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\begin{document}

\title{Monitoring online media reports for the early detection of unknown
diseases: insights from a retrospective study of COVID-19 emergence}



\author[1]{Sarah Valentin}%
\author[2]{Alizé Mercier}%
\author[3]{Renaud Lancelot}%
\author[4]{Mathieu Roche}%
\author[2]{Elena Arsevska}%
\affil[1]{Cirad, UMR Land, Environment, Remote Sensing and Spatial Information (Tetis)}%
\affil[2]{Cirad}%
\affil[3]{CIRAD}%
\affil[4]{Cirad, UMR Land, Environment, Remote Sensing and Spatial Information (Tetis) }%


\vspace{-1em}



  
  \date{May 08, 2024}


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\selectlanguage{english}
\begin{abstract}
Event-based surveillance (EBS) systems monitor a broad range of
information sources to detect early signals of disease emergence,
including new and unknown diseases. Following the emergence of a newly
identified coronavirus --so-called COVID-19, in humans in December 2019
in Wuhan, China, we conducted a retrospective analysis of the capacity
of three Event-Based Systems (EBS) systems (ProMED, HealthMap and
PADI-web) to detect early signals of this emergence. We evaluated the
changes in the online news vocabulary coinciding with the period before
/ after the identification of COVID-19, as well as the assessment of its
contagiousness and pandemic potential. ProMED was the timeliest EBS,
detecting signals one day before the official notification. At this
early stage, the specific vocabulary was related to ``pneumonia
symptoms'' and ``mystery illness''. Once COVID-19 was identified, the
vocabulary changed to virus family and specific COVID-19 acronyms. Our
results suggest the three EBS systems are complementary regarding data
sources, and all need improvements regarding timeliness. EBS methods
should be adapted to the different stages of disease emergence to
improve the early detection of future emergence of unknown pathogens.%
\end{abstract}%



\sloppy


\textbf{Title:}

\textbf{Running head:} Detection of COVID-19 emergence in online news.

\selectlanguage{ngerman}\textbf{Sarah Valentin\textsuperscript{1,2}, Alizé
Mercier\textsuperscript{2}, Renaud Lancelot\textsuperscript{2}, Mathieu
Roche\textsuperscript{1} and Elena Arsevska\textsuperscript{2}}
\selectlanguage{english}\textsuperscript{*+}

\emph{\textsuperscript{1}UMR TETIS, CIRAD, F-34398 Montpellier, France.
TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier,
France.\textsuperscript{2}UMR ASTRE, CIRAD, F-34398 Montpellier, France.
ASTRE, Univ Montpellier, CIRAD, INRAE, Montpellier,
France.}*Corresponding author. \selectlanguage{english}\textsuperscript{+}E-mail:
elena.arsevska@cirad.fr\textbf{Article type:} Rapid
communication\textbf{Words counts:} 2,115 words\textbf{Abstract}
Event-based surveillance (EBS) systems monitor a broad range of
information sources to detect early signals of disease emergence,
including new and unknown diseases. Following the emergence of a newly
identified coronavirus --so-called COVID-19, in humans in December 2019
in Wuhan, China, we conducted a retrospective analysis of the capacity
of three Event-Based Systems (EBS) systems (ProMED, HealthMap and
PADI-web) to detect early signals of this emergence. We evaluated the
changes in the online news vocabulary coinciding with the period before
/ after the identification of COVID-19, as well as the assessment of its
contagiousness and pandemic potential. ProMED was the timeliest EBS,
detecting signals one day before the official notification. At this
early stage, the specific vocabulary was related to ``pneumonia
symptoms'' and ``mystery illness''. Once COVID-19 was identified, the
vocabulary changed to virus family and specific COVID-19 acronyms. Our
results suggest the three EBS systems are complementary regarding data
sources, and all need improvements regarding timeliness. EBS methods
should be adapted to the different stages of disease emergence to
improve the early detection of future emergence of unknown
pathogens.\textbf{Keywords:} epidemic intelligence, online news,
emerging disease, PADI-web, COVID-19, One Health

\textbf{Introduction}

Epidemic intelligence (EI) aims to detect, monitor and assess potential
health threats for early warning and rapid response (Paquet et al.,
2006). In addition to the indicator-based surveillance of official
sources, public and animal health agencies increasingly integrate an
event-based surveillance (EBS) component to their EI system. The EBS
uses unstructured data from unofficial sources such as online news to
improve the early detection of emerging infectious diseases (EIDs).
Since the late 1990s, several free-access EBS systems support the EI
process, such as the Program for Monitoring Emerging Disease or ProMED
(Woodall, 2001), HealthMap (Brownstein et al., 2008) and since recently
PADI-web (Valentin, Arsevska, et al., 2020). ProMED is a human-curated
system launched in 1994 by the~International Society for Infectious
Diseases~(ISID). The system relies on a large network of experts
worldwide who produce and share verified reports on disease outbreaks in
a common platform (Carrion \& Madoff, 2017). HealthMap is a
semi-automated system founded by the Boston Children's Hospital in 2006.
The tool monitors both official and non-official news sources on the web
(Freifeld et al., 2008). Both HealthMap and ProMED monitor a broad range
of human, animal and environmental known and unknown threats. The
Platform for Automated extraction of animal Disease Information from the
web (PADI-web) was created in 2016 to monitor online animal
health-related news for the French Epidemic Intelligence System (FEIS)
(Arsevska et al., 2018, Valentin et al., 2020). Both HealthMap and
PADI-web automatically retrieve health-related news from Google News
using customized Really Simple Syndication (RSS) feeds. For the
detection of news, the two systems use terms for known diseases and
terms for clinical signs and syndromes (Arsevska et al., 2016). All
three EBS systems monitor news in multiple languages, including Chinese.

On Dec. 31 2019, local health officials of the Chinese city of Wuhan
reported a cluster of 27 cases of ''pneumonia of unknown cause''. These
cases were linked to a wholesale seafood market in the city. In January
2020, the first death was reported, and the aetiology was identified as
a new coronavirus, SARS-Cov-2, and the disease was named COVID-19. The
first epidemiological study on patients with laboratory-confirmed
COVID-19 infection reported an onset of illness as early as Dec. 1 2019
(Huang et al., 2020).

This retrospective study aimed first to evaluate three EBS systems
(ProMED, HealthMap and PADI-web) and their capacity to detect the
COVID-19 emergence in China timely. Secondly, we focused on PADI-web to
understand how an animal health EBS system contributed to the detection
of a human EID. We analysed the RSS feeds from PADI-web that detected
the COVID-19-related news articles (further referred to as ``news'').
Thirdly, we assessed the vocabulary in the news detected by PADI-web and
its change in relation to the identification of the pathogen and the
spread of the EID.

\textbf{Material and methods}

\textbf{2.1. Detection of COVID-19-related news}

To assess the timeliness of the three EBS, we searched for news from
Dec. 1\textsuperscript{st}, to Dec. 31, 2019. We compared the first news
regarding publication date, language, and source.

To understand how PADI-web detected the COVID-19 emergence, we further
filtered a second corpus of news published from Dec. 31, 2019, to Jan.
26, 2020, containing at least one of the following words in the title
and body of the news: ``pneumonia'', ``respiratory illness'',
``coronavirus'', ``nCoV'' (an early name for COVID-19), and ``Wuhan''.
After manual verification of their relevance, we retained for analysis
275 out of 333 news (Valentin, Mercier, et al., 2020).

To assess the link between the animal health RSS feeds from PADI-web and
the detected news, we analysed them according to the RSS feed used for
their retrieval. To this end, we read each news and categorized it into
i) disease-specific RSS feeds (containing specific disease names), and
ii) syndromic RSS feeds (containing combinations of symptoms and animal
hosts).

\textbf{2.2. Vocabulary in the news}

To analyse the vocabulary change through the period ranging from
COVID-19 discovery to its spread outside China, we extracted terms from
the whole corpus. For this purpose, we used a ranking function based on
terms frequency and importance11Using the F-TFIDF-C measure
(Lossio-Ventura et al., 2014) with the support of \emph{BioTex}, a
text-mining tool adapted to the biomedical area (Lossio-Ventura et al.,
2016). \emph{BioTex} is based on the use of (i) a relevant combination
of information retrieval techniques and statistical methods, and (ii) a
list of syntactic structures of the terms that have been learnt with
relevant sources (e.g. MeSH). The terms extracted with \emph{BioTex} can
be simple (e.g. \emph{influenza}), or compound (e.g. \emph{avian
influenza}), and are lowercased. We further identified the terms
referring to COVID-19, such as ``new virus'' and ``mystery pneumonia''.
We manually categorized the terms as ``mystery'' (terms referring to the
unknown threat), ``pneumonia'' (terms referring to the clinical signs),
``coronavirus'' (terms referring to the virus taxonomy) and
``technical'' (technical acronyms for the virus itself). One news can
contain terms from different categories. We calculated the daily
proportion of each category, expressed as the sum of the occurrences of
the category divided by the total number of occurrences.

\textbf{Results}

\textbf{3.1. Detection of news}ProMED was the first to detect and report
a news from a Chinese online
source22https://promedmail.org/promed-post/?id=6864153\}. The ProMED
report dated from Dec. 30, 2019, one day before the first official
notification of pneumonia-like cases in Wuhan (Wuhan Municipal Health
Commission, 2020). PADI-web and HealthMap respectively detected three
and one COVID-19-related news on Dec. 31, 2019, the same day as the
first official notification of pneumonia-like cases in Wuhan (one
HealthMap news from an English source, three PADI-web news from two
English and one Chinese source). The news detected by the three EBS
originated from five different media sources. From 275 COVID-19-related
news retrieved by PADI-web, 45.5\% (\emph{n}=125) were retrieved by
disease-specific RSS, and the remaining 54.5\% (\emph{n}=150) were
retrieved by syndromic RSS feeds (\textbf{Table 1}). Content-wise,
31.7\% (\emph{n}=87) of the news compared COVID-19 to five animal
diseases (avian influenza, African swine fever, classical swine fever,
West Nile virus, and Rift Valley fever), 24.4\% (\emph{n}=67) of the
news described the broad range of animal species susceptible to
coronaviruses, 18.2\% (\emph{n}=50) described ruling-out avian influenza
from diagnosis of COVID-19, and 7.7\% (\emph{n}=21) described ongoing
outbreaks in addition to COVID-19 (avian influenza, African swine fever,
classical swine fever, and foot-and-mouth disease), 2.5\% (\emph{n}=7)
referred to animal species present in the Chinese markets as being the
potential COVID-19 source, and 0.7\% (\emph{n}=2) news advised to avoid
contact with animals. Irrelevant keywords matches were found in 12 news
(e.g. finding a host keyword in the name of a source), and no link could
be established between the RSS feed and the article for 29 remaining
news (10.5\%).

\textbf{3.2. Vocabulary of the news}

From the terms referring either to the virus or the disease, 18 terms
belonged to the category ``pneumonia'', 8 terms belonged to the category
``mystery'', 3 terms belonged to the category ``coronavirus'' (one of
them, ``coronovirus'' being a misspelt form for ``coronavirus''), and 7
terms belonged to the category ``technical'' (\textbf{Table 2} ).

Before identification of the virus (Dec. 31, 2019 - Jan. 8, 2020),
58.1\% (\emph{n} =317/586) of the COVID-19 terms were from category
``pneumonia'', 29.1\% (\emph{n} =159/586) were from category ``mystery''
and 12.8\% (\emph{n} =70/586) were from category ``coronavirus''. From
the official identification to the first report of a case outside China
(Jan. 09, 2020 - Jan. 12, 2020), 48.5\% (\emph{n} =127/262) of the terms
were from category ``coronavirus'', 34.7\% (\emph{n} =91/262) were from
category ``pneumonia'', and 16.8\% (\emph{n} =44/262) were from category
``mystery''. From this first report to the confirmation of
human-to-human transmission (Jan. 13, 2020 - Jan. 19, 2020), 58.3\%
(\emph{n} =196/336) of the terms were from category ``coronavirus'',
27.4\% (\emph{n} =92/336) were from category ``pneumonia'', 11.3\%
(\emph{n} =38/336) were from category ``mystery'', and 3.0\% (\emph{n}
=10/336) were from category ``technical''. From the confirmation of
human-to-human transmission to the end of the studied period 62.9\%
(\emph{n} =906/1440) of the terms were from category ``coronavirus'',
17.4\% (\emph{n} =250/1440) were from category ``technical'', 14.1\%
(\emph{n} =203/1140) were from category ``pneumonia'', and 5.6\%
(\emph{n} =81/1140) were from category ``mystery'' (\textbf{Figure 1} ).

Among the three compared EBS systems, only ProMED relies on local expert
information to alert on health threats. This result suggests the network
of local experts rooted in the field is crucial for the detection of EID
events and their reporting. On the other hand, HealthMap and PADI-web
detected the news on the same day than the official reporting. Thus, it
is important to understand their current limitations and stress the role
of experts in EBS systems. Further studies should also assess whether
the timeliness of automated systems depends on the communication
strategies of online media, what their threshold for reporting health
events is, and how these features influence the sensitivity of web-based
EBS systems.

The three EBS systems included in this study monitor media in multiple
languages, thus making it possible to detect news from local media. A
further increase in the number of available languages should improve the
sensitivity of the EBS systems (Barboza et al., 2014). Our study also
showed that the three EBS systems are complementary regarding scope
(animal health, animal and public health), moderation (manual,
semi-automated, automated), and the number of covered languages.

PADI-web could retrieve news through animal-health related RSS feeds,
thus proving useful to detect relevant information for public health
risk assessors. For example, many of the news detected by PADI-web
compared the magnitude and the economic impact of COVID-19 with avian
influenza and African swine fever outbreaks in China. Indeed, before the
identification of COVID-19, the pneumonia-like illness was compared to
avian influenza zoonotic infections. Some news also presented a summary
of several recent disease outbreaks in China, including African swine
fever (which is not a zoonotic disease), thus explaining why they were
captured.

The ability of EBS tools to embrace a broad scope of health-related
topics through a limited number of queries (RSS feeds) is a significant
strength compared to formal sources. This capacity largely depends on
the intrinsic features of online news in which outbreak-related content
is often enriched with additional information, such as comparisons with
previous disease outbreaks, thus increasing the probability of being
detected by EBS tools. However, the probability of detection of an EID
event might be higher for (actually or assumed) zoonotic diseases and
countries with ongoing animal disease outbreaks. This is not a strong
limitation in practice.

In addition to syndromic feeds, disease-specific feeds equally
contributed to the first detection of COVID-19 by PADI-web, thus
highlighting the importance of combining both disease-specific and
syndromic feeds. Furthermore, the vocabulary used to describe COVID-19
emergence before the virus identification included terms semantically
related to ``unknown'' and ``mysterious'' events. Therefore, their
integration into existing RSS feeds may increase the detection and
retrieval of relevant news. We suggest enriching the identification of
classic epidemiological entities (e.g. disease, hosts, locations, dates)
in the news content with this category of terms.

Our results indicated that the vocabulary changes as the disease
spreads. Thus, EBS methods used to retrieve and analyse news from the
web should be adapted to the different stages of disease epidemiology.
With MERS-CoV in 2014 and SARS in 2003, COVID-19 is the third
coronavirus emergence in the past two decades, highlighting the need to
monitor closely the emergence of pneumonia-like illnesses using existing
EBS systems. Our results highlight the complementarity of existing
systems and underline the need for collaborative development.
Mutualizing resources from veterinary and public health seems crucial to
improve the early detection of unknown diseases in a One Health context.
Future work will focus on identifying the most relevant keywords for the
rapid detection of unknown threats, in collaboration with other EBS
systems. Also, EBS tools may be used in a broader perspective, such as
monitoring the implementation of protective and control measures. These
efforts invested in improving the timeliness and sensitivity of EBS
systems only make sense if their output (alerts of EID events) is
designed, supervised, and interpreted by epidemiologists in
collaboration with disease experts and reference laboratories. Most
importantly, this output should feed the information flow used by the
health managers and decision-makers, for an early reaction to control
EID events before they can spread.\textbf{Acknowledgements} The authors
acknowledge ProMED for data sharing. This work was in part funded by the
H2020 ``Monitoring outbreak events for disease surveillance in a data
science context'' (MOOD) project under grant agreement No 874850
(\emph{https://mood-h2020.eu/})., the French General Directorate for
Food (DGAL), the French Agricultural Research Centre for International
Development (CIRAD) and the SONGES Project FEDER and Occitanie. This
work was supported by the French National Research Agency under the
Investments for the Future Program, referred to as
ANR-16-CONV-0004.\textbf{Data Sharing and Accessibility}The data that
support the findings of this study are openly available in CIRAD
Dataverse at http://doi.org/doi:10.18167/DVN1/MSLEFC.\textbf{Conflict of
interests}

The authors declare no conflict of interests.

\textbf{Ethics Statement}

The authors confirm that the ethical policies of the journal, as noted
on the journal's author guidelines page, have been adhered to. No
ethical approval was required because this study did not involve any
experimental protocol on humans or animals, and only uses publicly
available online data.

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Chinese{]}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Link with COVID-2019 & Type of RSS feed & Type of RSS feed & Type of RSS
feed\tabularnewline
\midrule
\endhead
& Disease-specific & Syndromic & Total\tabularnewline
Comparison with another disease & 20.4\% (\emph{n}=56) & 11.3\%
(\emph{n}=31) & 31.7\% (\emph{n}=87)\tabularnewline
Disease ruled out & 17.8\% (\emph{n}=49) & 0.4\% (\emph{n}=1) & 18.2\%
(\emph{n}=50)\tabularnewline
Aggregation with other disease outbreaks & 6.2\% (\emph{n}=17) & 1.5\%
(\emph{n}=4) & 7.7\% (\emph{n}=21)\tabularnewline
Coronaviruses in animals & - & 24.4\% (\emph{n}=67) & 24.4\%
(\emph{n}=67)\tabularnewline
Market animals & - & 2.5\% (\emph{n}=7) & 2.5\%
(\emph{n}=7)\tabularnewline
Avoid contact with animals & - & 0.7\% (\emph{n}=2) & 0.7\%
(\emph{n}=2)\tabularnewline
Irrelevant keywords matches & 0.4\% (\emph{n}=1) & 4.0\% (\emph{n}=11) &
4.4\% (\emph{n}=12)\tabularnewline
Unknown & 0.7\% (\emph{n}=2) & 9.8\% (\emph{n}=27) & 10.5\%
(\emph{n}=29)\tabularnewline
Total & \textbf{45.5\% (n=125)} & \textbf{54.5\% (n=150)} &
\textbf{100\% (275)}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 1.} Percentage (\%) and number (n) of COVID-19-related
news retrieved by PADI-web from 31\textsuperscript{st} Dec. 2019 to
26\textsuperscript{th} Jan. 2020. Each article is categorized by type of
feed (disease-related or syndromic) according to the link between the
feed and COVID-19.\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
Category & Terms\tabularnewline
\midrule
\endhead
pneumonia & pneumonia, respiratory outbreak, lung disease, respiratory
tract illness, respiratory illness, respiratory infection,
pneumonia-like disease, upper-respiratory illness, respiratory
condition, lung infection, pneumonia-like cases, pneumonia-like illness,
respiratory virus, lung virus, pneumonia-like virus, pneumonia-causing
virus, pneumonialike virus\tabularnewline
mystery & mystery, mysterious, unidentified, undocumented, disease x,
unknown, abnormal, unexplained\tabularnewline
technical & 2019-ncov, ncov, 2019 novel coronavirus, n-cov2019, novel
coronavirus 2019, ncov2019, cov2019\tabularnewline
coronavirus & coronavirus, betacoronavirus, coronovirus\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2.} Terms, used to describe SARS-CoV-2 and COVID-19 in the
corpus and their corresponding category after manual classification.\selectlanguage{english}
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