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Navigating Global Public Influenza Surveillance Systems for Reliable Forecasting

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Integrated Science of Global Epidemics

Part of the book series: Integrated Science ((IS,volume 14))

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Summary

Global public health surveillance systems are imperative for monitoring, managing, and mitigating worldwide public health risks. Sustaining and enhancing these systems is imperative to detect, track, model, and predict outbreaks and pandemics of infections. Our understanding of the complex dynamic systems and governing principles of outbreaks’ transmission and control depends on coordinated and integrated information gathering and processing. The interdisciplinary vision for such integration, data sharing, and curation warrants the development of reliable forecasts for circulating and emerging infections. Health professionals, researchers, and policymakers rely on the ongoing curation of public surveillance data. This chapter describes the role that public health surveillance data can play when modeling infectious disease outbreaks. We provide an overview of our previous work extracting, describing, and analyzing public and private health surveillance data to calculate trend and seasonality features of disease outbreaks. We describe important health surveillance data attributes that must be assessed for enabling modern analytical tools, including time series analysis, machine learning, and artificial intelligence methods. We illustrate these attributes’ importance for modeling disease outbreaks using the publicly available global influenza surveillance database FluNet. We conclude the chapter by discussing the importance of data integrity for the future of disease forecasting. Given the economic, social, political, and public health repercussions of the ongoing novel coronavirus (COVID-19) 2019–20 pandemic, greater attention is necessary on the attributes of surveillance data that render it usable for describing, explaining, and predicting disease outbreaks.

Graphical Abstract/Art Performance

A world map for global strides for harmonized, complete, and reliable monitoring and forecasting and completeness of influenza. The scale ranges from 50 to 80 and grey-shaded areas have no data.

Global strides for harmonized, complete, and reliable monitoring and forecasting: completeness of influenza reporting in 166 FluNet-reporting countries and territories (2008–2019).

The code of this chapter is 01110010 01,110,011 01,100,101 01,100,111 01,000,110 01,100,011 01,100,001 01,101,001 01,101,111 01,101,110 01,110,100.

The future will be determined in part by happenings that it is impossible to foresee; it will also be influenced by trends that are now existent and observable.

Emily Greene Balch

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    Authors and Affiliations

    1. Division of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA

      Ryan B. Simpson, Jordyn Gottlieb, Bingjie Zhou, Shiwei Liang, Xu Jiang, Meghan A. Hartwick & Elena N. Naumova

    2. Tufts Initiative for the Forecasting and Modeling of Infectious Diseases, Tufts University, 150 Harrison Avenue, Jaharis-264, Boston, MA, 02111, USA

      Meghan A. Hartwick & Elena N. Naumova

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      Nima Rezaei

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    Simpson, R.B. et al. (2023). Navigating Global Public Influenza Surveillance Systems for Reliable Forecasting. In: Rezaei, N. (eds) Integrated Science of Global Epidemics. Integrated Science, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-031-17778-1_6

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