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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.<ref>{{Cite journal|last=Frérot|first=Mathilde|last2=Lefebvre|first2=Annick|last3=Aho|first3=Simon|last4=Callier|first4=Patrick|last5=Astruc|first5=Karine|last6=Glélé|first6=Ludwig Serge Aho|date=2018-12-10|title=What is epidemiology? Changing definitions of epidemiology 1978-2017|url=https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0208442|journal=PLOS ONE|language=en|volume=13|issue=12|pages=e0208442|doi=10.1371/journal.pone.0208442|issn=1932-6203|pmc=6287859|pmid=30532230}}</ref>
'''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.<ref>{{Cite journal|last1=Frérot|first1=Mathilde|last2=Lefebvre|first2=Annick|last3=Aho|first3=Simon|last4=Callier|first4=Patrick|last5=Astruc|first5=Karine|last6=Glélé|first6=Ludwig Serge Aho|date=2018-12-10|title=What is epidemiology? Changing definitions of epidemiology 1978-2017|journal=PLOS ONE|language=en|volume=13|issue=12|pages=e0208442|doi=10.1371/journal.pone.0208442|issn=1932-6203|pmc=6287859|pmid=30532230|bibcode=2018PLoSO..1308442F|doi-access=free}}</ref>


In many cases, [[Epidemiology|epidemiologists]] have worked across multiple disciplines and used different technologies in order to find answers pertaining to disease distribution. For example, in the 1800’s, [[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.<ref>{{Citation|last=Tulchinsky|first=Theodore H.|title=John Snow, Cholera, the Broad Street Pump; Waterborne Diseases Then and Now|date=2018|url=http://dx.doi.org/10.1016/b978-0-12-804571-8.00017-2|work=Case Studies in Public Health|pages=77–99|publisher=Elsevier|access-date=2021-12-08}}</ref>
In many cases, [[Epidemiology|epidemiologists]] have worked across multiple disciplines and used different technologies in order to find answers pertaining to disease distribution. For example, in the 1800’s, [[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.<ref>{{Citation|last=Tulchinsky|first=Theodore H.|title=John Snow, Cholera, the Broad Street Pump; Waterborne Diseases Then and Now|date=2018|url=http://dx.doi.org/10.1016/b978-0-12-804571-8.00017-2|work=Case Studies in Public Health|pages=77–99|publisher=Elsevier|doi=10.1016/b978-0-12-804571-8.00017-2|isbn=9780128045718|s2cid=134374719|access-date=2021-12-08}}</ref>


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.<ref>{{Cite web|date=2021-11-17|title=COVID-19 Genomic Epidemiology Toolkit {{!}} Advanced Molecular Detection (AMD) {{!}} CDC|url=https://www.cdc.gov/amd/training/covid-19-gen-epi-toolkit.html|access-date=2021-12-08|website=www.cdc.gov|language=en-us}}</ref>
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.<ref>{{Cite web|date=2021-11-17|title=COVID-19 Genomic Epidemiology Toolkit {{!}} Advanced Molecular Detection (AMD) {{!}} CDC|url=https://www.cdc.gov/amd/training/covid-19-gen-epi-toolkit.html|access-date=2021-12-08|website=www.cdc.gov|language=en-us}}</ref>


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.<ref>{{Cite journal|last=Sara|first=Jaskanwal Deep Singh|last2=Maor|first2=Elad|last3=Borlaug|first3=Barry|last4=Lewis|first4=Bradley R.|last5=Orbelo|first5=Diana|last6=Lerman|first6=Lliach O.|last7=Lerman|first7=Amir|date=2020|title=Non-invasive vocal biomarker is associated with pulmonary hypertension|url=https://pubmed.ncbi.nlm.nih.gov/32298301/|journal=PloS One|volume=15|issue=4|pages=e0231441|doi=10.1371/journal.pone.0231441|issn=1932-6203|pmc=7162478|pmid=32298301}}</ref><ref>{{Cite journal|last=Maor|first=Elad|last2=Tsur|first2=Nir|last3=Barkai|first3=Galia|last4=Meister|first4=Ido|last5=Makmel|first5=Shmuel|last6=Friedman|first6=Eli|last7=Aronovich|first7=Daniel|last8=Mevorach|first8=Dana|last9=Lerman|first9=Amir|last10=Zimlichman|first10=Eyal|last11=Bachar|first11=Gideon|date=2021|title=Noninvasive Vocal Biomarker is Associated With Severe Acute Respiratory Syndrome Coronavirus 2 Infection|url=https://pubmed.ncbi.nlm.nih.gov/34007956/|journal=Mayo Clinic Proceedings. Innovations, Quality & Outcomes|volume=5|issue=3|pages=654–662|doi=10.1016/j.mayocpiqo.2021.05.007|issn=2542-4548|pmc=8120447|pmid=34007956}}</ref>
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.<ref>{{Cite journal|last1=Sara|first1=Jaskanwal Deep Singh|last2=Maor|first2=Elad|last3=Borlaug|first3=Barry|last4=Lewis|first4=Bradley R.|last5=Orbelo|first5=Diana|last6=Lerman|first6=Lliach O.|last7=Lerman|first7=Amir|date=2020|title=Non-invasive vocal biomarker is associated with pulmonary hypertension|journal=PLOS ONE|volume=15|issue=4|pages=e0231441|doi=10.1371/journal.pone.0231441|issn=1932-6203|pmc=7162478|pmid=32298301|bibcode=2020PLoSO..1531441S|doi-access=free}}</ref><ref>{{Cite journal|last1=Maor|first1=Elad|last2=Tsur|first2=Nir|last3=Barkai|first3=Galia|last4=Meister|first4=Ido|last5=Makmel|first5=Shmuel|last6=Friedman|first6=Eli|last7=Aronovich|first7=Daniel|last8=Mevorach|first8=Dana|last9=Lerman|first9=Amir|last10=Zimlichman|first10=Eyal|last11=Bachar|first11=Gideon|date=2021|title=Noninvasive Vocal Biomarker is Associated With Severe Acute Respiratory Syndrome Coronavirus 2 Infection|journal=Mayo Clinic Proceedings. Innovations, Quality & Outcomes|volume=5|issue=3|pages=654–662|doi=10.1016/j.mayocpiqo.2021.05.007|issn=2542-4548|pmc=8120447|pmid=34007956}}</ref>


== Clinical relevance ==
== 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.<ref>{{Cite journal|last=Imran|first=Ali|last2=Posokhova|first2=Iryna|last3=Qureshi|first3=Haneya N.|last4=Masood|first4=Usama|last5=Riaz|first5=Muhammad Sajid|last6=Ali|first6=Kamran|last7=John|first7=Charles N.|last8=Hussain|first8=MD Iftikhar|last9=Nabeel|first9=Muhammad|date=2020-01-01|title=AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app|url=https://www.sciencedirect.com/science/article/pii/S2352914820303026|journal=Informatics in Medicine Unlocked|language=en|volume=20|pages=100378|doi=10.1016/j.imu.2020.100378|issn=2352-9148}}</ref>
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.<ref>{{Cite journal|last1=Imran|first1=Ali|last2=Posokhova|first2=Iryna|last3=Qureshi|first3=Haneya N.|last4=Masood|first4=Usama|last5=Riaz|first5=Muhammad Sajid|last6=Ali|first6=Kamran|last7=John|first7=Charles N.|last8=Hussain|first8=MD Iftikhar|last9=Nabeel|first9=Muhammad|date=2020-01-01|title=AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app|journal=Informatics in Medicine Unlocked|language=en|volume=20|pages=100378|doi=10.1016/j.imu.2020.100378|pmid=32839734|pmc=7318970|arxiv=2004.01275|issn=2352-9148}}</ref>


=== Baseline Measurements and Deviations ===
=== Baseline Measurements and Deviations ===
Studying respiratory sounds and identifying deviations from baseline is an invaluable epidemiologic tool.<ref>{{Cite journal|last=Lau|first=Bryan|last2=Duggal|first2=Priya|last3=Ehrhardt|first3=Stephan|last4=Armenian|first4=Haroutune|last5=Branas|first5=Charles C|last6=Colditz|first6=Graham A|last7=Fox|first7=Matthew P|last8=Hawes|first8=Stephen E|last9=He|first9=Jiang|last10=Hofman|first10=Albert|last11=Keyes|first11=Katherine|date=2020-07-01|title=Perspectives on the Future of Epidemiology: A Framework for Training|url=https://doi.org/10.1093/aje/kwaa013|journal=American Journal of Epidemiology|volume=189|issue=7|pages=634–639|doi=10.1093/aje/kwaa013|issn=0002-9262}}</ref> 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.<ref>{{Cite web|title=Risk scores in real-time: the untapped potential of mobile health|url=https://global-uploads.webflow.com/601331581ba868154325e525/604287f3604756b49407a107_Hyfe%20Risk%20Score%20in%20Real-Time.pdf|url-status=live|archive-url=https://web.archive.org/web/20220119192747/https://global-uploads.webflow.com/601331581ba868154325e525/604287f3604756b49407a107_Hyfe%20Risk%20Score%20in%20Real-Time.pdf |archive-date=2022-01-19 }}</ref>
Studying respiratory sounds and identifying deviations from baseline is an invaluable epidemiologic tool.<ref>{{Cite journal|last1=Lau|first1=Bryan|last2=Duggal|first2=Priya|last3=Ehrhardt|first3=Stephan|last4=Armenian|first4=Haroutune|last5=Branas|first5=Charles C|last6=Colditz|first6=Graham A|last7=Fox|first7=Matthew P|last8=Hawes|first8=Stephen E|last9=He|first9=Jiang|last10=Hofman|first10=Albert|last11=Keyes|first11=Katherine|date=2020-07-01|title=Perspectives on the Future of Epidemiology: A Framework for Training|url=https://doi.org/10.1093/aje/kwaa013|journal=American Journal of Epidemiology|volume=189|issue=7|pages=634–639|doi=10.1093/aje/kwaa013|pmid=32003778|issn=0002-9262}}</ref> 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.<ref>{{Cite web|title=Risk scores in real-time: the untapped potential of mobile health|url=https://global-uploads.webflow.com/601331581ba868154325e525/604287f3604756b49407a107_Hyfe%20Risk%20Score%20in%20Real-Time.pdf|url-status=live|archive-url=https://web.archive.org/web/20220119192747/https://global-uploads.webflow.com/601331581ba868154325e525/604287f3604756b49407a107_Hyfe%20Risk%20Score%20in%20Real-Time.pdf |archive-date=2022-01-19 }}</ref>


=== Acoustic Epidemiology Through Use of Smartphone Apps ===
=== 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.<ref>{{Cite journal|last=Faezipour|first=Miad|last2=Abuzneid|first2=Abdelshakour|date=2020-10-01|title=Smartphone-Based Self-Testing of COVID-19 Using Breathing Sounds|url=https://www.liebertpub.com/doi/abs/10.1089/tmj.2020.0114|journal=Telemedicine and e-Health|volume=26|issue=10|pages=1202–1205|doi=10.1089/tmj.2020.0114|issn=1530-5627}}</ref>
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.<ref>{{Cite journal|last1=Faezipour|first1=Miad|last2=Abuzneid|first2=Abdelshakour|date=2020-10-01|title=Smartphone-Based Self-Testing of COVID-19 Using Breathing Sounds|url=https://www.liebertpub.com/doi/abs/10.1089/tmj.2020.0114|journal=Telemedicine and E-Health|volume=26|issue=10|pages=1202–1205|doi=10.1089/tmj.2020.0114|pmid=32487005|s2cid=219286869|issn=1530-5627}}</ref>


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.<ref>{{Cite journal|last=Gabaldon-Figueira|first=Juan Carlos|last2=Brew|first2=Joe|last3=Doré|first3=Dominique Hélène|last4=Umashankar|first4=Nita|last5=Chaccour|first5=Juliane|last6=Orrillo|first6=Virginia|last7=Tsang|first7=Lai Yu|last8=Blavia|first8=Isabel|last9=Fernández-Montero|first9=Alejandro|last10=Bartolomé|first10=Javier|last11=Lapierre|first11=Simon Grandjean|date=2021-07-01|title=Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study|url=https://bmjopen.bmj.com/content/11/7/e051278|journal=BMJ Open|language=en|volume=11|issue=7|pages=e051278|doi=10.1136/bmjopen-2021-051278|issn=2044-6055}}</ref>
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.<ref>{{Cite journal|last1=Gabaldon-Figueira|first1=Juan Carlos|last2=Brew|first2=Joe|last3=Doré|first3=Dominique Hélène|last4=Umashankar|first4=Nita|last5=Chaccour|first5=Juliane|last6=Orrillo|first6=Virginia|last7=Tsang|first7=Lai Yu|last8=Blavia|first8=Isabel|last9=Fernández-Montero|first9=Alejandro|last10=Bartolomé|first10=Javier|last11=Lapierre|first11=Simon Grandjean|date=2021-07-01|title=Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study|url=https://bmjopen.bmj.com/content/11/7/e051278|journal=BMJ Open|language=en|volume=11|issue=7|pages=e051278|doi=10.1136/bmjopen-2021-051278|pmid=34215614|pmc=8257291|issn=2044-6055}}</ref>


== Syndromic Surveillance ==
== 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]].<ref>{{Cite journal|last=Colón-González|first=Felipe J.|last2=Lake|first2=Iain R.|last3=Morbey|first3=Roger A.|last4=Elliot|first4=Alex J.|last5=Pebody|first5=Richard|last6=Smith|first6=Gillian E.|date=2018-04-24|title=A methodological framework for the evaluation of syndromic surveillance systems: a case study of England|url=https://doi.org/10.1186/s12889-018-5422-9|journal=BMC Public Health|volume=18|issue=1|pages=544|doi=10.1186/s12889-018-5422-9|issn=1471-2458}}</ref>
Syndromic surveillance is a complementary, and potentially faster method of health data collection and analysis as compared to standard methods of public [[health monitoring]].<ref>{{Cite journal|last1=Colón-González|first1=Felipe J.|last2=Lake|first2=Iain R.|last3=Morbey|first3=Roger A.|last4=Elliot|first4=Alex J.|last5=Pebody|first5=Richard|last6=Smith|first6=Gillian E.|date=2018-04-24|title=A methodological framework for the evaluation of syndromic surveillance systems: a case study of England|url=https://doi.org/10.1186/s12889-018-5422-9|journal=BMC Public Health|volume=18|issue=1|pages=544|doi=10.1186/s12889-018-5422-9|pmid=29699520|pmc=5921418|issn=1471-2458}}</ref>


=== Examples of Syndromic Surveillance ===
=== Examples of Syndromic Surveillance ===
Instances of syndromic surveillance are easy to find. Examples include:<ref>{{Cite journal|last=Heffernan|first=Richard|last2=Mostashari|first2=Farzad|last3=Das|first3=Debjani|last4=Karpati|first4=Adam|last5=Kulldorff|first5=Martin|last6=Weiss|first6=Don|title=Syndromic Surveillance in Public Health Practice, New York City - Volume 10, Number 5—May 2004 - Emerging Infectious Diseases journal - CDC|url=https://wwwnc.cdc.gov/eid/article/10/5/03-0646_article|language=en-us|doi=10.3201/eid1005.030646}}</ref>
Instances of syndromic surveillance are easy to find. Examples include:<ref>{{Cite journal|last1=Heffernan|first1=Richard|last2=Mostashari|first2=Farzad|last3=Das|first3=Debjani|last4=Karpati|first4=Adam|last5=Kulldorff|first5=Martin|last6=Weiss|first6=Don|title=Syndromic Surveillance in Public Health Practice, New York City - Volume 10, Number 5—May 2004 - Emerging Infectious Diseases journal - CDC|journal=Emerging Infectious Diseases|year=2004|volume=10|issue=5|pages=858–864|url=https://wwwnc.cdc.gov/eid/article/10/5/03-0646_article|language=en-us|doi=10.3201/eid1005.030646|pmid=15200820}}</ref>


* Logs that record missed school or work due to illness<ref>{{Cite journal|last=Faezipour|first=Miad|last2=Abuzneid|first2=Abdelshakour|date=2020-10-01|title=Smartphone-Based Self-Testing of COVID-19 Using Breathing Sounds|url=https://www.liebertpub.com/doi/abs/10.1089/tmj.2020.0114|journal=Telemedicine and e-Health|volume=26|issue=10|pages=1202–1205|doi=10.1089/tmj.2020.0114|issn=1530-5627}}</ref>
* Logs that record missed school or work due to illness<ref>{{Cite journal|last1=Faezipour|first1=Miad|last2=Abuzneid|first2=Abdelshakour|date=2020-10-01|title=Smartphone-Based Self-Testing of COVID-19 Using Breathing Sounds|url=https://www.liebertpub.com/doi/abs/10.1089/tmj.2020.0114|journal=Telemedicine and E-Health|volume=26|issue=10|pages=1202–1205|doi=10.1089/tmj.2020.0114|pmid=32487005|s2cid=219286869|issn=1530-5627}}</ref>


* Symptoms recorded on patients in [[Emergency department|emergency rooms]]<ref>{{Cite journal|last=Heffernan|first=Richard|last2=Mostashari|first2=Farzad|last3=Das|first3=Debjani|last4=Karpati|first4=Adam|last5=Kulldorff|first5=Martin|last6=Weiss|first6=Don|title=Syndromic Surveillance in Public Health Practice, New York City - Volume 10, Number 5—May 2004 - Emerging Infectious Diseases journal - CDC|url=https://wwwnc.cdc.gov/eid/article/10/5/03-0646_article|language=en-us|doi=10.3201/eid1005.030646}}</ref>
* Symptoms recorded on patients in [[Emergency department|emergency rooms]]<ref>{{Cite journal|last1=Heffernan|first1=Richard|last2=Mostashari|first2=Farzad|last3=Das|first3=Debjani|last4=Karpati|first4=Adam|last5=Kulldorff|first5=Martin|last6=Weiss|first6=Don|title=Syndromic Surveillance in Public Health Practice, New York City - Volume 10, Number 5—May 2004 - Emerging Infectious Diseases journal - CDC|journal=Emerging Infectious Diseases|year=2004|volume=10|issue=5|pages=858–864|url=https://wwwnc.cdc.gov/eid/article/10/5/03-0646_article|language=en-us|doi=10.3201/eid1005.030646|pmid=15200820}}</ref>
* How often certain lab tests are ordered and performed<ref>{{Cite journal|last=Heffernan|first=Richard|last2=Mostashari|first2=Farzad|last3=Das|first3=Debjani|last4=Karpati|first4=Adam|last5=Kulldorff|first5=Martin|last6=Weiss|first6=Don|title=Syndromic Surveillance in Public Health Practice, New York City - Volume 10, Number 5—May 2004 - Emerging Infectious Diseases journal - CDC|url=https://wwwnc.cdc.gov/eid/article/10/5/03-0646_article|language=en-us|doi=10.3201/eid1005.030646}}</ref>
* How often certain lab tests are ordered and performed<ref>{{Cite journal|last1=Heffernan|first1=Richard|last2=Mostashari|first2=Farzad|last3=Das|first3=Debjani|last4=Karpati|first4=Adam|last5=Kulldorff|first5=Martin|last6=Weiss|first6=Don|title=Syndromic Surveillance in Public Health Practice, New York City - Volume 10, Number 5—May 2004 - Emerging Infectious Diseases journal - CDC|journal=Emerging Infectious Diseases|year=2004|volume=10|issue=5|pages=858–864|url=https://wwwnc.cdc.gov/eid/article/10/5/03-0646_article|language=en-us|doi=10.3201/eid1005.030646|pmid=15200820}}</ref>


=== Bias in Syndromic Surveillance ===
=== Bias in Syndromic Surveillance ===
Sources for [[Public health surveillance|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.<ref>{{Cite journal|last=Gabaldon-Figueira|first=Juan Carlos|last2=Brew|first2=Joe|last3=Doré|first3=Dominique Hélène|last4=Umashankar|first4=Nita|last5=Chaccour|first5=Juliane|last6=Orrillo|first6=Virginia|last7=Tsang|first7=Lai Yu|last8=Blavia|first8=Isabel|last9=Fernández-Montero|first9=Alejandro|last10=Bartolomé|first10=Javier|last11=Grandjean Lapierre|first11=Simon|date=2021-07-02|title=Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study|url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8257291/|journal=BMJ Open|volume=11|issue=7|pages=e051278|doi=10.1136/bmjopen-2021-051278|issn=2044-6055|pmc=8257291|pmid=34215614}}</ref><ref>{{Cite journal|last=Clinica Universidad de Navarra, Universidad de Navarra|date=2021-09-08|others=Centre de Recherche du Centre Hospitalier de l'Université de Montréal|title=Digital Acoustic Surveillance for Early Detection of Respiratory Disease Outbreaks: An Exploratory Observational Study in Navarra, Spain|url=https://clinicaltrials.gov/ct2/show/NCT04762693}}</ref>
Sources for [[Public health surveillance|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.<ref>{{Cite journal|last1=Gabaldon-Figueira|first1=Juan Carlos|last2=Brew|first2=Joe|last3=Doré|first3=Dominique Hélène|last4=Umashankar|first4=Nita|last5=Chaccour|first5=Juliane|last6=Orrillo|first6=Virginia|last7=Tsang|first7=Lai Yu|last8=Blavia|first8=Isabel|last9=Fernández-Montero|first9=Alejandro|last10=Bartolomé|first10=Javier|last11=Grandjean Lapierre|first11=Simon|date=2021-07-02|title=Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study|journal=BMJ Open|volume=11|issue=7|pages=e051278|doi=10.1136/bmjopen-2021-051278|issn=2044-6055|pmc=8257291|pmid=34215614}}</ref><ref>{{Cite journal|last=Clinica Universidad de Navarra, Universidad de Navarra|date=2021-09-08|others=Centre de Recherche du Centre Hospitalier de l'Université de Montréal|title=Digital Acoustic Surveillance for Early Detection of Respiratory Disease Outbreaks: An Exploratory Observational Study in Navarra, Spain|url=https://clinicaltrials.gov/ct2/show/NCT04762693}}</ref>


== The future of acoustic epidemiology ==
== 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.<ref name=":0">{{Cite journal|last=Rennoll|first=Valerie|last2=McLane|first2=Ian|last3=Emmanouilidou|first3=Dimitra|last4=West|first4=James|last5=Elhilali|first5=Mounya|date=2021|title=Electronic Stethoscope Filtering Mimics the Perceived Sound Characteristics of Acoustic Stethoscope|url=https://ieeexplore.ieee.org/document/9184043/|journal=IEEE Journal of Biomedical and Health Informatics|volume=25|issue=5|pages=1542–1549|doi=10.1109/JBHI.2020.3020494|issn=2168-2208|pmc=7917155|pmid=32870803}}</ref><ref name=":1">{{Cite journal|last=Birring|first=S. S.|last2=Fleming|first2=T.|last3=Matos|first3=S.|last4=Raj|first4=A. A.|last5=Evans|first5=D. H.|last6=Pavord|first6=I. D.|date=2008-05-01|title=The Leicester Cough Monitor: preliminary validation of an automated cough detection system in chronic cough|url=https://erj.ersjournals.com/content/31/5/1013|journal=European Respiratory Journal|language=en|volume=31|issue=5|pages=1013–1018|doi=10.1183/09031936.00057407|issn=0903-1936|pmid=18184683}}</ref> 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.<ref>{{Cite journal|last=Smith|first=Jaclyn A.|last2=Ashurst|first2=H. Louise|last3=Jack|first3=Sandy|last4=Woodcock|first4=Ashley A.|last5=Earis|first5=John E.|date=2006-01-25|title=The description of cough sounds by healthcare professionals|url=https://doi.org/10.1186/1745-9974-2-1|journal=Cough|volume=2|issue=1|pages=1|doi=10.1186/1745-9974-2-1|issn=1745-9974|pmc=1413549|pmid=16436200}}</ref><ref name=":0" /><ref name=":1" />
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.<ref name=":0">{{Cite journal|last1=Rennoll|first1=Valerie|last2=McLane|first2=Ian|last3=Emmanouilidou|first3=Dimitra|last4=West|first4=James|last5=Elhilali|first5=Mounya|date=2021|title=Electronic Stethoscope Filtering Mimics the Perceived Sound Characteristics of Acoustic Stethoscope|journal=IEEE Journal of Biomedical and Health Informatics|volume=25|issue=5|pages=1542–1549|doi=10.1109/JBHI.2020.3020494|issn=2168-2208|pmc=7917155|pmid=32870803}}</ref><ref name=":1">{{Cite journal|last1=Birring|first1=S. S.|last2=Fleming|first2=T.|last3=Matos|first3=S.|last4=Raj|first4=A. A.|last5=Evans|first5=D. H.|last6=Pavord|first6=I. D.|date=2008-05-01|title=The Leicester Cough Monitor: preliminary validation of an automated cough detection system in chronic cough|url=https://erj.ersjournals.com/content/31/5/1013|journal=European Respiratory Journal|language=en|volume=31|issue=5|pages=1013–1018|doi=10.1183/09031936.00057407|issn=0903-1936|pmid=18184683|s2cid=11219873}}</ref> 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.<ref>{{Cite journal|last1=Smith|first1=Jaclyn A.|last2=Ashurst|first2=H. Louise|last3=Jack|first3=Sandy|last4=Woodcock|first4=Ashley A.|last5=Earis|first5=John E.|date=2006-01-25|title=The description of cough sounds by healthcare professionals|url=https://doi.org/10.1186/1745-9974-2-1|journal=Cough|volume=2|issue=1|pages=1|doi=10.1186/1745-9974-2-1|issn=1745-9974|pmc=1413549|pmid=16436200}}</ref><ref name=":0" /><ref name=":1" />


== See also ==
== See also ==

Revision as of 13:18, 4 February 2022

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]

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 1800’s, 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]

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.[11]

Examples of Syndromic Surveillance

Instances of syndromic surveillance are easy to find. Examples include:[12]

  • Logs that record missed school or work due to illness[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

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, retrieved 2021-12-08
  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. 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. ^ 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. ^ 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.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  12. ^ 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.
  13. ^ 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.
  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. ^ Clinica Universidad de Navarra, Universidad de Navarra (2021-09-08). "Digital Acoustic Surveillance for Early Detection of Respiratory Disease Outbreaks: An Exploratory Observational Study in Navarra, Spain". Centre de Recherche du Centre Hospitalier de l'Université de Montréal. {{cite journal}}: Cite journal requires |journal= (help)
  18. ^ a b 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. ^ a b 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.{{cite journal}}: CS1 maint: unflagged free DOI (link)
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