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      Will Participatory Syndromic Surveillance Work in Latin America? Piloting a Mobile Approach to Crowdsource Influenza-Like Illness Data in Guatemala

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          Abstract

          Background

          In many Latin American countries, official influenza reports are neither timely nor complete, and surveillance of influenza-like illness (ILI) remains thin in consistency and precision. Public participation with mobile technology may offer new ways of identifying nonmedically attended cases and reduce reporting delays, but no published studies to date have assessed the viability of ILI surveillance with mobile tools in Latin America. We implemented and assessed an ILI-tailored mobile health (mHealth) participatory reporting system.

          Objective

          The objectives of this study were to evaluate the quality and characteristics of electronically collected data, the user acceptability of the symptom reporting platform, and the costs of running the system and of identifying ILI cases, and to use the collected data to characterize cases of reported ILI.

          Methods

          We recruited the heads of 189 households comprising 584 persons during randomly selected home visits in Guatemala. From August 2016 to March 2017, participants used text messages or an app to report symptoms of ILI at home, the ages of the ILI cases, if medical attention was sought, and if medicines were bought in pharmacies. We sent weekly reminders to participants and compensated those who sent reports with phone credit. We assessed the simplicity, flexibility, acceptability, stability, timeliness, and data quality of the system.

          Results

          Nearly half of the participants (47.1%, 89/189) sent one or more reports. We received 468 reports, 83.5% (391/468) via text message and 16.4% (77/468) via app. Nine-tenths of the reports (93.6%, 438/468) were received within 48 hours of the transmission of reminders. Over a quarter of the reports (26.5%, 124/468) indicated that at least someone at home had ILI symptoms. We identified 202 ILI cases and collected age information from almost three-fifths (58.4%, 118/202): 20 were aged between 0 and 5 years, 95 were aged between 6 and 64 years, and three were aged 65 years or older. Medications were purchased from pharmacies, without medical consultation, in 33.1% (41/124) of reported cases. Medical attention was sought in 27.4% (34/124) of reported cases. The cost of identifying an ILI case was US $6.00. We found a positive correlation (Pearson correlation coefficient=.8) between reported ILI and official surveillance data for noninfluenza viruses from weeks 41 (2016) to 13 (2017).

          Conclusions

          Our system has the potential to serve as a practical complement to respiratory virus surveillance in Guatemala. Its strongest attributes are simplicity, flexibility, and timeliness. The biggest challenge was low enrollment caused by people’s fear of victimization and lack of phone credit. Authorities in Central America could test similar methods to improve the timeliness, and extend the breadth, of disease surveillance. It may allow them to rapidly detect localized or unusual circulation of acute respiratory illness and trigger appropriate public health actions.

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          Most cited references41

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          Predicting the Present with Google Trends

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            Google trends: a web-based tool for real-time surveillance of disease outbreaks.

            Google Flu Trends can detect regional outbreaks of influenza 7-10 days before conventional Centers for Disease Control and Prevention surveillance systems. We describe the Google Trends tool, explain how the data are processed, present examples, and discuss its strengths and limitations. Google Trends shows great promise as a timely, robust, and sensitive surveillance system. It is best used for surveillance of epidemics and diseases with high prevalences and is currently better suited to track disease activity in developed countries, because to be most effective, it requires large populations of Web search users. Spikes in search volume are currently hard to interpret but have the benefit of increasing vigilance. Google should work with public health care practitioners to develop specialized tools, using Google Flu Trends as a blueprint, to track infectious diseases. Suitable Web search query proxies for diseases need to be established for specialized tools or syndromic surveillance. This unique and innovative technology takes us one step closer to true real-time outbreak surveillance.
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              Using internet searches for influenza surveillance.

              The Internet is an important source of health information. Thus, the frequency of Internet searches may provide information regarding infectious disease activity. As an example, we examined the relationship between searches for influenza and actual influenza occurrence. Using search queries from the Yahoo! search engine ( http://search.yahoo.com ) from March 2004 through May 2008, we counted daily unique queries originating in the United States that contained influenza-related search terms. Counts were divided by the total number of searches, and the resulting daily fraction of searches was averaged over the week. We estimated linear models, using searches with 1-10-week lead times as explanatory variables to predict the percentage of cultures positive for influenza and deaths attributable to pneumonia and influenza in the United States. With use of the frequency of searches, our models predicted an increase in cultures positive for influenza 1-3 weeks in advance of when they occurred (P < .001), and similar models predicted an increase in mortality attributable to pneumonia and influenza up to 5 weeks in advance (P < .001). Search-term surveillance may provide an additional tool for disease surveillance.
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                Author and article information

                Contributors
                Journal
                JMIR Public Health Surveill
                JMIR Public Health Surveill
                JPH
                JMIR Public Health and Surveillance
                JMIR Publications (Toronto, Canada )
                2369-2960
                Oct-Dec 2017
                14 November 2017
                : 3
                : 4
                : e87
                Affiliations
                [1] 1 Center for Health Studies, Universidad del Valle de Guatemala Guatemala City Guatemala
                [2] 2 Centre de Recherche en Gestion Centre National de la Recherche Scientifique, Ecole Polytechnique Palaiseau France
                [3] 3 Center for Studies in Applied Informatics Universidad del Valle de Guatemala Guatemala City Guatemala
                [4] 4 Ministry of Health Guatemala City Guatemala
                [5] 5 Influenza Program US Centers for Disease Control and Prevention Guatemala City Guatemala
                Author notes
                Corresponding Author: José Tomás Prieto josetomasprieto@ 123456gmail.com
                Author information
                http://orcid.org/0000-0002-5156-395X
                http://orcid.org/0000-0003-4198-9380
                http://orcid.org/0000-0002-1226-9344
                http://orcid.org/0000-0003-3770-424X
                http://orcid.org/0000-0001-9970-3758
                http://orcid.org/0000-0002-8129-1609
                http://orcid.org/0000-0002-4985-4088
                http://orcid.org/0000-0002-5442-4023
                Article
                v3i4e87
                10.2196/publichealth.8610
                5705859
                29138128
                1ee0d4fb-6f76-47e2-8400-e3ba21004c93
                ©José Tomás Prieto, Jorge H Jara, Juan Pablo Alvis, Luis R Furlan, Christian Travis Murray, Judith Garcia, Pierre-Jean Benghozi, Susan Cornelia Kaydos-Daniels. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 14.11.2017.

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

                History
                : 31 July 2017
                : 7 September 2017
                : 10 September 2017
                : 4 October 2017
                Categories
                Original Paper
                Original Paper

                crowdsourcing,human flu,influenza,grippe,mhealth,texting,mobile apps,short message service,text message,developing countries

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