Early Detection of Public Health Emergencies of International Concern through Undiagnosed Disease Reports in ProMED-Mail

Rolland, Camille; Lazarus, C.; Giese, Coralie; Monate, Bastien; Travert, Anne-Sophie; Salomon, Jérôme

doi:10.3201/eid2602.191043

Cited by 13 publications

(11 citation statements)

References 6 publications

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“…ProMED (The Program for Monitoring Emerging Diseases) is an internet-based reporting system for emerging infectious or toxinmediated diseases [15][16][17][18][19][20]. It was founded in 1994 and is currently a https://doi.org/10.1016/j.tmaid.2020.101575 Received 27 January 2020; Received in revised form 3 February 2020; Accepted 4 February 2020 program of the International Society for Infectious Diseases [15][16][17]21].…”

Section: Introductionmentioning

confidence: 99%

Coronavirus infections reported by ProMED, February 2000–January 2020

Bonilla-Aldana

Holguin-Rivera

Cortés-Bonilla

et al. 2020

Travel Medicine and Infectious Disease

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Section: Introductionmentioning

confidence: 99%

Coronavirus infections reported by ProMED, February 2000–January 2020

Bonilla-Aldana

Holguin-Rivera

Cortés-Bonilla

et al. 2020

Travel Medicine and Infectious Disease

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“…However, little is known about the impact of direct or indirect exposure to public health emergencies on citizen assessments of government actions. This omission is serious because public health emergencies have been increasing (e.g., the 2009 H1N1 pandemic, the 2015-2016 Zika virus epidemic, the ongoing 2018-20 Kivu Ebola epidemic, and the ongoing COVID-19 outbreak) international concern in recent years (Modjarrad et al, 2016;Rolland et al, 2020).…”

mentioning

confidence: 99%

Citizen Assessments of Government Actions in the COVID-19 Outbreak in China

Zhang¹,

Zhang

Zhao³

et al. 2020

Chinese Public Administration Review

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This study investigates citizen assessments of government actions in the COVID-19 outbreak in China.Empirical analyses based on a large-scale online survey indicate that the Chinese public expects the government to improve its support for the frontline medical staff, management of public stress and anxiety, and disclosure of government information. Specifically, indirect exposure to COVID-19 through second-hand information is negatively associated with citizen assessments of government actions; by contrast, the first-hand frontline experience with the epidemic is positively associated with citizen assessments of government actions. Findings suggest that citizens with first-hand experience might be more able to judge government actions under the actual constraints of resources and opportunities and are less likely to overemphasize the costs or risks associated with government actions than others without frontline experience. Our work suggests that governments should effectively communicate vivid information regarding government actions to the public during public health emergencies, as more informed citizens might be more supportive of governments with limited resources and, probably, more actively collaborate with governments.

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“…The current COVID-19 pandemic has also had a huge impact on the surveillance and control of other infectious diseases [ 65 ]. In addition, because ProMED-mail records and follows up undiagnosed diseases in different countries [ 66 ], this abundant disease surveillance information is unstructured and is thus not able to be efficiently used by public health workers or scientists. Our proposed deep neural network provides a good way to extract outbreak information from unstructured text, which can then be further analyzed.…”

Section: Discussionmentioning

confidence: 99%

Linguistic Pattern–Infused Dual-Channel Bidirectional Long Short-term Memory With Attention for Dengue Case Summary Generation From the Program for Monitoring Emerging Diseases–Mail Database: Algorithm Development Study

Chang¹,

Chiu²,

Chuang³

2022

JMIR Public Health Surveill

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Background Globalization and environmental changes have intensified the emergence or re-emergence of infectious diseases worldwide, such as outbreaks of dengue fever in Southeast Asia. Collaboration on region-wide infectious disease surveillance systems is therefore critical but difficult to achieve because of the different transparency levels of health information systems in different countries. Although the Program for Monitoring Emerging Diseases (ProMED)–mail is the most comprehensive international expert–curated platform providing rich disease outbreak information on humans, animals, and plants, the unstructured text content of the reports makes analysis for further application difficult. Objective To make monitoring the epidemic situation in Southeast Asia more efficient, this study aims to develop an automatic summary of the alert articles from ProMED-mail, a huge textual data source. In this paper, we proposed a text summarization method that uses natural language processing technology to automatically extract important sentences from alert articles in ProMED-mail emails to generate summaries. Using our method, we can quickly capture crucial information to help make important decisions regarding epidemic surveillance. Methods Our data, which span a period from 1994 to 2019, come from the ProMED-mail website. We analyzed the collected data to establish a unique Taiwan dengue corpus that was validated with professionals’ annotations to achieve almost perfect agreement (Cohen κ=90%). To generate a ProMED-mail summary, we developed a dual-channel bidirectional long short-term memory with attention mechanism with infused latent syntactic features to identify key sentences from the alerting article. Results Our method is superior to many well-known machine learning and neural network approaches in identifying important sentences, achieving a macroaverage F1 score of 93%. Moreover, it can successfully extract the relevant correct information on dengue fever from a ProMED-mail alerting article, which can help researchers or general users to quickly understand the essence of the alerting article at first glance. In addition to verifying the model, we also recruited 3 professional experts and 2 students from related fields to participate in a satisfaction survey on the generated summaries, and the results show that 84% (63/75) of the summaries received high satisfaction ratings. Conclusions The proposed approach successfully fuses latent syntactic features into a deep neural network to analyze the syntactic, semantic, and contextual information in the text. It then exploits the derived information to identify crucial sentences in the ProMED-mail alerting article. The experiment results show that the proposed method is not only effective but also outperforms the compared methods. Our approach also demonstrates the potential for case summary generation from ProMED-mail alerting articles. In terms of practical application, when a new alerting article arrives, our method can quickly identify the relevant case information, which is the most critical part, to use as a reference or for further analysis.

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Early Detection of Public Health Emergencies of International Concern through Undiagnosed Disease Reports in ProMED-Mail

Cited by 13 publications

References 6 publications

Coronavirus infections reported by ProMED, February 2000–January 2020

Coronavirus infections reported by ProMED, February 2000–January 2020

Citizen Assessments of Government Actions in the COVID-19 Outbreak in China

Linguistic Pattern–Infused Dual-Channel Bidirectional Long Short-term Memory With Attention for Dengue Case Summary Generation From the Program for Monitoring Emerging Diseases–Mail Database: Algorithm Development Study

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