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Objectives: To investigate the differences in vaccine hesitancy and preference of the currently available COVID-19 vaccines between two countries, namely, China and the United States (U.S.). Method: A cross-national survey was conducted in both China and the United States, and discrete choice experiments, as well as Likert scales, were utilized to assess vaccine preference and the underlying factors contributing to vaccination acceptance. Propensity score matching (PSM) was performed to enable a direct comparison between the two countries. Results: A total of 9077 (5375 and 3702 from China and the United States, respectively) respondents completed the survey. After propensity score matching, over 82.0% of respondents from China positively accepted the COVID-19 vaccination, while 72.2% of respondents from the United States positively accepted it. Specifically, only 31.9% of Chinese respondents were recommended by a doctor to have COVID-19 vaccination, while more than half of the U.S. respondents were recommended by a doctor (50.2%), local health board (59.4%), or friends and families (64.8%). The discrete choice experiments revealed that respondents from the United States attached the greatest importance to the efficacy of COVID-19 vaccines (44.41%), followed by the cost of vaccination (29.57%), whereas those from China held a different viewpoint, that the cost of vaccination covered the largest proportion in their trade-off (30.66%), and efficacy ranked as the second most important attribute (26.34%). Additionally, respondents from China tended to be much more concerned about the adverse effect of vaccination (19.68% vs. 6.12%) and have a lower perceived severity of being infected with COVID-19. Conclusion: Although the overall acceptance and hesitancy of COVID-19 vaccination in both countries are high, underpinned distinctions between these countries were observed. Owing to the differences in COVID-19 incidence rates, cultural backgrounds, and the availability of specific COVID-19 vaccines in the two countries, vaccine rollout strategies should be nation-dependent.
Taoran Liu; Zonglin He; Jian Huang; Ni Yan; Qian Chen; Fengqiu Huang; Yuejia Zhang; Omolola Akinwunmi; Babatunde Akinwunmi; Casper Zhang; Yibo Wu; Wai-Kit Ming. A Comparison of Vaccine Hesitancy of COVID-19 Vaccination in China and the United States. Vaccines 2021, 9, 649 .
AMA StyleTaoran Liu, Zonglin He, Jian Huang, Ni Yan, Qian Chen, Fengqiu Huang, Yuejia Zhang, Omolola Akinwunmi, Babatunde Akinwunmi, Casper Zhang, Yibo Wu, Wai-Kit Ming. A Comparison of Vaccine Hesitancy of COVID-19 Vaccination in China and the United States. Vaccines. 2021; 9 (6):649.
Chicago/Turabian StyleTaoran Liu; Zonglin He; Jian Huang; Ni Yan; Qian Chen; Fengqiu Huang; Yuejia Zhang; Omolola Akinwunmi; Babatunde Akinwunmi; Casper Zhang; Yibo Wu; Wai-Kit Ming. 2021. "A Comparison of Vaccine Hesitancy of COVID-19 Vaccination in China and the United States." Vaccines 9, no. 6: 649.
Background: The role of low-carbohydrate ketogenic diet (LCKD) as an adjuvant therapy in antitumor treatment is not well established. This systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to investigate the efficacy of LCKD as an adjuvant therapy in antitumor treatment compared to non-ketogenic diet in terms of lipid profile, body weight, fasting glucose level, insulin, and adverse effects; Methods: In this study, databases such as PubMed, Web of Science, Scopus, CINAHL, and Cochrane trials were searched. Only RCTs that involved cancer participants that were assigned to dietary interventions including a LCKD group and a control group (any non-ketogenic dietary intervention) were selected. Three reviewers independently extracted the data, and the meta-analysis was performed using a fixed effects model or random effects model depending on the I2 value or p-value; Results: A total of six articles met the inclusion/exclusion criteria. In the overall analysis, the post-intervention results = standard mean difference, SMD (95% CI) showed total cholesterol (TC) level = 0.25 (−0.17, 0.67), HDL-cholesterol = −0.07 (−0.50, 0.35), LDL-cholesterol = 0.21 (−0.21, 0.63), triglyceride (TG) = 0.09 (−0.33, 0.51), body weight (BW) = −0.34 (−1.33, 0.65), fasting blood glucose (FBG) = −0.40 (−1.23, 0.42) and insulin = 0.11 (−1.33, 1.55). There were three outcomes showing significant results in those in LCKD group: the tumor marker PSA, p = 0.03, the achievement of ketosis p = 0.010, and the level of satisfaction, p = 0.005; Conclusions: There was inadequate evidence to support the beneficial effects of LCKDs on antitumor therapy. More trials comparing LCKD and non-KD with a larger sample size are necessary to give a more conclusive result.
Ya-Feng Yang; Preety Mattamel; Tanya Joseph; Jian Huang; Qian Chen; Babatunde Akinwunmi; Casper Zhang; Wai-Kit Ming. Efficacy of Low-Carbohydrate Ketogenic Diet as an Adjuvant Cancer Therapy: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Nutrients 2021, 13, 1388 .
AMA StyleYa-Feng Yang, Preety Mattamel, Tanya Joseph, Jian Huang, Qian Chen, Babatunde Akinwunmi, Casper Zhang, Wai-Kit Ming. Efficacy of Low-Carbohydrate Ketogenic Diet as an Adjuvant Cancer Therapy: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Nutrients. 2021; 13 (5):1388.
Chicago/Turabian StyleYa-Feng Yang; Preety Mattamel; Tanya Joseph; Jian Huang; Qian Chen; Babatunde Akinwunmi; Casper Zhang; Wai-Kit Ming. 2021. "Efficacy of Low-Carbohydrate Ketogenic Diet as an Adjuvant Cancer Therapy: A Systematic Review and Meta-Analysis of Randomized Controlled Trials." Nutrients 13, no. 5: 1388.
Genome-wide association studies (GWAS) have identified genetic loci associated with risk of Alzheimer’s disease (AD), but underlying mechanisms are largely unknown. We conducted a metabolome-wide association study (MWAS) of AD-associated loci from GWAS using untargeted metabolic profiling (metabolomics) by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). We identified an association of lactosylceramides (LacCer) with AD-related single nucleotide polymorphisms (SNPs) in ABCA7 (P = 5.0x 10−5 to 1.3 x 10−44). We show that plasma LacCer concentrations are associated with cognitive performance in humans and concentrations of sphingomyelins, ceramides, and hexose-ceramides were altered in brain tissue from ABCA7 knock out mice, compared to wild type (WT) (P =0.049 to 1.4 x10−5). We then used Mendelian randomisation to show that the association of LacCer with AD risk is potentially causal. Our work suggests that risk for AD arising from functional variations in ABCA7 are mediated at least in part through ceramides. Modulation of their metabolism or downstream signalling may offer new therapeutic opportunities for AD.
Abbas Dehghan; Rui Pinto; Ibrahim Karaman; Jian Huang; Brenan R Durainayagam; Sonia Liggi; Luke Whiley; Rima Mustafa; Miia Kivipelto; Alina Solomon; Tiia Ngandu; Takahisa Kanekiyo; Tomonori Aikawa; Elena Chekmeneva; Stephane Camuzeaux; Matthew R. Lewis; Manuja R Kaluarachchi; Mohsen Ghanbari; M Arfan Ikram; Elaine Holmes; Ioanna Tzoulaki; Paul M. Matthews; Julian L. Griffin; Paul Elliott. Metabolome-wide association study on ABCA7 demonstrates a role for ceramide metabolism in impaired cognitive performance and Alzheimer’s disease. 2021, 1 .
AMA StyleAbbas Dehghan, Rui Pinto, Ibrahim Karaman, Jian Huang, Brenan R Durainayagam, Sonia Liggi, Luke Whiley, Rima Mustafa, Miia Kivipelto, Alina Solomon, Tiia Ngandu, Takahisa Kanekiyo, Tomonori Aikawa, Elena Chekmeneva, Stephane Camuzeaux, Matthew R. Lewis, Manuja R Kaluarachchi, Mohsen Ghanbari, M Arfan Ikram, Elaine Holmes, Ioanna Tzoulaki, Paul M. Matthews, Julian L. Griffin, Paul Elliott. Metabolome-wide association study on ABCA7 demonstrates a role for ceramide metabolism in impaired cognitive performance and Alzheimer’s disease. . 2021; ():1.
Chicago/Turabian StyleAbbas Dehghan; Rui Pinto; Ibrahim Karaman; Jian Huang; Brenan R Durainayagam; Sonia Liggi; Luke Whiley; Rima Mustafa; Miia Kivipelto; Alina Solomon; Tiia Ngandu; Takahisa Kanekiyo; Tomonori Aikawa; Elena Chekmeneva; Stephane Camuzeaux; Matthew R. Lewis; Manuja R Kaluarachchi; Mohsen Ghanbari; M Arfan Ikram; Elaine Holmes; Ioanna Tzoulaki; Paul M. Matthews; Julian L. Griffin; Paul Elliott. 2021. "Metabolome-wide association study on ABCA7 demonstrates a role for ceramide metabolism in impaired cognitive performance and Alzheimer’s disease." , no. : 1.
Background Artificial intelligence (AI) methods can potentially be used to relieve the pressure that the COVID-19 pandemic has exerted on public health. In cases of medical resource shortages caused by the pandemic, changes in people’s preferences for AI clinicians and traditional clinicians are worth exploring. Objective We aimed to quantify and compare people’s preferences for AI clinicians and traditional clinicians before and during the COVID-19 pandemic, and to assess whether people’s preferences were affected by the pressure of pandemic. Methods We used the propensity score matching method to match two different groups of respondents with similar demographic characteristics. Respondents were recruited in 2017 and 2020. A total of 2048 respondents (2017: n=1520; 2020: n=528) completed the questionnaire and were included in the analysis. Multinomial logit models and latent class models were used to assess people’s preferences for different diagnosis methods. Results In total, 84.7% (1115/1317) of respondents in the 2017 group and 91.3% (482/528) of respondents in the 2020 group were confident that AI diagnosis methods would outperform human clinician diagnosis methods in the future. Both groups of matched respondents believed that the most important attribute of diagnosis was accuracy, and they preferred to receive combined diagnoses from both AI and human clinicians (2017: odds ratio [OR] 1.645, 95% CI 1.535-1.763; P<.001; 2020: OR 1.513, 95% CI 1.413-1.621; P<.001; reference: clinician diagnoses). The latent class model identified three classes with different attribute priorities. In class 1, preferences for combined diagnoses and accuracy remained constant in 2017 and 2020, and high accuracy (eg, 100% accuracy in 2017: OR 1.357, 95% CI 1.164-1.581) was preferred. In class 2, the matched data from 2017 were similar to those from 2020; combined diagnoses from both AI and human clinicians (2017: OR 1.204, 95% CI 1.039-1.394; P=.011; 2020: OR 2.009, 95% CI 1.826-2.211; P<.001; reference: clinician diagnoses) and an outpatient waiting time of 20 minutes (2017: OR 1.349, 95% CI 1.065-1.708; P<.001; 2020: OR 1.488, 95% CI 1.287-1.721; P<.001; reference: 0 minutes) were consistently preferred. In class 3, the respondents in the 2017 and 2020 groups preferred different diagnosis methods; respondents in the 2017 group preferred clinician diagnoses, whereas respondents in the 2020 group preferred AI diagnoses. In the latent class, which was stratified according to sex, all male and female respondents in the 2017 and 2020 groups believed that accuracy was the most important attribute of diagnosis. Conclusions Individuals’ preferences for receiving clinical diagnoses from AI and human clinicians were generally unaffected by the pandemic. Respondents believed that accuracy and expense were the most important attributes of diagnosis. These findings can be used to guide policies that are relevant to the development of AI-based health care.
Taoran Liu; Winghei Tsang; Yifei Xie; Kang Tian; Fengqiu Huang; Yanhui Chen; Oiying Lau; Guanrui Feng; Jianhao Du; Bojia Chu; Tingyu Shi; Junjie Zhao; Yiming Cai; Xueyan Hu; Babatunde Akinwunmi; Jian Huang; Casper J P Zhang; Wai-Kit Ming. Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study. Journal of Medical Internet Research 2021, 23, e26997 .
AMA StyleTaoran Liu, Winghei Tsang, Yifei Xie, Kang Tian, Fengqiu Huang, Yanhui Chen, Oiying Lau, Guanrui Feng, Jianhao Du, Bojia Chu, Tingyu Shi, Junjie Zhao, Yiming Cai, Xueyan Hu, Babatunde Akinwunmi, Jian Huang, Casper J P Zhang, Wai-Kit Ming. Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study. Journal of Medical Internet Research. 2021; 23 (3):e26997.
Chicago/Turabian StyleTaoran Liu; Winghei Tsang; Yifei Xie; Kang Tian; Fengqiu Huang; Yanhui Chen; Oiying Lau; Guanrui Feng; Jianhao Du; Bojia Chu; Tingyu Shi; Junjie Zhao; Yiming Cai; Xueyan Hu; Babatunde Akinwunmi; Jian Huang; Casper J P Zhang; Wai-Kit Ming. 2021. "Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study." Journal of Medical Internet Research 23, no. 3: e26997.
Background The influence of meteorological factors on the transmission and spread of COVID-19 is of interest and has not been investigated. Objective This study aimed to investigate the associations between meteorological factors and the daily number of new cases of COVID-19 in 9 Asian cities. Methods Pearson correlation and generalized additive modeling (GAM) were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available. Results The Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=–0.565, P<.001), Shanghai (r=–0.47, P<.001), and Guangzhou (r=–0.53, P<.001). In Japan, however, a positive correlation was observed (r=0.416, P<.001). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), GAM analysis showed the number of daily new confirmed cases to be positively associated with both average temperature and relative humidity, especially using lagged 3D modeling where the positive influence of temperature on daily new confirmed cases was discerned in 5 cities (exceptions: Beijing, Wuhan, Korea, and Malaysia). Moreover, the sensitivity analysis showed, by incorporating the city grade and public health measures into the model, that higher temperatures can increase daily new case numbers (beta=0.073, Z=11.594, P<.001) in the lagged 3-day model. Conclusions The findings suggest that increased temperature yield increases in daily new cases of COVID-19. Hence, large-scale public health measures and expanded regional research are still required until a vaccine becomes widely available and herd immunity is established.
Zonglin He; Yiqiao Chin; Shinning Yu; Jian Huang; Casper J P Zhang; Ke Zhu; Nima Azarakhsh; Jie Sheng; Yi He; Pallavi Jayavanth; Qian Liu; Babatunde O Akinwunmi; Wai-Kit Ming. The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis. JMIR Public Health and Surveillance 2021, 7, e20495 .
AMA StyleZonglin He, Yiqiao Chin, Shinning Yu, Jian Huang, Casper J P Zhang, Ke Zhu, Nima Azarakhsh, Jie Sheng, Yi He, Pallavi Jayavanth, Qian Liu, Babatunde O Akinwunmi, Wai-Kit Ming. The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis. JMIR Public Health and Surveillance. 2021; 7 (1):e20495.
Chicago/Turabian StyleZonglin He; Yiqiao Chin; Shinning Yu; Jian Huang; Casper J P Zhang; Ke Zhu; Nima Azarakhsh; Jie Sheng; Yi He; Pallavi Jayavanth; Qian Liu; Babatunde O Akinwunmi; Wai-Kit Ming. 2021. "The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis." JMIR Public Health and Surveillance 7, no. 1: e20495.
BACKGROUND Artificial intelligence (AI) methods can potentially be used to relieve the pressure that the COVID-19 pandemic has exerted on public health. In cases of medical resource shortages caused by the pandemic, changes in people’s preferences for AI clinicians and traditional clinicians are worth exploring. OBJECTIVE We aimed to quantify and compare people’s preferences for AI clinicians and traditional clinicians before and during the COVID-19 pandemic, and to assess whether people’s preferences were affected by the pressure of pandemic. METHODS We used the propensity score matching method to match two different groups of respondents with similar demographic characteristics. Respondents were recruited in 2017 and 2020. A total of 2048 respondents (2017: n=1520; 2020: n=528) completed the questionnaire and were included in the analysis. Multinomial logit models and latent class models were used to assess people’s preferences for different diagnosis methods. RESULTS In total, 84.7% (1115/1317) of respondents in the 2017 group and 91.3% (482/528) of respondents in the 2020 group were confident that AI diagnosis methods would outperform human clinician diagnosis methods in the future. Both groups of matched respondents believed that the most important attribute of diagnosis was accuracy, and they preferred to receive combined diagnoses from both AI and human clinicians (2017: odds ratio [OR] 1.645, 95% CI 1.535-1.763; P<.001; 2020: OR 1.513, 95% CI 1.413-1.621; P<.001; reference: clinician diagnoses). The latent class model identified three classes with different attribute priorities. In class 1, preferences for combined diagnoses and accuracy remained constant in 2017 and 2020, and high accuracy (eg, 100% accuracy in 2017: OR 1.357, 95% CI 1.164-1.581) was preferred. In class 2, the matched data from 2017 were similar to those from 2020; combined diagnoses from both AI and human clinicians (2017: OR 1.204, 95% CI 1.039-1.394; P=.011; 2020: OR 2.009, 95% CI 1.826-2.211; P<.001; reference: clinician diagnoses) and an outpatient waiting time of 20 minutes (2017: OR 1.349, 95% CI 1.065-1.708; P<.001; 2020: OR 1.488, 95% CI 1.287-1.721; P<.001; reference: 0 minutes) were consistently preferred. In class 3, the respondents in the 2017 and 2020 groups preferred different diagnosis methods; respondents in the 2017 group preferred clinician diagnoses, whereas respondents in the 2020 group preferred AI diagnoses. In the latent class, which was stratified according to sex, all male and female respondents in the 2017 and 2020 groups believed that accuracy was the most important attribute of diagnosis. CONCLUSIONS Individuals’ preferences for receiving clinical diagnoses from AI and human clinicians were generally unaffected by the pandemic. Respondents believed that accuracy and expense were the most important attributes of diagnosis. These findings can be used to guide policies that are relevant to the development of AI-based health care.
Taoran Liu; Winghei Tsang; Yifei Xie; Kang Tian; Fengqiu Huang; Yanhui Chen; Oiying Lau; Guanrui Feng; Jianhao Du; Bojia Chu; Tingyu Shi; Junjie Zhao; Yiming Cai; Xueyan Hu; Babatunde Akinwunmi; Jian Huang; Casper J P Zhang; Wai-Kit Ming. Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study (Preprint). 2021, 1 .
AMA StyleTaoran Liu, Winghei Tsang, Yifei Xie, Kang Tian, Fengqiu Huang, Yanhui Chen, Oiying Lau, Guanrui Feng, Jianhao Du, Bojia Chu, Tingyu Shi, Junjie Zhao, Yiming Cai, Xueyan Hu, Babatunde Akinwunmi, Jian Huang, Casper J P Zhang, Wai-Kit Ming. Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study (Preprint). . 2021; ():1.
Chicago/Turabian StyleTaoran Liu; Winghei Tsang; Yifei Xie; Kang Tian; Fengqiu Huang; Yanhui Chen; Oiying Lau; Guanrui Feng; Jianhao Du; Bojia Chu; Tingyu Shi; Junjie Zhao; Yiming Cai; Xueyan Hu; Babatunde Akinwunmi; Jian Huang; Casper J P Zhang; Wai-Kit Ming. 2021. "Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study (Preprint)." , no. : 1.
BACKGROUND Due to the COVID-19 pandemic, health information related to COVID-19 has spread across the news media worldwide. Google is among the most used Internet search engines, and the Google Trends tool can reflect how the public seek COVID-related health information during this period. OBJECTIVE To understand health communication through Google Trends and news coverage and to explore their relationship with early prevention and control of COVID-19 in the early epidemic stage. METHODS To achieve the study objectives, we analyzed the public’s information-seeking behaviors on Google and news media coverage on COVID-19. We collected data on COVID-19 news coverage and Google search queries for eight countries (United States, United Kingdom, Canada, Singapore, Ireland, Australia, South Africa, and New Zealand) between January 1, 2020 and April 29, 2020, and depicted the trend of news coverage on COVID-19 over time, as well as search trends on the topics of COVID-19 related diseases, treatments and medical resources, symptoms and signs, and public measures. The characteristics of various trends in different countries were described and analyzed. RESULTS Across all search trends in eight countries, search peaks were formed almost between March and April 2020, and declines occurred in April 2020. Regarding COVID-19 related diseases, the searched peak for all terms occurred near or after March 11, 2020 across all countries, except Singapore. For treatments and medical resources, the term “mask” formed multiple search peaks while “ventilator” fluctuated modestly. In the topic of symptoms and signs, “fever” and “cough” were the most searched terms. The topic of public measures was the least searched. Besides, when combing the search trends with news coverage, there were mainly three patterns: the American pattern, the Singapore pattern, and the other-countries pattern. The Singapore pattern mainly saw two search peaks, while the trends of news coverage and search queries in the American pattern were in opposite directions. CONCLUSIONS Our findings reveal public concern about facemasks, disease control, and public health measures. As a source of information, news media can influence the search behaviors of the public. According to public concerns, news media can be used to spread more valuable information, and thus achieve more effective health communication. Also, because public concerns varied in different countries and periods, news media can deliver more effective health communication based on the change of public interest. Governments and health care systems can recognize Google Trends as public needs in the early stages of a COVID-19 crisis, and can translate public needs into practices to better control the spread of COVID-19. Therefore, news coverage trends and Google search trends also contribute to the prevention and control of epidemics in the early epidemic stage.
Qian Liu; Wai-Kit Ming; Fengqiu Huang; Qiuyi Chen; AoAo Jiao; Taoran Liu; Huailiang Wu; Babatunde Akinwunmi; Jia Li; Guan Liu; Casper Jp Zhang; Jian Huang. Understanding Health Communication Through Google Trends and News Coverage for COVID-19: A Multinational Study in Eight Countries (Preprint). 2020, 1 .
AMA StyleQian Liu, Wai-Kit Ming, Fengqiu Huang, Qiuyi Chen, AoAo Jiao, Taoran Liu, Huailiang Wu, Babatunde Akinwunmi, Jia Li, Guan Liu, Casper Jp Zhang, Jian Huang. Understanding Health Communication Through Google Trends and News Coverage for COVID-19: A Multinational Study in Eight Countries (Preprint). . 2020; ():1.
Chicago/Turabian StyleQian Liu; Wai-Kit Ming; Fengqiu Huang; Qiuyi Chen; AoAo Jiao; Taoran Liu; Huailiang Wu; Babatunde Akinwunmi; Jia Li; Guan Liu; Casper Jp Zhang; Jian Huang. 2020. "Understanding Health Communication Through Google Trends and News Coverage for COVID-19: A Multinational Study in Eight Countries (Preprint)." , no. : 1.
A novel pneumonia-like coronavirus disease (COVID-19) caused by a novel coronavirus named SARS-CoV-2 has swept across China and the world. Public health measures that were effective in previous infection outbreaks (eg, wearing a face mask, quarantining) were implemented in this outbreak. Available multidimensional social network data that take advantage of the recent rapid development of information and communication technologies allow for an exploration of disease spread and control via a modernized epidemiological approach. By using spatiotemporal data and real-time information, we can provide more accurate estimates of disease spread patterns related to human activities and enable more efficient responses to the outbreak. Two real cases during the COVID-19 outbreak demonstrated the application of emerging technologies and digital data in monitoring human movements related to disease spread. Although the ethical issues related to using digital epidemiology are still under debate, the cases reported in this article may enable the identification of more effective public health measures, as well as future applications of such digitally directed epidemiological approaches in controlling infectious disease outbreaks, which offer an alternative and modern outlook on addressing the long-standing challenges in population health.
Zonglin He; Casper J P Zhang; Jian Huang; Jingyan Zhai; Shuang Zhou; Joyce Wai-Ting Chiu; Jie Sheng; Winghei Tsang; Babatunde O Akinwunmi; Wai-Kit Ming. A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China. Journal of Medical Internet Research 2020, 22, e21685 .
AMA StyleZonglin He, Casper J P Zhang, Jian Huang, Jingyan Zhai, Shuang Zhou, Joyce Wai-Ting Chiu, Jie Sheng, Winghei Tsang, Babatunde O Akinwunmi, Wai-Kit Ming. A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China. Journal of Medical Internet Research. 2020; 22 (9):e21685.
Chicago/Turabian StyleZonglin He; Casper J P Zhang; Jian Huang; Jingyan Zhai; Shuang Zhou; Joyce Wai-Ting Chiu; Jie Sheng; Winghei Tsang; Babatunde O Akinwunmi; Wai-Kit Ming. 2020. "A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China." Journal of Medical Internet Research 22, no. 9: e21685.
Background Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. Objective This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. Methods An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. Results The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. Conclusions Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients’ age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.
Jiayi Shen; Jiebin Chen; Zequan Zheng; Jiabin Zheng; Zherui Liu; Jian Song; Sum Yi Wong; Xiaoling Wang; Mengqi Huang; Po-Han Fang; Bangsheng Jiang; Winghei Tsang; Zonglin He; Taoran Liu; Babatunde Akinwunmi; Chi Chiu Wang; Casper J P Zhang; Jian Huang; Wai-Kit Ming. An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study. Journal of Medical Internet Research 2020, 22, e21573 .
AMA StyleJiayi Shen, Jiebin Chen, Zequan Zheng, Jiabin Zheng, Zherui Liu, Jian Song, Sum Yi Wong, Xiaoling Wang, Mengqi Huang, Po-Han Fang, Bangsheng Jiang, Winghei Tsang, Zonglin He, Taoran Liu, Babatunde Akinwunmi, Chi Chiu Wang, Casper J P Zhang, Jian Huang, Wai-Kit Ming. An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study. Journal of Medical Internet Research. 2020; 22 (9):e21573.
Chicago/Turabian StyleJiayi Shen; Jiebin Chen; Zequan Zheng; Jiabin Zheng; Zherui Liu; Jian Song; Sum Yi Wong; Xiaoling Wang; Mengqi Huang; Po-Han Fang; Bangsheng Jiang; Winghei Tsang; Zonglin He; Taoran Liu; Babatunde Akinwunmi; Chi Chiu Wang; Casper J P Zhang; Jian Huang; Wai-Kit Ming. 2020. "An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study." Journal of Medical Internet Research 22, no. 9: e21573.
Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart–brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers. Using magnetic resonance images of the heart and aorta from 26,893 individuals in the UK Biobank, a phenome-wide association study associates cardiovascular imaging phenotypes with a wide range of demographic, lifestyle and clinical features.
Wenjia Bai; Hideaki Suzuki; Jian Huang; Catherine Francis; Shuo Wang; Giacomo Tarroni; Florian Guitton; Nay Aung; Kenneth Fung; Steffen E. Petersen; Stefan K. Piechnik; Stefan Neubauer; Evangelos Evangelou; Abbas Dehghan; Declan P. O’Regan; Martin R. Wilkins; Yike Guo; Paul M. Matthews; Daniel Rueckert. A population-based phenome-wide association study of cardiac and aortic structure and function. Nature Medicine 2020, 26, 1654 -1662.
AMA StyleWenjia Bai, Hideaki Suzuki, Jian Huang, Catherine Francis, Shuo Wang, Giacomo Tarroni, Florian Guitton, Nay Aung, Kenneth Fung, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Evangelos Evangelou, Abbas Dehghan, Declan P. O’Regan, Martin R. Wilkins, Yike Guo, Paul M. Matthews, Daniel Rueckert. A population-based phenome-wide association study of cardiac and aortic structure and function. Nature Medicine. 2020; 26 (10):1654-1662.
Chicago/Turabian StyleWenjia Bai; Hideaki Suzuki; Jian Huang; Catherine Francis; Shuo Wang; Giacomo Tarroni; Florian Guitton; Nay Aung; Kenneth Fung; Steffen E. Petersen; Stefan K. Piechnik; Stefan Neubauer; Evangelos Evangelou; Abbas Dehghan; Declan P. O’Regan; Martin R. Wilkins; Yike Guo; Paul M. Matthews; Daniel Rueckert. 2020. "A population-based phenome-wide association study of cardiac and aortic structure and function." Nature Medicine 26, no. 10: 1654-1662.
Objective To explore the causal relationships between sleep, major depressive disorder (MDD), and Alzheimer disease (AD). Methods We conducted bidirectional 2-sample Mendelian randomization analyses. Genetic associations were obtained from the largest genome-wide association studies currently available in UK Biobank (n = 446,118), Psychiatric Genomics Consortium (n = 18,759), and International Genomics of Alzheimer's Project (n = 63,926). We used the inverse variance–weighted Mendelian randomization method to estimate causal effects and weighted median and Mendelian randomization–Egger for sensitivity analyses to test for pleiotropic effects. Results We found that higher risk of AD was significantly associated with being a “morning person” (odds ratio [OR] 1.01, p = 0.001), shorter sleep duration (self-reported: β = −0.006, p = 1.9 × 10−4; accelerometer based: β = −0.015, p = 6.9 × 10−5), less likely to report long sleep (β = −0.003, p = 7.3 × 10−7), earlier timing of the least active 5 hours (β = −0.024, p = 1.7 × 10−13), and a smaller number of sleep episodes (β = −0.025, p = 5.7 × 10−14) after adjustment for multiple comparisons. We also found that higher risk of AD was associated with lower risk of insomnia (OR 0.99, p = 7 × 10−13). However, we did not find evidence that these abnormal sleep patterns were causally related to AD or for a significant causal relationship between MDD and risk of AD. Conclusion We found that AD may causally influence sleep patterns. However, we did not find evidence supporting a causal role of disturbed sleep patterns for AD or evidence for a causal relationship between MDD and AD.
Jian Huang; Verena Zuber; Paul M. Matthews; Paul Elliott; Joanna Tzoulaki; Abbas Dehghan. Sleep, major depressive disorder, and Alzheimer disease. Neurology 2020, 95, e1963 -e1970.
AMA StyleJian Huang, Verena Zuber, Paul M. Matthews, Paul Elliott, Joanna Tzoulaki, Abbas Dehghan. Sleep, major depressive disorder, and Alzheimer disease. Neurology. 2020; 95 (14):e1963-e1970.
Chicago/Turabian StyleJian Huang; Verena Zuber; Paul M. Matthews; Paul Elliott; Joanna Tzoulaki; Abbas Dehghan. 2020. "Sleep, major depressive disorder, and Alzheimer disease." Neurology 95, no. 14: e1963-e1970.
UNSTRUCTURED A novel pneumonia-like coronavirus disease (COVID-19) caused by a novel coronavirus named SARS-CoV-2 has swept across China and the world. Public health measures that were effective in previous infection outbreaks (eg, wearing a face mask, quarantining) were implemented in this outbreak. Available multidimensional social network data that take advantage of the recent rapid development of information and communication technologies allow for an exploration of disease spread and control via a modernized epidemiological approach. By using spatiotemporal data and real-time information, we can provide more accurate estimates of disease spread patterns related to human activities and enable more efficient responses to the outbreak. Two real cases during the COVID-19 outbreak demonstrated the application of emerging technologies and digital data in monitoring human movements related to disease spread. Although the ethical issues related to using digital epidemiology are still under debate, the cases reported in this article may enable the identification of more effective public health measures, as well as future applications of such digitally directed epidemiological approaches in controlling infectious disease outbreaks, which offer an alternative and modern outlook on addressing the long-standing challenges in population health.
Zonglin He; Casper J P Zhang; Jian Huang; Jingyan Zhai; Shuang Zhou; Joyce Wai-Ting Chiu; Jie Sheng; Winghei Tsang; Babatunde O Akinwunmi; Wai-Kit Ming. A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China (Preprint). 2020, 1 .
AMA StyleZonglin He, Casper J P Zhang, Jian Huang, Jingyan Zhai, Shuang Zhou, Joyce Wai-Ting Chiu, Jie Sheng, Winghei Tsang, Babatunde O Akinwunmi, Wai-Kit Ming. A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China (Preprint). . 2020; ():1.
Chicago/Turabian StyleZonglin He; Casper J P Zhang; Jian Huang; Jingyan Zhai; Shuang Zhou; Joyce Wai-Ting Chiu; Jie Sheng; Winghei Tsang; Babatunde O Akinwunmi; Wai-Kit Ming. 2020. "A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China (Preprint)." , no. : 1.
BACKGROUND Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. OBJECTIVE This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. METHODS An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. RESULTS The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. CONCLUSIONS Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients’ age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.
Jiayi Shen; Jiebin Chen; Zequan Zheng; Jiabin Zheng; Zherui Liu; Jian Song; Sum Yi Wong; Xiaoling Wang; Mengqi Huang; Po-Han Fang; Bangsheng Jiang; Winghei Tsang; Zonglin He; Taoran Liu; Babatunde Akinwunmi; Chi Chiu Wang; Casper J P Zhang; Jian Huang; Wai-Kit Ming. An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study (Preprint). 2020, 1 .
AMA StyleJiayi Shen, Jiebin Chen, Zequan Zheng, Jiabin Zheng, Zherui Liu, Jian Song, Sum Yi Wong, Xiaoling Wang, Mengqi Huang, Po-Han Fang, Bangsheng Jiang, Winghei Tsang, Zonglin He, Taoran Liu, Babatunde Akinwunmi, Chi Chiu Wang, Casper J P Zhang, Jian Huang, Wai-Kit Ming. An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study (Preprint). . 2020; ():1.
Chicago/Turabian StyleJiayi Shen; Jiebin Chen; Zequan Zheng; Jiabin Zheng; Zherui Liu; Jian Song; Sum Yi Wong; Xiaoling Wang; Mengqi Huang; Po-Han Fang; Bangsheng Jiang; Winghei Tsang; Zonglin He; Taoran Liu; Babatunde Akinwunmi; Chi Chiu Wang; Casper J P Zhang; Jian Huang; Wai-Kit Ming. 2020. "An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study (Preprint)." , no. : 1.
BACKGROUND The influence of meteorological factors on the transmission and spread of COVID-19 is of interest and has not been investigated. OBJECTIVE This study aimed to investigate the associations between meteorological factors and the daily number of new cases of COVID-19 in 9 Asian cities. METHODS Pearson correlation and generalized additive modeling (GAM) were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available. RESULTS The Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=–0.565, P<.001), Shanghai (r=–0.47, P<.001), and Guangzhou (r=–0.53, P<.001). In Japan, however, a positive correlation was observed (r=0.416, P<.001). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), GAM analysis showed the number of daily new confirmed cases to be positively associated with both average temperature and relative humidity, especially using lagged 3D modeling where the positive influence of temperature on daily new confirmed cases was discerned in 5 cities (exceptions: Beijing, Wuhan, Korea, and Malaysia). Moreover, the sensitivity analysis showed, by incorporating the city grade and public health measures into the model, that higher temperatures can increase daily new case numbers (beta=0.073, Z=11.594, P<.001) in the lagged 3-day model. CONCLUSIONS The findings suggest that increased temperature yield increases in daily new cases of COVID-19. Hence, large-scale public health measures and expanded regional research are still required until a vaccine becomes widely available and herd immunity is established.
Zonglin He; Yiqiao Chin; Shinning Yu; Jian Huang; Casper J P Zhang; Ke Zhu; Nima Azarakhsh; Jie Sheng; Yi He; Pallavi Jayavanth; Qian Liu; Babatunde O Akinwunmi; Wai-Kit Ming. The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis (Preprint). 2020, 1 .
AMA StyleZonglin He, Yiqiao Chin, Shinning Yu, Jian Huang, Casper J P Zhang, Ke Zhu, Nima Azarakhsh, Jie Sheng, Yi He, Pallavi Jayavanth, Qian Liu, Babatunde O Akinwunmi, Wai-Kit Ming. The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis (Preprint). . 2020; ():1.
Chicago/Turabian StyleZonglin He; Yiqiao Chin; Shinning Yu; Jian Huang; Casper J P Zhang; Ke Zhu; Nima Azarakhsh; Jie Sheng; Yi He; Pallavi Jayavanth; Qian Liu; Babatunde O Akinwunmi; Wai-Kit Ming. 2020. "The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis (Preprint)." , no. : 1.
BACKGROUND By middle April 2020, the novel coronavirus disease (COVID-19) pandemic has spread globally and caused more than 2 million confirmed cases and 140 thousand deaths. During this ongoing outbreak, the psychological demands of pregnant women needed to be acknowledged because they are likely to present symptoms of depression, stress or other mental discomfort during pregnancy. OBJECTIVE By middle April 2020, the novel coronavirus disease (COVID-19) pandemic has spread globally and caused more than 2 million confirmed cases and 140 thousand deaths. During this ongoing outbreak, the psychological demands of pregnant women needed to be acknowledged because they are likely to present symptoms of depression, stress or other mental discomfort during pregnancy. METHODS A population-based cross-sectional survey was carried out to collect the data of sociodemographic and other psychological assessments and responses through a national online platform. A total of 1901 pregnant women were included in this study. Each participant finished standardized rating scales on stress, depression, and responses to COVID-19. Independent t-test and chi-squared tests were used to compare outcomes between epicenter and non-epicenters in China. RESULTS Pregnant women in the epicenter appeared to have a significantly higher prevalence rate of suspected PSTD than the non-epicentral region. A slightly higher proportion of pregnant women in epicenter worried about infectious risks and outcomes related to their fetus. However, no significant difference was found between the probable PPD levels of the two regions. Pregnant women in both epicenter and non-epicentral regions have adapted their behaviors to mitigate the infection risks. CONCLUSIONS Epidemic situations could result in higher risks of psychological problems during pregnancy. Even outside of the epicenter, the depressive symptoms of pregnant women were more severe than during regular times. Several implications in antenatal care are also yielded for clinical application especially for countries in the early COVID-19 outbreak due to pandemic.
Huailiang Wu; Nga-Kwo Chan; Casper C.J. Zhang; Jian Huang; Huiyun Wang; Zongzhi Yin; Babatunde Akinwunmi; Wai-Kit Ming. Psychological and behavioral responses of pregnant women during the early COVID-19 outbreak: Epicenter and non-epicenter regions (Preprint). 2020, 1 .
AMA StyleHuailiang Wu, Nga-Kwo Chan, Casper C.J. Zhang, Jian Huang, Huiyun Wang, Zongzhi Yin, Babatunde Akinwunmi, Wai-Kit Ming. Psychological and behavioral responses of pregnant women during the early COVID-19 outbreak: Epicenter and non-epicenter regions (Preprint). . 2020; ():1.
Chicago/Turabian StyleHuailiang Wu; Nga-Kwo Chan; Casper C.J. Zhang; Jian Huang; Huiyun Wang; Zongzhi Yin; Babatunde Akinwunmi; Wai-Kit Ming. 2020. "Psychological and behavioral responses of pregnant women during the early COVID-19 outbreak: Epicenter and non-epicenter regions (Preprint)." , no. : 1.
Background In December 2019, a few coronavirus disease (COVID-19) cases were first reported in Wuhan, Hubei, China. Soon after, increasing numbers of cases were detected in other parts of China, eventually leading to a disease outbreak in China. As this dreadful disease spreads rapidly, the mass media has been active in community education on COVID-19 by delivering health information about this novel coronavirus, such as its pathogenesis, spread, prevention, and containment. Objective The aim of this study was to collect media reports on COVID-19 and investigate the patterns of media-directed health communications as well as the role of the media in this ongoing COVID-19 crisis in China. Methods We adopted the WiseSearch database to extract related news articles about the coronavirus from major press media between January 1, 2020, and February 20, 2020. We then sorted and analyzed the data using Python software and Python package Jieba. We sought a suitable topic number with evidence of the coherence number. We operated latent Dirichlet allocation topic modeling with a suitable topic number and generated corresponding keywords and topic names. We then divided these topics into different themes by plotting them into a 2D plane via multidimensional scaling. Results After removing duplications and irrelevant reports, our search identified 7791 relevant news reports. We listed the number of articles published per day. According to the coherence value, we chose 20 as the number of topics and generated the topics’ themes and keywords. These topics were categorized into nine main primary themes based on the topic visualization figure. The top three most popular themes were prevention and control procedures, medical treatment and research, and global or local social and economic influences, accounting for 32.57% (n=2538), 16.08% (n=1258), and 11.79% (n=919) of the collected reports, respectively. Conclusions Topic modeling of news articles can produce useful information about the significance of mass media for early health communication. Comparing the number of articles for each day and the outbreak development, we noted that mass media news reports in China lagged behind the development of COVID-19. The major themes accounted for around half the content and tended to focus on the larger society rather than on individuals. The COVID-19 crisis has become a worldwide issue, and society has become concerned about donations and support as well as mental health among others. We recommend that future work addresses the mass media’s actual impact on readers during the COVID-19 crisis through sentiment analysis of news data.
Qian Liu; Zequan Zheng; Jiabin Zheng; Qiuyi Chen; Guan Liu; Sihan Chen; Bojia Chu; Hongyu Zhu; Babatunde Akinwunmi; Jian Huang; Casper J P Zhang; Wai-Kit Ming. Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach. Journal of Medical Internet Research 2020, 22, e19118 .
AMA StyleQian Liu, Zequan Zheng, Jiabin Zheng, Qiuyi Chen, Guan Liu, Sihan Chen, Bojia Chu, Hongyu Zhu, Babatunde Akinwunmi, Jian Huang, Casper J P Zhang, Wai-Kit Ming. Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach. Journal of Medical Internet Research. 2020; 22 (4):e19118.
Chicago/Turabian StyleQian Liu; Zequan Zheng; Jiabin Zheng; Qiuyi Chen; Guan Liu; Sihan Chen; Bojia Chu; Hongyu Zhu; Babatunde Akinwunmi; Jian Huang; Casper J P Zhang; Wai-Kit Ming. 2020. "Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach." Journal of Medical Internet Research 22, no. 4: e19118.
AIMTo investigate the associations of meteorological factors and the daily new cases of coronavirus disease (COVID-19) in nine Asian cities.METHODPearson’s correlation and generalized additive modeling were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available.RESULTSThe Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=-0.565, PCONCLUSIONThe associations between meteorological factors and the number of COVID-19 daily cases are inconsistent across cities and lagged time. Large-scale public health measures and expanded regional research are still required until a vaccine becomes available and herd immunity is established.Significance statementWith increasing COVID-19 cases across China and the world, and previous studies showing that meteorological factors may be associated with infectious disease transmission, the saying has it that when summer comes, the epidemic of COVID-19 may simultaneously fade away. We demonstrated the influence of meteorological factors on the daily domestic new cases of coronavirus disease (COVID-19) in nine Asian cities. And we found that the associations between meteorological factors and the number of COVID-19 daily cases are inconsistent across cities and time. We think this important topic may give better clues on prevention, management, and preparation for new events or new changes that could happen in the COVID-19 epidemiology in various geographical regions and as we move towards Summer.
Zonglin He; Yiqiao Chin; Jian Huang; Yi He; Babatunde O. Akinwunmi; Shinning Yu; Casper J.P. Zhang; Wai-Kit Ming. Meteorological factors and domestic new cases of coronavirus disease (COVID-19) in nine Asian cities: A time-series analysis. 2020, 1 .
AMA StyleZonglin He, Yiqiao Chin, Jian Huang, Yi He, Babatunde O. Akinwunmi, Shinning Yu, Casper J.P. Zhang, Wai-Kit Ming. Meteorological factors and domestic new cases of coronavirus disease (COVID-19) in nine Asian cities: A time-series analysis. . 2020; ():1.
Chicago/Turabian StyleZonglin He; Yiqiao Chin; Jian Huang; Yi He; Babatunde O. Akinwunmi; Shinning Yu; Casper J.P. Zhang; Wai-Kit Ming. 2020. "Meteorological factors and domestic new cases of coronavirus disease (COVID-19) in nine Asian cities: A time-series analysis." , no. : 1.
BACKGROUND In December 2019, a few coronavirus disease (COVID-19) cases were first reported in Wuhan, Hubei, China. Soon after, increasing numbers of cases were detected in other parts of China, eventually leading to a disease outbreak in China. As this dreadful disease spreads rapidly, the mass media has been active in community education on COVID-19 by delivering health information about this novel coronavirus, such as its pathogenesis, spread, prevention, and containment. OBJECTIVE The aim of this study was to collect media reports on COVID-19 and investigate the patterns of media-directed health communications as well as the role of the media in this ongoing COVID-19 crisis in China. METHODS We adopted the WiseSearch database to extract related news articles about the coronavirus from major press media between January 1, 2020, and February 20, 2020. We then sorted and analyzed the data using Python software and Python package Jieba. We sought a suitable topic number with evidence of the coherence number. We operated latent Dirichlet allocation topic modeling with a suitable topic number and generated corresponding keywords and topic names. We then divided these topics into different themes by plotting them into a 2D plane via multidimensional scaling. RESULTS After removing duplications and irrelevant reports, our search identified 7791 relevant news reports. We listed the number of articles published per day. According to the coherence value, we chose 20 as the number of topics and generated the topics’ themes and keywords. These topics were categorized into nine main primary themes based on the topic visualization figure. The top three most popular themes were prevention and control procedures, medical treatment and research, and global or local social and economic influences, accounting for 32.57% (n=2538), 16.08% (n=1258), and 11.79% (n=919) of the collected reports, respectively. CONCLUSIONS Topic modeling of news articles can produce useful information about the significance of mass media for early health communication. Comparing the number of articles for each day and the outbreak development, we noted that mass media news reports in China lagged behind the development of COVID-19. The major themes accounted for around half the content and tended to focus on the larger society rather than on individuals. The COVID-19 crisis has become a worldwide issue, and society has become concerned about donations and support as well as mental health among others. We recommend that future work addresses the mass media’s actual impact on readers during the COVID-19 crisis through sentiment analysis of news data.
Qian Liu; Zequan Zheng; Jiabin Zheng; Qiuyi Chen; Guan Liu; Sihan Chen; Bojia Chu; Hongyu Zhu; Babatunde Akinwunmi; Jian Huang; Casper J. P. Zhang; Wai-Kit Ming. Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach (Preprint). 2020, 1 .
AMA StyleQian Liu, Zequan Zheng, Jiabin Zheng, Qiuyi Chen, Guan Liu, Sihan Chen, Bojia Chu, Hongyu Zhu, Babatunde Akinwunmi, Jian Huang, Casper J. P. Zhang, Wai-Kit Ming. Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach (Preprint). . 2020; ():1.
Chicago/Turabian StyleQian Liu; Zequan Zheng; Jiabin Zheng; Qiuyi Chen; Guan Liu; Sihan Chen; Bojia Chu; Hongyu Zhu; Babatunde Akinwunmi; Jian Huang; Casper J. P. Zhang; Wai-Kit Ming. 2020. "Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach (Preprint)." , no. : 1.
Abstract Background A novel coronavirus disease (COVID-19) outbreak due to the severe respiratory syndrome coronavirus (SARS-CoV-2) infection occurred in China in late December 2019. Facemask wearing with proper hand hygiene is considered an effective measure to prevent SARS-CoV-2 transmission, but facemask wearing has become a social concern due to the global facemask shortage. China is the major facemask producer in the world, contributing to 50% of global production. However, a universal facemask wearing policy would put an enormous burden on the facemask supply. Methods We performed a policy review concerning facemasks using government websites and mathematical modelling shortage analyses based on data obtained from the National Health Commission (NHC), the Ministry of Industry and Information Technology (MIIT), the Centre for Disease Control and Prevention (CDC), and General Administration of Customs (GAC) of the People's Republic of China. Three scenarios with respect to wearing facemasks were considered: (1) a universal facemask wearing policy implementation in all regions of mainland China; (2) a universal facemask wearing policy implementation only in the epicentre (Hubei province, China); and (3) no implementation of a universal facemask wearing policy. Findings Regardless of different universal facemask wearing policy scenarios, facemask shortage would occur but eventually end during our prediction period (from 20 Jan 2020 to 30 Jun 2020). The duration of the facemask shortage described in the scenarios of a country-wide universal facemask wearing policy, a universal facemask wearing policy in the epicentre, and no universal facemask wearing policy were 132, seven, and four days, respectively. During the prediction period, the largest daily facemask shortages were predicted to be 589·5, 49·3, and 37·5 million in each of the three scenarios, respectively. In any scenario, an N95 mask shortage was predicted to occur on 24 January 2020 with a daily facemask shortage of 2·2 million. Interpretation Implementing a universal facemask wearing policy in the whole of China could lead to severe facemask shortage. Without effective public communication, a universal facemask wearing policy could result in societal panic and subsequently, increase the nationwide and worldwide demand for facemasks. These increased demands could cause a facemask shortage for healthcare workers and reduce the effectiveness of outbreak control in the affected regions, eventually leading to a pandemic. To fight novel infectious disease outbreaks, such as COVID-19, governments should monitor domestic facemask supplies and give priority to healthcare workers. The risk of asymptomatic transmission and facemask shortages should be carefully evaluated before introducing a universal facemask wearing policy in high-risk regions. Public health measures aimed at improving hand hygiene and effective public communication should be considered along with the facemask policy.
Huai-Liang Wu; Jian Huang; Casper J.P. Zhang; Zonglin He; Wai-Kit Ming. Facemask shortage and the novel coronavirus disease (COVID-19) outbreak: Reflections on public health measures. EClinicalMedicine 2020, 21, 1 .
AMA StyleHuai-Liang Wu, Jian Huang, Casper J.P. Zhang, Zonglin He, Wai-Kit Ming. Facemask shortage and the novel coronavirus disease (COVID-19) outbreak: Reflections on public health measures. EClinicalMedicine. 2020; 21 ():1.
Chicago/Turabian StyleHuai-Liang Wu; Jian Huang; Casper J.P. Zhang; Zonglin He; Wai-Kit Ming. 2020. "Facemask shortage and the novel coronavirus disease (COVID-19) outbreak: Reflections on public health measures." EClinicalMedicine 21, no. : 1.
BackgroundIn December 2019, some COVID-19 cases were first reported and soon the disease broke out. As this dreadful disease spreads rapidly, the mass media has been active in community education on COVID-19 by delivering health information about this novel coronavirus.MethodsWe adopted the Huike database to extract news articles about coronavirus from major press media, between January 1st, 2020, to February 20th, 2020. The data were sorted and analyzed by Python software and Python package Jieba. We sought a suitable topic number using the coherence number. We operated Latent Dirichlet Allocation (LDA) topic modeling with the suitable topic number and generated corresponding keywords and topic names. We divided these topics into different themes by plotting them into two-dimensional plane via multidimensional scaling.FindingsAfter removing duplicates, 7791 relevant news reports were identified. We listed the number of articles published per day. According to the coherence value, we chose 20 as our number of topics and obtained their names and keywords. These topics were categorized into nine primary themes based on the topic visualization figure. The top three popular themes were prevention and control procedures, medical treatment and research, global/local social/economic influences, accounting for 32·6%, 16·6%, 11·8% of the collected reports respectively.InterpretationThe Chinese mass media news reports lag behind the COVID-19 outbreak development. The major themes accounted for around half the content and tended to focus on the larger society than on individuals. The COVID-19 crisis has become a global issue, and society has also become concerned about donation and support as well as mental health. We recommend that future work should address the mass media’s actual impact on readers during the COVID-19 crisis through sentiment analysis of news data.FundingNational Social Science Foundation of China (18CXW021)Evidence before this studyThe novel coronavirus related news reports have engaged public attention in China during the COVID-19 crisis. Topic modeling of these news articles can produce useful information about the significance of mass media for early health communication. We searched the Huike database, the most professional Chinese media content database, using the search term “coronavirus” for related news articles published from January 1st, 2020, to February 20th, 2020. We found that these articles can be classified into different themes according to their emphasis, however, we found no other studies apply topic modeling method to study them.Added value of this studyTo our knowledge, this study is the first to investigate the patterns of health communications through media and the role the media have played and are still playing in the light of the current COVID-19 crisis in China with topic modeling method. We compared the number of articles each day with the outbreak development and identified there’s a delay in reporting COVID-19 outbreak progression for Chinese mass media. We identify nine main themes for 7791 collected news reports and detail their emphasis respectively.Implications of all the available evidenceOur results show that the mass media news reports play a significant role in health communication during the COVID-19 crisis, government can strengthen the report dynamics and enlarge the news coverage next time another disease strikes. Sentiment analysis of news data are needed to assess the actual effect of the news reports.
Qian Liu; Zequan Zheng; Jiabin Zheng; Qiuyi Chen; Guan Liu; Sihan Chen; Bojia Chu; Hongyu Zhu; Babatunde Akinwunmi; Jian Huang; Casper J. P. Zhang; Wai-Kit Ming. Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: A Digital Topic Modeling Approach. 2020, 1 .
AMA StyleQian Liu, Zequan Zheng, Jiabin Zheng, Qiuyi Chen, Guan Liu, Sihan Chen, Bojia Chu, Hongyu Zhu, Babatunde Akinwunmi, Jian Huang, Casper J. P. Zhang, Wai-Kit Ming. Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: A Digital Topic Modeling Approach. . 2020; ():1.
Chicago/Turabian StyleQian Liu; Zequan Zheng; Jiabin Zheng; Qiuyi Chen; Guan Liu; Sihan Chen; Bojia Chu; Hongyu Zhu; Babatunde Akinwunmi; Jian Huang; Casper J. P. Zhang; Wai-Kit Ming. 2020. "Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: A Digital Topic Modeling Approach." , no. : 1.