Mizzi, Giovanni (2019) Improving dengue fever surveillance with online data. PhD thesis, University of Warwick.

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Abstract

Dengue is a major threat to public health in Brazil, the world’s sixth largest country by population, with nearly 1.5 million cases reported in 2016 alone and case counts continuing to grow. However, official data on the current number of dengue cases can often be severely delayed, with incremental delivery of data and a wait of up to six months for full case count information. Previous studies have sought to exploit rapidly available data on dengue-related Google searches or Twitter messages to deliver improved estimates of dengue cases, but have not accounted for the true nature of the delays in dengue data across Brazil, rendering operational usage of these approaches unrealistic. Here, we develop a model which uses online data to deliver improved weekly estimates of dengue cases in Rio de Janeiro, whilst explicitly accounting for the structure of the delays in incoming dengue case count data. In contrast to previous approaches, we draw on data from Google Trends and Twitter in tandem, and demonstrate that this leads to better estimates compared to models using only one of these data streams alone. We also demonstrate how our model can be extended to forecast future dengue incidence. To underline the robustness of our approach, we apply our model to a range of Brazilian cities. Our results provide evidence that online data can be used to improve both estimates and predictions of disease incidence, even where the underlying case count data are severely delayed. Crucially, the model we present is operationally realistic, and can therefore be used in practice to support the decision-making processes of health authorities.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	H Social Sciences > HM Sociology R Medicine > RA Public aspects of medicine R Medicine > RC Internal medicine Z Bibliography. Library Science. Information Resources > ZA Information resources
Library of Congress Subject Headings (LCSH):	Dengue -- Brazil, Dengue -- Brazil -- Epidemiology, Epidemiology -- Research -- Methodology, Epidemiology -- Mathematical models, User-generated content, Online social networks
Official Date:	April 2019
Dates:	Date Event April 2019 UNSPECIFIED
Institution:	University of Warwick
Theses Department:	Mathematics for Real-World Systems Centre for Doctoral Training
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Moat, Suzy ; Preis, Toby, 1981-
Format of File:	pdf
Extent:	vii, 173 leaves : charts
Language:	eng

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