Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review - Thieme Connect
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Published online: 2021-04-21
© 2021 IMIA and Georg Thieme Verlag KG
Role of Participatory Health Informatics
in Detecting and Managing Pandemics:
Literature Review
Elia Gabarron1*, Octavio Rivera-Romero 2*, Talya Miron-Shatz3, 4, Rebecca Grainger5,
Kerstin Denecke6
1
Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway
2
Department of Electronic Technology, Universidad de Sevilla, Spain
3
Faculty of Business Administration, Ono Academic College, Israel
4
Winton Centre for Risk and Evidence Communication, Cambridge University, England
5
Department of Medicine, University of Otago, Wellington, New Zealand
6
Institute for Medical Informatics, Bern University of Applied Sciences, Bern, Switzerland
Summary IEEE Xplore, ACM (Association for Computing Machinery) Digital vented reaching firm conclusions about the utility of PHI in de-
Objectives: Using participatory health informatics (PHI) to detect Library, and Cochrane Library. Data from studies fulfilling the tecting and monitoring these disease outbreaks. For others, e.g.,
disease outbreaks or learn about pandemics has gained interest eligibility criteria were extracted and synthesized narratively. COVID-19, social media and online search patterns corresponded
in recent years. However, the role of PHI in understanding and Results: Out of 417 citations retrieved, 53 studies were to disease patterns, and detected disease outbreak earlier than
managing pandemics, citizens’ role in this context, and which included in this review. Most research focused on influenza-like conventional public health methods, thereby suggesting that PHI
methods are relevant for collecting and processing data are still illnesses or COVID-19 with at least three papers on other epi- can contribute to disease and pandemic monitoring.
unclear, as is which types of data are relevant. This paper aims to demics (Ebola, Zika or measles). The geographic scope ranged
clarify these issues and explore the role of PHI in managing and from global to concentrating on specific countries. Multiple Keywords
detecting pandemics. processing and analysis methods were reported, although often Epidemics, public health surveillance, social media
Methods: Through a literature review we identified studies that missing relevant information. The majority of outcomes are
explore the role of PHI in detecting and managing pandemics. reported for two application areas: crisis communication and Yearb Med Inform 2021:
Studies from five databases were screened: PubMed, CINAHL detection of disease outbreaks. http://dx.doi.org/10.1055/s-0041-1726486
(Cumulative Index to Nursing and Allied Health Literature), Conclusions: For most diseases, the small number of studies pre-
1 Introduction time delays. In the last 20 years, the use of
journalistic and other unofficial online in-
PHI is a multidisciplinary field that uses
information technology as provided through
Detecting the spread of infection in an formation sources for epidemic intelligence the web, smartphones, or wearables to in-
epidemic allows health systems and has gained interest. News aggregators such crease participation of individuals in their
governments to implement timely public as HealthMap [2] or MediSys [3] collect care process and to enable them in practicing
health interventions. The term ‘epidemic online news and information from websites self-care and shared decision-making [5]. PHI
intelligence’ refers to “all the activities or blogs to provide an overview of the deals with the resources, devices, and methods
related to early identification of potential worldwide disease situation for the purpose required to support active participation and
health threats, their verification, assessment of real-time surveillance. Data are also col- engagement of the stakeholders, such as social
and investigation in order to recommend lected from search engines or messages on media [5]. Goals to be achieved through PHI
public health measures to control them” social media, such as Twitter. These data, include improving and maintaining health and
[1]. Traditional epidemic intelligence sys- which are now being utilized as a form well-being; improving the healthcare system
tems mainly use clinical epidemiological of participatory health informatics (PHI), and health outcomes; sharing experiences;
data, such as reports from hospitals and may provide a complementary source of achieving life goals; and gaining self-edu-
healthcare providers, which often lead to information to traditional sources such as cation [6]. Beyond eliciting epidemic intelli-
health system, thereby helping to detect and gence, participatory health is used to engage
predict the volume and spread of infection or inform citizens of disease outbreaks or
*
Contributed equally and share first authorship in epidemics [4]. governmental activities related to an outbreak.
IMIA Yearbook of Medical Informatics 2021Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review
The COVID-19 pandemic has highlight- OR Crimean-Congo haemorrhagic fever studies or did not report results (i.e., study
ed the potential role of PHI in pandemics. OR Ebola virus disease OR Hendra virus protocols, opinion, frameworks or review
For example, during the COVID-19 crisis infection OR Influenza OR Lassa fever papers); or were published in languages
Chinese central government agencies used OR Marburg virus disease OR Meningitis other than English.
social media to promote citizen engagement OR MERS-CoV OR Monkeypox OR The selected articles were divided among
[4, 7]. Other studies during the COVID-19 Nipah virus infection OR coronavirus three authors (EG, KD, OR) for data ex-
pandemic have used online data to assess OR 2019-nCoV OR Covid OR Plague OR traction. We extracted: 1) Disease/epidemic,
citizens‘ risk perceptions or attitudes and Rift Valley fever OR SARS OR Smallpox settings and country; 2) Objective, data
opinions related to the pandemic [8, 9]. OR Tularaemia OR Yellow fever OR Zika source, type of information provided, user
Given these research developments, we virus disease. group, epidemic and considered region; 3)
aim to examine which methods and features • Keywords related to participatory Data preprocessing, analysis techniques and
of PHI are considered, and which roles PHI health: Social media OR social network features; 4) Outcome and reasons that limit
plays in assessing, managing and controlling site OR online social network OR online the outcome. Data were abstracted into a
pandemics. Furthermore, we aimed to iden- community OR Facebook OR Twitter OR Microsoft Excel spreadsheet standardized
tify and summarize the research about what YouTube OR Instagram OR WhatsApp for this review, piloted and refined with 10
roles citizens play in this process. Specifi- OR mHealth OR mobile health OR preliminary papers. The selected articles
cally, we aim to use a literature search and e-health OR ehealth OR mobile applica- were included in the narrative synthesis.
synthesis to address the following research tions OR apps.
questions: • Keywords related to treatment / inter-
• Which epidemics have been studied by ventions: Management OR Detection
means of a PHI approach to disease sur-
veillance?
OR surveillance OR Infoveillance OR
infodemic.
3 Results
• Which tools of PHI and which methods 3.1 Sample
are used to analyze citizens‘ contribu- The database search retrieved 461 records,
tions?
• Does citizen input correspond with epi-
2.2 Eligibility with 417 records remaining after duplicate
We uploaded all search references to Rayyan removal; 53 papers met the inclusion criteria
demic data? after full text review and were included in
• What are the barriers to the use of social (https://rayyan.qcri.org) and removed dupli-
cates. To assess the eligibility of the articles, the qualitative synthesis (Figure 1). A sum-
media for pandemic detection and man- mary of the included studies can be found
agement? in a first step, all titles and abstracts were
divided among two reviewers (EG, OR) in Appendix 2.
where each reviewer looked at half of the
papers. After title and abstract screening, in
2 Methods a second step, full text of all potentially eli- 3.2 Which Epidemics Have Been
gible articles was obtained, and articles were
We undertook a literature review to identify reviewed to confirm their eligibility by two Studied?
studies that help in answering the listed ques- reviewers independently (EG, OR). Conflicts The 53 papers considered various epidemics;
tions above. The Preferred Reporting Items were discussed with a third reviewer (KD) influenza-like illnesses (27/53; 51%) and
for Systematic Reviews and Meta-Analyses until consensus was reached. COVID-19 (11/53; 19%) were the most
(PRISMA) criteria guided the conduct and frequently studied epidemics (Appendix 3).
reporting of the review [10]. Almost all studies analyzed retrospectively
collected social media data where contrib-
2.3 Inclusion and Exclusion Criteria utors were unaware of the usage of their
Articles were included if they: a) focused on content for the purpose of epidemic surveil-
2.1 Search Strategy epidemics, disease outbreaks or any of the lance. In all these papers, the data sets were
The full search was carried out on June 10th, 20 pandemic diseases recognized by WHO created based on predefined keywords such
2020 (see Appendix 1). The search covered (https://www.who.int/emergencies/diseas- as example tweets collected using keywords
PubMed; ACM Digital Library; IEEE es/en/); b) addressed the role or features of like flu, influenza etc. (e.g., [11,12]). Only
Xplore; CINAHL and Cochrane library social media, mobile health, or other PHI; c) one study was conducted prospectively [13]
using the following keywords: were primary studies reporting results; and with data actively generated by users.
• Keywords related to epidemics or d) were in the English language. Articles About a quarter (14/53; 26%) of the pa-
pandemics: Epidemics (MeSH) OR Pan- were excluded if they: did not deal with pers had a global scope, with 10/53 (19%)
demics (MeSH) OR Disease Outbreaks epidemics, outbreak diseases or pandemics; focused on the USA and Canada, and 7/53
(MeSH) OR Chikungunya OR Cholera did not deal with PHI; were not primary (13%) on China. Australia and Malaysia
IMIA Yearbook of Medical Informatics 2021Gabarron et al.
A preprocessing stage that prepares data
to be analyzed is required when automated
analyses techniques are used. Preprocessing
techniques clean data and transform them
to the predictable and analyzable format
required by analysis algorithms. Examples
of common related techniques in natural
language processing are lowercasing, stem-
ming, lemmatization, stop word removal,
and normalization. Preprocessing is crucial
when data sources present a dynamic and
specific language like those used in social
networks. Reporting the data preprocessing
techniques used in automated analysis is rel-
evant for research reproducibility. Thirteen
studies (13/53; 25%) did not require a data
preprocessing stage for two main reasons
[11, 13, 18, 25, 26, 28, 30, 39, 41, 58, 59,
61, 62]: they were based on quantitative
data such as web search index, or authors
followed a manual approach to analyze the
collected data. Although a preprocessing
stage was required in the remaining included
studies, four of them (4/53; 7.5%) did not
report any information regarding this stage
[14, 17, 29, 60].
A total of 36 studies reported data
preprocessing. Among those, data prepro-
cessing and filtering was reported in 19
studies (19/40; 48%). Several approaches to
Fig. 1 PRISMA Flowchart of the paper selection procedure. filtering data were reported. Nineteen of the
studies reporting preprocessing information
included a data preparation stage (19/40;
48%). Information regarding data cleaning
were considered a region in three papers 3.3 Which Tools of PHI Were was the most frequently reported. However,
(6%). Japan, Madagascar, the Netherlands, many of these studies reported vaguely about
and Italy were the focus of two papers each Examined, and Which Methods data preparation that did not include details
(4%). Greece or the UK were target locations Were Used? on specific techniques and tools used. Data
of one paper (2%). Just over one in ten papers Social media was the data source in the ma- aggregation was implemented in 14 studies
(6/53, 11%) did not specify a region. jority of the included studies (49/53; 92%). (14/40; 35%) in which data from several
The objectives of the 53 studies were Among those, Twitter was the most common- sources such as Google Trends data, Twitter,
classified into six main categories: analyzing ly used channel (34/53; 64%), followed by Web search indexes, or official Centre for
contents related to epidemics (i.e., reactions, Sina Weibo (10/53, 19%) (Table 1). Other data Disease control (CDC) data were combined
opinion, attitudes, quality of the information, sources were official disease outbreak data on a weekly or daily basis. See Appendix 6
sentiment analysis, distribution patterns, (15/53; 28%), Google Trends (6/53; 11%), for further details.
etc.); detecting disease; disease monitoring and Internet search engines (6/53; 11%). Regarding the analysis techniques, the
or disease surveillance; comparing number More than two thirds of the included stud- most common analyses in the included
of posts with official disease numbers; pre- ies provided information about the content studies were the correlation analysis (23/53;
dicting future epidemics; and tracing con- of social media posts (37/53; 70%). The next 43%), followed by spatiotemporal analysis
tacts (Appendix 4). The three most common most common types of provided information using various techniques (22/53; 42%), and
objectives were analyzing content related to were the reporting of spatiotemporal data classification problem (14/53; 26%) (Table 2).
the epidemic (20/53; 38%), detecting disease from social media, found in 14 papers (26%); When it comes to features used in the
(12/53; 23%), and monitoring or surveillance and Internet search queries in 12 papers analysis, spatiotemporal features were the
(10/53; 19%). (23%) (Appendix 5). most common data used in the analysis of
IMIA Yearbook of Medical Informatics 2021Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review
Table 1 Data source used by the studies included in the review (n=53) Some papers mentioned a positive effect
of using social media for crisis communica-
tion. Posting of latest news and information
Data source References using this data source Number (%) of on how the government is handling the
papers citing this
data source
pandemic can affect citizens’ engagement
[7, 59], thereby demonstrating that posting
Social media [7,11,12,14-58] 49 (92%) can be valuable for citizen education [18].
Twitter [11,12,15,16,20,23,25–30,32–38,40,42– 34 (64%) Other papers question the usefulness or
52,55,57,59] effectiveness of social media for commu-
Sina Weibo [7,17–19,22,24,31,39,53,54] 10 (19%) nication on an epidemic [29, 32, 52] since
Baidu [17,18,31,39] 4 (7.5%) there are discrepancies between the interest
Social media (in general) [17,21,56,59] 4 (7.5%) or concerns of the population in general
Coosto (social media monitoring tool) [32,41] 1 (2%)
and the provided information by public
health authorities. Misinformation and false
Facebook [32] 1 (2%)
alarms were mentioned in two papers [48,
WeChat [14] 1 (2%) 58] (Table 3).
Wikipedia [28] 1 (2%)
YouTube [58] 1 (2%)
Official disease outbreak data (CDC, [12,17,18,20,21,24,29,31,38,39,41,46,59–61] 15 (28%)
WHO, laboratory,… etc) 3.5 Barriers to the Use of Social
Google Trends [11,12,20,39,41,55] 6 (11%) Media for Pandemic Detection and
Internet search / search engines [17,23,26,28,61,62] 6 (11%) Management
News, online news [18,20,26,56,59] 5 (9%)
In the included papers, we identified four
groups of issues that impact the outcome of
Surveys (any type) [13,21] 2 (4%) PHI for detecting or retrieving knowledge on
Spinn3r (Web and social media [60] 1 (2%) pandemics. These are usage of app data, data
indexing service) collection, behavior of individuals, and anal-
ysis and interpretation of social media data.
*
Note: some studies included more than one data source When apps are used for disease surveil-
lance, privacy issues and concerns about
personal confidentiality can hamper the data
usage. Authorizing social media apps to use
the included studies. Thirty-four studies 3.4 Does Citizen Input Correspond personal data including personal informa-
used time stamp or time series in the tion, activity status data, and spatiotemporal
analysis (34/53; 64%). Most of the studies With Epidemic Data? data is still not acceptable [14]. Another
used the location information to filter data Table 3 summarizes the reported outcomes barrier relates to data collection. Specifical-
out in the collection stage, but only 16 by disease. Several papers reported correla- ly, not all data generated on social media is
of them compared results from different tions between social media data related to available for analysis. Several research pa-
geolocation (areas, cities, or countries) COVID-19, influenza-like illnesses, mea- pers used the Twitter API which only allows
(16/53; 30%). sles, MERS-CoV, MRSA and the plague a collection of a subset of the data posted on
Twenty-three of the included studies and official case numbers [11, 30, 41, 53, Twitter, which may have resulted in leaving
analyzed features extracted from post 61, 62]. Results regarding the timeliness of relevant tweets unconsidered [12, 33, 34].
contents. Seven of those studies analyzed the detected events, i.e., whether PHI can Only one paper considered popular tweets,
words or keywords (7/53; 13%). Six stud- help in detecting outbreaks ahead of official i.e., those that were re-tweeted many times
ies used word frequencies or the Term statistics were rare and contradictory. For [52], which means that these tweets are not
Frequency-Inverse Document Frequency conjunctivitis and COVID-19, two papers representative of all tweets.
(TF-IDF) values (6/53; 11%). Five studies (3.8%) reported that social media data can Another bias may arise from censorship
used “bag of words” in their analysis (5/53; detect outbreaks as early as, or earlier than, in countries like China, thus limiting the
9%). Other features such as linguistic the official reporting mechanisms [53, 62]. completeness of the data [22]. Data col-
characteristics were used in a single study. For Ebola, one paper concluded it is un- lection from Google Trends only provides
Further details about features used in the likely that online surveillance provides an relative and not absolute values, thus hin-
analyses are reported in Appendix 7. alert more than a week before the official dering the possibility of further refining and
announcement [47]. processing them [61]. Finally, data related to
IMIA Yearbook of Medical Informatics 2021Gabarron et al.
Table 2 Analysis techniques used in included papers (n=53) disease surveillance, such as geolocation
or other user data, might be unavailable
Analysis Algorithm or technique (References using this technique) Number of or imprecise (e.g., when using search logs
papers citing this from Google or access rates of Wikipedia
technique pages) [28, 62].
Correlation analysis Spearman ([11,18,31,33,39,54]) 23 (43%) The third group of problems is related
Pearson ([19,20,37,38,43,44,48,49,55,60]) to the behavior of individuals, or citizens´
unspecified ([12,22,25,30,50,51,61]) input. When a disease (e.g., influenza) be-
Spatiotemporal analysis comes a hot topic, people do not post about
Time series analysis FDR [23], DBNM [24], KLD [47], JI [51], GCt [53], DFt [53], ARIMAX 17 (32%) it [15]. In contrast, when celebrities are con-
[55], ESA [57] cerned about a disease, there are more people
unspecified ([13,14,30,35,38,43,49,50,61,62]) posting about it [62] which may generate
Seasonal analysis SARIMA [17], SDA [17], LiR [17], BCP [28], SH-ESD [42], STL [54] 5 (9%) false concern. Public awareness of disease
unspecified [48] surveillance methods using social media
Classification
could influence behavior and consequently
Relevance classification SVM ([15,16,27,35,36,38,44,49,53]) 13 (25%) lead to false reporting [16].
LR ([35,46,55]) The fourth group of issues concerns
NB ([36,42]) the analysis and interpretation of data, i.e.,
LiR ([46]) data processing for the purpose of disease
ME ([48]) surveillance. First of all, when considering
RF [53], DT ([53]), ET ([53]), KNN ([53]), MP ([53]) free text as a data source, misspellings,
Emotions classification NB [47] 1 (2%) abbreviations, and use of slang hamper
Detection processing [27]. Second, social media data
Topic Identification LDA ([11,27,44,54]) 8 (15%) is dynamic: new words can appear (e.g., new
BTM ([33]) slang referring to a disease). This requires
RF ([54]) retraining classifiers so that new vocabulary
Km ([57]) and new anomalies in social signals can be
unspecified ([40,51]) learned [16, 27].
Symptom Recognition LDA ([21]) 1 (2%)
LR ([21])
Language detection Unspecified ([32]) 1 (2%)
Prediction/Estimation LiR ([12,21,22,37,38,46]) 8 (15%)
4 Discussion
MRM ([30,46,61])
4.1 Summary of Findings
Sentiment analysis NB ([34]) 5 (9%) This literature review and synthesis con-
ME ([34])
DLMC ([34])
firms that PHI has been used to address a
SSA ([47]) wide variety of public health issues relating
unspecified ([7,47,50]) to pandemics. Most literature has focused
on influenza or influenza-like illness or,
Other analysis
in 2020, COVID-19. The vast majority of
Qualitative analysis Thematic analysis ([59]) 2 (4%)
Classification ([31])
studies have used data from social media
posts or web search patterns with a wide
Link analysis, Influence GNA ([60]) 1 (2%)
variety of data analysis techniques. For
analysis, and/or
Communities identification most diseases, the small number of studies
identified means that firm conclusions
*
Note: some studies reported more than one type of information about the utility of PHI in detecting and
FDR: False Discovery Rate; DBNM: Dynamic Bayesian Network Model; KLD: Kullback-Leibler Divergence; monitoring these disease outbreaks cannot
JI: Jaccard Index; GCt: Granger Causality Test; DFt: Dickey-Fuller tests; ARIMAX: AutoRegressive Integrated
Moving Average model with eXogenous covariates; ESA: Exponential Smoothing Algorithm; SARIMA: Seasonal
be reached. In comparison, the extensive
AutoRegressive Integrated Moving Average; SDA: Seasonal Decomposition Analysis; LiR: Linear Regression; literature on influenza and COVID-19 (in
BCP: Bayesian Change Point; SH-ESD: Seasonal-Hybrid Extreme Studentized Deviate; STL: Seasonal-Trend spite of the fact that the literature search
decomposition based on Loess; SVM: Supported Vector Machine; LR: Logistic Regression; NB: Naïve Bayes; ended in June 2020) provides valuable
ME: Maximum Entropy; RF: Random Forest; DT: Decision Tree; ET: Extra Tree; KNN: K nearest neighbors;
MP: Multilayer Perceptron; LDA: Latent Dirichlet Allocation; BTM; Biterm Topic Model; Km: K-means; MRM:
insights into the potential for PHI to pro-
Multivariable Regression Model; DLMC: Dynamic Language Model Classifier; SSA: Stanford Sentiment Analyzer; vide additional, more timely or efficient
GNA: Girvan-Newman Algorithm. pandemic monitoring.
IMIA Yearbook of Medical Informatics 2021Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review
Table 3 Reported outcomes related to PHI per epidemic in the reviewed papers tributed across the population. For example,
a recent secondary analysis of survey data
Epidemic Outcome revealed that women were more likely to
post on COVID-19 than men; that black,
Conjunctivitis PHI (Google search data) enable earlier outbreak detection. [62]
Latino, and other non-white males were
COVID-19 PHI (number of tweets) correlates positively with daily case numbers [19,22,39,54] more likely to post on the topic than whites,
PHI (Reports of symptoms and diagnosis of COVID-19 on social media) enabled to predict daily case and that people age 65 and above were
counts up to 14 days ahead of official statistics [53] more likely to post than younger people
Value for communication and information provision: Media richness negatively predicts citizen [68]. However, existing analysis tools take
engagement through government social media, but dialogic loop facilitates engagement. Information this into account, and use the frequency
relating to the latest news about the crisis and the government’s handling of the event positively of posting as a variable in and of itself.
affects citizen engagement through government social media [7]
Research has yet to study the association
Dengue Social media user trust in information shared by health professionals and others in their online social between differential frequency of posting,
networks [40] and outbreak detection.
Ebola PHI did not enable to earlier outbreak detection [26]. It should also be noted that PHI is varied
Analysis of emotions in social media microblogging data (Twitter) may be utilized as a source of and, as such, some types of PHI are better
evidence for disease outbreak detection and monitoring [47]. at answering specific questions than oth-
Influenza, Seasonal Outbreak detection:
ers. For example, search data on the CDC
flu, Influenza-like Partially positive correlation between local influenza-like illness percentages and tweet rates [16]
website was better and faster at detecting
illnesses, Avian Positive correlation between the number of tweets, search volume or frequent daily discussions influenza trends on a national, but not state
influenza and daily case numbers [20,24,25,27,31,35,36,38,43,44,49,50,56,60]. level [69]. There is a need to clarify which
PHI (Twitter) enabled earlier detection of outbreaks [42]. Differences in the degree of sensitivity questions are best suited to be answered by
exist between social media: A high sensitivity of 92% was found for Google and a low sensitivity which source of PHI. The same holds true
of 50% was calculated for Twitter. Wikipedia had the lowest sensitivity of 33% [28]. for analysis techniques. As individuals gen-
PHI led to false alarms: Twitter flu surveillance erroneously indicated a typical flu season during erate increasingly more PHI, and its use for
2011-2012. detecting and managing pandemics persists,
Measles Value for communication and information provision: Social media provides insight into the opinions newer, more refined tools and analyses are
regarding the pandemic that are at a certain moment salient among the public [59] required to assess how PHI best assists in
There is a positive correlation between the weekly number of social media messages and the weekly promoting health and, along with that, what
number of online news articles [59] its characteristics are.
MERS-CoV High correlations between social media data and the number of confirmed MERS cases. High Finally, recent work analyzing Tweets to
correlations between social media data and the number of quarantined cases [11] capture public sentiment about COVID-19,
Methicillin-resistant
identified five dominant themes: health care
PHI enabled rapid identification of potential MRSA outbreaks [41]
Staphylococcus environment, emotional support, business
aureus (MRSA) economy, social change, and psychological
stress [70]. These are not captured in elec-
Plague (bubonic Statistically significant positive correlations were found between Google trends search data and tronic health records, and yet they provide
plague, pneumonic confirmed, suspected, and probable cases [30,61]
plague)
invaluable insights into population needs
and concern which should receive public
Zika Value for communication and information provision: Social media is unlikely to be useful or effective health attention. Since some papers claim
for communication on an epidemic; There are discrepancies between what the general public was that early alerts cannot be achieved from
most interested in, or concerned about, and what public health authorities provided [29,32,52] social media, more information needs to be
collected on various diseases to understand
to what degree patterns generalize.
4.2 Epidemics and PHI probably facilitated fast developments 4.3 PHI Tools, Methods and
in relation to the COVID-19 emergency.
Although most of the articles included Likewise, PHI research on COVID-19 may Citizens´ Input
in our review focused on influenza-like be applicable for detecting and managing Analysis of social media and web searches
illnesses and COVID-19, other epidem- future epidemics. shows that posting and search frequencies
ics have also been considered in PHI PHI is an imperfect source of data with have consistent positive correlations with
research (i.e., Ebola, Zika, Dengue, etc.). its own biases such that its content and official disease incidence numbers in the
PHI research on previous pandemics has frequency of posting are not equally dis- cases of influenza, COVID-19 and for the
IMIA Yearbook of Medical Informatics 2021Gabarron et al.
related MERS Co-V. Most recent studies The analysis of social media posts can data characteristics, several processing
in COVID-19 suggest that analysis of these also be useful for assessing the effectiveness stages are required to prepare data to be
data may even predict an increase in case of government or health authority communi- used by analysis models. However, artifi-
numbers ahead of official health system cation with their populations. Again, issues cial intelligence supporting participatory
generated data [53]. Previous literature of how representative social media users are health is still in its infancy [78]. Although
synthesis has also concluded that online of the wider population remain unresolved. the most common application of artificial
social networks generate data that can At present, each analysis requires a bespoke intelligence in participatory health is the
track pandemic development [63] and approach to data collection and analysis. secondary analysis of social media data
other disease outbreaks [64]. As these It would seem likely that this use of social [78], there are several challenges that must
data are created by citizens in their daily media and web browsing data will comple- be addressed [78]. Additionally, a combina-
lives and do not incur additional data col- ment traditional research approaches with tion of epidemiologic expertise, analytical
lection costs, they therefore represent an the advantages of more immediacy of data. expertise, and advanced computational
attractive additional source of information skills are required to interpretate data for
to complement traditional disease surveil- pandemic surveillance [77].
lance data. The timeliness of PHI provides 4.4 Barriers of Using Social Media
other significant benefits, such as noting a
certain level of awareness of and concern During Pandemics 4.5 Limitations
with COVID-19, which epidemiological We found several barriers of using social
media for detecting or retrieving knowledge One limitation of our work is that the
records do not convey. Further studies
on pandemics: data privacy and concerns data collection ended on June 10th, 2020,
validating these observations are a high
about personal confidentiality, data collec- while the COVID-19 pandemic continued
priority, particularly as the COVID-19
tion (technical limitation like the Twitter to evolve. Thus, for example, we did not
pandemic unfolds.
API data sample, lack of data completeness, include a study published in August 2020
Since citizen behavior and input may
censorship, or potential inaccuracies), on how media coverage influences Google
change as the pandemic evolves, these cor-
behavior trends, and complexity of data search trends, so that they cannot be as-
relations between infection incidence and
analysis and interpretation. sumed to only reflect people’s health [66].
secondary data sources may not remain
Data privacy and personal confidenti- Likewise, we did not include a study on
stable. On the other hand, this might reflect
ality are two of the most relevant issues of natural language processing that revealed
psychological responses to the pandemic
using social media for participatory health changes in large mental health groups (e.g.,
which are related to, but not the same
purposes [72, 73]. Included studies reported SuicideWatch and Depression) on Reddit
as, the actual prevalence of COVID-19.
privacy and confidentiality barriers showing during the COVID-19 pandemic [67]. This
Furthermore, lack of correlation between
that there are still unresolved ethical, legal, novel, illuminating work was published
disease incidence and media trends might
and technological questions. Therefore, new after our inclusion date. Regardless, we
result from adaptation and familiarity,
models of responsible and transparent data believe it was important to end the search
leading, for example, to fewer information
searches (e.g., on Wikipedia). collection and treatment addressing these at that date so that COVID-19 was still in-
The requirements for data cleaning questions are needed, especially in public cluded in the review and so that the results
and analytic methods, which may need health emergencies [74]. Limitations in of the review are released when COVID-19
to rapidly evolve, may present additional data collection from social media sources is still of relevance, so they can be utilized
barriers to this approach to disease sur- is an issue that is commonly reported in by health officials and researchers.
veillance being widely adopted in multiple the scientific literature [75, 76]. The social
geographic settings. The availability of influence that an individual’s posts on so-
standardized surveillance approaches and cial media may have on others’ behaviors In order to move the field of PHI forward for
efficient development of effective algo- is also reported as a relevant aspect to be detecting and managing future pandemics
rithms have previously been identified as considered in digital surveillance systems we recommend:
barriers to use of social media in surveil- using social media [77]. Simple methods Finding the best way to deal with the
lance of illicit drug use [65]. Furthermore, are commonly used to collect data from current barriers to fuller impact of PHI
the uncertainties about how representative social media sources, resulting in a dataset data (i.e., privacy issues, commercial
data are and if sufficient population cover- including noise (data not related to specific practices, governmental practices, etc.)
age is reached remain unresolved. pandemic). Then, a filtering stage is re- Clarifying which questions are best suited
Our synthesis of the outcomes of using quired to select efficiently the data sample. to be answered by which source of PHI
PHI in pandemics suggests that analysis of Both manual and automated filtering are Creating more refined tools and analyses
social media posting is useful in assessing commonly used to classify collected data. is required to assess how PHI best assists
disease-related informational needs, such Most automated methods are based on in promoting health during pandemics]
as reducing vaccination hesitancy [71]. artificial intelligence. Due to social media
IMIA Yearbook of Medical Informatics 2021Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review
5 Conclusions and Rivera-Romero O, Franco-Martín M, De la Torre-
Díez I. Suicide Risk Assessment Using Machine
Ebola in West Africa: Evidence from the Online
Big Data Platform. Int J Environ Res Public Health
Recommendations for Future Learning and Social Networks: a Scoping Review.
J Med Syst 2020 Nov 9;44(12):205.
2016 Aug 4;13(8):780.
19. Zhao Y, Cheng S, Yu X, Xu H. Chinese Public‘s
Research 6. Syed-Abdul S, Gabarron E, Lau AY, Househ M.
Chapter 1 - An introduction to participatory health
Attention to the COVID-19 Epidemic on Social
Media: Observational Descriptive Study. J Med
This paper explored the role of PHI in through social media. In: Participatory Health Internet Res 2020 May 4;22(5):e18825.
Through Social Media. Academic Press; 2016 Jan 20. Samaras L, García-Barriocanal E, Sicilia MA.
managing and detecting pandemics. We Comparing Social media and Google to detect
1. p. 1-9.
conclude that PHI provides an unmediated, 7. Chen Q, Min C, Zhang W, Wang G, Ma X, Evans R. and predict severe epidemics. Sci Rep 2020 Mar
authentic, and readily available source of Unpacking the black box: How to promote citizen 16;10(1):4747.
information that can be highly useful in the engagement through government social media 21. Daughton AR, Chunara R, Paul MJ. Comparison
during the COVID-19 crisis. Comput Human of Social Media, Syndromic Surveillance, and
detection and management of pandemics. Microbiologic Acute Respiratory Infection Data:
Our findings highlight the ways in which Behav 2020 Sep;110:106380.
8. Lohiniva AL, Sane J, Sibenberg K, Puumalainen T, Observational Study. JMIR Public Health Surveill
social media can be used as a form of Salminen M. Understanding coronavirus disease 2020 Apr 24;6(2):e14986.
participatory health, to manage and detect (COVID-19) risk perceptions among the public to 22. Li J, Xu Q, Cuomo R, Purushothaman V, Mackey T.
pandemics. They also illuminate the barri- enhance risk communication efforts: a practical Data Mining and Content Analysis of the Chinese
approach for outbreaks, Finland, February 2020. Social Media Platform Weibo During the Early
ers to fuller impact of such data: some of COVID-19 Outbreak: Retrospective Observational
these barriers stem from privacy issues, Euro Surveill 2020 Apr;25(13):2000317.
9. World Health Organization. Coronavirus Infoveillance Study. JMIR Public Health Surveill
some from commercial practices such as (COVID-19) events as they happen. Geneva: 2020 Apr 21;6(2):e18700.
providing relative but not absolute rankings World Health Organization; 2020:1-0. Available 23. Yom-Tov E, Borsa D, Cox IJ, McKendry RA.
Detecting disease outbreaks in mass gatherings
of trends, while others are rooted in govern- from: https://www.who.int/emergencies/diseases/
using Internet data. J Med Internet Res 2014 Jun
mental practices such as censorship. There novel-coronavirus-2019/interactive-timeline/
18;16(6):e154.
10. Liberati A, Altman DG, Tetzlaff J, Mulrow C,
is a series of questions that future studies Gøtzsche PC, Ioannidis JP, et al. The PRISMA 24. Huang J, Zhao H, Zhang J. Detecting flu trans-
could aim to answer: What are the issues statement for reporting systematic reviews and mission by social sensor in China. In: 2013 IEEE
that hamper citizens’ contribution and the International Conference on Green Computing and
meta-analyses of studies that evaluate health care
Communications and IEEE Internet of Things and
value of their contribution, and what can interventions: explanation and elaboration. J Clin
IEEE Cyber, Physical and Social Computing 2013
facilitate their contribution? To what extent Epidemiol 2009 Oct;62(10):e1-34.
Aug 20. IEEE; 2013. p. 1242-7
11. Shin SY, Seo DW, An J, Kwak H, Kim SH, Gwack
are citizens invited to contribute to outbreak J, et al. High correlation of Middle East respiratory 25. Lee B, Yoon J, Kim S, Hwang BY. Detecting social
detection and crisis communication using signals of flu symptoms. In: 8th International Con-
syndrome spread with Google search and Twitter
ference on Collaborative Computing: Networking,
PHI? Given that citizen input is instru- trends in Korea. Sci Rep 2016 Sep 6;6:32920.
Applications and Worksharing (CollaborateCom)
mental in early detection of diseases and is 12. Comito C, Forestiero A, Pizzuti C. Improving
2012 Oct 14. IEEE; 2012. p. 544-5.
influenza forecasting with web-based social data.
crucial in detection of mental distress re- In: 2018 IEEE/ACM International Conference on
26. Yom-Tov E. Ebola data from the Internet: An oppor-
sulting from diseases, governments should tunity for syndromic surveillance or a news event?
Advances in Social Networks Analysis and Mining
strive to invite such input in a standardized, In: Proceedings of the 5th international conference
(ASONAM) 2018 Aug 28; IEEE; 2018. p. 963-70.
on digital health 2015; May 18. p. 115-9.
anonymized manner. 13. Moberley S, Carlson S, Durrheim D, Dalton C.
27. Kagashe I, Yan Z, Suheryani I. Enhancing Seasonal
Flutracking: Weekly online community-based
Influenza Surveillance: Topic Analysis of Widely
surveillance of influenza-like illness in Australia.
Used Medicinal Drugs Using Twitter Data. J Med
2017 Annual Report. Commun Dis Intell 2019;43. Internet Res 2017;19:e315
References 14. Wang S, Ding S, Xiong L. A New System for
Surveillance and Digital Contact Tracing for
28. Sharpe JD, Hopkins RS, Cook RL, Striley CW.
Evaluating Google, Twitter, and Wikipedia as Tools
1. Kaiser R, Coulombier D, Baldari M, Morgan D, COVID-19: Spatiotemporal Reporting Over Net- for Influenza Surveillance Using Bayesian Change
Paquet C. What is epidemic intelligence, and how work and GPS. JMIR Mhealth Uhealth 2020 Jun Point Analysis: A Comparative Analysis. JMIR
is it being improved in Europe? Euro Surveill 2006 10;8(6):e19457. Public Health Surveill 2016 Oct 20;2(2):e161.
Feb 2;11(2):E060202.4 15. Wakamiya S, Kawai Y, Aramaki E. After the boom 29. Khatua A, Khatua A. Immediate and long-term
2. Freifeld CC, Mandl KD, Reis BY, Brownstein JS. no one tweets: microblog-based influenza detection effects of 2016 Zika Outbreak: A Twitter-based
HealthMap: global infectious disease monitoring incorporating indirect information. In: Proceedings study. In: 2016 IEEE 18th International Confer-
through automated classification and visualization of the Sixth International Conference on Emerging ence on e-Health Networking, Applications and
of Internet media reports. J Am Med Inform Assoc Databases: Technologies, Applications, and Theory Services (Healthcom) 2016 Sep 14. IEEE; 2016.
2008 Mar-Apr;15(2):150-7. 2016 Oct 17. p. 17-25. p. 1-6.
3. Rortais A, Belyaeva J, Gemo M, Van der Goot E, 16. Allen C, Tsou MH, Aslam A, Nagel A, Gawron 30. Al-Mohrej A, Agha S. Are Saudi medical students
Linge JP. MedISys: An early-warning system for JM. Applying GIS and Machine Learning Meth- aware of middle east respiratory syndrome coro-
the detection of (re-) emerging food-and feed- ods to Twitter Data for Multiscale Surveillance of navirus during an outbreak? J Infect Public Health
borne hazards. Food Research International 2010 Influenza. PLoS One 2016 Jul 25;11(7):e0157734. 2017 Jul-Aug;10(4):388-95.
Jun 1;43(5):1553-6. 17. Chen Y, Zhang Y, Xu Z, Wang X, Lu J, Hu W. 31. Gu H, Chen B, Zhu H, Jiang T, Wang X, Chen L,
4. Samaras L, García-Barriocanal E, Sicilia MA. Avian Influenza A (H7N9) and related Internet et al. Importance of Internet surveillance in public
Syndromic surveillance using web data: a sys- search query data in China. Sci Rep 2019 Jul health emergency control and prevention: evidence
tematic review. Innovation in Health Informatics 18;9(1):10434. from a digital epidemiologic study during avian
2020:39-77. 18. Liu K, Li L, Jiang T, Chen B, Jiang Z, Wang Z, influenza A H7N9 outbreaks. J Med Internet Res
5. Castillo-Sánchez G, Marques G, Dorronzoro E, et al. Chinese Public Attention to the Outbreak of 2014 Jan 17;16(1):e20.
IMIA Yearbook of Medical Informatics 2021Gabarron et al.
32. Barata G, Shores K, Alperin JP. Local chatter or (SOMA’10), 2010 Jul 25. p. 115-22. Study. JMIR Public Health Surveill 2019 Mar
international buzz? Language differences on posts 46. Ofoghi B, Mann M, Verspoor K. Ealry discovery 8;5(1):e13142.
about Zika research on Twitter and Facebook. PLoS of salient health threats: a social media emotion 61. Deiner MS, McLeod SD, Wong J, Chodosh J,
One 2018 Jan 5;13(1):e0190482. classification technique. Pac Symp Biocomput Lietman TM, Porco TC. Google Searches and
33. Mackey T, Purushothaman V, Li J, Shah N, Nali 2016;21:504-15. Detection of Conjunctivitis Epidemics Worldwide.
M, Bardier C, et al. Machine Learning to Detect 47. Mowery J. Twitter Influenza Surveillance: Quan- Ophthalmology 2019 Sep;126(9):1219-1229.
Self-Reporting of Symptoms, Testing Access, and tifying Seasonal Misdiagnosis Patterns and their 62. Al-Garadi MA, Khan MS, Varathan KD, Mujtaba
Recovery Associated With COVID-19 on Twitter: Impact on Surveillance Estimates. Online J Public G, Al-Kabsi AM. Using online social networks to
Retrospective Big Data Infoveillance Study. JMIR Health Inform 2016 Dec 28;8(3):e198. track a pandemic: A systematic review. J Biomed
Public Health Surveill 2020 Jun 8;6(2):e19509. 48. Wakamiya S, Kawai Y, Aramaki E. Twitter-Based Inform 2016 Aug;62:1-11.
34. Byrd K, Mansurov A, Baysal O. Mining Twitter Influenza Detection After Flu Peak via Tweets With 63. Bernardo TM, Rajic A, Young I, Robiadek K,
Data for Influenza Detection and Surveillance. Indirect Information: Text Mining Study. JMIR Pham MT, Funk JA. Scoping review on search
In: 2016 IEEE/ACM International Workshop Public Health Surveill 2018 Sep 25;4(3):e65. queries and social media for disease surveillance:
on Software Engineering in Healthcare Systems 49. Comito C, Forestiero A, Pizzuti C. Twitter-based a chronology of innovation. J Med Internet Res
(SEHS) May 14. IEEE; 2016. p. 43-9. Influenza Surveillance: An Analysis of the 2013 Jul 18;15(7):e147.
35. Broniatowski DA, Paul MJ, Dredze M. National 2016-2017 and 2017-2018 Seasons in Italy. In: 64. Kazemi DM, Borsari B, Levine MJ, Dooley B.
and local influenza surveillance through Twitter: Proceedings of the 22nd International Database Systematic review of surveillance by social media
an analysis of the 2012-2013 influenza epidemic. Engineering & Applications Symposium 2018 Jun platforms for illicit drug use. J Public Health (Oxf)
PLoS One 2013 Dec 9;8(12):e83672. 18. p. 175-82. 2017 Dec 1;39(4):763-76.
36. Collier N, Son NT, Nguyen NM. OMG U got flu? 50. Kanhabua N, Nejdl W. Understanding the diversity 65. Sousa-Pinto B, Anto A, Czarlewski W, Anto JM,
Analysis of shared health messages for bio-sur- of tweets in the time of outbreaks. In: Proceedings Fonseca JA, Bousquet J. Assessment of the Impact
veillance. J Biomed Semantics 2011 Oct 6;2 Suppl of the 22nd International Conference on World of Media Coverage on COVID-19–Related Google
5(Suppl 5):S9. Wide Web 2013 May 13; p. 1335-42. Trends Data: Infodemiology Study. J Med Internet
37. Chew C, Eysenbach G. Pandemics in the age 51. Gui X, Wang Y, Kou Y, Reynolds TL, Chen Y, Mei Res 2020 Aug 10;22(8):e19611.
of Twitter: content analysis of Tweets during Q, et al. Understanding the Patterns of Health In- 66. Low DM, Rumker L, Talkar T, Torous J, Cecchi G,
the 2009 H1N1 outbreak. PLoS One 2010 Nov formation Dissemination on Social Media during Ghosh SS. Natural Language Processing Reveals
29;5(11):e14118. the Zika Outbreak. AMIA Annu Symp Proc 2018 Vulnerable Mental Health Support Groups and
38. Zhang K, Arablouei R, Jurdak R. Predicting prev- Apr 16;2017:820-9. Heightened Health Anxiety on Reddit During
alence of influenza-like illness from geo-tagged 52. Shen C, Chen A, Luo C, Zhang J, Feng B, Liao COVID-19: Observational Study. J Med Internet
tweets. In: Proceedings of the 26th International W. Using Reports of Symptoms and Diagnoses Res 2020 Oct 12;22(10):e22635.
Conference on World Wide Web Companion 2017 on Social Media to Predict COVID-19 Case 67. Campos-Castillo C, Laestadius LI. Racial and Eth-
Apr 3; p. 1327-34. Counts in Mainland China: Observational Info- nic Digital Divides in Posting COVID-19 Content
39. Garazzino S, Montagnani C, Donà D, Meini veillance Study. J Med Internet Res 2020 May on Social Media Among US Adults: Secondary
A, Felici E, Vergine G, et al. Italian SITIP-SIP 28;22(5):e19421. Survey Analysis. J Med Internet Res 2020 Jul
SARS-CoV-2 paediatric infection study group. 53. Han X, Wang J, Zhang M, Wang X. Using Social 3;22(7):e20472.
Multicentre Italian study of SARS-CoV-2 in- Media to Mine and Analyze Public Opinion Relat- 68. Caldwell WK, Fairchild G, Del Valle SY. Surveil-
fection in children and adolescents, preliminary ed to COVID-19 in China. Int J Environ Res Public ling Influenza Incidence With Centers for Disease
data as at 10 April 2020. Euro Surveill 2020 Health 2020 Apr 17;17(8):2788. Control and Prevention Web Traffic Data: Demon-
May;25(18):2000600. 54. Broniatowski DA, Dredze M, Paul MJ, Dugas A. stration Using a Novel Dataset. J Med Internet Res
40. He Z, Zhang CJ, Huang J, Zhai J, Zhou S, Chiu JW, Using Social Media to Perform Local Influenza 2020 Jul 3;22(7):e14337.
Sheng et al. A New Era of Epidemiology: Digital Surveillance in an Inner-City Hospital: A Retro- 69. Hung M, Lauren E, Hon ES, Birmingham WC,
Epidemiology for Investigating the COVID-19 spective Observational Study. JMIR Public Health Xu J, Su S, et al. Social Network Analysis of
Outbreak in China. J Med Internet Res 2020 Sep Surveill 2015 Jan-Jun;1(1):e5. COVID-19 Sentiments: Application of Artifi-
17;22(9):e21685. 55. Corley CD, Cook DJ, Mikler AR, Singh KP. Adv cial Intelligence. J Med Internet Res 2020 Aug
41. Yousefinaghani S, Dara R, Poljak Z, Bernardo TM, Exp Med Biol 2010;680:559-64. 18;22(8):e22590.
Sharif S. The Assessment of Twitter‘s Potential for 56. Odlum M, Yoon S. What can we learn about the 70. Wilson SL, Wiysonge C. Social media and vac-
Outbreak Detection: Avian Influenza Case Study. Ebola outbreak from tweets? Am J Infect Control cine hesitancy. BMJ Global Health 2020 Oct
Sci Rep 2019 Dec 3;9(1):18147. 2015 Jun;43(6):563-71. 1;5(10):e004206.
42. Nagel AC, Tsou MH, Spitzberg BH, An L, Gawron 57. Li HO, Bailey A, Huynh D, Chan J. YouTube as a 71. Rivera-Romero O, Konstantinidis S, Denecke K,
JM, Gupta DK, et al. The complex relationship source of information on COVID-19: a pandemic Gabarrón E, Petersen C, Househ M, et al. Ethical
of realspace events and messages in cyberspace: of misinformation? BMJ Glob Health 2020 Considerations for Participatory Health through
case study of influenza and pertussis using tweets. May;5(5):e002604. Social Media: Healthcare Workforce and Policy
J Med Internet Res 2013 Oct 24;15(10):e237. 58. Mollema L, Harmsen IA, Broekhuizen E, Clijnk R, Maker Perspectives: Contribution of the IMIA
43. Aslam AA, Tsou MH, Spitzberg BH, An L, De Melker H, Paulussen T, et al. Disease detection Participatory Health and Social Media Working
Gawron JM, Gupta DK, et al. The reliability of or public opinion reflection? Content analysis of Group. Yearb Med Inform 2020 Aug;29(1):71-6.
tweets as a supplementary method of seasonal tweets, other social media, and online newspapers 72. Golinelli D, Boetto E, Carullo G, Nuzzolese AG,
influenza surveillance. J Med Internet Res 2014 during the measles outbreak in The Netherlands in Landini MP, Fantini MP. Adoption of Digital
Nov 14;16(11):e250. 2013. J Med Internet Res 2015 May 26;17(5):e128. Technologies in Health Care During the COVID-19
44. Abd-Alrazaq A, Alhuwail D, Househ M, Hamdi 59. Corley CD, Cook DJ, Mikler AR, Singh KP. Text Pandemic: Systematic Review of Early Scien-
M, Shah Z. Top Concerns of Tweeters During the and structural data mining of influenza mentions tific Literature. J Med Internet Res 2020 Nov
COVID-19 Pandemic: Infoveillance Study. J Med in web and social media. Int J Environ Res Public 6;22(11):e22280.
Internet Res 2020 Apr 21;22(4):e19016. Health 2010 Feb;7(2):596-615. 73. Almeida BD, Doneda D, Ichihara MY, Barral-Netto
45. Culotta A. Towards detecting influenza epidemics 60. Bragazzi NL, Mahroum N. Google Trends Pre- M, Matta GC, Rabello ET, et al. Personal data
by analyzing Twitter messages. In Proceedings dicts Present and Future Plague Cases During the usage and privacy considerations in the COVID-19
of the 1st Workshop on Social Media Analytics Plague Outbreak in Madagascar: Infodemiological global pandemic. Ciência & Saúde Coletiva 2020
IMIA Yearbook of Medical Informatics 2021Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review
Jun 5;25:2487-92. Inform 2016 Aug;62:1-11. Correspondence to:
74. Castillo-Sánchez G, Marques G, Dorronzoro E, 76. Salathé M, Bengtsson L, Bodnar TJ, Brewer DD, Kerstin Denecke
Rivera-Romero O, Franco-Martín M, De la Torre- Brownstein JS, Buckee C, et al. Digital epidemi- Bern University of Applied Sciences
Díez I. Suicide Risk Assessment Using Machine ology. PLoS Comput Biol 2012;8(7):e1002616. Institute for Medical Informatics
Learning and Social Networks: a Scoping Review. 77. Denecke K, Gabarron E, Grainger R, Konstan- Quellgasse 1
J Med Syst 2020 Nov 9;44(12):205. tinidis ST, Lau A, Rivera-Romero O, et al. Artificial 2502 Biel / Switzerland
75. Al-Garadi MA, Khan MS, Dewi K, Varathan GM, Intelligence for Participatory Health: Applications, E-mail: kerstin.denecke@bfh.ch
Al-Kabsi AM. Using online social networks to Impact, and Future Implications. Yearb Med In-
track a pandemic: A systematic review. J Biomed form 2019 Aug;28(1):165-73.
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