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Journal Description

The Journal of Medical Internet Research (JMIR), now in its 21st year, is the pioneer open access eHealth journal and is the flagship journal of JMIR Publications. It is the leading digital health journal globally in terms of quality/visibility (Impact Factor 2018: 4.945, ranked #1 out of 26 journals in the medical informatics category) and in terms of size (number of papers published). The journal focuses on emerging technologies, medical devices, apps, engineering, and informatics applications for patient education, prevention, population health and clinical care. As a leading high-impact journal in its disciplines (health informatics and health services research), it is selective, but it is now complemented by almost 30 specialty JMIR sister journals, which have a broader scope. Peer-review reports are portable across JMIR journals and papers can be transferred, so authors save time by not having to resubmit a paper to different journals. 

As an open access journal, we are read by clinicians, allied health professionals, informal caregivers, and patients alike, and have (as with all JMIR journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies. We publish original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews).

We are also a leader in participatory and open science approaches, and offer the option to publish new submissions immediately as preprints, which receive DOIs for immediate citation (eg, in grant proposals), and for open peer-review purposes. We also invite patients to participate (eg, as peer-reviewers) and have patient representatives on editorial boards.

Be a widely cited leader in the digitial health revolution and submit your paper today!


Recent Articles:

  • Source: freepik; Copyright: freepik; URL:; License: Licensed by JMIR.

    Racial and Ethnic Digital Divides in Posting COVID-19 Content on Social Media Among US Adults: Secondary Survey Analysis


    Background: Public health surveillance experts are leveraging user-generated content on social media to track the spread and effects of COVID-19. However, racial and ethnic digital divides, which are disparities among people who have internet access and post on social media, can bias inferences. This bias is particularly problematic in the context of the COVID-19 pandemic because due to structural inequalities, members of racial and ethnic minority groups are disproportionately vulnerable to contracting the virus and to the deleterious economic and social effects from mitigation efforts. Further, important demographic intersections with race and ethnicity, such as gender and age, are rarely investigated in work characterizing social media users; however, they reflect additional axes of inequality shaping differential exposure to COVID-19 and its effects. Objective: The aim of this study was to characterize how the race and ethnicity of US adults are associated with their odds of posting COVID-19 content on social media and how gender and age modify these odds. Methods: We performed a secondary analysis of a survey conducted by the Pew Research Center from March 19 to 24, 2020, using a national probability sample (N=10,510). Respondents were recruited from an online panel, where panelists without an internet-enabled device were given one to keep at no cost. The binary dependent variable was responses to an item asking whether respondents “used social media to share or post information about the coronavirus.” We used survey-weighted logistic regressions to estimate the odds of responding in the affirmative based on the race and ethnicity of respondents (white, black, Latino, other race/ethnicity), adjusted for covariates measuring sociodemographic background and COVID-19 experiences. We examined how gender (female, male) and age (18 to 30 years, 31 to 50 years, 51 to 64 years, and 65 years and older) intersected with race and ethnicity by estimating interactions. Results: Respondents who identified as black (odds ratio [OR] 1.29, 95% CI 1.02-1.64; P=.03), Latino (OR 1.66, 95% CI 1.36-2.04; P<.001), or other races/ethnicities (OR 1.33, 95% CI 1.02-1.72; P=.03) had higher odds than respondents who identified as white of reporting that they posted COVID-19 content on social media. Women had higher odds of posting than men regardless of race and ethnicity (OR 1.58, 95% CI 1.39-1.80; P<.001). Among men, respondents who identified as black, Latino, or members of other races/ethnicities were significantly more likely to post than respondents who identified as white. Older adults (65 years or older) had significantly lower odds (OR 0.73, 95% CI 0.57-0.94; P=.01) of posting compared to younger adults (18-29 years), particularly among those identifying as other races/ethnicities. Latino respondents were the most likely to report posting across all age groups. Conclusions: In the United States, members of racial and ethnic minority groups are most likely to contribute to COVID-19 content on social media, particularly among groups traditionally less likely to use social media (older adults and men). The next step is to ensure that data collection procedures capture this diversity by encompassing a breadth of search criteria and social media platforms.

  • Source: Wikimedia Commons; Copyright: Whoisjohngalt; URL:; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).

    Surveilling Influenza Incidence With Centers for Disease Control and Prevention Web Traffic Data: Demonstration Using a Novel Dataset


    Background: Influenza epidemics result in a public health and economic burden worldwide. Traditional surveillance techniques, which rely on doctor visits, provide data with a delay of 1 to 2 weeks. A means of obtaining real-time data and forecasting future outbreaks is desirable to provide more timely responses to influenza epidemics. Objective: This study aimed to present the first implementation of a novel dataset by demonstrating its ability to supplement traditional disease surveillance at multiple spatial resolutions. Methods: We used internet traffic data from the Centers for Disease Control and Prevention (CDC) website to determine the potential usability of this data source. We tested the traffic generated by 10 influenza-related pages in 8 states and 9 census divisions within the United States and compared it against clinical surveillance data. Results: Our results yielded an r2 value of 0.955 in the most successful case, promising results for some cases, and unsuccessful results for other cases. In the interest of scientific transparency to further the understanding of when internet data streams are an appropriate supplemental data source, we also included negative results (ie, unsuccessful models). Models that focused on a single influenza season were more successful than those that attempted to model multiple influenza seasons. Geographic resolution appeared to play a key role, with national and regional models being more successful, overall, than models at the state level. Conclusions: These results demonstrate that internet data may be able to complement traditional influenza surveillance in some cases but not in others. Specifically, our results show that the CDC website traffic may inform national- and division-level models but not models for each individual state. In addition, our results show better agreement when the data were broken up by seasons instead of aggregated over several years. We anticipate that this work will lead to more complex nowcasting and forecasting models using this data stream.

  • Amount raised by state and city. Source: Figure 7 from; Copyright: the authors; License: Creative Commons Attribution (CC-BY).

    Understanding the Dimensions of Medical Crowdfunding: A Visual Analytics Approach


    Background: Medical crowdfunding has emerged as a growing field for fundraising opportunities. Some environmental trends have driven the emergence of campaigns to raise funds for medical care. These trends include lack of medical insurance, economic backlash following the 2008 financial collapse, and shortcomings of health care regulations. Objective: Research regarding crowdfunding campaign use, reasons, and effects on the provision of medical care and individual relationships in health systems is limited. This study aimed to explore the nature and dimensions of the phenomenon of medical crowdfunding using a visual analytics approach and data crawled from the GoFundMe crowdfunding platform in 2019. We aimed to explore and identify the factors that contribute to a successful campaign. Methods: This data-driven study used a visual analytics approach. It focused on descriptive analytics to obtain a panoramic insight into medical projects funded through the GoFundMe crowdfunding platform. Results: This study highlighted the relevance of positioning the campaign for fundraising. In terms of motivating donors, it appears that people are typically more generous in contributing to campaigns for children rather than those for adults. The results emphasized the differing dynamics that a picture posted in the campaign brings to the potential for medical crowdfunding. In terms of donor’s motivation, the results show that a picture depicting the pediatric patient by himself or herself is the most effective. In addition, a picture depicting the current medical condition of the patient as severe is more effective than one depicting relative normalcy in the condition. This study also drew attention to the optimum length of the title. Finally, an interesting trend in the trajectory of donations is that the average amount of a donation decreases with an increase in the number of donors. This indicates that the first donors tend to be the most generous. Conclusions: This study examines the relationship between social media, the characteristics of a campaign, and the potential for fundraising. Its analysis of medical crowdfunding campaigns across the states offers a window into the status of the country’s health care affordability. This study shows the nurturing role that social media can play in the domain of medical crowdfunding. In addition, it discusses the drivers of a successful fundraising campaign with respect to the GoFundMe platform.

  • A doctor accessing a patient's telemedicine form stored on the cloud while talking with the patient using WeChat. Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Monitoring and Management of Home-Quarantined Patients With COVID-19 Using a WeChat-Based Telemedicine System: Retrospective Cohort Study


    Background: Most patients with coronavirus disease (COVID-19) who show mild symptoms are sent home by physicians to recover. However, the condition of some of these patients becomes severe or critical as the disease progresses. Objective: The aim of this study was to evaluate a telemedicine model that was developed to address the challenges of treating patients with progressive COVID-19 who are home-quarantined and shortages in the medical workforce. Methods: A telemedicine system was developed to continuously monitor the progression of home-quarantined patients with COVID-19. The system was built based on a popular social media smartphone app called WeChat; the app was used to establish two-way communication between a multidisciplinary team consisting of 7 medical workers and 188 home-quarantined individuals (including 74 confirmed patients with COVID-19). The system helped patients self-assess their conditions and update the multidisciplinary team through a telemedicine form stored on a cloud service, based on which the multidisciplinary team made treatment decisions. We evaluated this telemedicine system via a single-center retrospective study conducted at Tongji Hospital in Wuhan, China, in January 2020. Results: Among 188 individuals using the telemedicine system, 114 (60.6%) were not infected with COVID-19 and were dismissed. Of the 74 confirmed patients with COVID-19, 26 (35%) recovered during the study period and voluntarily stopped using the system. The remaining 48/76 confirmed patients with COVID-19 (63%) used the system until the end of the study, including 6 patients whose conditions progressed to severe or critical. These 6 patients were admitted to hospital and were stabilized (one received extracorporeal membrane oxygenation support for 17 days). All 74 patients with COVID-19 eventually recovered. Through a comparison of the monitored symptoms between hospitalized and nonhospitalized patients, we found prolonged persistence and deterioration of fever, dyspnea, lack of strength, and muscle soreness to be diagnostic of need for hospitalization. Conclusions: By continuously monitoring the changes in several key symptoms, the telemedicine system reduces the risks of delayed hospitalization due to disease progression for patients with COVID-19 quarantined at home. The system uses a set of scales for quarantine management assessment that enables patients to self-assess their conditions. The results are useful for medical staff to identify disease progression and, hence, make appropriate and timely treatment decisions. The system requires few staff to manage a large cohort of patients. In addition, the system can solicit help from recovered but self-quarantined medical workers to alleviate shortages in the medical workforce and free healthy medical workers to fight COVID-19 on the front line. Thus, it optimizes the usage of local medical resources and prevents cross-infections among medical workers and patients.

  • Source: Freepik; Copyright: kaboompics; URL:; License: Licensed by JMIR.

    Mapping of Health Literacy and Social Panic Via Web Search Data During the COVID-19 Public Health Emergency: Infodemiological Study


    Background: Coronavirus disease (COVID-19) is a type of pneumonia caused by a novel coronavirus that was discovered in 2019. As of May 6, 2020, 84,407 cases and 4643 deaths have been confirmed in China. The Chinese population has expressed great concern since the COVID-19 outbreak. Meanwhile, an average of 1 billion people per day are using the Baidu search engine to find COVID-19–related health information. Objective: The aim of this paper is to analyze web search data volumes related to COVID-19 in China. Methods: We conducted an infodemiological study to analyze web search data volumes related to COVID-19. Using Baidu Index data, we assessed the search frequencies of specific search terms in Baidu to describe the impact of COVID-19 on public health, psychology, behaviors, lifestyles, and social policies (from February 11, 2020, to March 17, 2020). Results: The search frequency related to COVID-19 has increased significantly since February 11th. Our heat maps demonstrate that citizens in Wuhan, Hubei Province, express more concern about COVID-19 than citizens from other cities since the outbreak first occurred in Wuhan. Wuhan citizens frequently searched for content related to “medical help,” “protective materials,” and “pandemic progress.” Web searches for “return to work” and “go back to school” have increased eight-fold compared to the previous month. Searches for content related to “closed community and remote office” have continued to rise, and searches for “remote office demand” have risen by 663% from the previous quarter. Employees who have returned to work have mainly engaged in the following web searches: “return to work and prevention measures,” “return to work guarantee policy,” and “time to return to work.” Provinces with large, educated populations (eg, Henan, Hebei, and Shandong) have been focusing on “online education” whereas medium-sized cities have been paying more attention to “online medical care.” Conclusions: Our findings suggest that web search data may reflect changes in health literacy, social panic, and prevention and control policies in response to COVID-19.

  • Source: Freepik; Copyright: Freepik; URL:; License: Licensed by JMIR.

    Identification of Symptoms Prognostic of COVID-19 Severity: Multivariate Data Analysis of a Case Series in Henan Province


    Background: The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease (COVID-19), has been declared a global pandemic. Identifying individuals whose infection can potentially become severe is critical to control the case fatality rate of COVID-19. However, knowledge of symptoms that are prognostic of COVID-19 severity is lacking. Objective: The objective of our study was to identify symptoms prognostic of COVID-19 infection severity. Methods: We analyzed documented symptoms, including fever, cough, fatigue, expectoration, sore throat, chest distress, headache, diarrhea, rhinorrhea, stuffed nose, nausea, vomiting, muscle or joint ache, shortness of breath, and their associations with disease severity using a case series, including 655 confirmed cases from January 23 to February 5, 2020 in Henan Province, China. We also analyzed the influence of individual characteristics, including age, gender, and comorbidities, on symptoms with prognostic value. Results: Fatigue (95% CI 0.141 to 0.334, P<.001), expectoration (95% CI 0.107 to 0.305, P<.001) and stuffed nose (95% CI –0.499 to –0.082, P=.006) were identified as the prognostic symptoms of COVID-19 patients from the multivariate analysis. Fever occurred in 603/655 (92.1%) of the patients but was not associated with disease severity. Fatigue accounted for 184/655 (28.1%) of the patients and was linearly associated with infection severity with statistical significance. Expectoration occurred in 169/655 (25.8%) patients in the cohort and was the sole prognostic factor for patients with cardiovascular complications, including hypertension. Shortness of breath, chest distress, muscle or joint ache, and dry cough, which occurred in 33 (5%), 83 (12.7%), 78 (11.9%), and 276 (42.1%) of the 655 patients, respectively, were significantly enriched among patients classified as severe. Stuffed nose and nausea were associated with favorable disease severity, especially among male patients. More female than male patients were documented as having muscle or joint ache. Headache was most enriched in patents aged 15 to 39 years, followed by those aged 40 to 64 years, with statistical significance. Conclusions: Fatigue and expectoration are signs of severe COVID-19 infection. Shortness of breath, chest distress, muscle or joint ache, and dry cough are prevalent in severe patients. Expectoration is commonly present in older individuals and patients with cardiovascular disorders, including hypertension. Shortness of breath is prognostic of severe infection in male patients. Stuffed nose and nausea are favorable prognostic factors of severe infection, especially among male patients.

  • Source: Image created by the authors; Copyright: The Authors; URL:; License: Licensed by the authors.

    Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With...


    Background: Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1% to 42.7% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient’s quality of life. The TrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)—Android and iOS—to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider. Objective: In this study, we explored whether the mobile OS—Android and iOS—used during user assessments can be predicted by the dynamic daily-life TYT data. Methods: TYT mainly applies the paradigms ecological momentary assessment (EMA) and mobile crowdsensing to collect dynamic EMA (EMA-D) daily-life data. The dynamic daily-life TYT data that were analyzed included eight questions as part of the EMA-D questionnaire. In this study, 518 TYT users were analyzed, who each completed at least 11 EMA-D questionnaires. Out of these, 221 were iOS users and 297 were Android users. The iOS users completed, in total, 14,708 EMA-D questionnaires; the number of EMA-D questionnaires completed by the Android users was randomly reduced to the same number to properly address the research question of the study. Machine learning methods—a feedforward neural network, a decision tree, a random forest classifier, and a support vector machine—were applied to address the research question. Results: Machine learning was able to predict the mobile OS used with an accuracy up to 78.94% based on the provided EMA-D questionnaires on the assessment level. In this context, the daily measurements regarding how users concentrate on the actual activity were particularly suitable for the prediction of the mobile OS used. Conclusions: In the work at hand, two particular aspects have been revealed. First, machine learning can contribute to EMA-D data in the medical context. Second, based on the EMA-D data of TYT, we found that the accuracy in predicting the mobile OS used has several implications. Particularly, in clinical studies using mobile devices, the OS should be assessed as a covariate, as it might be a confounder.

  • The DDC19 platform. Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    A COVID-19 Risk Assessment Decision Support System for General Practitioners: Design and Development Study


    Background: The coronavirus disease (COVID-19) has become an urgent and serious global public health crisis. Community engagement is the first line of defense in the fight against infectious diseases, and general practitioners (GPs) play an important role in it. GPs are facing unique challenges from disasters and pandemics in delivering health care. However, there is still no suitable mobile management system that can help GPs collect data, dynamically assess risks, and effectively triage or follow-up with patients with COVID-19. Objective: The aim of this study is to design, develop, and deploy a mobile-based decision support system for COVID-19 (DDC19) to assist GPs in collecting data, assessing risk, triaging, managing, and following up with patients during the COVID-19 outbreak. Methods: Based on the actual scenarios and the process of patients using health care, we analyzed the key issues that need to be solved and designed the main business flowchart of DDC19. We then constructed a COVID-19 dynamic risk stratification model with high recall and clinical interpretability, which was based on a multiclass logistic regression algorithm. Finally, through a 10-fold cross-validation to quantitatively evaluate the risk stratification ability of the model, a total of 2243 clinical data consisting of 36 dimension clinical features from fever clinics were used for training and evaluation of the model. Results: DDC19 is composed of three parts: mobile terminal apps for the patient-end and GP-end, and the database system. All mobile terminal devices were wirelessly connected to the back end data center to implement request sending and data transmission. We used low risk, moderate risk, and high risk as labels, and adopted a 10-fold cross-validation method to evaluate and test the COVID-19 dynamic risk stratification model in different scenarios (different dimensions of personal clinical data accessible at an earlier stage). The data set dimensions were (2243, 15) when only using the data of patients’ demographic information, clinical symptoms, and contact history; (2243, 35) when the results of blood tests were added; and (2243, 36) after obtaining the computed tomography imaging results of the patient. The average value of the three classification results of the macro–area under the curve were all above 0.71 in each scenario. Conclusions: DCC19 is a mobile decision support system designed and developed to assist GPs in providing dynamic risk assessments for patients with suspected COVID-19 during the outbreak, and the model had a good ability to predict risk levels in any scenario it covered.

  • AI-based automatic detection of COVID-19 with CT. Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation


    Background: Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. Objective: We aimed to rapidly develop an AI technique to diagnose COVID-19 pneumonia in CT images and differentiate it from non–COVID-19 pneumonia and nonpneumonia diseases. Methods: A simple 2D deep learning framework, named the fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning using one of four state-of-the-art pretrained deep learning models (VGG16, ResNet-50, Inception-v3, or Xception) as a backbone. For training and testing of FCONet, we collected 3993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and nonpneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training set and a testing set at a ratio of 8:2. For the testing data set, the diagnostic performance of the four pretrained FCONet models to diagnose COVID-19 pneumonia was compared. In addition, we tested the FCONet models on an external testing data set extracted from embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. Results: Among the four pretrained models of FCONet, ResNet-50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100.00%, and accuracy 99.87%) and outperformed the other three pretrained models in the testing data set. In the additional external testing data set using low-quality CT images, the detection accuracy of the ResNet-50 model was the highest (96.97%), followed by Xception, Inception-v3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). Conclusions: FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing data set, the FCONet model based on ResNet-50 appears to be the best model, as it outperformed other FCONet models based on VGG16, Xception, and Inception-v3.

  • Source: Pexels; Copyright: Cytonn Photography; URL:; License: Licensed by JMIR.

    Toward the Development of Data Governance Standards for Using Clinical Free-Text Data in Health Research: Position Paper


    Background: Clinical free-text data (eg, outpatient letters or nursing notes) represent a vast, untapped source of rich information that, if more accessible for research, would clarify and supplement information coded in structured data fields. Data usually need to be deidentified or anonymized before they can be reused for research, but there is a lack of established guidelines to govern effective deidentification and use of free-text information and avoid damaging data utility as a by-product. Objective: This study aimed to develop recommendations for the creation of data governance standards to integrate with existing frameworks for personal data use, to enable free-text data to be used safely for research for patient and public benefit. Methods: We outlined data protection legislation and regulations relating to the United Kingdom for context and conducted a rapid literature review and UK-based case studies to explore data governance models used in working with free-text data. We also engaged with stakeholders, including text-mining researchers and the general public, to explore perceived barriers and solutions in working with clinical free-text. Results: We proposed a set of recommendations, including the need for authoritative guidance on data governance for the reuse of free-text data, to ensure public transparency in data flows and uses, to treat deidentified free-text data as potentially identifiable with use limited to accredited data safe havens, and to commit to a culture of continuous improvement to understand the relationships between the efficacy of deidentification and reidentification risks, so this can be communicated to all stakeholders. Conclusions: By drawing together the findings of a combination of activities, we present a position paper to contribute to the development of data governance standards for the reuse of clinical free-text data for secondary purposes. While working in accordance with existing data governance frameworks, there is a need for further work to take forward the recommendations we have proposed, with commitment and investment, to assure and expand the safe reuse of clinical free-text data for public benefit.

  • The Information “Cake” Model. The four pillars of infodemic management are information monitoring (infoveillance; top left); building eHealth Literacy and science literacy (top right); encouraging knowledge refinement and quality improvement processes for information providers, such as fact checking and peer review (bottom left); and Knowledge Translation, meaning to translate knowledge from one layer to another, while minimizing distorting factors (bottom right). eHealth: electronic health; KT: knowledge translation. Source: Figure 1 from; Copyright: the authors; License: Creative Commons Attribution (CC-BY).

    How to Fight an Infodemic: The Four Pillars of Infodemic Management

    Authors List:


    In this issue of the Journal of Medical Internet Research, the World Health Organization (WHO) is presenting a framework for managing the coronavirus disease (COVID-19) infodemic. Infodemiology is now acknowledged by public health organizations and the WHO as an important emerging scientific field and critical area of practice during a pandemic. From the perspective of being the first “infodemiolgist” who originally coined the term almost two decades ago, I am positing four pillars of infodemic management: (1) information monitoring (infoveillance); (2) building eHealth Literacy and science literacy capacity; (3) encouraging knowledge refinement and quality improvement processes such as fact checking and peer-review; and (4) accurate and timely knowledge translation, minimizing distorting factors such as political or commercial influences. In the current COVID-19 pandemic, the United Nations has advocated that facts and science should be promoted and that these constitute the antidote to the current infodemic. This is in stark contrast to the realities of infodemic mismanagement and misguided upstream filtering, where social media platforms such as Twitter have advertising policies that sideline science organizations and science publishers, treating peer-reviewed science as “inappropriate content.”

  • Illustration of the infodemic by @WHO/Sam Bradd. Source: World Health Organization / Sam Bradd; Copyright: World Health Organization / Sam Bradd; URL:; License: CC BY-NC-SA 3.0 IGO

    Framework for Managing the COVID-19 Infodemic: Methods and Results of an Online, Crowdsourced WHO Technical Consultation


    Background: An infodemic is an overabundance of information—some accurate and some not—that occurs during an epidemic. In a similar manner to an epidemic, it spreads between humans via digital and physical information systems. It makes it hard for people to find trustworthy sources and reliable guidance when they need it. Objective: A World Health Organization (WHO) technical consultation on responding to the infodemic related to the coronavirus disease (COVID-19) pandemic was held, entirely online, to crowdsource suggested actions for a framework for infodemic management. Methods: A group of policy makers, public health professionals, researchers, students, and other concerned stakeholders was joined by representatives of the media, social media platforms, various private sector organizations, and civil society to suggest and discuss actions for all parts of society, and multiple related professional and scientific disciplines, methods, and technologies. A total of 594 ideas for actions were crowdsourced online during the discussions and consolidated into suggestions for an infodemic management framework. Results: The analysis team distilled the suggestions into a set of 50 proposed actions for a framework for managing infodemics in health emergencies. The consultation revealed six policy implications to consider. First, interventions and messages must be based on science and evidence, and must reach citizens and enable them to make informed decisions on how to protect themselves and their communities in a health emergency. Second, knowledge should be translated into actionable behavior-change messages, presented in ways that are understood by and accessible to all individuals in all parts of all societies. Third, governments should reach out to key communities to ensure their concerns and information needs are understood, tailoring advice and messages to address the audiences they represent. Fourth, to strengthen the analysis and amplification of information impact, strategic partnerships should be formed across all sectors, including but not limited to the social media and technology sectors, academia, and civil society. Fifth, health authorities should ensure that these actions are informed by reliable information that helps them understand the circulating narratives and changes in the flow of information, questions, and misinformation in communities. Sixth, following experiences to date in responding to the COVID-19 infodemic and the lessons from other disease outbreaks, infodemic management approaches should be further developed to support preparedness and response, and to inform risk mitigation, and be enhanced through data science and sociobehavioral and other research. Conclusions: The first version of this framework proposes five action areas in which WHO Member States and actors within society can apply, according to their mandate, an infodemic management approach adapted to national contexts and practices. Responses to the COVID-19 pandemic and the related infodemic require swift, regular, systematic, and coordinated action from multiple sectors of society and government. It remains crucial that we promote trusted information and fight misinformation, thereby helping save lives.

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  • Evaluation of problem-based learning innovation in medical education during the COVID-19 pandemic: a qualitative study among medical students

    Date Submitted: Jul 3, 2020

    Open Peer Review Period: Jul 3, 2020 - Jul 10, 2020

    Background: Most educators affected by the COVID-19 epidemic have had to find the most appropriate teaching approach to deal with this emergency teaching situation, i.e., an approach that can make goo...

    Background: Most educators affected by the COVID-19 epidemic have had to find the most appropriate teaching approach to deal with this emergency teaching situation, i.e., an approach that can make good use of various teaching resources to achieve high adaptability. According to the characteristics of medical students and the course content of clinical clerkship, we adopted the problem-based learning (PBL) method to redesign the pedagogy of clerkship. At the end of the semester, the feasibility of the PBL teaching model in emergency remote teaching (ERT), which has an impact on students' learning experience and preference, was evaluated. Compared with other countries affected by the COVID-19 epidemic, the outbreak time and recovery time in China are both earlier; thus, the evaluation and feedback of ERT can provide the referential evidence to global educators and institutions. Objective: The objective of this study was to explore medical students' learning experience in ERT to understand how these positive and negative perceptions influence their learning preferences in different teaching modes and how the PBL model combined digital technology to influence teaching quality. Methods: Among 123 medical students from Jinan University, China, who participated in the questionnaire at the end of the ERT in the clinical clerkship course, 25 volunteered to have a further in-depth interview. We randomly select five veterinary students to participate in the one-on-one in-depth online interview, which was conducted within 30 minutes. After coding of the transcription by the NVivo 12.0 software, the collected qualitative data would undergo a thematic analysis. Results: The thematic analysis indicated two main themes. One is that the adoption of PBL is the crucial for overseas medical students to evaluate ERT positively, and is depicted by one sub-theme: positive comment contributions. The other theme is that clinical practice as the core of medical education has a decisive influence on the teaching mode preference of medical students, as depicted by two sub-themes: negative comment contributions; and preference in different teaching methods. Conclusions: Although medical students preferred an offline teaching mode due to practical requirements, they generally gave positive comments on this ERT because of the PBL method pedagogy. This indicated the feasibility of the online PBL teaching method in medical education. Moreover, medical students' preferences in the combination of the online and offline teaching mode revealed a revolutionary new direction of revolution in medical education.

  • Noncommunicable chronic disease and the risk of COVID-19: a population-based case-control study

    Date Submitted: Jul 2, 2020

    Open Peer Review Period: Jul 2, 2020 - Aug 27, 2020

    Objective: To investigate the association of the non-communicable chronic disease (NCD) with the risk of coronavirus disease 2019 (COVID-19). Methods: A case-control study was conducted. The cases we...

    Objective: To investigate the association of the non-communicable chronic disease (NCD) with the risk of coronavirus disease 2019 (COVID-19). Methods: A case-control study was conducted. The cases were laboratory-confirmed COVID-19 who were treated in the Union Hospital in Wuhan. The healthy controls were randomly selected from the participants of the Hunan Government Employee Cohort study who were not infected with COVID-19, matching by age and sex. NCDs including hypertension, diabetes, coronary heart disease, chronic pulmonary disease, and cancer were determined by self-reportings, use of medications, measurements, and/or laboratory testings. The severity of COVID-19 was determined by physicians according to the guideline. Logistic regression was used to estimate the association, in terms of odds ratio (OR). Results: A total of 468 cases and 1404 controls (1:3) were included in the analysis with a mean age of 59.1±12.8 years and 51.7% male. The case group comprised 134 moderately ill, 275 severely ill, and 59 critically ill COVID-19 patients. Patients with diabetes (OR=3.23, P<0.001), chronic pulmonary disease (OR=5.99, P<0.001), and hypertension (OR=1.45, P=0.001) showed a significantly increased risk of COVID-19 infection compared to the healthy controls. Additionally, diabetes, chronic pulmonary disease, hypertension, and the number of comorbid NCDs were associated with the severity of COVID-19 dose-dependently. Conclusions: Patients with diabetes, hypertension, and chronic pulmonary disease are at a higher risk of having COVID-19 and developing severe type of the disease.

  • A text-mining analysis of public perceptions and topic modeling during the COVID-19 pandemic using Twitter data

    Date Submitted: Jun 30, 2020

    Open Peer Review Period: Jun 30, 2020 - Jul 8, 2020

    Background: Coronavirus disease (COVID-19) is a scientifically and medically novel disease that is not fully understood as it needs to be consistently and deeply studied. In the past, research on the...

    Background: Coronavirus disease (COVID-19) is a scientifically and medically novel disease that is not fully understood as it needs to be consistently and deeply studied. In the past, research on the COVID-19 outbreak was only able to predict quantity data such as the number of outbreaks, but not infoveillance data. Objective: This study aims to understand public perceptions on the trends of the COVID-19 pandemic and uncover meaningful themes of concern posted by Twitter users during the pandemic throughout the world. Methods: Data mining on Twitter was conducted to collect a total of 107,990 tweets between December 13 and March 9, 2020. The analysis included time series, sentiment analysis and topic modeling to identify the most common topics in the tweets as well as to categorize clusters and find themes from keyword analysis. Results: The results indicate three main aspects of public awareness and concerns regarding the COVID-19 pandemic. Firstly, the study indicated the trend of the spread and symptoms of COVID-19, which was divided into three stages. Secondly, the results of the sentiment analysis and emotional tendency showed that the people had a negative outlook toward COVID-19. Thirdly, topic modeling and themes relating to COVID-19 and the outbreak were divided into three categories, including (1) emergency of COVID-19 impact, (2) the epidemic situation and how to control it, and (3) news and social media reporting on the epidemic. Conclusions: Sentiment analysis and topic modeling can produce useful information about the trend of COVID-19 pandemic and alternative perspectives to investigate the COVID-19 crisis which has created considerable public awareness around the world. This finding shows that Twitter is a good communication channel for understanding both public concern and awareness about COVID-19 disease. These findings can help health departments to communicate information as to what the public thinks about the disease.

  • Clinical Characteristics and Prognostic Factors for ICU Admission of Patients with COVID-19 Using Machine Learning And Natural Language Processing

    Date Submitted: Jun 29, 2020

    Open Peer Review Period: Jun 28, 2020 - Jul 7, 2020

    Background: There remain many unknowns regarding the onset and clinical course of the ongoing COVID-19 pandemic. Objective: Here we aimed to describe the clinical characteristics and predictors of ICU...

    Background: There remain many unknowns regarding the onset and clinical course of the ongoing COVID-19 pandemic. Objective: Here we aimed to describe the clinical characteristics and predictors of ICU use in a large cohort of COVID-19 patients in real time. Methods: To achieve the research objective, we used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling), to analyse the electronic health records (EHRs) of patients with COVID-19. Results: A total of 10,504 patients with a clinical or PCR-confirmed diagnosis of COVID-19 were identified, 52.5% males, with a mean age of 58.2 and S.D. 19.7 years. Upon admission, the most common symptoms were cough, fever, and dyspnoea, but all in less than half of cases. Overall, 6% of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm we identified that a combination of age, fever, and tachypnoea was the most parsimonious predictor of ICU admission: those younger than 56 years, without tachypnoea, and temperature <39º C, (or >39º C without respiratory crackles), were free of ICU admission. On the contrary, COVID-19 patients aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnoea and delayed their visit to the ER after being seen in primary care. Conclusions: Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnoea with/without respiratory crackles) predicts which COVID-19 patients require ICU admission.

  • Older Persons’ Knowledge, Attitudes, and Practices towards COVID-19: A Quick Online Cross-sectional Survey in Malaysia

    Date Submitted: Jun 28, 2020

    Open Peer Review Period: Jun 28, 2020 - Aug 23, 2020

    Background: Due to the factor of age and pre-existing medication conditions, older persons pose a higher risk of COVID-19 infection and experience more severe complication compared to others. Adherenc...

    Background: Due to the factor of age and pre-existing medication conditions, older persons pose a higher risk of COVID-19 infection and experience more severe complication compared to others. Adherence preventive measures become one of the best ways in fighting COVID-19, which largely influenced by knowledge, attitudes, and practices (KAP). Objective: This study aims to determine level of knowledge, attitudes, and practices towards COVID-19 among Malaysian older persons. Methods: An online cross-sectional study was conducted among 501 Malaysian older persons from 18th to 21st June 2020. The KAP instrument consisted of demographics details, knowledge (14 items), attitudes (3 items) and practices (2 items), adapted from previous study. Results: Results of this study showed participants had an overall correct rate of 91.3%, indicating a good knowledge level. Participants showed positive attitudes towards social distancing (98.6%), compliance to health authorities’ precautions (99.0%) and successful control (84.6%) of spreading of COVID-19 in Malaysia. The participants also taking preventive measures by refraining themselves from visiting crowded place (88.2%) and wore mask when leaving home (97.4%). Findings from this study showed participants’ high knowledge level of COVID-19 translated into good and safe preventive measures, during the recovery MCO in Malaysia. Conclusions: Continuous education and outreach from health authorities are essential to improve knowledge of COVID-19 and promote the newly adapted cultural norms, especially among older persons.

  • Mobile Health Application in China: Cross-sectional Study

    Date Submitted: Jun 26, 2020

    Open Peer Review Period: Jun 26, 2020 - Aug 21, 2020

    Background: Mobile health applications are emerging as a novel platform to obtain data pertinent to wellness and disease diagnosis, prevention, and management. As the future general trend of medical i...

    Background: Mobile health applications are emerging as a novel platform to obtain data pertinent to wellness and disease diagnosis, prevention, and management. As the future general trend of medical informatization, mobile health is an indispensable way to promote universal medical care to reduce disease burden. The features of these mHealth apps in China is unclear, so we collected a wide range of application information to evaluate these apps effectively. Objective: We aim to provide a landscape of mHealth apps on the existing market in China. We expect that based on the actual state, this study can give future development directions of mHealth apps. Methods: We searched mHealth apps from five android app stores (Huawei, Oppo, Vivo, Tencent, and 360), Apple App Store (IOS), and Baidu search engine up to October 25, 2019. We also searched the inventory of the top 100 Chinese hospitals in 2018 and four online shopping sites (Tmall, JD, Pinduoduo, and Suning) to identify apps of Internet hospitals and intelligent devices, respectively. Results: We identified 2425 mHealth apps (93.3% android, 69.2% iPhone) in the primary analysis, whose intentional users were ordinary consumers (Android 1808/2262, 79.9%; IOS 1350/1677, 80.5%). 56.1% (1168/2081) of app developers were Internet companies. More than 90% of apps were available free of cost (Android 2111/2262, 93.3%; IOS 1615/1677, 96.3%), but in-app purchases accounted for more than 60% (Android 1397/2262, 61.8%; IOS 1189/1677, 70.9%). Of the 1285 public available apps, 1248 were for health management, of which 26.3% (328/1248) were related to bodybuilding, and 13.7% (171/1248) were related to women's health. The other 697 apps were used for medical support, and 289 of them were related to inquiries. The permissions required by the apps include claiming the network (2081/2107), reading the status and identity of the phone (1881/2107) , and location (1799/2107). Conclusions: With the increasing condition of the paid and membership system, rising profit of mHealth app drives various industries to move forward. This study guides research designs of future apps in mHealth field. The prospect of mHealth app is bright, but there exists a critical condition in claiming excessive permissions, security, and legal management, which need to be further strengthened.