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Varrelman TJ, Rader B, Remmel C, Tuli G, Han AR, Astley CM, Brownstein JS. Vaccine effectiveness against emerging COVID-19 variants using digital health data. COMMUNICATIONS MEDICINE 2024; 4:81. [PMID: 38710936 DOI: 10.1038/s43856-024-00508-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 04/24/2024] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND Participatory surveillance of self-reported symptoms and vaccination status can be used to supplement traditional public health surveillance and provide insights into vaccine effectiveness and changes in the symptoms produced by an infectious disease. The University of Maryland COVID Trends and Impact Survey provides an example of participatory surveillance that leveraged Facebook's active user base to provide self-reported symptom and vaccination data in near real-time. METHODS Here, we develop a methodology for identifying changes in vaccine effectiveness and COVID-19 symptomatology using the University of Maryland COVID Trends and Impact Survey data from three middle-income countries (Guatemala, Mexico, and South Africa). We implement conditional logistic regression to develop estimates of vaccine effectiveness conditioned on the prevalence of various definitions of self-reported COVID-like illness in lieu of confirmed diagnostic test results. RESULTS We highlight a reduction in vaccine effectiveness during Omicron-dominated waves of infections when compared to periods dominated by the Delta variant (median change across COVID-like illness definitions: -0.40, IQR[-0.45, -0.35]. Further, we identify a shift in COVID-19 symptomatology towards upper respiratory type symptoms (i.e., cough and sore throat) during Omicron periods of infections. Stratifying COVID-like illness by the National Institutes of Health's (NIH) description of mild and severe COVID-19 symptoms reveals a similar level of vaccine protection across different levels of COVID-19 severity during the Omicron period. CONCLUSIONS Participatory surveillance data alongside methodologies described in this study are particularly useful for resource-constrained settings where diagnostic testing results may be delayed or limited.
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Affiliation(s)
- Tanner J Varrelman
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, 02115, USA.
| | - Benjamin Rader
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Epidemiology, Boston University, Boston, MA, 02118, USA
| | - Christopher Remmel
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Gaurav Tuli
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Aimee R Han
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Christina M Astley
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, 02115, USA
- Division of Endocrinology, Boston Children's Hospital, Boston, MA, 02115, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - John S Brownstein
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, 02115, USA
- Harvard Medical School, Boston, MA, 02115, USA
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Mendoza K, Villalobos-Daniel VE, Jáuregui A, Valero-Morales I, Hernández-Alcaraz C, Zacarías-Alejandro N, Alarcon-Guevara RO, Barquera S. Development of a crowdsourcing- and gamification-based mobile application to collect epidemiological information and promote healthy lifestyles in Mexico. Sci Rep 2024; 14:6174. [PMID: 38486091 PMCID: PMC10940696 DOI: 10.1038/s41598-024-56761-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 03/11/2024] [Indexed: 03/18/2024] Open
Abstract
We developed a mobile application to promote healthy lifestyles and collect non-communicable disease (NCD) data in Mexico. Its theoretical foundations are supported by a framework-guided literature review. With design sprints, Scrum, Model-View-Controller, and Representational State Transfer architecture, we operationalized evidence-based nutrition/physical activity information into a crowdsourcing- and gamification-based application. The application was piloted for three months to monitor the response of 520 adults. Potential improvements were characterized, considering benchmarking, expert guidance, and standards. Salud Activa (English: Active Health) has two crowdsourcing modules: Nutritional scanner, scanning products' bar codes, providing nutritional data, and allowing new product registry feeding our databases; Surveys, comprising gradually-released NCD questions. Three intervention modules were generated: Drinks diary, a beverage assessment component to receive hydration recommendations; Step counter, monitoring users' steps via Google Fit/Health-iOS; Metabolic Avatar, interconnecting modules and changing as a function of beverage and step records. The 3-month median of Salud Activa use was seven days (IQR = 3-12), up to 35% of participants completed a Survey section, and 157 food products were registered through Nutritional scanner. Better customization might benefit usability and user engagement. Quantitative and qualitative data will enhance Salud Activa's design, user uptake, and efficacy in interventions delivered through this platform.
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Affiliation(s)
- Kenny Mendoza
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
- Center for Nutrition and Health Research (CINyS), National Institute of Public Health (INSP), Cuernavaca, Morelos, Mexico
| | - Víctor Eduardo Villalobos-Daniel
- Center for Nutrition and Health Research (CINyS), National Institute of Public Health (INSP), Cuernavaca, Morelos, Mexico
- Department of Noncommunicable Diseases and Mental Health, Pan American Health Organization, Washington, DC, USA
| | - Alejandra Jáuregui
- Center for Nutrition and Health Research (CINyS), National Institute of Public Health (INSP), Cuernavaca, Morelos, Mexico
| | - Isabel Valero-Morales
- Center for Nutrition and Health Research (CINyS), National Institute of Public Health (INSP), Cuernavaca, Morelos, Mexico
- Queen Mary University of London, London, UK
| | - César Hernández-Alcaraz
- Center for Nutrition and Health Research (CINyS), National Institute of Public Health (INSP), Cuernavaca, Morelos, Mexico
| | | | - Ricardo Omar Alarcon-Guevara
- Center for Nutrition and Health Research (CINyS), National Institute of Public Health (INSP), Cuernavaca, Morelos, Mexico
| | - Simón Barquera
- Center for Nutrition and Health Research (CINyS), National Institute of Public Health (INSP), Cuernavaca, Morelos, Mexico.
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3
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Trevisan G, Morris P, Silva GS, Nakkirt P, Wang C, Main R, Zimmerman J. Active Participatory Regional Surveillance for Notifiable Swine Pathogens. Animals (Basel) 2024; 14:233. [PMID: 38254402 PMCID: PMC10812401 DOI: 10.3390/ani14020233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 12/30/2023] [Accepted: 01/05/2024] [Indexed: 01/24/2024] Open
Abstract
We evaluated an active participatory design for the regional surveillance of notifiable swine pathogens based on testing 10 samples collected by farm personnel in each participating farm. To evaluate the performance of the design, public domain software was used to simulate the introduction and spread of a pathogen among 17,521 farms in a geographic region of 1,615,246 km2. Using the simulated pathogen spread data, the probability of detecting ≥ 1 positive farms in the region was estimated as a function of the percent of participating farms (20%, 40%, 60%, 80%, 100%), farm-level detection probability (10%, 20%, 30%, 40%, 50%), and regional farm-level prevalence. At 0.1% prevalence (18 positive farms among 17,521 farms) and a farm-level detection probability of 30%, the participatory surveillance design achieved 67%, 90%, and 97% probability of detecting ≥ 1 positive farms in the region when producer participation was 20%, 40%, and 60%, respectively. The cost analysis assumed that 10 individual pig samples per farm would be pooled into 2 samples (5 pigs each) for testing. Depending on the specimen collected (serum or swab sample) and test format (nucleic acid or antibody detection), the cost per round of sampling ranged from EUR 0.017 to EUR 0.032 (USD 0.017 to USD 0.034) per pig in the region. Thus, the analysis suggested that an active regional participatory surveillance design could achieve detection at low prevalence and at a sustainable cost.
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Affiliation(s)
- Giovani Trevisan
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Patterson Hall, 1800 Christensen Drive, Ames, IA 50011-1134, USA; (G.T.); (G.S.S.); (C.W.); (R.M.)
| | - Paul Morris
- Department of Statistics, College of Liberal Arts and Sciences, Iowa State University, Snedecor Hall, 2438 Osborn Drive, Ames, IA 50011-4009, USA; (P.M.); (P.N.)
| | - Gustavo S. Silva
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Patterson Hall, 1800 Christensen Drive, Ames, IA 50011-1134, USA; (G.T.); (G.S.S.); (C.W.); (R.M.)
| | - Pormate Nakkirt
- Department of Statistics, College of Liberal Arts and Sciences, Iowa State University, Snedecor Hall, 2438 Osborn Drive, Ames, IA 50011-4009, USA; (P.M.); (P.N.)
| | - Chong Wang
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Patterson Hall, 1800 Christensen Drive, Ames, IA 50011-1134, USA; (G.T.); (G.S.S.); (C.W.); (R.M.)
- Department of Statistics, College of Liberal Arts and Sciences, Iowa State University, Snedecor Hall, 2438 Osborn Drive, Ames, IA 50011-4009, USA; (P.M.); (P.N.)
| | - Rodger Main
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Patterson Hall, 1800 Christensen Drive, Ames, IA 50011-1134, USA; (G.T.); (G.S.S.); (C.W.); (R.M.)
| | - Jeffrey Zimmerman
- Department of Statistics, College of Liberal Arts and Sciences, Iowa State University, Snedecor Hall, 2438 Osborn Drive, Ames, IA 50011-4009, USA; (P.M.); (P.N.)
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Ben Moussa M, Rahal A, Lee L, Mukhi S. Syndromic surveillance performance in Canada throughout the COVID-19 pandemic, March 1, 2020 to March 4, 2023. CANADA COMMUNICABLE DISEASE REPORT = RELEVE DES MALADIES TRANSMISSIBLES AU CANADA 2023; 49:501-509. [PMID: 38504875 PMCID: PMC10946582 DOI: 10.14745/ccdr.v49i1112a06] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has highlighted the need for robust surveillance of respiratory viruses. Syndromic surveillance continues to be an important surveillance component recommended by the World Health Organization (WHO). While FluWatchers, Canada's syndromic surveillance system, has been in place since 2015, the COVID-19 pandemic provided a valuable opportunity to expand the program's scope and underlying technology infrastructure. Following some structural changes to FluWatchers syndromic questionnaire, participants are now able to contribute valuable data to the non-specific surveillance of respiratory virus activity across Canada. This article examines the performance of FluWatchers' syndromic surveillance over the three years of the COVID-19 pandemic in Canada. More specifically, this article examines FluWatchers' performance with respect to the correlation between the FluWatchers influenza-like illness (ILI) and acute respiratory infection (ARI) indicators and total respiratory virus detections (RVDs) in Canada, including influenza, respiratory syncytial virus (RSV), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and other respiratory viruses.
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Affiliation(s)
- Myriam Ben Moussa
- Centre for Emerging and Respiratory Infections and Pandemic Preparedness, Public Health Agency of Canada, Ottawa, ON
| | - Abbas Rahal
- Centre for Emerging and Respiratory Infections and Pandemic Preparedness, Public Health Agency of Canada, Ottawa, ON
| | - Liza Lee
- Centre for Emerging and Respiratory Infections and Pandemic Preparedness, Public Health Agency of Canada, Ottawa, ON
| | - Shamir Mukhi
- Canadian Network for Public Health Intelligence, National Microbiology Laboratory, Edmonton, AB
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5
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Atkins N, Harikar M, Duggan K, Zawiejska A, Vardhan V, Vokey L, Dozier M, de los Godos EF, Mcswiggan E, Mcquillan R, Theodoratou E, Shi T. What are the characteristics of participatory surveillance systems for influenza-like-illness? J Glob Health 2023; 13:04130. [PMID: 37856769 PMCID: PMC10587643 DOI: 10.7189/jogh.13.04130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023] Open
Abstract
Background Seasonal influenza causes significant morbidity and mortality, with an estimated 9.4 million hospitalisations and 290 000-650 000 respiratory related-deaths globally each year. Influenza can also cause mild illness, which is why not all symptomatic persons might necessarily be tested for influenza. To monitor influenza activity, healthcare facility-based syndromic surveillance for influenza-like illness is often implemented. Participatory surveillance systems for influenza-like illness (ILI) play an important role in influenza surveillance and can complement traditional facility-based surveillance systems to provide real-time estimates of influenza-like illness activity. However, such systems differ in designs between countries and contexts, making it necessary to identify their characteristics to better understand how they fit traditional surveillance systems. Consequently, we aimed to investigate the performance of participatory surveillance systems for ILI worldwide. Methods We systematically searched four databases for relevant articles on influenza participatory surveillance systems for ILI. We extracted data from the included, eligible studies and assessed their quality using the Joanna Briggs Critical Appraisal Tools. We then synthesised the findings using narrative synthesis. Results We included 39 out of 3797 retrieved articles for analysis. We identified 26 participatory surveillance systems, most of which sought to capture the burden and trends of influenza-like illness and acute respiratory infections among cohorts with risk factors for influenza-like illness. Of all the surveillance system attributes assessed, 52% reported on correlation with other surveillance systems, 27% on representativeness, and 21% on acceptability. Among studies that reported these attributes, all systems were rated highly in terms of simplicity, flexibility, sensitivity, utility, and timeliness. Most systems (87.5%) were also well accepted by users, though participation rates varied widely. However, despite their potential for greater reach and accessibility, most systems (90%) fared poorly in terms of representativeness of the population. Stability was a concern for some systems (60%), as was completeness (50%). Conclusions The analysis of participatory surveillance system attributes showed their potential in providing timely and reliable influenza data, especially in combination with traditional hospital- and laboratory led-surveillance systems. Further research is needed to design future systems with greater uptake and utility.
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Affiliation(s)
- Nadege Atkins
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
- Joint first authorship
| | - Mandara Harikar
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
- Joint first authorship
| | - Kirsten Duggan
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Agnieszka Zawiejska
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Vaishali Vardhan
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Laura Vokey
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Marshall Dozier
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Emma F de los Godos
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
- Equal contribution
| | - Emilie Mcswiggan
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Ruth Mcquillan
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
- Equal contribution
| | - Evropi Theodoratou
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
- Equal contribution
| | - Ting Shi
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- Equal contribution
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6
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Francis SD, Mwima G, Lethoko M, Chang C, Farley SM, Asiimwe F, Chen Q, West C, Greenleaf AR. Comparison of Influenza-Like Illness (ILI) incidence data from the novel LeCellPHIA participatory surveillance system with COVID-19 case count data, Lesotho, July 2020 - July 2021. BMC Infect Dis 2023; 23:688. [PMID: 37845641 PMCID: PMC10577929 DOI: 10.1186/s12879-023-08664-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 10/03/2023] [Indexed: 10/18/2023] Open
Abstract
BACKGROUND While laboratory testing for infectious diseases such as COVID-19 is the surveillance gold standard, it is not always feasible, particularly in settings where resources are scarce. In the small country of Lesotho, located in sub-Saharan Africa, COVID-19 testing has been limited, thus surveillance data available to local authorities are limited. The goal of this study was to compare a participatory influenza-like illness (ILI) surveillance system in Lesotho with COVID-19 case count data, and ultimately to determine whether the participatory surveillance system adequately estimates the case count data. METHODS A nationally-representative sample was called on their mobile phones weekly to create an estimate of incidence of ILI between July 2020 and July 2021. Case counts from the website Our World in Data (OWID) were used as the gold standard to which our participatory surveillance data were compared. We calculated Spearman's and Pearson's correlation coefficients to compare the weekly incidence of ILI reports to COVID-19 case count data. RESULTS Over course of the study period, an ILI symptom was reported 1,085 times via participatory surveillance for an average annual cumulative incidence of 45.7 per 100 people (95% Confidence Interval [CI]: 40.7 - 51.4). The cumulative incidence of reports of ILI symptoms was similar among males (46.5, 95% CI: 39.6 - 54.4) and females (45.1, 95% CI: 39.8 - 51.1). There was a slightly higher annual cumulative incidence of ILI among persons living in peri-urban (49.5, 95% CI: 31.7 - 77.3) and urban settings compared to rural areas. The January peak of the participatory surveillance system ILI estimates correlated significantly with the January peak of the COVID-19 case count data (Spearman's correlation coefficient = 0.49; P < 0.001) (Pearson's correlation coefficient = 0.67; P < 0.0001). CONCLUSIONS The ILI trends captured by the participatory surveillance system in Lesotho mirrored trends of the COVID-19 case count data from Our World in Data. Public health practitioners in geographies that lack the resources to conduct direct surveillance of infectious diseases may be able to use cell phone-based data collection to monitor trends.
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Affiliation(s)
- Sarah D Francis
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, USA.
| | | | | | | | - Shannon M Farley
- ICAP at Columbia, New York, USA
- Department of Population and Family Health, Mailman School of Public Health, Columbia University, New York, USA
| | | | - Qixuan Chen
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, USA
| | - Christine West
- Centers for Disease Control (CDC), Atlanta Global Health Center/Division of Global HIV and TB, Atlanta, USA
| | - Abigail R Greenleaf
- ICAP at Columbia, New York, USA
- Department of Population and Family Health, Mailman School of Public Health, Columbia University, New York, USA
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Tamayo LD, Condori-Pino CE, Sanchez Z, Gonçalves R, Málaga Chávez FS, Castillo-Neyra R, Levy MZ, Paz-Soldan VA. An effective internet-based system for surveillance and elimination of triatomine insects: AlertaChirimacha. PLoS Negl Trop Dis 2023; 17:e0011694. [PMID: 37844066 PMCID: PMC10602375 DOI: 10.1371/journal.pntd.0011694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 10/26/2023] [Accepted: 10/02/2023] [Indexed: 10/18/2023] Open
Abstract
Vector-borne diseases remain a significant public health threat in many regions of the world. Traditional vector surveillance and control methods have relied on active and passive surveillance programs, which are often costly and time-consuming. New internet-based vector surveillance systems have shown promise in removing some of the cost and labor burden from health authorities. We developed and evaluated the effectiveness of a new internet-based surveillance system, "AlertaChirimacha", for detecting Triatoma infestans (known locally by its Quechua name, Chirimacha), the Chagas disease vector, in the city of Arequipa, Peru. In the first 26 months post-implementation, AlertaChirimacha received 206 reports of residents suspecting or fearing triatomines in their homes or neighborhoods, of which we confirmed, through pictures or inspections, 11 (5.3%) to be Triatoma infestans. After microscopic examination, none of the specimens collected were infected with Trypanosoma cruzi. AlertaChirimacha received 57% more confirmed reports than the traditional surveillance system and detected 10% more infested houses than active and passive surveillance approaches combined. Through in-depth interviews we evaluate the reach, bilateral engagement, and response promptness and efficiency of AlertaChirimacha. Our study highlights the potential of internet-based vector surveillance systems, such as AlertaChirimacha, to improve vector surveillance and control efforts in resource-limited settings. This approach could decrease the cost and time horizon for the elimination of vector-mediated Chagas disease in the region.
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Affiliation(s)
- Laura D. Tamayo
- Zoonotic Disease Research Laboratory, One Health Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Arequipa, Perú
| | - Carlos E. Condori-Pino
- Zoonotic Disease Research Laboratory, One Health Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Arequipa, Perú
| | - Zoee Sanchez
- Department of Tropical Medicine and Infectious Disease, Tulane University, School of Public Health and Tropical Medicine, New Orleans, Lousiana, United States of America
| | - Raquel Gonçalves
- Zoonotic Disease Research Laboratory, One Health Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Arequipa, Perú
| | | | - Ricardo Castillo-Neyra
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Michael Z. Levy
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Valerie A. Paz-Soldan
- Department of Tropical Medicine and Infectious Disease, Tulane University, School of Public Health and Tropical Medicine, New Orleans, Lousiana, United States of America
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8
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Tseng YJ, Olson KL, Bloch D, Mandl KD. Engaging a national-scale cohort of smart thermometer users in participatory surveillance. NPJ Digit Med 2023; 6:175. [PMID: 37730764 PMCID: PMC10511532 DOI: 10.1038/s41746-023-00917-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 09/04/2023] [Indexed: 09/22/2023] Open
Abstract
Participatory surveillance systems crowdsource individual reports to rapidly assess population health phenomena. The value of these systems increases when more people join and persistently contribute. We examine the level of and factors associated with engagement in participatory surveillance among a retrospective, national-scale cohort of individuals using smartphone-connected thermometers with a companion app that allows them to report demographic and symptom information. Between January 1, 2020 and October 29, 2022, 1,325,845 participants took 20,617,435 temperature readings, yielding 3,529,377 episodes of consecutive readings. There were 1,735,805 (49.2%) episodes with self-reported symptoms (including reports of no symptoms). Compared to before the pandemic, participants were more likely to report their symptoms during pandemic waves, especially after the winter wave began (September 13, 2020) (OR across pandemic periods range from 3.0 to 4.0). Further, symptoms were more likely to be reported during febrile episodes (OR = 2.6, 95% CI = 2.6-2.6), and for new participants, during their first episode (OR = 2.4, 95% CI = 2.4-2.5). Compared with participants aged 50-65 years old, participants over 65 years were less likely to report their symptoms (OR = 0.3, 95% CI = 0.3-0.3). Participants in a household with both adults and children (OR = 1.6 [1.6-1.7]) were more likely to report symptoms. We find that the use of smart thermometers with companion apps facilitates the collection of data on a large, national scale, and provides real time insight into transmissible disease phenomena. Nearly half of individuals using these devices are willing to report their symptoms after taking their temperature, although participation varies among individuals and over pandemic stages.
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Affiliation(s)
- Yi-Ju Tseng
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Karen L Olson
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | | | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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9
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Leal Neto O, Paolotti D, Dalton C, Carlson S, Susumpow P, Parker M, Phetra P, Lau EHY, Colizza V, Jan van Hoek A, Kjelsø C, Brownstein JS, Smolinski MS. Enabling Multicentric Participatory Disease Surveillance for Global Health Enhancement: Viewpoint on Global Flu View. JMIR Public Health Surveill 2023; 9:e46644. [PMID: 37490846 PMCID: PMC10504624 DOI: 10.2196/46644] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/21/2023] [Accepted: 07/25/2023] [Indexed: 07/27/2023] Open
Abstract
Participatory surveillance (PS) has been defined as the bidirectional process of transmitting and receiving data for action by directly engaging the target population. Often represented as self-reported symptoms directly from the public, PS can provide evidence of an emerging disease or concentration of symptoms in certain areas, potentially identifying signs of an early outbreak. The construction of sets of symptoms to represent various disease syndromes provides a mechanism for the early detection of multiple health threats. Global Flu View (GFV) is the first-ever system that merges influenza-like illness (ILI) data from more than 8 countries plus 1 region (Hong Kong) on 4 continents for global monitoring of this annual health threat. GFV provides a digital ecosystem for spatial and temporal visualization of syndromic aggregates compatible with ILI from the various systems currently participating in GFV in near real time, updated weekly. In 2018, the first prototype of a digital platform to combine data from several ILI PS programs was created. At that time, the priority was to have a digital environment that brought together different programs through an application program interface, providing a real time map of syndromic trends that could demonstrate where and when ILI was spreading in various regions of the globe. After 2 years running as an experimental model and incorporating feedback from partner programs, GFV was restructured to empower the community of public health practitioners, data scientists, and researchers by providing an open data channel among these contributors for sharing experiences across the network. GFV was redesigned to serve not only as a data hub but also as a dynamic knowledge network around participatory ILI surveillance by providing knowledge exchange among programs. Connectivity between existing PS systems enables a network of cooperation and collaboration with great potential for continuous public health impact. The exchange of knowledge within this network is not limited only to health professionals and researchers but also provides an opportunity for the general public to have an active voice in the collective construction of health settings. The focus on preparing the next generation of epidemiologists will be of great importance to scale innovative approaches like PS. GFV provides a useful example of the value of globally integrated PS data to help reduce the risks and damages of the next pandemic.
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Affiliation(s)
- Onicio Leal Neto
- Ending Pandemics, San Francisco, CA, United States
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | | | | | | | | | | | | | - Eric H Y Lau
- School of Public Health, University of Hong Kong, Hong Kong, China
| | - Vittoria Colizza
- Pierre Louis Institute of Epidemiology and Public Health, INSERM, Sorbonne Université, Paris, France
| | - Albert Jan van Hoek
- National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | | | - John S Brownstein
- Boston Children Hospital, Harvard University, Boston, MA, United States
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Tseng YJ, Olson KL, Bloch D, Mandl KD. Smart Thermometer-Based Participatory Surveillance to Discern the Role of Children in Household Viral Transmission During the COVID-19 Pandemic. JAMA Netw Open 2023; 6:e2316190. [PMID: 37261828 PMCID: PMC10236238 DOI: 10.1001/jamanetworkopen.2023.16190] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/18/2023] [Indexed: 06/02/2023] Open
Abstract
Importance Children's role in spreading virus during the COVID-19 pandemic is yet to be elucidated, and measuring household transmission traditionally requires contact tracing. Objective To discern children's role in household viral transmission during the pandemic when enveloped viruses were at historic lows and the predominance of viral illnesses were attributed to COVID-19. Design, Setting, and Participants This cohort study of a voluntary US cohort tracked data from participatory surveillance using commercially available thermometers with a companion smartphone app from October 2019 to October 2022. Eligible participants were individuals with temperature measurements in households with multiple members between October 2019 and October 2022 who opted into data sharing. Main Outcomes and Measures Proportion of household transmissions with a pediatric index case and changes in transmissions during school breaks were assessed using app and thermometer data. Results A total of 862 577 individuals from 320 073 households with multiple participants (462 000 female [53.6%] and 463 368 adults [53.7%]) were included. The number of febrile episodes forecast new COVID-19 cases. Within-household transmission was inferred in 54 506 (15.4%) febrile episodes and increased from the fourth pandemic period, March to July 2021 (3263 of 32 294 [10.1%]) to the Omicron BA.1/BA.2 wave (16 516 of 94 316 [17.5%]; P < .001). Among 38 787 transmissions in 166 170 households with adults and children, a median (IQR) 70.4% (61.4%-77.6%) had a pediatric index case; proportions fluctuated weekly from 36.9% to 84.6%. A pediatric index case was 0.6 to 0.8 times less frequent during typical school breaks. The winter break decrease was from 68.4% (95% CI, 57.1%-77.8%) to 41.7% (95% CI, 34.3%-49.5%) at the end of 2020 (P < .001). At the beginning of 2022, it dropped from 80.3% (95% CI, 75.1%-84.6%) to 54.5% (95% CI, 51.3%-57.7%) (P < .001). During summer breaks, rates dropped from 81.4% (95% CI, 74.0%-87.1%) to 62.5% (95% CI, 56.3%-68.3%) by August 2021 (P = .02) and from 83.8% (95% CI, 79.2%-87.5) to 62.8% (95% CI, 57.1%-68.1%) by July 2022 (P < .001). These patterns persisted over 2 school years. Conclusions and Relevance In this cohort study using participatory surveillance to measure within-household transmission at a national scale, we discerned an important role for children in the spread of viral infection within households during the COVID-19 pandemic, heightened when schools were in session, supporting a role for school attendance in COVID-19 spread.
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Affiliation(s)
- Yi-Ju Tseng
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Karen L. Olson
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
| | | | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
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11
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Sarker F, Chowdhury MH, Ratul IJ, Islam S, Mamun KA. An interactive national digital surveillance system to fight against COVID-19 in Bangladesh. Front Digit Health 2023; 5:1059446. [PMID: 37250527 PMCID: PMC10210141 DOI: 10.3389/fdgth.2023.1059446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 03/06/2023] [Indexed: 05/31/2023] Open
Abstract
Background COVID-19 has affected many people globally, including in Bangladesh. Due to a lack of preparedness and resources, Bangladesh has experienced a catastrophic health crisis, and the devastation caused by this deadly virus has not yet been halted. Hence, precise and rapid diagnostics and infection tracing are essential for managing the condition and limiting its spread. The conventional screening procedure, such as reverse transcription polymerase chain reaction (RT-PCR), is not available in most rural areas and is time-consuming. Therefore, a data-driven intelligent surveillance system can be advantageous for rapid COVID-19 screening and risk estimation. Objectives This study describes the design, development, implementation, and characteristics of a nationwide web-based surveillance system for educating, screening, and tracking COVID-19 at the community level in Bangladesh. Methods The system consists of a mobile phone application and a cloud server. The data is collected by community health professionals via home visits or telephone calls and analyzed using rule-based artificial intelligence (AI). Depending on the results of the screening procedure, a further decision is made regarding the patient. This digital surveillance system in Bangladesh provides a platform to support government and non-government organizations, including health workers and healthcare facilities, in identifying patients at risk of COVID-19. It refers people to the nearest government healthcare facility, collecting and testing samples, tracking and tracing positive cases, following up with patients, and documenting patient outcomes. Results This study began in April 2020, and the results are provided in this paper till December 2022. The system has successfully completed 1,980,323 screenings. Our rule-based AI model categorized them into five separate risk groups based on the acquired patient information. According to the data, around 51% of the overall screened populations are safe, 35% are low risk, 9% are high risk, 4% are mid risk, and the remaining 1% is very high risk. The dashboard integrates all collected data from around the nation onto a single platform. Conclusion This screening can help the symptomatic patient take immediate action, such as isolation or hospitalization, depending on the severity. This surveillance system can also be utilized for risk mapping, planning, and allocating health resources to more vulnerable areas to reduce the virus's severity.
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Affiliation(s)
- Farhana Sarker
- CMED Health Ltd., Dhaka, Bangladesh
- Department of CSE, University of Liberal Arts, Dhaka, Bangladesh
| | | | - Ishrak Jahan Ratul
- Advanced Intelligent Multidisciplinary Systems Lab (AIMS Lab), United International University, Dhaka, Bangladesh
| | - Shariful Islam
- School of Exercise & Nutrition Sciences, Faculty of Health, Deakin University, Burwood, VIC, Australia
| | - Khondaker A. Mamun
- CMED Health Ltd., Dhaka, Bangladesh
- Advanced Intelligent Multidisciplinary Systems Lab (AIMS Lab), United International University, Dhaka, Bangladesh
- Department of CSE, United International University, Dhaka, Bangladesh
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12
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Shausan A, Nazarathy Y, Dyda A. Emerging data inputs for infectious diseases surveillance and decision making. Front Digit Health 2023; 5:1131731. [PMID: 37082524 PMCID: PMC10111015 DOI: 10.3389/fdgth.2023.1131731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 03/20/2023] [Indexed: 04/07/2023] Open
Abstract
Infectious diseases create a significant health and social burden globally and can lead to outbreaks and epidemics. Timely surveillance for infectious diseases is required to inform both short and long term public responses and health policies. Novel data inputs for infectious disease surveillance and public health decision making are emerging, accelerated by the COVID-19 pandemic. These include the use of technology-enabled physiological measurements, crowd sourcing, field experiments, and artificial intelligence (AI). These technologies may provide benefits in relation to improved timeliness and reduced resource requirements in comparison to traditional methods. In this review paper, we describe current and emerging data inputs being used for infectious disease surveillance and summarize key benefits and limitations.
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Affiliation(s)
- Aminath Shausan
- School of Public Health, The University of Queensland, Brisbane, QLD, Australia
- School of Mathematics and Physics, The University of Queensland, Brisbane, QLD, Australia
| | - Yoni Nazarathy
- School of Mathematics and Physics, The University of Queensland, Brisbane, QLD, Australia
| | - Amalie Dyda
- School of Public Health, The University of Queensland, Brisbane, QLD, Australia
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13
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Wittwer S, Paolotti D, Lichand G, Leal Neto O. Participatory surveillance for COVID-19 trends detection in Brazil: Cross-section study. JMIR Public Health Surveill 2023; 9:e44517. [PMID: 36888908 PMCID: PMC10138922 DOI: 10.2196/44517] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/25/2023] [Accepted: 03/07/2023] [Indexed: 03/09/2023] Open
Abstract
BACKGROUND The ongoing COVID-19 pandemic has emphasized the necessity of a well-functioning surveillance system to detect and mitigate disease outbreaks. Traditional surveillance (TS) usually relies on healthcare providers and generally suffers from reporting lags that prevent immediate response plans. Participatory surveillance (PS), an innovative digital approach whereby individuals voluntarily monitor and report on their own health status via Web-based surveys, has emerged in the past decade to complement traditional data collections approaches. OBJECTIVE This study compares novel PS data on COVID-19 infection rates across nine Brazilian cities with official TS data to examine the opportunities and challenges of using the former, and the potential advantages of combining the two approaches. METHODS The traditional surveillance data for Brazil, prospectively called the TS data, is publicly accessible on GitHub. The participatory surveillance data was collected through the Brazil Sem Corona - a Colab platform. To gather information on an individual's health status, each participant was asked to fill out a daily questionnaire into the Colab app on symptoms as well as exposure. RESULTS We find that high participation rates are key for PS data to adequately mirror TS infection rates. Where participation was high, we document a significant trend correlation between lagged PS data and TS infection rates, suggesting that the former could be used for early detection. In our data, forecasting models integrating both approaches increased accuracy up to 3% relative to a 14-day forecast horizon model based exclusively on TS data. Furthermore, we show that the PS data captures a population that significantly differs from the traditional observation. CONCLUSIONS In the traditional system, the new recorded COVID-19 cases per day are aggregated based on positive lab-confirmed tests. In contrast, the PS data shows a significant share of reports categorized as potential COVID-19 case that are not lab-confirmed. Quantifying the economic value of a PS system implementation remains hard. But scarce public funds as well as persisting constraints to the TS system motivate for a PS system, making it an important avenue for future research. The decision to set up a PS system requires careful evaluation of its expected benefits, relative to the costs of setting up platforms and incentivizing engagement to increase both coverage and consistent reporting over time. The ability to compute such economic trade-offs might be key to have PS become a more integral part of policy toolkits moving forward. These results corroborate previous studies when it comes to the benefits of an integrated and comprehensive surveillance system, but also shed lights on its limitations, and on the need for additional research to improve future implementations of PS platforms. CLINICALTRIAL
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Affiliation(s)
- Salome Wittwer
- Department of Economics, University of Zurich, Schönberggasse 1, Zurich, CH
| | - Daniela Paolotti
- Data Science for Social Impact and Sustainability, ISI Foundation, Turin, IT
| | - Guilherme Lichand
- Department of Economics, University of Zurich, Schönberggasse 1, Zurich, CH
| | - Onicio Leal Neto
- Department of Computer Science, ETH Zürich, Universitätstrasse 6, Zurich, CH
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14
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Diwan V, Sharma U, Ganeshkumar P, Thangaraj JWV, Muthappan S, Venkatasamy V, Parashar V, Soni P, Garg A, Pawar NS, Pathak A, Purohit MR, Madhanraj K, Hulth A, Ponnaiah M. Syndromic surveillance system during mass gathering of Panchkroshi Yatra festival, Ujjain, Madhya Pradesh, India. New Microbes New Infect 2023; 52:101097. [PMID: 36864894 PMCID: PMC9971318 DOI: 10.1016/j.nmni.2023.101097] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 01/29/2023] [Accepted: 02/02/2023] [Indexed: 02/11/2023] Open
Abstract
Background The health implications surrounding a mass gathering pose significant challenges to public health officials. The use of syndromic surveillance provides an ideal method for achieving the public health goals and objectives at such events. In the absence of published reports of systematic documentation of public health preparedness in mass gatherings in the local context, we describe the public health preparedness and demonstrate the operational feasibility of a tablet-based participatory syndromic surveillance among pilgrims during the annual ritual circumambulation- Panchkroshi Yatra. Methods A real-time surveillance system was established from 2017-2019 to capture all the health consultations done at the designated points (medical camps) in the Panchkroshi yatra area of the city Ujjain in Madhya Pradesh. We also surveyed a subset of pilgrims in 2017 to gauge satisfaction with the public health measures such as sanitation, water, safety, food, and cleanliness. Results In 2019, injuries were reported in the highest proportion (16.7%; 794/4744); most numbers of fever cases (10.6%; 598/5600) were reported in 2018, while 2017 saw the highest number of patient presentations of abdominal pain (7.73%; 498/6435). Conclusion Public health and safety measures were satisfactory except for the need for setting up urinals along the fixed route of the circumambulation. A systematic data collection of selected symptoms among yatris and their surveillance through tablet could be established during the panchkroshi yatra, which can complement the existing surveillance for detecting early warning signals. We recommend the implementation of such tablet-based surveillance during such mass gathering events.
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Affiliation(s)
- Vishal Diwan
- ICMR- National Institute for Research in Environmental Health, Bhopal, India,Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden,Corresponding author. ICMR- National Institute for Research in Environmental Health, Bhopal, India.
| | - Upasana Sharma
- ICMR- National Institute of Epidemiology, Chennai, India
| | | | | | | | | | | | | | - Ankit Garg
- R.D Gardi Medical College, Ujjain, India
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15
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Suy Lan C, Sok S, Chheang K, Lan DM, Soung V, Divi N, Ly S, Smolinski M. Cambodia national health hotline - Participatory surveillance for early detection and response to disease outbreaks. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2022; 29:100584. [PMID: 36605884 PMCID: PMC9808424 DOI: 10.1016/j.lanwpc.2022.100584] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Affiliation(s)
- Channé Suy Lan
- InSTEDD iLab Southeast Asia, Phnom Penh, Cambodia
- Corresponding author.
| | - Samnang Sok
- Communicable Disease Control Department, Ministry of Health, Cambodia
| | | | | | | | | | - Sovann Ly
- Communicable Disease Control Department, Ministry of Health, Cambodia
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16
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Herbert C, Shi Q, Kheterpal V, Nowak C, Suvarna T, Durnan B, Schrader S, Behar S, Naeem S, Tarrant S, Kalibala B, Singh A, Gerber B, Barton B, Lin H, Cohen-Wolkowiez M, Corbie-Smith G, Kibbe W, Marquez J, Baek J, Hafer N, Gibson L, O’Connor L, Broach J, Heetderks W, McManus D, Soni A. Use of a Digital Assistant to Report COVID-19 Rapid Antigen Self-test Results to Health Departments in 6 US Communities. JAMA Netw Open 2022; 5:e2228885. [PMID: 36018589 PMCID: PMC9419013 DOI: 10.1001/jamanetworkopen.2022.28885] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 07/06/2022] [Indexed: 11/14/2022] Open
Abstract
Importance Widespread distribution of rapid antigen tests is integral to the US strategy to address COVID-19; however, it is estimated that few rapid antigen test results are reported to local departments of health. Objective To characterize how often individuals in 6 communities throughout the United States used a digital assistant to log rapid antigen test results and report them to their local departments of health. Design, Setting, and Participants This prospective cohort study is based on anonymously collected data from the beneficiaries of the Say Yes! Covid Test program, which distributed more than 3 000 000 rapid antigen tests at no cost to residents of 6 communities (Louisville, Kentucky; Indianapolis, Indiana; Fulton County, Georgia; O'ahu, Hawaii; Ann Arbor and Ypsilanti, Michigan; and Chattanooga, Tennessee) between April and October 2021. A descriptive evaluation of beneficiary use of a digital assistant for logging and reporting their rapid antigen test results was performed. Interventions Widespread community distribution of rapid antigen tests. Main Outcomes and Measures Number and proportion of tests logged and reported to the local department of health through the digital assistant. Results A total of 313 000 test kits were distributed, including 178 785 test kits that were ordered using the digital assistant. Among all distributed kits, 14 398 households (4.6%) used the digital assistant, but beneficiaries reported three-quarters of their rapid antigen test results to their state public health departments (30 965 tests reported of 41 465 total test results [75.0%]). The reporting behavior varied by community and was significantly higher among communities that were incentivized for reporting test results vs those that were not incentivized or partially incentivized (90.5% [95% CI, 89.9%-91.2%] vs 70.5%; [95% CI, 70.0%-71.0%]). In all communities, positive tests were less frequently reported than negative tests (60.4% [95% CI, 58.1%-62.8%] vs 75.5% [95% CI, 75.1%-76.0%]). Conclusions and Relevance These results suggest that application-based reporting with incentives may be associated with increased reporting of rapid tests for COVID-19. However, increasing the adoption of the digital assistant may be a critical first step.
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Affiliation(s)
- Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Qiming Shi
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester
- Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester
| | | | | | | | | | | | - Stephanie Behar
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Syed Naeem
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Seanan Tarrant
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Ben Kalibala
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Aditi Singh
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Ben Gerber
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester
| | - Honghuang Lin
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | | | - Giselle Corbie-Smith
- Center for Health Equity Research, Department of Social Medicine, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill
| | - Warren Kibbe
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina
| | - Juan Marquez
- Washtenaw County Health Department, Washtenaw, Michigan
| | - Jonggyu Baek
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester
| | - Nathaniel Hafer
- Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester
| | - Laura Gibson
- Division of Infectious Disease, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Laurel O’Connor
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester
| | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, via contract with Kelly Services, Bethesda, Maryland
| | - David McManus
- Division of Cardiology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Apurv Soni
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester
- Division of Clinical Informatics, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
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Ghazvini K, Keikha M. Social networks and human monkeypox outbreak 2022: Hazards and opportunities - Correspondence. Int J Surg 2022; 104:106831. [PMID: 35961495 PMCID: PMC9534009 DOI: 10.1016/j.ijsu.2022.106831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 08/05/2022] [Indexed: 11/25/2022]
Affiliation(s)
- Kiarash Ghazvini
- Antimicrobial Resistance Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Microbiology and Virology, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Masoud Keikha
- Antimicrobial Resistance Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Microbiology and Virology, Mashhad University of Medical Sciences, Mashhad, Iran.
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19
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Smartphone apps in the COVID-19 pandemic. Nat Biotechnol 2022; 40:1013-1022. [PMID: 35726090 DOI: 10.1038/s41587-022-01350-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 05/04/2022] [Indexed: 01/08/2023]
Abstract
At the beginning of the COVID-19 pandemic, analog tools such as nasopharyngeal swabs for PCR tests were center stage and the major prevention tactics of masking and physical distancing were a throwback to the 1918 influenza pandemic. Overall, there has been scant regard for digital tools, particularly those based on smartphone apps, which is surprising given the ubiquity of smartphones across the globe. Smartphone apps, given accessibility in the time of physical distancing, were widely used for tracking, tracing and educating the public about COVID-19. Despite limitations, such as concerns around data privacy, data security, digital health illiteracy and structural inequities, there is ample evidence that apps are beneficial for understanding outbreak epidemiology, individual screening and contact tracing. While there were successes and failures in each category, outbreak epidemiology and individual screening were substantially enhanced by the reach of smartphone apps and accessory wearables. Continued use of apps within the digital infrastructure promises to provide an important tool for rigorous investigation of outcomes both in the ongoing outbreak and in future epidemics.
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Tan YR, Agrawal A, Matsoso MP, Katz R, Davis SLM, Winkler AS, Huber A, Joshi A, El-Mohandes A, Mellado B, Mubaira CA, Canlas FC, Asiki G, Khosa H, Lazarus JV, Choisy M, Recamonde-Mendoza M, Keiser O, Okwen P, English R, Stinckwich S, Kiwuwa-Muyingo S, Kutadza T, Sethi T, Mathaha T, Nguyen VK, Gill A, Yap P. A call for citizen science in pandemic preparedness and response: beyond data collection. BMJ Glob Health 2022; 7:bmjgh-2022-009389. [PMID: 35760438 PMCID: PMC9237878 DOI: 10.1136/bmjgh-2022-009389] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 06/10/2022] [Indexed: 12/16/2022] Open
Abstract
The COVID-19 pandemic has underlined the need to partner with the community in pandemic preparedness and response in order to enable trust-building among stakeholders, which is key in pandemic management. Citizen science, defined here as a practice of public participation and collaboration in all aspects of scientific research to increase knowledge and build trust with governments and researchers, is a crucial approach to promoting community engagement. By harnessing the potential of digitally enabled citizen science, one could translate data into accessible, comprehensible and actionable outputs at the population level. The application of citizen science in health has grown over the years, but most of these approaches remain at the level of participatory data collection. This narrative review examines citizen science approaches in participatory data generation, modelling and visualisation, and calls for truly participatory and co-creation approaches across all domains of pandemic preparedness and response. Further research is needed to identify approaches that optimally generate short-term and long-term value for communities participating in population health. Feasible, sustainable and contextualised citizen science approaches that meaningfully engage affected communities for the long-term will need to be inclusive of all populations and their cultures, comprehensive of all domains, digitally enabled and viewed as a key component to allow trust-building among the stakeholders. The impact of COVID-19 on people’s lives has created an opportune time to advance people’s agency in science, particularly in pandemic preparedness and response.
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Affiliation(s)
- Yi-Roe Tan
- International Digital Health & AI Research Collaborative (I-DAIR), Geneva, Switzerland
| | - Anurag Agrawal
- Trivedi School of Biosciences, Ashoka University, Sonepath, Haryana, India
| | - Malebona Precious Matsoso
- Pharmacy & Pharmacology, University of Witwatersrand, Member of IPPPR, Johannesburg-Braamfontein, South Africa
| | - Rebecca Katz
- Center for Global Health Science and Security, Georgetown University, Washington, District of Columbia, USA
| | - Sara L M Davis
- Global Health Centre, Graduate Institute Geneva, Geneva, Switzerland
| | - Andrea Sylvia Winkler
- Center for Global Health, Department of Neurology, Technical University of Munich, Munchen, Germany.,Centre for Global Health, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Annalena Huber
- Center for Global Health, Department of Neurology, Technical University of Munich, Munchen, Germany
| | - Ashish Joshi
- Graduate School of Public Health and Health Policy, City University of New York, New York, New York, USA
| | - Ayman El-Mohandes
- Graduate School of Public Health and Health Policy, City University of New York, New York, New York, USA
| | - Bruce Mellado
- School of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa.,Subatomic Physics, iThemba Laboratory for Accelerator Based Sciences, Somerset West, South Africa
| | | | | | - Gershim Asiki
- African Population and Health Research Center, Nairobi, Kenya
| | - Harjyot Khosa
- International Planned Parenthood Federation, New Delhi, India
| | - Jeffrey Victor Lazarus
- Hospital Cliínic, University of Barcelona, Instituto de Salud Global de Barcelona, Barcelona, Spain
| | - Marc Choisy
- Centre for Tropical Medicine and Global Health, Univerity of Oxford Nuffield Department of Medicine, Oxford, Oxfordshire, UK.,Oxford University Clinical Research Unit, Ho Chi Minh City, Ho Chi MInh, Viet Nam
| | - Mariana Recamonde-Mendoza
- Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.,Bioinformatics Core, HCPA, Porto Alegre, Brazil
| | - Olivia Keiser
- Institute of Global Health, Universite de Geneve, Geneva, GE, Switzerland
| | | | - Rene English
- Division of Health Systems and Public Health, Department of Global Health, Stellenbosch University Faculty of Medicine and Health Sciences, Cape Town, Western Cape, South Africa
| | | | | | - Tariro Kutadza
- Zimbabwe National Network of People Living with HIV (ZNNP+), Harare, Zimbabwe
| | - Tavpritesh Sethi
- Computational Biology, Indraprastha Institute of Information Technology Delhi, New Delhi, Delhi, India
| | - Thuso Mathaha
- School of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa
| | - Vinh Kim Nguyen
- Global Health Centre, Graduate Institute Geneva, Geneva, Switzerland
| | - Amandeep Gill
- International Digital Health & AI Research Collaborative (I-DAIR), Geneva, Switzerland
| | - Peiling Yap
- International Digital Health & AI Research Collaborative (I-DAIR), Geneva, Switzerland
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Herbert C, Shi Q, Kheterpal V, Nowak C, Suvarna T, Durnam B, Schrader S, Behar S, Naeem S, Tarrant S, Kalibala B, Singh A, Gerber B, Barton B, Lin H, Cohen-Wolkowiez M, Corbie-Smith G, Kibbe W, Marquez J, Baek J, Hafer N, Gibson L, O'Connor L, Broach J, Heetderks W, McManus D, Soni A. If you build it, will they use it? Use of a Digital Assistant for Self-Reporting of COVID-19 Rapid Antigen Test Results during Large Nationwide Community Testing Initiative. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.03.31.22273242. [PMID: 35411338 PMCID: PMC8996627 DOI: 10.1101/2022.03.31.22273242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Importance Wide-spread distribution of rapid-antigen tests is integral to the United States' strategy to address COVID-19; however, it is estimated that few rapid-antigen test results are reported to local departments of health. Objective To characterize how often individuals in six communities throughout the United States used a digital assistant to log rapid-antigen test results and report them to their local Department of Health. Design This prospective cohort study is based on anonymously collected data from the beneficiaries of The Say Yes! Covid Test program, which distributed 3,000,000 rapid antigen tests at no cost to residents of six communities between April and October 2021. We provide a descriptive evaluation of beneficiaries' use of digital assistant for logging and reporting their rapid antigen test results. Main Outcome and Measures Number and proportion of tests logged and reported to the Department of Health through the digital assistant. Results A total of 178,785 test kits were ordered by the digital assistant, and 14,398 households used the digital assistant to log 41,465 test results. Overall, a small proportion of beneficiaries used the digital assistant (8%), but over 75% of those who used it reported their rapid antigen test results to their state public health department. The reporting behavior varied between communities and was significantly different for communities that were incentivized for reporting test results (p < 0.001). In all communities, positive tests were less reported than negative tests (60.4% vs 75.5%; p<0.001). Conclusions and Relevance These results indicate that app-based reporting with incentives may be an effective way to increase reporting of rapid tests for COVID-19; however, increasing the adoption of the digital assistant is a critical first step.
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Zhang X, Xu Y. Business Cycle and Public Health: The Moderating Role of Health Education and Digital Economy. Front Public Health 2022; 9:793404. [PMID: 35087786 PMCID: PMC8787688 DOI: 10.3389/fpubh.2021.793404] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 10/25/2021] [Indexed: 01/22/2023] Open
Abstract
The cyclicality of public health in the emerging market is underexplored in existing literature. In this study, we used a fixed effect model and provincial data to document how public health varies with the business cycle in China over the period of 2010-2019. The estimated results showed that the business cycle is negatively correlated with the mortality of infectious disease, a proxy variable of public health, thus indicating that public health exhibits a countercyclical pattern in China. Furthermore, we investigated the potential moderating role of public health education and digital economy development in the relationship between business cycle and public health. Our findings suggested that public health education and digital economy development can mitigate the damage of economic conditions on public health in China. Health education helps the public obtain more professional knowledge about diseases and then induces effective preventions. Compared with traditional economic growth, digital economy development can avoid environmental pollution which affects public health. Also, it ensures that state-of-the-art medical services are available for the public through e-health. In addition, digitalization assures that remote working is practicable and reduces close contact during epidemics such as COVID-19. The conclusions stand when subjected to several endogeneity and robustness checks. Therefore, the paper implies that these improvements in public health education and digitalization can help the government in promoting public health.
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Affiliation(s)
- Xing Zhang
- School of Finance, Renmin University of China, Beijing, China
| | - Yingying Xu
- School of Economics and Management, University of Science and Technology Beijing, Beijing, China
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Iyamu I, Gómez-Ramírez O, Xu AXT, Chang HJ, Watt S, Mckee G, Gilbert M. Challenges in the development of digital public health interventions and mapped solutions: Findings from a scoping review. Digit Health 2022; 8:20552076221102255. [PMID: 35656283 PMCID: PMC9152201 DOI: 10.1177/20552076221102255] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Background “Digital public health” has emerged from an interest in integrating digital technologies into public health. However, significant challenges which limit the scale and extent of this digital integration in various public health domains have been described. We summarized the literature about these challenges and identified strategies to overcome them. Methods We adopted Arksey and O’Malley's framework (2005) integrating adaptations by Levac et al. (2010). OVID Medline, Embase, Google Scholar, and 14 government and intergovernmental agency websites were searched using terms related to “digital” and “public health.” We included conceptual and explicit descriptions of digital technologies in public health published in English between 2000 and June 2020. We excluded primary research articles about digital health interventions. Data were extracted using a codebook created using the European Public Health Association's conceptual framework for digital public health. Results and analysis Overall, 163 publications were included from 6953 retrieved articles with the majority (64%, n = 105) published between 2015 and June 2020. Nontechnical challenges to digital integration in public health concerned ethics, policy and governance, health equity, resource gaps, and quality of evidence. Technical challenges included fragmented and unsustainable systems, lack of clear standards, unreliability of available data, infrastructure gaps, and workforce capacity gaps. Identified strategies included securing political commitment, intersectoral collaboration, economic investments, standardized ethical, legal, and regulatory frameworks, adaptive research and evaluation, health workforce capacity building, and transparent communication and public engagement. Conclusion Developing and implementing digital public health interventions requires efforts that leverage identified strategies to overcome diverse challenges encountered in integrating digital technologies in public health.
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Affiliation(s)
- Ihoghosa Iyamu
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Oralia Gómez-Ramírez
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
- CIHR Canadian HIV Trials Network, Vancouver, BC, Canada
| | - Alice XT Xu
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Hsiu-Ju Chang
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Sarah Watt
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Geoff Mckee
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Mark Gilbert
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
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Leal-Neto O, Egger T, Schlegel M, Flury D, Sumer J, Albrich W, Babouee Flury B, Kuster S, Vernazza P, Kahlert C, Kohler P. Digital SARS-CoV-2 Detection Among Hospital Employees: Participatory Surveillance Study. JMIR Public Health Surveill 2021; 7:e33576. [PMID: 34727046 PMCID: PMC8610449 DOI: 10.2196/33576] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/05/2021] [Accepted: 10/05/2021] [Indexed: 12/24/2022] Open
Abstract
Background The implementation of novel techniques as a complement to traditional disease surveillance systems represents an additional opportunity for rapid analysis. Objective The objective of this work is to describe a web-based participatory surveillance strategy among health care workers (HCWs) in two Swiss hospitals during the first wave of COVID-19. Methods A prospective cohort of HCWs was recruited in March 2020 at the Cantonal Hospital of St. Gallen and the Eastern Switzerland Children’s Hospital. For data analysis, we used a combination of the following techniques: locally estimated scatterplot smoothing (LOESS) regression, Spearman correlation, anomaly detection, and random forest. Results From March 23 to August 23, 2020, a total of 127,684 SMS text messages were sent, generating 90,414 valid reports among 1004 participants, achieving a weekly average of 4.5 (SD 1.9) reports per user. The symptom showing the strongest correlation with a positive polymerase chain reaction test result was loss of taste. Symptoms like red eyes or a runny nose were negatively associated with a positive test. The area under the receiver operating characteristic curve showed favorable performance of the classification tree, with an accuracy of 88% for the training data and 89% for the test data. Nevertheless, while the prediction matrix showed good specificity (80.0%), sensitivity was low (10.6%). Conclusions Loss of taste was the symptom that was most aligned with COVID-19 activity at the population level. At the individual level—using machine learning–based random forest classification—reporting loss of taste and limb/muscle pain as well as the absence of runny nose and red eyes were the best predictors of COVID-19.
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Affiliation(s)
- Onicio Leal-Neto
- Department of Economics, University of Zurich, Zurich, Switzerland
| | - Thomas Egger
- Clinic for Infectious Diseases and Hospital Epidemiology, Cantonal Hospital St. Gallen, St Gallen, Switzerland
| | - Matthias Schlegel
- Clinic for Infectious Diseases and Hospital Epidemiology, Cantonal Hospital St. Gallen, St Gallen, Switzerland
| | - Domenica Flury
- Clinic for Infectious Diseases and Hospital Epidemiology, Cantonal Hospital St. Gallen, St Gallen, Switzerland
| | - Johannes Sumer
- Clinic for Infectious Diseases and Hospital Epidemiology, Cantonal Hospital St. Gallen, St Gallen, Switzerland
| | - Werner Albrich
- Clinic for Infectious Diseases and Hospital Epidemiology, Cantonal Hospital St. Gallen, St Gallen, Switzerland
| | - Baharak Babouee Flury
- Clinic for Infectious Diseases and Hospital Epidemiology, Cantonal Hospital St. Gallen, St Gallen, Switzerland.,Medical Research Center, Cantonal Hospital St. Gallen, St Gallen, Switzerland
| | | | - Pietro Vernazza
- Clinic for Infectious Diseases and Hospital Epidemiology, Cantonal Hospital St. Gallen, St Gallen, Switzerland
| | - Christian Kahlert
- Clinic for Infectious Diseases and Hospital Epidemiology, Cantonal Hospital St. Gallen, St Gallen, Switzerland.,Department of Infectious Diseases and Hospital Epidemiology, Children's Hospital of Eastern Switzerland, St Gallen, Switzerland
| | - Philipp Kohler
- Clinic for Infectious Diseases and Hospital Epidemiology, Cantonal Hospital St. Gallen, St Gallen, Switzerland
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Mremi IR, George J, Rumisha SF, Sindato C, Kimera SI, Mboera LEG. Twenty years of integrated disease surveillance and response in Sub-Saharan Africa: challenges and opportunities for effective management of infectious disease epidemics. ONE HEALTH OUTLOOK 2021; 3:22. [PMID: 34749835 PMCID: PMC8575546 DOI: 10.1186/s42522-021-00052-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 08/18/2021] [Indexed: 05/15/2023]
Abstract
INTRODUCTION This systematic review aimed to analyse the performance of the Integrated Disease Surveillance and Response (IDSR) strategy in Sub-Saharan Africa (SSA) and how its implementation has embraced advancement in information technology, big data analytics techniques and wealth of data sources. METHODS HINARI, PubMed, and advanced Google Scholar databases were searched for eligible articles. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols. RESULTS A total of 1,809 articles were identified and screened at two stages. Forty-five studies met the inclusion criteria, of which 35 were country-specific, seven covered the SSA region, and three covered 3-4 countries. Twenty-six studies assessed the IDSR core functions, 43 the support functions, while 24 addressed both functions. Most of the studies involved Tanzania (9), Ghana (6) and Uganda (5). The routine Health Management Information System (HMIS), which collects data from health care facilities, has remained the primary source of IDSR data. However, the system is characterised by inadequate data completeness, timeliness, quality, analysis and utilisation, and lack of integration of data from other sources. Under-use of advanced and big data analytical technologies in performing disease surveillance and relating multiple indicators minimises the optimisation of clinical and practice evidence-based decision-making. CONCLUSIONS This review indicates that most countries in SSA rely mainly on traditional indicator-based disease surveillance utilising data from healthcare facilities with limited use of data from other sources. It is high time that SSA countries consider and adopt multi-sectoral, multi-disease and multi-indicator platforms that integrate other sources of health information to provide support to effective detection and prompt response to public health threats.
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Affiliation(s)
- Irene R Mremi
- Department of Veterinary Medicine and Public Health, Sokoine University of Agriculture, Morogoro, Tanzania.
- SACIDS Foundation for One Health, Sokoine University of Agriculture, Morogoro, Tanzania.
- National Institute for Medical Research, Dar es Salaam, Tanzania.
| | - Janeth George
- Department of Veterinary Medicine and Public Health, Sokoine University of Agriculture, Morogoro, Tanzania
- SACIDS Foundation for One Health, Sokoine University of Agriculture, Morogoro, Tanzania
| | - Susan F Rumisha
- National Institute for Medical Research, Dar es Salaam, Tanzania
- Malaria Atlas Project, Geospatial Health and Development, Telethon Kids Institute, West Perth, Australia
| | - Calvin Sindato
- SACIDS Foundation for One Health, Sokoine University of Agriculture, Morogoro, Tanzania
- National Institute for Medical Research, Tabora Research Centre, Tabora, Tanzania
| | - Sharadhuli I Kimera
- Department of Veterinary Medicine and Public Health, Sokoine University of Agriculture, Morogoro, Tanzania
| | - Leonard E G Mboera
- SACIDS Foundation for One Health, Sokoine University of Agriculture, Morogoro, Tanzania
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Malik A, Antonino A, Khan ML, Nieminen M. Characterizing HIV discussions and engagement on Twitter. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-021-00577-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
AbstractThe novel settings provided by social media facilitate users to seek and share information on a wide array of subjects, including healthcare and wellness. Analyzing health-related opinions and discussions on these platforms complement traditional public health surveillance systems to support timely and effective interventions. This study aims to characterize the HIV-related conversations on Twitter by identifying the prevalent topics and the key events and actors involved in these discussions. Through Twitter API, we collected tweets containing the hashtag #HIV for a one-year period. After pre-processing the collected data, we conducted engagement analysis, temporal analysis, and topic modeling algorithm on the analytical sample (n = 122,807). Tweets by HIV/AIDS/LGBTQ activists and physicians received the highest level of engagement. An upsurge in tweet volume and engagement was observed during global and local events such as World Aids Day and HIV/AIDS awareness and testing days for trans-genders, blacks, women, and the aged population. Eight topics were identified that include “stigma”, “prevention”, “epidemic in the developing countries”, “World Aids Day”, “treatment”, “events”, “PrEP”, and “testing”. Social media discussions offer a nuanced understanding of public opinions, beliefs, and sentiments about numerous health-related issues. The current study reports various dimensions of HIV-related posts on Twitter. Based on the findings, public health agencies and pertinent entities need to proactively use Twitter and other social media by engaging the public through involving influencers. The undertaken methodological choices may be applied to further assess HIV discourse on other popular social media platforms.
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Lee L, Desroches M, Mukhi S, Bancej C. FluWatchers: Evaluation of a crowdsourced influenza-like illness surveillance application for Canadian influenza seasons 2015-2016 to 2018-2019. CANADA COMMUNICABLE DISEASE REPORT = RELEVE DES MALADIES TRANSMISSIBLES AU CANADA 2021; 47:357-363. [PMID: 34650332 PMCID: PMC8448189 DOI: 10.14745/ccdr.v47i09a02] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
BACKGROUND Sentinel influenza-like illness (ILI) surveillance is an essential component of a comprehensive influenza surveillance program. Community-based ILI surveillance systems that rely solely on sentinel healthcare practices omit important segments of the population, including those who do not seek medical care. Participatory surveillance, which relies on community participation in surveillance, may address some limitations of traditional ILI systems. OBJECTIVE We aimed to evaluate FluWatchers, a crowdsourced ILI application developed to complement and complete ILI surveillance in Canada. METHODS Using established frameworks for surveillance evaluations, we assessed the acceptability, reliability, accuracy and usefulness of the FluWatchers system 2015-2016, through 2018-2019. Evaluation indicators were compared against national surveillance indicators of ILI and of laboratory confirmed respiratory virus infections. RESULTS The acceptability of FluWatchers was demonstrated by growth of 50%-100% in season-over-season participation, and a consistent season-over-season retention of 80%. Reliability was greater for FluWatchers than for our traditional ILI system, although both systems had week-over-week fluctuations in the number of participants responding. FluWatchers' ILI rates had moderate correlation with weekly influenza laboratory detection rates and other winter seasonal respiratory virus detections including respiratory syncytial virus and seasonal coronaviruses. Finally, FluWatchers has demonstrated its usefulness as a source of core FluWatch surveillance information and has the potential to fill data gaps in current programs for influenza surveillance and control. CONCLUSION FluWatchers is an example of an innovative digital participatory surveillance program that was created to address limitations of traditional ILI surveillance in Canada. It fulfills the surveillance system evaluation criteria of acceptability, reliability, accuracy and usefulness.
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Affiliation(s)
- Liza Lee
- Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, ON
| | - Mireille Desroches
- Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, ON
| | - Shamir Mukhi
- National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB
| | - Christina Bancej
- Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, ON
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Hswen Y, Yom-Tov E. Analysis of a Vaping-Associated Lung Injury Outbreak through Participatory Surveillance and Archival Internet Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18158203. [PMID: 34360495 PMCID: PMC8346109 DOI: 10.3390/ijerph18158203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 07/28/2021] [Accepted: 07/30/2021] [Indexed: 11/22/2022]
Abstract
The US Centers for Disease Control and Prevention alerted of a suspected outbreak of lung illness associated with using E-cigarette products in September 2019. At the time that the CDC published its alert little was known about the causes of the outbreak or who was at risk for it. Here we provide insights into the outbreak through analysis of passive reporting and participatory surveillance. We collected data about vaping habits and associated adverse reactions from four data sources pertaining to people in the USA: A participatory surveillance platform (YouVape), Reddit, Google Trends, and Bing. Data were analyzed to identify vaping behaviors and reported adverse events. These were correlated among sources and with prior reports. Data was obtained from 720 YouVape users, 4331 Reddit users, and over 1 million Bing users. Large geographic variation was observed across vaping products. Significant correlation was found among the data sources in reported adverse reactions. Models of participatory surveillance data found specific product and adverse reaction associations. Specifically, cannabidiol was found to be associated with fever, while tetrahydrocannabinol was found to be correlated with diarrhea. Our results demonstrate that utilization of different, complementary, online data sources provide a holistic view of vaping associated lung injury while augmenting traditional data sources.
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Affiliation(s)
- Yulin Hswen
- Department of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, CA 94158, USA;
- Bakar Computational Health Sciences Institute, University of California at San Francisco, San Francisco, CA 94143, USA
- Innovation Program, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Elad Yom-Tov
- Microsoft Research Israel, 3 Alan Turing Str., Herzeliya 4672415, Israel
- Faculty of Industrial Engineering and Management, Technion, Haifa 3200000, Israel
- Correspondence:
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Runkle JD, Sugg MM, Graham G, Hodge B, March T, Mullendore J, Tove F, Salyers M, Valeika S, Vaughan E. Participatory COVID-19 Surveillance Tool in Rural Appalachia : Real-Time Disease Monitoring and Regional Response. Public Health Rep 2021; 136:327-337. [PMID: 33601984 PMCID: PMC8580398 DOI: 10.1177/0033354921990372] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/06/2021] [Indexed: 01/19/2023] Open
Abstract
INTRODUCTION Few US studies have examined the usefulness of participatory surveillance during the coronavirus disease 2019 (COVID-19) pandemic for enhancing local health response efforts, particularly in rural settings. We report on the development and implementation of an internet-based COVID-19 participatory surveillance tool in rural Appalachia. METHODS A regional collaboration among public health partners culminated in the design and implementation of the COVID-19 Self-Checker, a local online symptom tracker. The tool collected data on participant demographic characteristics and health history. County residents were then invited to take part in an automated daily electronic follow-up to monitor symptom progression, assess barriers to care and testing, and collect data on COVID-19 test results and symptom resolution. RESULTS Nearly 6500 county residents visited and 1755 residents completed the COVID-19 Self-Checker from April 30 through June 9, 2020. Of the 579 residents who reported severe or mild COVID-19 symptoms, COVID-19 symptoms were primarily reported among women (n = 408, 70.5%), adults with preexisting health conditions (n = 246, 70.5%), adults aged 18-44 (n = 301, 52.0%), and users who reported not having a health care provider (n = 131, 22.6%). Initial findings showed underrepresentation of some racial/ethnic and non-English-speaking groups. PRACTICAL IMPLICATIONS This low-cost internet-based platform provided a flexible means to collect participatory surveillance data on local changes in COVID-19 symptoms and adapt to guidance. Data from this tool can be used to monitor the efficacy of public health response measures at the local level in rural Appalachia.
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Affiliation(s)
- Jennifer D. Runkle
- North Carolina Institute for Climate Studies, North Carolina State University, Asheville, NC, USA
| | - Maggie M. Sugg
- Department of Geography and Planning, Appalachian State University, Boone, NC, USA
| | - Garrett Graham
- North Carolina Institute for Climate Studies, North Carolina State University, Asheville, NC, USA
| | - Bryan Hodge
- Mountain Area Health Education, Asheville, NC, USA
| | - Terri March
- Hendersonville Family Medicine Residency, Mountain Area Health Education, Asheville, NC, USA
| | | | - Fletcher Tove
- Buncombe County Health and Human Services, Asheville, NC, USA
| | - Martha Salyers
- Public Health and Human Services Division, Eastern Band of the Cherokee Indians, Cherokee, NC, USA
| | - Steve Valeika
- Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ellis Vaughan
- Buncombe County Health and Human Services, Asheville, NC, USA
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Mahmud AS, Chowdhury S, Sojib KH, Chowdhury A, Quader MT, Paul S, Saidy MS, Uddin R, Engø-Monsen K, Buckee CO. Participatory syndromic surveillance as a tool for tracking COVID-19 in Bangladesh. Epidemics 2021; 35:100462. [PMID: 33887643 PMCID: PMC8054699 DOI: 10.1016/j.epidem.2021.100462] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 03/19/2021] [Accepted: 04/12/2021] [Indexed: 12/29/2022] Open
Abstract
Limitations in laboratory diagnostic capacity and reporting delays have hampered efforts to mitigate and control the ongoing coronavirus disease 2019 (COVID-19) pandemic globally. To augment traditional lab and hospital-based surveillance, Bangladesh established a participatory surveillance system for the public to self-report symptoms consistent with COVID-19 through multiple channels. Here, we report on the use of this system, which received over 3 million responses within two months, for tracking the COVID-19 outbreak in Bangladesh. Although we observe considerable noise in the data and initial volatility in the use of the different reporting mechanisms, the self-reported syndromic data exhibits a strong association with lab-confirmed cases at a local scale. Moreover, the syndromic data also suggests an earlier spread of the outbreak across Bangladesh than is evident from the confirmed case counts, consistent with predicted spread of the outbreak based on population mobility data. Our results highlight the usefulness of participatory syndromic surveillance for mapping disease burden generally, and particularly during the initial phases of an emerging outbreak.
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Affiliation(s)
- Ayesha S Mahmud
- Department of Demography, University of California, Berkeley, USA.
| | | | | | | | | | | | | | | | | | - Caroline O Buckee
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, USA
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Platt M, Hasselgren A, Román-Belmonte JM, Tuler de Oliveira M, De la Corte-Rodríguez H, Delgado Olabarriaga S, Rodríguez-Merchán EC, Mackey TK. Test, Trace, and Put on the Blockchain?: A Viewpoint Evaluating the Use of Decentralized Systems for Algorithmic Contact Tracing to Combat a Global Pandemic. JMIR Public Health Surveill 2021; 7:e26460. [PMID: 33727212 PMCID: PMC8108567 DOI: 10.2196/26460] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 02/20/2021] [Accepted: 03/08/2021] [Indexed: 01/29/2023] Open
Abstract
The enormous pressure of the increasing case numbers experienced during the COVID-19 pandemic has given rise to a variety of novel digital systems designed to provide solutions to unprecedented challenges in public health. The field of algorithmic contact tracing, in particular, an area of research that had previously received limited attention, has moved into the spotlight as a crucial factor in containing the pandemic. The use of digital tools to enable more robust and expedited contact tracing and notification, while maintaining privacy and trust in the data generated, is viewed as key to identifying chains of transmission and close contacts, and, consequently, to enabling effective case investigations. Scaling these tools has never been more critical, as global case numbers have exceeded 100 million, as many asymptomatic patients remain undetected, and as COVID-19 variants begin to emerge around the world. In this context, there is increasing attention on blockchain technology as a part of systems for enhanced digital algorithmic contact tracing and reporting. By analyzing the literature that has emerged from this trend, the common characteristics of the designs proposed become apparent. An archetypal system architecture can be derived, taking these characteristics into consideration. However, assessing the utility of this architecture using a recognized evaluation framework shows that the added benefits and features of blockchain technology do not provide significant advantages over conventional centralized systems for algorithmic contact tracing and reporting. From our study, it, therefore, seems that blockchain technology may provide a more significant benefit in other areas of public health beyond contact tracing.
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Affiliation(s)
- Moritz Platt
- Department of Informatics, King's College London, London, United Kingdom
| | - Anton Hasselgren
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Juan Manuel Román-Belmonte
- Department of Physical Medicine and Rehabilitation, Hospital Central de la Cruz Roja San José y Santa Adela, Madrid, Spain
| | | | | | | | - E Carlos Rodríguez-Merchán
- Department of Orthopaedic Surgery, La Paz University Hospital, Madrid, Spain
- Osteoarticular Surgery Research, Hospital La Paz Institute for Health Research, IdiPAZ, Madrid, Spain
| | - Tim Ken Mackey
- Department of Anesthesiology, Division of Infectious Diseases and Global Public Health, School of Medicine, UC San Diego, La Jolla, CA, United States
- BlockLAB, San Diego Supercomputer Center, UC San Diego, La Jolla, CA, United States
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Masthi R, Jahan A, Bharathi D, Abhilash P, Kaniyarakkal V, Tv S, Gowda G, Ts R, Goud R, Rao S, Hegde A. Postcode based participatory disease surveillance systems : a comparison with traditional risk-based surveillance and its application in the COVID-19 pandemic. JMIR Public Health Surveill 2021. [PMID: 33481758 DOI: 10.2196/20746] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Background: The SARS-Cov-2 infection has rapidly saturated health systems and traditional surveillance networks are finding hard to keep pace with its spread. We designed a participatory disease surveillance (PDS) system, to capture symptoms of Influenza-like illness (ILI) to estimate SARS-CoV-2 infection in the community. While data generated by these platforms can help public health organisations find community hotspots and effectively direct control measures, it has never been compared to traditional systems. OBJECTIVE Methods and Objectives: A completely anonymised web based PDS system, www.trackcovid-19.org was developed. We evaluated the symptomatic responses received form the PDS system to the traditional risk based surveillance carried out by the Bruhat Bengaluru Mahanagara Palike over a period of 45 days in the South Indian city of Bengaluru. METHODS Methods and Objectives: A completely anonymised web based PDS system, www.trackcovid-19.org was developed. We evaluated the symptomatic responses received form the PDS system to the traditional risk based surveillance carried out by the Bruhat Bengaluru Mahanagara Palike over a period of 45 days in the South Indian city of Bengaluru. RESULTS Results: The PDS system recorded 11062 entries from 106 Postal codes. A healthy response was obtained from 10863 users while 199 (1.8%) reported symptomatic. Subgroup analysis of a 14 day symptomatic window recorded 33 (0.29%) responses. Risk based surveillance was carried out covering a population of 605,284 with 209 (0.03%) individuals identified symptomatic. CONCLUSIONS Conclusion: Web PDS platforms provide better visualisation of community infection when compared to traditional risk based surveillance systems. They are extremely useful by providing real time information in the extended battle against this pandemic. When integrated into national disease surveillance systems, they can provide long term community surveillance adding an important cost-effective layer to already available data sources.
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Affiliation(s)
- Ramesh Masthi
- Kempegowda Institute of Medical Sciences, Bangalore, IN
| | - Afraz Jahan
- Kempegowda Institute of Medical Sciences, Bangalore, IN
| | | | | | | | - Sanjay Tv
- Kempegowda Institute of Medical Sciences, Bangalore, IN
| | | | - Ranganath Ts
- Bangalore Medical College & Research Institute, Bangalore, IN
| | | | | | - Ajay Hegde
- Trackcovid-19.org, 349, 4th Main, Sadashivananagr, Bangalore, IN
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Corsi A, de Souza FF, Pagani RN, Kovaleski JL. Big data analytics as a tool for fighting pandemics: a systematic review of literature. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021; 12:9163-9180. [PMID: 33144892 PMCID: PMC7595572 DOI: 10.1007/s12652-020-02617-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 10/10/2020] [Indexed: 05/09/2023]
Abstract
Infectious and contagious diseases represent a major challenge for health systems worldwide, either in private or public sectors. More recently, with the increase in cases related to these problems, combined with the recent global pandemic of COVID-19, the need to study strategies to treat these health disturbs is even more latent. Big Data, as well as Big Data Analytics techniques, have been addressed in this context with the possibility of predicting, mapping, tracking, monitoring, and raising awareness about these epidemics and pandemics. Thus, the purpose of this study is to identify how BDA can help in cases of pandemics and epidemics. To achieve this purpose, a systematic review of literature was carried out using the methodology Methodi Ordinatio. The rigorous search resulted in a portfolio of 45 articles, retrived from scientific databases. For the collection and analysis of data, the softwares NVivo 12 and VOSviewer were used. The content analysis sought to identify how Big Data and Big Data Analytics can help fighting epidemics and pandemics. The types and sources of data used in cases of previous epidemics and pandemics were identified, as well as techniques for treating these data. The results showed that the main sources of data come from social media and Internet search engines. The most common techniques for analyzing these data involve the use of statistics, such as correlation and regression, combined with other techniques. Results shows that there is a fruitiful field of study to be explored by both areas, Big Data and Health.
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Affiliation(s)
- Alana Corsi
- Federal University of Technology-Paraná (UTFPR) Câmpus Ponta Grossa, Av. Monteiro Lobato, s/n-Km 04, Ponta Grossa, PR 84016-210 Brazil
| | - Fabiane Florencio de Souza
- Federal University of Technology-Paraná (UTFPR) Câmpus Ponta Grossa, Av. Monteiro Lobato, s/n-Km 04, Ponta Grossa, PR 84016-210 Brazil
| | - Regina Negri Pagani
- Federal University of Technology-Paraná (UTFPR) Câmpus Ponta Grossa, Av. Monteiro Lobato, s/n-Km 04, Ponta Grossa, PR 84016-210 Brazil
| | - João Luiz Kovaleski
- Federal University of Technology-Paraná (UTFPR) Câmpus Ponta Grossa, Av. Monteiro Lobato, s/n-Km 04, Ponta Grossa, PR 84016-210 Brazil
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Vasconcellos JS, Ratzlaff FR, Vogel FS, Giotto Ê, Veiga HG, Botton SA, Sangioni LA. Information technology by mobile communication for the notification of canine visceral leishmaniasis. PESQUISA VETERINÁRIA BRASILEIRA 2021. [DOI: 10.1590/1678-5150-pvb-6671] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
ABSTRACT: Visceral leishmaniasis is the most severe form of a human and animal disease complex entitled leishmaniasis, which is endemic to 70 countries. It is imperative to develop and offer technologies capable of increasing the resolution ability of control programs of this zoonosis. In the search for technological innovations in health, especially in environmental surveillance, the objective is to develop a mobile application (App) for smartphones in order to facilitate and systematize the notification of positive cases of canine visceral leishmaniasis (CVL) by veterinarians working in clinics for assisting the municipal health surveillance in the management of this zoonosis. Thus, we developed an App, C7 LVC - Canine Visceral Leishmaniasis Notification System, with formatting based on the CR Campeiro 7® software. The technology created enables the filling of important gaps in information systems, facilitating the transmission of data and the use of this data by public management bodies to take CVL prevention and control actions.
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Leal-Neto OB, Santos FAS, Lee JY, Albuquerque JO, Souza WV. Prioritizing COVID-19 tests based on participatory surveillance and spatial scanning. Int J Med Inform 2020; 143:104263. [PMID: 32877853 PMCID: PMC7449898 DOI: 10.1016/j.ijmedinf.2020.104263] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 08/20/2020] [Accepted: 08/24/2020] [Indexed: 12/24/2022]
Abstract
OBJECTIVES This study aimed to identify, describe and analyze priority areas for COVID-19 testing combining participatory surveillance and traditional surveillance. DESIGN It was carried out a descriptive transversal study in the city of Caruaru, Pernambuco state, Brazil, within the period of 20/02/2020 to 05/05/2020. Data included all official reports for influenza-like illness notified by the municipality health department and the self-reports collected through the participatory surveillance platform Brasil Sem Corona. METHODS We used linear regression and loess regression to verify a correlation between Participatory Surveillance (PS) and Traditional Surveillance (TS). Also a spatial scanning approach was deployed in order to identify risk clusters for COVID-19. RESULTS In Caruaru, the PS had 861 active users, presenting an average of 1.2 reports per user per week. The platform Brasil Sem Corona started on March 20th and since then, has been officially used by the Caruaru health authority to improve the quality of information from the traditional surveillance system. Regarding the respiratory syndrome cases from TS, 1588 individuals were positive for this clinical outcome. The spatial scanning analysis detected 18 clusters and 6 of them presented statistical significance (p-value < 0.1). Clusters 3 and 4 presented an overlapping area that was chosen by the local authority to deploy the COVID-19 serology, where 50 individuals were tested. From there, 32 % (n = 16) presented reagent results for antibodies related to COVID-19. CONCLUSION Participatory surveillance is an effective epidemiological method to complement the traditional surveillance system in response to the COVID-19 pandemic by adding real-time spatial data to detect priority areas for COVID-19 testing.
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Affiliation(s)
- O B Leal-Neto
- Department of Economics, University of Zurich, Zurich, Switzerland; Epitrack, Recife, Brazil.
| | - F A S Santos
- Agreste Academic Center, Federal University of Pernambuco, Caruaru, Brazil
| | | | - J O Albuquerque
- Epitrack, Recife, Brazil; Immunopathology Laboratory Keizo Asami, Federal University of Pernambuco, Recife, Brazil
| | - W V Souza
- Aggeu Magalhães Research Center, Oswaldo Cruz Foundation, Recife, Brazil
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Gozzi N, Tizzani M, Starnini M, Ciulla F, Paolotti D, Panisson A, Perra N. Collective Response to Media Coverage of the COVID-19 Pandemic on Reddit and Wikipedia: Mixed-Methods Analysis. J Med Internet Res 2020; 22:e21597. [PMID: 32960775 PMCID: PMC7553788 DOI: 10.2196/21597] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 07/31/2020] [Accepted: 09/09/2020] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND The exposure and consumption of information during epidemic outbreaks may alter people's risk perception and trigger behavioral changes, which can ultimately affect the evolution of the disease. It is thus of utmost importance to map the dissemination of information by mainstream media outlets and the public response to this information. However, our understanding of this exposure-response dynamic during the COVID-19 pandemic is still limited. OBJECTIVE The goal of this study is to characterize the media coverage and collective internet response to the COVID-19 pandemic in four countries: Italy, the United Kingdom, the United States, and Canada. METHODS We collected a heterogeneous data set including 227,768 web-based news articles and 13,448 YouTube videos published by mainstream media outlets, 107,898 user posts and 3,829,309 comments on the social media platform Reddit, and 278,456,892 views of COVID-19-related Wikipedia pages. To analyze the relationship between media coverage, epidemic progression, and users' collective web-based response, we considered a linear regression model that predicts the public response for each country given the amount of news exposure. We also applied topic modelling to the data set using nonnegative matrix factorization. RESULTS Our results show that public attention, quantified as user activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage; meanwhile, this activity declines rapidly while news exposure and COVID-19 incidence remain high. Furthermore, using an unsupervised, dynamic topic modeling approach, we show that while the levels of attention dedicated to different topics by media outlets and internet users are in good accordance, interesting deviations emerge in their temporal patterns. CONCLUSIONS Overall, our findings offer an additional key to interpret public perception and response to the current global health emergency and raise questions about the effects of attention saturation on people's collective awareness and risk perception and thus on their tendencies toward behavioral change.
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De la Puente-León M, Levy MZ, Toledo AM, Recuenco S, Shinnick J, Castillo-Neyra R. Spatial Inequality Hides the Burden of Dog Bites and the Risk of Dog-Mediated Human Rabies. Am J Trop Med Hyg 2020; 103:1247-1257. [PMID: 32662391 PMCID: PMC7470517 DOI: 10.4269/ajtmh.20-0180] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 05/25/2020] [Indexed: 12/21/2022] Open
Abstract
Since its reintroduction in 2015, rabies has been established as an enzootic disease among the dog population of Arequipa, Peru. Given the unknown rate of dog bites, the risk of human rabies transmission is concerning. Our objective was to estimate the rate of dog bites in the city and to identify factors associated with seeking health care in a medical facility for wound care and rabies prevention follow-up. To this end, we conducted a door-to-door survey with 4,370 adults in 21 urban and 21 peri-urban communities. We then analyzed associations between seeking health care following dog bites and various socioeconomic factors, stratifying by urban and peri-urban localities. We found a high annual rate of dog bites in peri-urban communities (12.4%), which was 2.6 times higher than that in urban areas (4.8%). Among those who were bitten, the percentage of people who sought medical treatment was almost twice as high in urban areas (39.1%) as in peri-urban areas (21.4%).
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Affiliation(s)
- Micaela De la Puente-León
- Zoonotic Disease Research Laboratory, One Health Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Arequipa, Perú
| | - Michael Z. Levy
- Zoonotic Disease Research Laboratory, One Health Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Arequipa, Perú
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Amparo M. Toledo
- Zoonotic Disease Research Laboratory, One Health Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Arequipa, Perú
| | - Sergio Recuenco
- Centro de Investigaciones Tecnológicas, Biomédicas y Medioambientales, Universidad Nacional Mayor de San Marcos, Lima, Perú
| | - Julianna Shinnick
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ricardo Castillo-Neyra
- Zoonotic Disease Research Laboratory, One Health Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Arequipa, Perú
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
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Chan AT, Drew DA, Nguyen LH, Joshi AD, Ma W, Guo CG, Lo CH, Mehta RS, Kwon S, Sikavi DR, Magicheva-Gupta MV, Fatehi ZS, Flynn JJ, Leonardo BM, Albert CM, Andreotti G, Beane-Freeman LE, Balasubramanian BA, Brownstein JS, Bruinsma F, Cowan AN, Deka A, Ernst ME, Figueiredo JC, Franks PW, Gardner CD, Ghobrial IM, Haiman CA, Hall JE, Deming-Halverson SL, Kirpach B, Lacey JV, Marchand LL, Marinac CR, Martinez ME, Milne RL, Murray AM, Nash D, Palmer JR, Patel AV, Rosenberg L, Sandler DP, Sharma SV, Schurman SH, Wilkens LR, Chavarro JE, Eliassen AH, Hart JE, Kang JH, Koenen KC, Kubzansky LD, Mucci LA, Ourselin S, Rich-Edwards JW, Song M, Stampfer MJ, Steves CJ, Willett WC, Wolf J, Spector T. The COronavirus Pandemic Epidemiology (COPE) Consortium: A Call to Action. Cancer Epidemiol Biomarkers Prev 2020; 29:1283-1289. [PMID: 32371551 PMCID: PMC7357669 DOI: 10.1158/1055-9965.epi-20-0606] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 05/01/2020] [Accepted: 05/04/2020] [Indexed: 01/08/2023] Open
Abstract
The rapid pace of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; COVID-19) pandemic presents challenges to the real-time collection of population-scale data to inform near-term public health needs as well as future investigations. We established the COronavirus Pandemic Epidemiology (COPE) consortium to address this unprecedented crisis on behalf of the epidemiology research community. As a central component of this initiative, we have developed a COVID Symptom Study (previously known as the COVID Symptom Tracker) mobile application as a common data collection tool for epidemiologic cohort studies with active study participants. This mobile application collects information on risk factors, daily symptoms, and outcomes through a user-friendly interface that minimizes participant burden. Combined with our efforts within the general population, data collected from nearly 3 million participants in the United States and United Kingdom are being used to address critical needs in the emergency response, including identifying potential hot spots of disease and clinically actionable risk factors. The linkage of symptom data collected in the app with information and biospecimens already collected in epidemiology cohorts will position us to address key questions related to diet, lifestyle, environmental, and socioeconomic factors on susceptibility to COVID-19, clinical outcomes related to infection, and long-term physical, mental health, and financial sequalae. We call upon additional epidemiology cohorts to join this collective effort to strengthen our impact on the current health crisis and generate a new model for a collaborative and nimble research infrastructure that will lead to more rapid translation of our work for the betterment of public health.
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Affiliation(s)
- Andrew T Chan
- Clinical & Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - David A Drew
- Clinical & Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Long H Nguyen
- Clinical & Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Amit D Joshi
- Clinical & Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Wenjie Ma
- Clinical & Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Chuan-Guo Guo
- Clinical & Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Chun-Han Lo
- Clinical & Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Raaj S Mehta
- Clinical & Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Sohee Kwon
- Clinical & Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Daniel R Sikavi
- Clinical & Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Marina V Magicheva-Gupta
- Clinical & Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Zahra S Fatehi
- Clinical & Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Jacqueline J Flynn
- Clinical & Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Brianna M Leonardo
- Clinical & Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Christine M Albert
- Department of Cardiology, Cedars-Sinai Hospital, Los Angeles, California
| | - Gabriella Andreotti
- Division of Cancer Epidemiology & Genetics, Occupational and Environmental Epidemiology Branch, National Institutes of Health, National Cancer Institute, Bethesda, Maryland
| | - Laura E Beane-Freeman
- Division of Cancer Epidemiology & Genetics, Occupational and Environmental Epidemiology Branch, National Institutes of Health, National Cancer Institute, Bethesda, Maryland
| | - Bijal A Balasubramanian
- Department of Epidemiology, Human Genetics, and Environmental Science, UTHealth School of Public Health, Houston, Texas
| | - John S Brownstein
- Computational Epidemiology Group, Boston Children's Hospital, Boston, Massachusetts
| | - Fiona Bruinsma
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Annie N Cowan
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | | | | | - Jane C Figueiredo
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Hospital, Los Angeles, California
| | - Paul W Franks
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Genetic and Molecular Epidemiology, Lund University, Malmo, Sweden
| | - Christopher D Gardner
- Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, California
| | - Irene M Ghobrial
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, Norris Comprehensive Cancer Center and the Epidemiology and Genetics Division, Department of Preventive Medicine, University of Southern California, Los Angeles, California
| | - Janet E Hall
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina
| | | | - Brenda Kirpach
- Hennepin Health Care Research Institute, Berman Center for Outcomes and Clinical Research, Minneapolis, Minnesota
| | - James V Lacey
- Division of Health Analytics, Department of Computational and Quantitative Medicine, City of Hope, Duarte, California
| | | | - Catherine R Marinac
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Maria Elena Martinez
- Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, California
- Moores Cancer Center, University of California, San Diego, La Jolla California
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Anne M Murray
- Hennepin Health Care Research Institute, Berman Center for Outcomes and Clinical Research, Minneapolis, Minnesota
| | - Denis Nash
- Institute for Implementation Science in Population Health, City University of New York (CUNY), New York, New York
- Department of Epidemiology and Biostatistics, School of Public Health, City University of New York (CUNY), New York, New York
| | - Julie R Palmer
- Slone Epidemiology Center, School of Medicine, Boston University, Boston, Massachusetts
| | | | - Lynn Rosenberg
- Slone Epidemiology Center, School of Medicine, Boston University, Boston, Massachusetts
| | - Dale P Sandler
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina
| | - Shreela V Sharma
- Department of Epidemiology, Human Genetics, and Environmental Science, UTHealth School of Public Health, Houston, Texas
| | - Shepherd H Schurman
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina
| | | | - Jorge E Chavarro
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - A Heather Eliassen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Jaime E Hart
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Jae Hee Kang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Laura D Kubzansky
- Department of Social and Behavioral Sciences and Lee Kum Sheung Center for Health and Happiness, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Lorelei A Mucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Sebastien Ourselin
- Department of Twin Research & Genetic Epidemiology, Kings College, London, United Kingdom
| | - Janet W Rich-Edwards
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Mingyang Song
- Clinical & Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Meir J Stampfer
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Claire J Steves
- Department of Twin Research & Genetic Epidemiology, Kings College, London, United Kingdom
| | - Walter C Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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Si Y, Wu H, Liu Q. Factors Influencing Doctors' Participation in the Provision of Medical Services Through Crowdsourced Health Care Information Websites: Elaboration-Likelihood Perspective Study. JMIR Med Inform 2020; 8:e16704. [PMID: 32597787 PMCID: PMC7367514 DOI: 10.2196/16704] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 11/16/2019] [Accepted: 02/01/2020] [Indexed: 12/19/2022] Open
Abstract
Background Web-based crowdsourcing promotes the goals achieved effectively by gaining solutions from public groups via the internet, and it has gained extensive attention in both business and academia. As a new mode of sourcing, crowdsourcing has been proven to improve efficiency, quality, and diversity of tasks. However, little attention has been given to crowdsourcing in the health sector. Objective Crowdsourced health care information websites enable patients to post their questions in the question pool, which is accessible to all doctors, and the patients wait for doctors to respond to their questions. Since the sustainable development of crowdsourced health care information websites depends on the participation of the doctors, we aimed to investigate the factors influencing doctors’ participation in providing health care information in these websites from the perspective of the elaboration-likelihood model. Methods We collected 1524 questions with complete patient-doctor interaction processes from an online health community in China to test all the hypotheses. We divided the doctors into 2 groups based on the sequence of the answers: (1) doctor who answered the patient’s question first and (2) the doctors who answered that question after the doctor who answered first. All analyses were conducted using the ordinary least squares method. Results First, the ability of the doctor who first answered the health-related question was found to positively influence the participation of the following doctors who answered after the first doctor responded to the question (βoffline1=.177, P<.001; βoffline2=.063, P=.048; βonline=.418, P<.001). Second, the reward that the patient offered for the best answer showed a positive effect on doctors’ participation (β=.019, P<.001). Third, the question’s complexity was found to positively moderate the relationships between the ability of the first doctor who answered and the participation of the following doctors (β=.186, P=.05) and to mitigate the effect between the reward and the participation of the following doctors (β=–.003, P=.10). Conclusions This study has both theoretical and practical contributions. Online health community managers can build effective incentive mechanisms to encourage highly competent doctors to participate in the provision of medical services in crowdsourced health care information websites and they can increase the reward incentives for each question to increase the participation of the doctors.
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Affiliation(s)
- Yan Si
- School of Business, Wuxi Vocational College of Science and Technology, Wuxi, China
| | - Hong Wu
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qing Liu
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Garg S, Bhatnagar N, Gangadharan N. A Case for Participatory Disease Surveillance of the COVID-19 Pandemic in India. JMIR Public Health Surveill 2020; 6:e18795. [PMID: 32287038 PMCID: PMC7164788 DOI: 10.2196/18795] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 04/09/2020] [Accepted: 04/13/2020] [Indexed: 12/26/2022] Open
Abstract
The coronavirus disease pandemic requires the deployment of novel surveillance strategies to curtail further spread of the disease in the community. Participatory disease surveillance mechanisms have already been adopted in countries for the current pandemic. India, with scarce resources, good telecom support, and a not-so-robust heath care system, makes a strong case for introducing participatory disease surveillance for the prevention and control of the pandemic. India has just launched Aarogya Setu, which is a first-of-its-kind participatory disease surveillance initiative in India. This will supplement the existing Integrated Disease Surveillance Programme in India by finding missing cases and having faster aggregation, analysis of data, and prompt response measures. This newly created platform empowers communities with the right information and guidance, enabling protection from infection and reducing unnecessary contact with the overburdened health care system. However, caution needs to be exercised to address participation from digitally isolated populations, ensure the reliability of data, and consider ethical concerns such as maintaining individual privacy.
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Affiliation(s)
- Suneela Garg
- Maulana Azad Medical College, Delhi University, Delhi, India
| | - Nidhi Bhatnagar
- Maulana Azad Medical College, Delhi University, Delhi, India
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Leal Neto O, Cruz O, Albuquerque J, Nacarato de Sousa M, Smolinski M, Pessoa Cesse EÂ, Libel M, Vieira de Souza W. Participatory Surveillance Based on Crowdsourcing During the Rio 2016 Olympic Games Using the Guardians of Health Platform: Descriptive Study. JMIR Public Health Surveill 2020; 6:e16119. [PMID: 32254042 PMCID: PMC7175192 DOI: 10.2196/16119] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 12/06/2019] [Accepted: 01/27/2020] [Indexed: 12/01/2022] Open
Abstract
Background With the evolution of digital media, areas such as public health are adding new platforms to complement traditional systems of epidemiological surveillance. Participatory surveillance and digital epidemiology have become innovative tools for the construction of epidemiological landscapes with citizens’ participation, improving traditional sources of information. Strategies such as these promote the timely detection of warning signs for outbreaks and epidemics in the region. Objective This study aims to describe the participatory surveillance platform Guardians of Health, which was used in a project conducted during the 2016 Olympic and Paralympic Games in Rio de Janeiro, Brazil, and officially used by the Brazilian Ministry of Health for the monitoring of outbreaks and epidemics. Methods This is a descriptive study carried out using secondary data from Guardians of Health available in a public digital repository. Based on syndromic signals, the information subsidy for decision making by policy makers and health managers becomes more dynamic and assertive. This type of information source can be used as an early route to understand the epidemiological scenario. Results The main result of this research was demonstrating the use of the participatory surveillance platform as an additional source of information for the epidemiological surveillance performed in Brazil during a mass gathering. The platform Guardians of Health had 7848 users who generated 12,746 reports about their health status. Among these reports, the following were identified: 161 users with diarrheal syndrome, 68 users with respiratory syndrome, and 145 users with rash syndrome. Conclusions It is hoped that epidemiological surveillance professionals, researchers, managers, and workers become aware of, and allow themselves to use, new tools that improve information management for decision making and knowledge production. This way, we may follow the path for a more intelligent, efficient, and pragmatic disease control system.
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Affiliation(s)
- Onicio Leal Neto
- University of Zurich, Zurich, Switzerland.,Epitrack, Recife, Brazil
| | - Oswaldo Cruz
- Scientific Computation Program, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Jones Albuquerque
- Epitrack, Recife, Brazil.,Immunopathology Lab Keizo Asami, Recife, Brazil
| | | | | | | | - Marlo Libel
- Ending Pandemics, San Francisco, CA, United States
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Hemedan AA, Abd Elaziz M, Jiao P, Alavi AH, Bahgat M, Ostaszewski M, Schneider R, Ghazy HA, Ewees AA, Lu S. Prediction of the Vaccine-derived Poliovirus Outbreak Incidence: A Hybrid Machine Learning Approach. Sci Rep 2020; 10:5058. [PMID: 32193487 PMCID: PMC7081356 DOI: 10.1038/s41598-020-61853-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 02/06/2020] [Indexed: 11/09/2022] Open
Abstract
Recently, significant attention has been devoted to vaccine-derived poliovirus (VDPV) surveillance due to its severe consequences. Prediction of the outbreak incidence of VDPF requires an accurate analysis of the alarming data. The overarching aim to this study is to develop a novel hybrid machine learning approach to identify the key parameters that dominate the outbreak incidence of VDPV. The proposed method is based on the integration of random vector functional link (RVFL) networks with a robust optimization algorithm called whale optimization algorithm (WOA). WOA is applied to improve the accuracy of the RVFL network by finding the suitable parameter configurations for the algorithm. The classification performance of the WOA-RVFL method is successfully validated using a number of datasets from the UCI machine learning repository. Thereafter, the method is implemented to track the VDPV outbreak incidences recently occurred in several provinces in Lao People's Democratic Republic. The results demonstrate the accuracy and efficiency of the WOA-RVFL algorithm in detecting the VDPV outbreak incidences, as well as its superior performance to the traditional RVFL method.
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Affiliation(s)
- Ahmed A Hemedan
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, EschsurAlzette, Luxembourg
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt.
- School of Computer Science& Technology, Huazhong university of Science and Technology, Wuhan, 430074, China.
| | - Pengcheng Jiao
- Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, China
| | - Amir H Alavi
- Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan
| | - Mahmoud Bahgat
- Research Group Immune- and Bio-markers for Infection, the Center of Excellence for Advanced Sciences, the National Research Center, Cairo, Egypt
- Therapeutic Chemistry Department, the National Research Center, Cairo, Egypt
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, EschsurAlzette, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, EschsurAlzette, Luxembourg
| | - Haneen A Ghazy
- Biotechnology department, Animal Health research institute, Kafrelsheikh, Egypt
| | - Ahmed A Ewees
- Department of Computer, Damietta University, Damietta El-Gadeeda City, Egypt
| | - Songfeng Lu
- School of Computer Science& Technology, Huazhong university of Science and Technology, Wuhan, 430074, China
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Ackley SF, Pilewski S, Petrovic VS, Worden L, Murray E, Porco TC. Assessing the utility of a smart thermometer and mobile application as a surveillance tool for influenza and influenza-like illness. Health Informatics J 2020; 26:2148-2158. [PMID: 31969046 DOI: 10.1177/1460458219897152] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Kinsa Inc. sells Food and Drug Administration-cleared smart thermometers, which synchronize with a mobile application, and may aid influenza forecasting efforts. We compare smart thermometer and mobile application data to regional influenza and influenza-like illness surveillance data from the California Department of Public Health. We evaluated the correlation between the regional California surveillance data and smart thermometer data, tested the hypothesis that smart thermometer readings and symptom reports provide regionally specific predictions, and determined whether smart thermometer and mobile application improved disease forecasts. Smart thermometer readings are highly correlated with regional surveillance data, are more predictive of surveillance data for their own region and season than for other times and places, and improve predictions of influenza, but not predictions of influenza-like illness. These results are consistent with the hypothesis that smart thermometer readings and symptom reports reflect underlying disease transmission in California. Data from such cloud-based devices could supplement syndromic influenza surveillance data.
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Affiliation(s)
| | | | | | - Lee Worden
- University of California, San Francisco, USA
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45
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Aiello AE, Renson A, Zivich PN. Social Media- and Internet-Based Disease Surveillance for Public Health. Annu Rev Public Health 2020; 41:101-118. [PMID: 31905322 DOI: 10.1146/annurev-publhealth-040119-094402] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Disease surveillance systems are a cornerstone of public health tracking and prevention. This review addresses the use, promise, perils, and ethics of social media- and Internet-based data collection for public health surveillance. Our review highlights untapped opportunities for integrating digital surveillance in public health and current applications that could be improved through better integration, validation, and clarity on rules surrounding ethical considerations. Promising developments include hybrid systems that couple traditional surveillance data with data from search queries, social media posts, and crowdsourcing. In the future, it will be important to identify opportunities for public and private partnerships, train public health experts in data science, reduce biases related to digital data (gathered from Internet use, wearable devices, etc.), and address privacy. We are on the precipice of an unprecedented opportunity to track, predict, and prevent global disease burdens in the population using digital data.
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Affiliation(s)
- Allison E Aiello
- Department of Epidemiology, Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7435, USA; , ,
| | - Audrey Renson
- Department of Epidemiology, Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7435, USA; , ,
| | - Paul N Zivich
- Department of Epidemiology, Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7435, USA; , ,
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Baltrusaitis K, Vespignani A, Rosenfeld R, Gray J, Raymond D, Santillana M. Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation. JMIR Public Health Surveill 2019; 5:e13403. [PMID: 31579019 PMCID: PMC6777281 DOI: 10.2196/13403] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 07/02/2019] [Accepted: 07/19/2019] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND The Centers for Disease Control and Prevention (CDC) tracks influenza-like illness (ILI) using information on patient visits to health care providers through the Outpatient Influenza-like Illness Surveillance Network (ILINet). As participation in this system is voluntary, the composition, coverage, and consistency of health care reports vary from state to state, leading to different measures of ILI activity between regions. The degree to which these measures reflect actual differences in influenza activity or systematic differences in the methods used to collect and aggregate the data is unclear. OBJECTIVE The objective of our study was to qualitatively and quantitatively compare national and region-specific ILI activity in the United States across 4 surveillance data sources-CDC ILINet, Flu Near You (FNY), athenahealth, and HealthTweets.org-to determine whether these data sources, commonly used as input in influenza modeling efforts, show geographical patterns that are similar to those observed in CDC ILINet's data. We also compared the yearly percentage of FNY participants who sought health care for ILI symptoms across geographical areas. METHODS We compared the national and regional 2018-2019 ILI activity baselines, calculated using noninfluenza weeks from previous years, for each surveillance data source. We also compared measures of ILI activity across geographical areas during 3 influenza seasons, 2015-2016, 2016-2017, and 2017-2018. Geographical differences in weekly ILI activity within each data source were also assessed using relative mean differences and time series heatmaps. National and regional age-adjusted health care-seeking percentages were calculated for each influenza season by dividing the number of FNY participants who sought medical care for ILI symptoms by the total number of ILI reports within an influenza season. Pearson correlations were used to assess the association between the health care-seeking percentages and baselines for each surveillance data source. RESULTS We observed consistent differences in ILI activity across geographical areas for CDC ILINet and athenahealth data. ILI activity for FNY displayed little variation across geographical areas, whereas differences in ILI activity for HealthTweets.org were associated with the total number of tweets within a geographical area. The percentage of FNY participants who sought health care for ILI symptoms differed slightly across geographical areas, and these percentages were positively correlated with CDC ILINet and athenahealth baselines. CONCLUSIONS Our findings suggest that differences in ILI activity across geographical areas as reported by a given surveillance system may not accurately reflect true differences in the prevalence of ILI. Instead, these differences may reflect systematic collection and aggregation biases that are particular to each system and consistent across influenza seasons. These findings are potentially relevant in the real-time analysis of the influenza season and in the definition of unbiased forecast models.
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Affiliation(s)
- Kristin Baltrusaitis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | | | - Roni Rosenfeld
- School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Josh Gray
- athenaResearch at athenahealth, Watertown, MA, United States
| | - Dorrie Raymond
- athenaResearch at athenahealth, Watertown, MA, United States
| | - Mauricio Santillana
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States.,Department of Pediatrics, Harvard Medical School, Boston, MA, United States
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Seitzinger P, Osgood N, Martin W, Tataryn J, Waldner C. Compliance Rates, Advantages, and Drawbacks of a Smartphone-Based Method of Collecting Food History and Foodborne Illness Data. J Food Prot 2019; 82:1061-1070. [PMID: 31124717 DOI: 10.4315/0362-028x.jfp-18-547] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Accepted: 02/23/2019] [Indexed: 11/11/2022]
Abstract
HIGHLIGHTS
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Affiliation(s)
- Patrick Seitzinger
- Northern Medical Program, Faculty of Medicine, University of British Columbia, Prince George, British Columbia, Canada V2N 4Z9
| | - Nathaniel Osgood
- Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada S7N 5B4
| | - Wanda Martin
- College of Nursing, University of Saskatchewan, Saskatoon, Saskatchewan, Canada S7N 5B4
| | - Joanne Tataryn
- Centre for Food-borne, Environmental and Zoonotic Infectious Diseases (CFEZID), Infectious Disease Prevention and Control Branch, Public Health Agency of Canada, Saskatoon, Saskatchewan, Canada S7N 5B4
| | - Cheryl Waldner
- Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada S7N 5B4
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Guerra J, Acharya P, Barnadas C. Community-based surveillance: A scoping review. PLoS One 2019; 14:e0215278. [PMID: 30978224 PMCID: PMC6461245 DOI: 10.1371/journal.pone.0215278] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Accepted: 03/31/2019] [Indexed: 12/22/2022] Open
Abstract
Background Involving community members in identifying and reporting health events for public health surveillance purposes, an approach commonly described as community-based surveillance (CBS), is increasingly gaining interest. We conducted a scoping review to list terms and definitions used to characterize CBS, to identify and summarize available guidance and recommendations, and to map information on past and existing in-country CBS systems. Methods We searched eight bibliographic databases and screened the worldwide web for any document mentioning an approach in which community members both collected and reported information on health events from their community for public health surveillance. Two independent reviewers performed double blind screening and data collection, any discrepancy was solved through discussion and consensus. Findings From the 134 included documents, several terms and definitions for CBS were retrieved. Guidance and recommendations for CBS were scattered through seven major guides and sixteen additional documents. Seventy-nine unique CBS systems implemented since 1958 in 42 countries were identified, mostly implemented in low and lower-middle income countries (79%). The systems appeared as fragmented (81% covering a limited geographical area and 70% solely implemented in a rural setting), vertical (67% with a single scope of interest), and of limited duration (median of 6 years for ongoing systems and 2 years for ended systems). Collection of information was mostly performed by recruited community members (80%). Interpretation While CBS has already been implemented in many countries, standardization is still required on the term and processes to be used. Further research is needed to ensure CBS integrates effectively into the overall public health surveillance system.
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Affiliation(s)
- José Guerra
- World Health Organization (WHO), Lyon, France
- * E-mail:
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Kalimeri K, Delfino M, Cattuto C, Perrotta D, Colizza V, Guerrisi C, Turbelin C, Duggan J, Edmunds J, Obi C, Pebody R, Franco AO, Moreno Y, Meloni S, Koppeschaar C, Kjelsø C, Mexia R, Paolotti D. Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms. PLoS Comput Biol 2019; 15:e1006173. [PMID: 30958817 PMCID: PMC6472822 DOI: 10.1371/journal.pcbi.1006173] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 04/18/2019] [Accepted: 03/01/2019] [Indexed: 11/18/2022] Open
Abstract
Seasonal influenza surveillance is usually carried out by sentinel general practitioners (GPs) who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This traditional practice for surveillance generally presents several issues, such as a delay of one week or more in releasing reports, population biases in the health-seeking behaviour, and the lack of a common definition of ILI case. On the other hand, the availability of novel data streams has recently led to the emergence of non-traditional approaches for disease surveillance that can alleviate these issues. In Europe, a participatory web-based surveillance system called Influenzanet represents a powerful tool for monitoring seasonal influenza epidemics thanks to aid of self-selected volunteers from the general population who monitor and report their health status through Internet-based surveys, thus allowing a real-time estimate of the level of influenza circulating in the population. In this work, we propose an unsupervised probabilistic framework that combines time series analysis of self-reported symptoms collected by the Influenzanet platforms and performs an algorithmic detection of groups of symptoms, called syndromes. The aim of this study is to show that participatory web-based surveillance systems are capable of detecting the temporal trends of influenza-like illness even without relying on a specific case definition. The methodology was applied to data collected by Influenzanet platforms over the course of six influenza seasons, from 2011-2012 to 2016-2017, with an average of 34,000 participants per season. Results show that our framework is capable of selecting temporal trends of syndromes that closely follow the ILI incidence rates reported by the traditional surveillance systems in the various countries (Pearson correlations ranging from 0.69 for Italy to 0.88 for the Netherlands, with the sole exception of Ireland with a correlation of 0.38). The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season (2016-2017) based only on the available information of the previous years (2011-2016). Furthermore, to broaden the scope of our approach, we applied it both in a forecasting fashion to predict the ILI trend of the 2016-2017 influenza season (Pearson correlations ranging from 0.60 for Ireland and UK, and 0.85 for the Netherlands) and also to detect gastrointestinal syndrome in France (Pearson correlation of 0.66). The final result is a near-real-time flexible surveillance framework not constrained by any specific case definition and capable of capturing the heterogeneity in symptoms circulation during influenza epidemics in the various European countries.
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Affiliation(s)
| | | | | | | | - Vittoria Colizza
- INSERM, Sorbonne Université, Institut Pierre Louis d’Epidémiologie et de Santé Publique, IPLESP, Paris, France
| | - Caroline Guerrisi
- Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, IPLESP, Paris, France
| | - Clement Turbelin
- Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, IPLESP, Paris, France
| | - Jim Duggan
- School of Computer Science, National University of Ireland Galway, Galway, Ireland
| | - John Edmunds
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Chinelo Obi
- Immunisation and Countermeasures Division, National Infections Service, Public Health England, London, United Kingdom
| | - Richard Pebody
- Immunisation and Countermeasures Division, National Infections Service, Public Health England, London, United Kingdom
| | | | - Yamir Moreno
- ISI Foundation, Turin, Italy
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain
- Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
| | - Sandro Meloni
- IFISC, Institute for Cross-Disciplinary Physics and Complex Systems (CSIC-UIB), Palma de Mallorca, Spain
| | | | | | - Ricardo Mexia
- Departamento de Epidemiologia, Instituto Nacional de Saúde Doutor Ricardo Jorge, Lisbon, Portugal
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Machine-learned epidemiology: real-time detection of foodborne illness at scale. NPJ Digit Med 2018; 1:36. [PMID: 31304318 PMCID: PMC6550174 DOI: 10.1038/s41746-018-0045-1] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 07/20/2018] [Accepted: 07/26/2018] [Indexed: 11/09/2022] Open
Abstract
Machine learning has become an increasingly powerful tool for solving complex problems, and its application in public health has been underutilized. The objective of this study is to test the efficacy of a machine-learned model of foodborne illness detection in a real-world setting. To this end, we built FINDER, a machine-learned model for real-time detection of foodborne illness using anonymous and aggregated web search and location data. We computed the fraction of people who visited a particular restaurant and later searched for terms indicative of food poisoning to identify potentially unsafe restaurants. We used this information to focus restaurant inspections in two cities and demonstrated that FINDER improves the accuracy of health inspections; restaurants identified by FINDER are 3.1 times as likely to be deemed unsafe during the inspection as restaurants identified by existing methods. Additionally, FINDER enables us to ascertain previously intractable epidemiological information, for example, in 38% of cases the restaurant potentially causing food poisoning was not the last one visited, which may explain the lower precision of complaint-based inspections. We found that FINDER is able to reliably identify restaurants that have an active lapse in food safety, allowing for implementation of corrective actions that would prevent the potential spread of foodborne illness.
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