Correlation Between UpToDate Searches and Reported Cases of Middle East Respiratory Syndrome During Outbreaks in Saudi Arabia

Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives

With the rapid development of the internet, the application of internet search data has been seen as a novel data source to offer timely infectious disease surveillance intelligence. Moreover, the advancements in internet search data, which include rich information at both space and time scales, enable investigators to sufficiently consider the spatiotemporal uncertainty, which can benefit researchers to better monitor infectious diseases and epidemics. In the present study, we present the necessary groundwork and critical appraisal of the use of internet search data and spatiotemporal analysis approaches in infectious disease surveillance by updating the current stage of knowledge on them. The study also provides future directions for researchers to investigate the combination of internet search data with the spatiotemporal analysis in infectious disease surveillance. Internet search data demonstrate a promising potential to offer timely epidemic intelligence, which can be seen as the prerequisite for improving infectious disease surveillance.

Dynamics of scientific publications on the MERS-CoV outbreaks in Saudi Arabia

Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is an emerging disease with a relatively high case fatality rate. Most cases have been reported from Saudi Arabia, and the disease epidemic potential is considered to be limited. However, human-human transmission has occurred, usually in the context of healthcare facility-associated outbreaks. The scientific and medical community depends on timely publication of epidemiological information on emerging diseases during outbreaks to appropriately target public health responses. In this review, we considered the academic response to four MERS CoV outbreaks that occurred in Al-Hasa in 2013, Jeddah in 2014 and Riyadh in 2014 and 2015. We analysed 68 relevant epidemiology articles. For articles for which submission dates were available, six articles were submitted during the course of an outbreak. One article was published within a month of the Al-Hasa outbreak, and one each was accepted during the Jeddah and Riyadh outbreaks. MERS-CoV epidemiology articles were cited more frequently than articles on other subjects in the same journal issues. Thus, most epidemiology articles on MERS-CoV were published with no preferential advantage over other articles. Collaboration of the research community and the scientific publishing industry is needed to facilitate timely publication of emerging infectious diseases.

Measuring Global Disease with Wikipedia: Success, Failure, and a Research Agenda

Effective disease monitoring provides a foundation for effective public health systems. This has historically been accomplished with patient contact and bureaucratic aggregation, which tends to be slow and expensive. Recent internet-based approaches promise to be real-time and cheap, with few parameters. However, the question of when and how these approaches work remains open. We addressed this question using Wikipedia access logs and category links. Our experiments, replicable and extensible using our open source code and data, test the effect of semantic article filtering, amount of training data, forecast horizon, and model staleness by comparing across 6 diseases and 4 countries using thousands of individual models. We found that our minimal-configuration, language-agnostic article selection process based on semantic relatedness is effective for improving predictions, and that our approach is relatively insensitive to the amount and age of training data. We also found, in contrast to prior work, very little forecasting value, and we argue that this is consistent with theoretical considerations about the nature of forecasting. These mixed results lead us to propose that the currently observational field of internet-based disease surveillance must pivot to include theoretical models of information flow as well as controlled experiments based on simulations of disease.

Information technology and social sciences: how can health IT be used to support the health professional?

Keeping up to date with the increasing amount of health-related knowledge and managing the increasing numbers of patients with more complex clinical problems is a challenge for healthcare professionals and healthcare systems. Health IT applications, such as electronic health records or decision-support systems, are meant to support both professionals and their support systems. However, for physicians using these applications, the applications often cause new problems, such as the impracticality of their use in clinical practice. This review adopts a social sciences perspective to understand these problems and derive suggestions for further development. Indeed, humans use tools to remediate the brain's weaknesses and enhance thinking. Available health IT tools have been shaped to fit administrative needs rather than physicians' needs. To increase the beneficial effect of health IT applications in health care, clinicians' style of thinking and their learning needs must be considered when designing and implementing such systems. New health IT tools must be shaped to fit health professionals' needs. To further ease the integration of new health IT tools into clinical practice, we must also consider the effects of implementing new tools on the wider social framework.