Nowcasting influenza outbreaks using open-source media report.

Abstract

We construct and verify a statistical method to nowcast influenza activity from a time-series of the frequency of reports concerning influenza related topics. Such reports are published electronically by both public health organizations as well as newspapers/media sources, and thus can be harvested easily via web crawlers. Since media reports are timely, whereas reports from public health organization are delayed by at least two weeks, using timely, open-source data to compensate for the lag in "official" reports can be useful. We use morbidity data from networks of sentinel physicians (both the Center of Disease Control's ILINet and France's Sentinelles network) as the gold standard of influenza-like illness (ILI) activity. The time-series of media reports is obtained from HealthMap (http://healthmap.org). We find that the time-series of media reports shows some correlation (≈ 0.5) with ILI activity; further, this can be leveraged into an autoregressive moving average model with exogenous inputs (ARMAX model) to nowcast ILI activity. We find that the ARMAX models have more predictive skill compared to autoregressive (AR) models fitted to ILI data i.e., it is possible to exploit the information content in the open-source data. We also find that when the open-source data are non-informative, the ARMAX models reproduce the performance of AR models. The statistical models are tested on data from the 2009 swine-flu outbreak as well as the mild 2011-2012 influenza season in the U.S.A.

Digital Inclusion in Nothern England: Training Women from Underrepresented Communities in Tech: A Data Analytics Case Study

“…An important distinction is whether such data are being used to predict the future, or to track the present. The latter, known as 'nowcasting', aims to utilise social and Internet derived data to quantify real-world phenomena in real-time [15], boasting previous research in present-moment happiness of nations [16], real-time mortality rates [17], influenza outbreaks [18] and voting intentions during political races [19]. In this paper we are focussing on nowcasting in the evolution and impact of the development of the TechUPWomen programme as it proceeds, using Twitter and Microsoft Teams data collected over a four-month period from July 2019 up until October 2019.…”