The Role of Heterogenous Real-world Data for Dengue Surveillance in Martinique: Observational Retrospective Study
Emmanuelle Sylvestre,Elsa Cecilia-Joseph,Guillaume Bouzillé,Fatiha Najioullah,Manuel Etienne,Fabrice Malouines,Jacques Rosine,Sandrine Julié,André Cabié,Marc Cuggia +9 more
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In this article , the authors used real-world data sources (hospitalization data, entomological data, and Google Trends) to identify associations with dengue outbreaks and their time lags.Abstract:
Background Traditionally, dengue prevention and control rely on vector control programs and reporting of symptomatic cases to a central health agency. However, case reporting is often delayed, and the true burden of dengue disease is often underestimated. Moreover, some countries do not have routine control measures for vector control. Therefore, researchers are constantly assessing novel data sources to improve traditional surveillance systems. These studies are mostly carried out in big territories and rarely in smaller endemic regions, such as Martinique and the Lesser Antilles. Objective The aim of this study was to determine whether heterogeneous real-world data sources could help reduce reporting delays and improve dengue monitoring in Martinique island, a small endemic region. Methods Heterogenous data sources (hospitalization data, entomological data, and Google Trends) and dengue surveillance reports for the last 14 years (January 2007 to February 2021) were analyzed to identify associations with dengue outbreaks and their time lags. Results The dengue hospitalization rate was the variable most strongly correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.70) with a time lag of −3 weeks. Weekly entomological interventions were also correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.59) with a time lag of −2 weeks. The most correlated query from Google Trends was the “Dengue” topic restricted to the Martinique region (Pearson correlation coefficient=0.637) with a time lag of −3 weeks. Conclusions Real-word data are valuable data sources for dengue surveillance in smaller territories. Many of these sources precede the increase in dengue cases by several weeks, and therefore can help to improve the ability of traditional surveillance systems to provide an early response in dengue outbreaks. All these sources should be better integrated to improve the early response to dengue outbreaks and vector-borne diseases in smaller endemic territories.read more
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The global distribution and burden of dengue
Samir Bhatt,Peter W. Gething,Oliver J. Brady,Jane P. Messina,Andrew Farlow,Catherine L. Moyes,John M. Drake,John M. Drake,John S. Brownstein,Anne G. Hoen,Osman Sankoh,Osman Sankoh,Monica F. Myers,Dylan B. George,Thomas Jaenisch,G. R. William Wint,Cameron P. Simmons,Thomas W. Scott,Thomas W. Scott,Jeremy Farrar,Jeremy Farrar,Simon I. Hay,Simon I. Hay +22 more
TL;DR: These new risk maps and infection estimates provide novel insights into the global, regional and national public health burden imposed by dengue and will help to guide improvements in disease control strategies using vaccine, drug and vector control methods, and in their economic evaluation.
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Detecting influenza epidemics using search engine query data
Jeremy Ginsberg,Matthew H. Mohebbi,Rajan Patel,Lynnette Brammer,Mark S. Smolinski,Lawrence B. Brilliant +5 more
TL;DR: A method of analysing large numbers of Google search queries to track influenza-like illness in a population and accurately estimate the current level of weekly influenza activity in each region of the United States with a reporting lag of about one day is presented.
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Time series analysis and its applications
TL;DR: Characteristics of Time Series * Time Series Regression and ARIMA Models * Dynamic Linear Models and Kalman Filtering * Spectral Analysis and Its Applications.
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Real-World Evidence — What Is It and What Can It Tell Us?
Rachel E. Sherman,Steven A. Anderson,Gerald J. Dal Pan,Gerry W. Gray,Thomas G. Gross,Nina L. Hunter,Lisa M. LaVange,Danica Marinac-Dabic,Peter W. Marks,Melissa A. Robb,Jeffrey Shuren,Robert Temple,Janet Woodcock,Lilly Q. Yue,Robert M. Califf +14 more
TL;DR: The FDA is developing guidance on the use of “real-world evidence” — health care information from atypical sources, including electronic health records, billing databases, and product and disease registries — to assess the safety and effectiveness of drugs and devices.
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Refining the global spatial limits of dengue virus transmission by evidence-based consensus
Oliver J. Brady,Peter W. Gething,Samir Bhatt,Jane P. Messina,John S. Brownstein,Anne G. Hoen,Catherine L. Moyes,Andrew Farlow,Thomas W. Scott,Thomas W. Scott,Simon I. Hay,Simon I. Hay +11 more
TL;DR: A contemporary global map of national-level dengue status is generated that assigns a relative measure of certainty and identifies gaps in the available evidence and provides a preliminary estimate of population at risk with an upper bound of 3.97 billion people.