Google Technology in the Surveillance of Hand Foot Mouth Disease in Asia

Cayce, Rachael; Hesterman, Kathleen; Bergstresser, Paul R.

doi:10.6000/ijipem/2014/4

Cited by 3 publications

(3 citation statements)

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“…Different types of surveillance systems, and therein syndromic surveillance, (summarised in Table 1 ), can involve active or passive engagement with participants, whether it is performed at the individual or clinic level, or being healthcare or community-based. For instance, non-participatory passive approaches to syndromic surveillance include monitoring Google search terms [ 3 , 4 ], or Tweets [ 5 , 6 ], making use of large pools of data not specifically collected from participants for syndromic surveillance, but which can be analysed for trends in disease-related keywords. More indirect measures of disease occurrence can also be used in passive surveillance, such as school or hospital staff absenteeism [ 7 ] and pharmaceutical sales [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Assessing Commitment and Reporting Fidelity to a Text Message-Based Participatory Surveillance in Rural Western Uganda

et al. 2016

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Syndromic surveillance, the collection of symptom data from individuals prior to or in the absence of diagnosis, is used throughout the developed world to provide rapid indications of outbreaks and unusual patterns of disease. However, the low cost of syndromic surveillance also makes it highly attractive for the developing world. We present a case study of electronic participatory syndromic surveillance, using participant-mobile phones in a rural region of Western Uganda, which has a high infectious disease burden, and frequent local and regional outbreaks. Our platform uses text messages to encode a suite of symptoms, their associated durations, and household disease burden, and we explore the ability of participants to correctly encode their symptoms, with an average of 75.2% of symptom reports correctly formatted between the second and 11th reporting timeslots. Concomitantly we identify divisions between participants able to rapidly adjust to this unusually participatory style of data collection, and those few for whom the study proved more challenging. We then perform analyses of the resulting syndromic time series, examining the clustering of symptoms by time and household to identify patterns such as a tendency towards the within-household sharing of respiratory illness.

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Section: Introductionmentioning

confidence: 99%

Assessing Commitment and Reporting Fidelity to a Text Message-Based Participatory Surveillance in Rural Western Uganda

et al. 2016

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“…These traces of disease observations are embedded in search queries [5, 7, 9, 12, 14, 17, 21, 25, 26, 31, 32, 33, 39, 49, 50, 53, 59, 63, 64, 71, 72 73, 77, 78, 81, 85, 87, 90, 97, 103, 104, 109, 119, 126, 127, 131, 132, 141, 142, 144, 146, 157, 158, 162, 163, 166, 168, 169, 170, 173, 177, 179, 180, 182], social media messages [1, 2, 8, 10, 20, 36, 40, 41, 42, 46, 51, 60, 62, 68, 76, 84, 89, 92, 93, 115, 116, 118, 123, 124, 148, 149, 151, 176], web server access logs [57, 79, 101, 105], and combinations thereof [13, 19, 30, 91, 136, 143, 167]. …”

Section: Related Workmentioning

confidence: 99%

“…The disease surveillance work cited above has been applied to a wide variety of infectious and non-infectious conditions: allergies [87], asthma [136, 176], avian influenza [25], cancer [39], chicken pox [109, 126], chikungunya [109], chlamydia [42, 78, 109], cholera [36, 57], dengue [7, 31, 32, 57, 62, 109], diabetes [42, 60], dysentery [180], Ebola [5, 57], erythromelalgia [63], food poisoning [12], gastroenteritis [45, 50, 71, 126], gonorrhea [77, 78, 109], hand foot and mouth disease [26, 167], heart disease [51, 60], hepatitis [109], HIV/AIDS [57, 76, 177, 180], influenza [1, 2, 8, 9, 10, 13, 19, 20, 21, 30, 33, 40, 41, 43, 46, 48, 53, 57, 59, 68, 72, 73, 79, 81, 84, 85, 89, 90, 91, 92, 93, 97, 101, 103, 104, 105, 109, 115, 116, 118, 123, 124, 126, 131, 132, 141, 14...…”

Section: Related Workmentioning

confidence: 99%

Measuring Global Disease with Wikipedia

Priedhorsky

Osthus

Daughton

et al. 2017

Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

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Effective disease monitoring provides a foundation for effective public health systems. This has historically been accomplished with patient contact and bureaucratic aggregation, which tends to be slow and expensive. Recent internet-based approaches promise to be real-time and cheap, with few parameters. However, the question of when and how these approaches work remains open. We addressed this question using Wikipedia access logs and category links. Our experiments, replicable and extensible using our open source code and data, test the effect of semantic article filtering, amount of training data, forecast horizon, and model staleness by comparing across 6 diseases and 4 countries using thousands of individual models. We found that our minimal-configuration, language-agnostic article selection process based on semantic relatedness is effective for improving predictions, and that our approach is relatively insensitive to the amount and age of training data. We also found, in contrast to prior work, very little forecasting value, and we argue that this is consistent with theoretical considerations about the nature of forecasting. These mixed results lead us to propose that the currently observational field of internet-based disease surveillance must pivot to include theoretical models of information flow as well as controlled experiments based on simulations of disease.

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Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data

et al. 2016

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The estimation of disease prevalence in online search engine data (e.g., Google Flu Trends (GFT)) has received a considerable amount of scholarly and public attention in recent years. While the utility of search engine data for disease surveillance has been demonstrated, the scientific community still seeks ways to identify and reduce biases that are embedded in search engine data. The primary goal of this study is to explore new ways of improving the accuracy of disease prevalence estimations by combining traditional disease data with search engine data. A novel method, Biased Sentinel Hospital-based Area Disease Estimation (B-SHADE), is introduced to reduce search engine data bias from a geographical perspective. To monitor search trends on Hand, Foot and Mouth Disease (HFMD) in Guangdong Province, China, we tested our approach by selecting 11 keywords from the Baidu index platform, a Chinese big data analyst similar to GFT. The correlation between the number of real cases and the composite index was 0.8. After decomposing the composite index at the city level, we found that only 10 cities presented a correlation of close to 0.8 or higher. These cities were found to be more stable with respect to search volume, and they were selected as sample cities in order to estimate the search volume of the entire province. After the estimation, the correlation improved from 0.8 to 0.864. After fitting the revised search volume with historical cases, the mean absolute error was 11.19% lower than it was when the original search volume and historical cases were combined. To our knowledge, this is the first study to reduce search engine data bias levels through the use of rigorous spatial sampling strategies.

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Google Technology in the Surveillance of Hand Foot Mouth Disease in Asia

Cited by 3 publications

References 0 publications

Assessing Commitment and Reporting Fidelity to a Text Message-Based Participatory Surveillance in Rural Western Uganda

Assessing Commitment and Reporting Fidelity to a Text Message-Based Participatory Surveillance in Rural Western Uganda

Measuring Global Disease with Wikipedia

Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data

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