Department of Health, add the absence of "any deliberate intervention in disease transmission". The basic reproduction number is not necessarily the same as the effective reproduction number , which is the number of cases generated in the current state of a population, which does not have to be the uninfected state. is a dimensionless number and not a time rate, which would have units of time−1, or units of time like doubling time.

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<span class="mw-page-title-main">Influenza-like illness</span> Medical diagnosis

Influenza-like illness (ILI), also known as flu-like syndrome or flu-like symptoms, is a medical diagnosis of possible influenza or other illness causing a set of common symptoms. These include fever, shivering, chills, malaise, dry cough, loss of appetite, body aches, nausea, and sneezing typically in connection with a sudden onset of illness. In most cases, the symptoms are caused by cytokines released by immune system activation, and are thus relatively non-specific.

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<span class="mw-page-title-main">COVID-19</span> Contagious disease caused by SARS-CoV-2

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by the coronavirus SARS-CoV-2. The first known case was identified in Wuhan, China, in December 2019. Most scientists believe the SARS-CoV-2 virus entered into human populations through natural zoonosis, similar to the SARS-CoV-1 and MERS-CoV outbreaks, and consistent with other pandemics in human history. Social and environmental factors including climate change, natural ecosystem destruction and wildlife trade increased the likelihood of such zoonotic spillover. The disease quickly spread worldwide, resulting in the COVID-19 pandemic.

<span class="mw-page-title-main">Caitlin Rivers</span> American epidemiologist

Caitlin M. Rivers is an American epidemiologist who as Senior Scholar at the Johns Hopkins Center for Health Security and assistant professor at the Johns Hopkins Bloomberg School of Public Health, specializing on improving epidemic preparedness. Rivers is currently working on the American response to the COVID-19 pandemic with a focus on the incorporation of infectious disease modeling and forecasting into public health decision making.

<span class="mw-page-title-main">COVID-19 surveillance</span> Measures to monitor the spread of the respiratory disease

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<span class="mw-page-title-main">Transmission of COVID-19</span> Mechanisms that spread coronavirus disease 2019

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<span class="mw-page-title-main">Martin Kulldorff</span> Professor of medicine, biostatistician

Martin Kulldorff is a Swedish biostatistician. He was a professor of medicine at Harvard Medical School from 2003 until his dismissal in 2024. He is a member of the US Food and Drug Administration's Drug Safety and Risk Management Advisory Committee and a former member of the Vaccine Safety Subgroup of the Advisory Committee on Immunization Practices at the Centers for Disease Control and Prevention.

References

  1. ↑ Frérot, Mathilde; Lefebvre, Annick; Aho, Simon; Callier, Patrick; Astruc, Karine; Glélé, Ludwig Serge Aho (2018-12-10). "What is epidemiology? Changing definitions of epidemiology 1978-2017". PLOS ONE. 13 (12): e0208442. Bibcode:2018PLoSO..1308442F. doi: 10.1371/journal.pone.0208442 . ISSN   1932-6203. PMC   6287859 . PMID   30532230.
  2. ↑ Tulchinsky, Theodore H. (2018), "John Snow, Cholera, the Broad Street Pump; Waterborne Diseases Then and Now", Case Studies in Public Health, Elsevier, pp. 77–99, doi: 10.1016/b978-0-12-804571-8.00017-2 , ISBN   9780128045718, S2CID   134374719
  3. ↑ "COVID-19 Genomic Epidemiology Toolkit | Advanced Molecular Detection (AMD) | CDC". www.cdc.gov. 2021-11-17. Retrieved 2021-12-08.
  4. ↑ Sara, Jaskanwal Deep Singh; Maor, Elad; Borlaug, Barry; Lewis, Bradley R.; Orbelo, Diana; Lerman, Lliach O.; Lerman, Amir (2020). "Non-invasive vocal biomarker is associated with pulmonary hypertension". PLOS ONE. 15 (4): e0231441. Bibcode:2020PLoSO..1531441S. doi: 10.1371/journal.pone.0231441 . ISSN   1932-6203. PMC   7162478 . PMID   32298301.
  5. ↑ Maor, Elad; Tsur, Nir; Barkai, Galia; Meister, Ido; Makmel, Shmuel; Friedman, Eli; Aronovich, Daniel; Mevorach, Dana; Lerman, Amir; Zimlichman, Eyal; Bachar, Gideon (2021). "Noninvasive Vocal Biomarker is Associated With Severe Acute Respiratory Syndrome Coronavirus 2 Infection". Mayo Clinic Proceedings. Innovations, Quality & Outcomes. 5 (3): 654–662. doi:10.1016/j.mayocpiqo.2021.05.007. ISSN   2542-4548. PMC   8120447 . PMID   34007956.
  6. ↑ Imran, Ali; Posokhova, Iryna; Qureshi, Haneya N.; Masood, Usama; Riaz, Muhammad Sajid; Ali, Kamran; John, Charles N.; Hussain, MD Iftikhar; Nabeel, Muhammad (2020-01-01). "AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app". Informatics in Medicine Unlocked. 20: 100378. arXiv: 2004.01275 . doi:10.1016/j.imu.2020.100378. ISSN   2352-9148. PMC   7318970 . PMID   32839734.
  7. ↑ Lau, Bryan; Duggal, Priya; Ehrhardt, Stephan; Armenian, Haroutune; Branas, Charles C; Colditz, Graham A; Fox, Matthew P; Hawes, Stephen E; He, Jiang; Hofman, Albert; Keyes, Katherine (2020-07-01). "Perspectives on the Future of Epidemiology: A Framework for Training". American Journal of Epidemiology. 189 (7): 634–639. doi: 10.1093/aje/kwaa013 . ISSN   0002-9262. PMC   11484601 . PMID   32003778.
  8. ↑ "Risk scores in real-time: the untapped potential of mobile health" (PDF). Archived (PDF) from the original on 2022-01-19.
  9. 1 2 Faezipour, Miad; Abuzneid, Abdelshakour (2020-10-01). "Smartphone-Based Self-Testing of COVID-19 Using Breathing Sounds". Telemedicine and e-Health. 26 (10): 1202–1205. doi:10.1089/tmj.2020.0114. ISSN   1530-5627. PMID   32487005. S2CID   219286869.
  10. ↑ Gabaldon-Figueira, Juan Carlos; Brew, Joe; Doré, Dominique Hélène; Umashankar, Nita; Chaccour, Juliane; Orrillo, Virginia; Tsang, Lai Yu; Blavia, Isabel; Fernández-Montero, Alejandro; Bartolomé, Javier; Lapierre, Simon Grandjean (2021-07-01). "Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study". BMJ Open. 11 (7): e051278. doi:10.1136/bmjopen-2021-051278. ISSN   2044-6055. PMC   8257291 . PMID   34215614.
  11. ↑ "Hyfe AI - Measure cough as an objective clinical finding". www.hyfe.ai. Retrieved 2022-05-17.
  12. ↑ Colón-González, Felipe J.; Lake, Iain R.; Morbey, Roger A.; Elliot, Alex J.; Pebody, Richard; Smith, Gillian E. (2018-04-24). "A methodological framework for the evaluation of syndromic surveillance systems: a case study of England". BMC Public Health. 18 (1): 544. doi: 10.1186/s12889-018-5422-9 . ISSN   1471-2458. PMC   5921418 . PMID   29699520.
  13. ↑ Heffernan, Richard; Mostashari, Farzad; Das, Debjani; Karpati, Adam; Kulldorff, Martin; Weiss, Don (2004). "Syndromic Surveillance in Public Health Practice, New York City - Volume 10, Number 5—May 2004 - Emerging Infectious Diseases journal - CDC". Emerging Infectious Diseases. 10 (5): 858–864. doi: 10.3201/eid1005.030646 . PMID   15200820.
  14. ↑ Heffernan, Richard; Mostashari, Farzad; Das, Debjani; Karpati, Adam; Kulldorff, Martin; Weiss, Don (2004). "Syndromic Surveillance in Public Health Practice, New York City - Volume 10, Number 5—May 2004 - Emerging Infectious Diseases journal - CDC". Emerging Infectious Diseases. 10 (5): 858–864. doi: 10.3201/eid1005.030646 . PMID   15200820.
  15. ↑ Heffernan, Richard; Mostashari, Farzad; Das, Debjani; Karpati, Adam; Kulldorff, Martin; Weiss, Don (2004). "Syndromic Surveillance in Public Health Practice, New York City - Volume 10, Number 5—May 2004 - Emerging Infectious Diseases journal - CDC". Emerging Infectious Diseases. 10 (5): 858–864. doi: 10.3201/eid1005.030646 . PMID   15200820.
  16. ↑ Gabaldon-Figueira, Juan Carlos; Brew, Joe; Doré, Dominique Hélène; Umashankar, Nita; Chaccour, Juliane; Orrillo, Virginia; Tsang, Lai Yu; Blavia, Isabel; Fernández-Montero, Alejandro; Bartolomé, Javier; Grandjean Lapierre, Simon (2021-07-02). "Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study". BMJ Open. 11 (7): e051278. doi:10.1136/bmjopen-2021-051278. ISSN   2044-6055. PMC   8257291 . PMID   34215614.
  17. ↑ Gabaldon-Figueira, Juan Carlos; Brew, Joe; Doré, Dominique Hélène; Umashankar, Nita; Chaccour, Juliane; Orrillo, Virginia; Tsang, Lai Yu; Blavia, Isabel; Fernández-Montero, Alejandro; Bartolomé, Javier; Grandjean Lapierre, Simon; Chaccour, C (2021). "Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study". BMJ Open. 11 (7). BMJ: e051278. doi:10.1136/bmjopen-2021-051278. ISSN   2044-6055. PMC   8257291 . PMID   34215614.
  18. 1 2 Rennoll, Valerie; McLane, Ian; Emmanouilidou, Dimitra; West, James; Elhilali, Mounya (2021). "Electronic Stethoscope Filtering Mimics the Perceived Sound Characteristics of Acoustic Stethoscope". IEEE Journal of Biomedical and Health Informatics. 25 (5): 1542–1549. doi:10.1109/JBHI.2020.3020494. ISSN   2168-2208. PMC   7917155 . PMID   32870803.
  19. 1 2 Birring, S. S.; Fleming, T.; Matos, S.; Raj, A. A.; Evans, D. H.; Pavord, I. D. (2008-05-01). "The Leicester Cough Monitor: preliminary validation of an automated cough detection system in chronic cough". European Respiratory Journal. 31 (5): 1013–1018. doi: 10.1183/09031936.00057407 . ISSN   0903-1936. PMID   18184683. S2CID   11219873.
  20. ↑ Smith, Jaclyn A.; Ashurst, H. Louise; Jack, Sandy; Woodcock, Ashley A.; Earis, John E. (2006-01-25). "The description of cough sounds by healthcare professionals". Cough. 2 (1): 1. doi: 10.1186/1745-9974-2-1 . ISSN   1745-9974. PMC   1413549 . PMID   16436200.
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