Enhancing Influenza Epidemics Forecasting Accuracy in China with Both Official and Unofficial Online News Articles, 2019-2020.
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ABSTRACT:
SUBMITTER: Li J
PROVIDER: S-EPMC8296334 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored.ObjectiveThe main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources.MethodsWe first used core terms and symptom-related keyword-based methods to extract COVID-19-related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used lagged Pearson correlations for COVID-19 forecasting timeliness analysis.ResultsOur proposed model achieved the highest accuracy in all 5 accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In mainland China, except for Hubei, the COVID-19 epidemic forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t198=-8.722, P<.001; model 2, t198=-5.000, P<.001, model 3, t198=-1.882, P=.06; model 4, t198=-4.644, P<.001; model 5, t198=-4.488, P<.001). In Hubei province, our proposed model's forecasting accuracy improved significantly compared with the baseline model using historical new confirmed COVID-19 case counts only (model 1, t198=-1.732, P=.09). Our results also showed that Internet-based sources could provide a 2- to 6-day earlier warning for COVID-19 outbreaks.ConclusionsOur approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for epidemics of COVID-19 and its variants, which may help improve public health agencies' interventions and resource allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics.
| S-EPMC9205424 | biostudies-literature
Project description:BackgroundGiven the variation in human papillomavirus (HPV) vaccine coverage across Canada, and debate regarding delivery of HPV vaccines in Catholic schools, we studied online comments on Canadian news websites to understand public perceptions of HPV and HPV vaccine.MethodsWe searched English- and French-language Canadian news websites for 2012 articles that contained the terms "HPV" or "human papillomavirus." Articles about HPV vaccinations that contained at least one comment were included. Two researchers independently coded comments, analyzing them for emerging themes.ResultsWe identified 3073 comments from 1198 individuals in response to 71 news articles; 630 (52.6%) individuals expressed positive sentiments about HPV vaccination (2.5 comments/individual), 404 (33.7%) were negative (3.0 comments/individual), 34 (2.8%) were mixed (1.5 comments/individual) and 130 (10.8%) were neutral (1.6 comments/individual). Vaccine-supportive commenters believed the vaccine is safe and effective. Common themes in negative comments included concerns regarding HPV vaccine safety and efficacy, distrust of pharmaceutical companies and government, and belief that school-age children are too young for HPV vaccine. Many comments focused on whether the Catholic Church has the right to inform health policy for students, and discussion often evolved into debates regarding HPV and sexual behaviour. We noted that many individuals doubted the credibility of vaccine safety information.ConclusionThe majority of commenters do not appear to be against HPV vaccination, but public health messaging that focuses on both the vaccine's safety profile, and its use as a means to prevent cancer rather than sexually transmitted HPV infection may facilitate its acceptance.
| S-EPMC4460033 | biostudies-literature
Project description:The objective of this study was to qualitatively summarise the content of online news articles pertaining to food parenting practices and determine whether this content is substantiated by the scientific literature. News article data were identified and collected from United Kingdom online news published during 2010-2017 period using the News on the Web corpus. A coding framework was used to categorise the content of news articles to identify information related to food parenting practices. Then, claims made about food parenting practices were extracted from relevant news articles. Each claim was evaluated to determine the extent to which any claims were supported by the available scientific research evidence. The study identified ten claims across thirty-two relevant online news articles. Claims made across the news articles reported on the following food parenting practices: food restrictions, food-based threats and bribes, pressure to eat, use of food to control negative emotions, food availability, food preparation, and meal and snack routines. Eight out of the ten claims identified did not refer to scientific research evidence. News articles frequently lacked detail and information to explain to readers why and how the use of certain food parenting practices could have a lasting impact on children's health outcomes. Considering the influence that news media has on parents, the reporting of food parenting practices in news articles should aim to provide a balanced view of the published scientific evidence and recognise the difficulties and barriers that prevent the use of helpful and healthy food parenting practices. The study results in this paper could be used to aid and structure of the dissemination of food parenting practice research findings in the media, inform public health education to influence perceptions of unhelpful food parenting practices, and promote parental use of responsive food parenting practices.
| S-EPMC9105157 | biostudies-literature
Project description:As a result of globalization, countries are collaborating more and sharing more information. People's perspectives now encompass both domestic and international news. In English as a Second Language (ESL) classes, the appropriate use of rich and vibrant online English news resources gives second language learners (L2 learners) direct access to first-hand, authentic English knowledge through independent reading and digestion, as well as peer-assisted classroom activities. Through weekly peer-assisted sharing of online English news, the goal of this study is to improve L2 learners' literacy, global competence, and appreciation of cultural diversity. This study examines the participants' perceptions and feedback using both quantitative and qualitative methods to confirm the efficacy of this pedagogy. After the semester-long experiment, significant differences were found in the English proficiency test. Particularly, the experimental class's reading and writing scores were significantly higher (p < 0.05) than the control class's, indicating that this pedagogy can effectively improve the literacy of ESL learners. The semi-structured interview revealed that participants were pleased with this instruction and perceived improvements in their language skills. ESL students' core English literacy has significantly improved by expanding their comprehension of news articles and cultivating a sense of social responsibility.
| S-EPMC10642956 | biostudies-literature
Project description:Recent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time. However, in subtropical and tropical regions, influenza epidemics can occur throughout the year, making routine forecast of influenza more challenging. Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics, using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered (SIR) model. We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013, including the 2009 pandemic. The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains (i.e., seasonal influenza A(H1N1), pandemic A(H1N1), A(H3N2), and B), as well as 19 aggregate epidemics caused by one or more of these influenza strains. Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead. Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads. These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions.
| S-EPMC4520691 | biostudies-literature
Project description:BackgroundRapid identification of subject experts for medical topics helps in improving the implementation of discoveries by speeding the time to market drugs and aiding in clinical trial recruitment, etc. Identifying such people who influence opinion through social network analysis is gaining prominence. In this work, we explore how to combine named entity recognition from unstructured news articles with social network analysis to discover opinion leaders for a given medical topic.MethodsWe employed a Conditional Random Field algorithm to extract three categories of entities from health-related new articles: Person, Organization and Location. We used the latter two to disambiguate polysemy and synonymy for the person names, used simple rules to identify the subject experts, and then applied social network analysis techniques to discover the opinion leaders among them based on their media presence. A network was created by linking each pair of subject experts who are mentioned together in an article. The social network analysis metrics (including centrality metrics such as Betweenness, Closeness, Degree and Eigenvector) are used for ranking the subject experts based on their power in information flow.ResultsWe extracted 734,204 person mentions from 147,528 news articles related to obesity from January 1, 2007 through July 22, 2010. Of these, 147,879 mentions have been marked as subject experts. The F-score of extracting person names is 88.5%. More than 80% of the subject experts who rank among top 20 in at least one of the metrics could be considered as opinion leaders in obesity.ConclusionThe analysis of the network of subject experts with media presence revealed that an opinion leader might have fewer mentions in the news articles, but a high network centrality measure and vice-versa. Betweenness, Closeness and Degree centrality measures were shown to supplement frequency counts in the task of finding subject experts. Further, opinion leaders missed in scientific publication network analysis could be retrieved from news articles.
| S-EPMC3338075 | biostudies-literature
Project description:We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance and detection for Health Departments, and predictive numbers of infection cases, which would allow them sufficient time to implement interventions. In this study, we found a strong correlation between Zika-related GTs and the cumulative numbers of reported cases (confirmed, suspected and total cases; p<0.001). Then, we used the correlation data from Zika-related online search in GTs and ZIKV epidemics between 12 February and 20 October 2016 to construct an autoregressive integrated moving average (ARIMA) model (0, 1, 3) for the dynamic estimation of ZIKV outbreaks. The forecasting results indicated that the predicted data by ARIMA model, which used the online search data as the external regressor to enhance the forecasting model and assist the historical epidemic data in improving the quality of the predictions, are quite similar to the actual data during ZIKV epidemic early November 2016. Integer-valued autoregression provides a useful base predictive model for ZVD cases. This is enhanced by the incorporation of GTs data, confirming the prognostic utility of search query based surveillance. This accessible and flexible dynamic forecast model could be used in the monitoring of ZVD to provide advanced warning of future ZIKV outbreaks.
| S-EPMC5217860 | biostudies-literature
Project description:Since the onset of the COVID-19 pandemic, countless disease prediction models have emerged, shaping the focus of news media, policymakers, and broader society. We reviewed the accuracy of forecasts made during prior twenty-first century epidemics, namely SARS, H1N1, and Ebola. We found that while disease prediction models were relatively nascent as a research focus during SARS and H1N1, for Ebola, numerous such forecasts were published. We found that forecasts of deaths for Ebola were often far from the eventual reality, with a strong tendency to over predict. Given the societal prominence of these models, it is crucial that their uncertainty be communicated. Otherwise, we will be unaware if we are being falsely lulled into complacency or unjustifiably shocked into action.
| S-EPMC7366467 | biostudies-literature
Project description:BackgroundDengue dynamics are driven by complex interactions between human-hosts, mosquito-vectors and viruses that are influenced by environmental and climatic factors. The objectives of this study were to analyze and model the relationships between climate, Aedes aegypti vectors and dengue outbreaks in Noumea (New Caledonia), and to provide an early warning system.Methodology/principal findingsEpidemiological and meteorological data were analyzed from 1971 to 2010 in Noumea. Entomological surveillance indices were available from March 2000 to December 2009. During epidemic years, the distribution of dengue cases was highly seasonal. The epidemic peak (March-April) lagged the warmest temperature by 1-2 months and was in phase with maximum precipitations, relative humidity and entomological indices. Significant inter-annual correlations were observed between the risk of outbreak and summertime temperature, precipitations or relative humidity but not ENSO. Climate-based multivariate non-linear models were developed to estimate the yearly risk of dengue outbreak in Noumea. The best explicative meteorological variables were the number of days with maximal temperature exceeding 32°C during January-February-March and the number of days with maximal relative humidity exceeding 95% during January. The best predictive variables were the maximal temperature in December and maximal relative humidity during October-November-December of the previous year. For a probability of dengue outbreak above 65% in leave-one-out cross validation, the explicative model predicted 94% of the epidemic years and 79% of the non epidemic years, and the predictive model 79% and 65%, respectively.Conclusions/significanceThe epidemic dynamics of dengue in Noumea were essentially driven by climate during the last forty years. Specific conditions based on maximal temperature and relative humidity thresholds were determinant in outbreaks occurrence. Their persistence was also crucial. An operational model that will enable health authorities to anticipate the outbreak risk was successfully developed. Similar models may be developed to improve dengue management in other countries.
| S-EPMC3279338 | biostudies-literature
Project description:The stock market is very complex and volatile. It is impacted by positive and negative sentiments which are based on media releases. The scope of the stock price analysis relies upon ability to recognise the stock movements. It is based on technical fundamentals and understanding the hidden trends which the market follows. Stock price prediction has consistently been an extremely dynamic field of exploration and research work. However, arriving at the ideal degree of precision is still an enticing challenge. In this paper, we are proposing a combined effort of using efficient machine learning techniques coupled with a deep learning technique-Long Short Term Memory (LSTM)-to use them to predict the stock prices with a high level of accuracy. Sentiments derived by users from news headlines have a tremendous effect on the buying and selling patterns of the traders as they easily get influenced by what they read. Hence, fusing one more dimension of sentiments along with technical analysis should improve the prediction accuracy. LSTM networks have proved to be a very useful tool to learn and predict temporal data having long term dependencies. In our work, the LSTM model uses historical stock data along with sentiments from news items to create a better predictive model.
| S-EPMC7924447 | biostudies-literature