How to combine spatio-temporal and thematic features in online news for enhanced animal disease surveillance?

AbstractSpatio-temporal count data relating to a set of non-overlapping areal units are prevalent in many fields, including epidemiology and social science. The spatial autocorrelation inherent in these data is typically modelled by a set of random effects that are assigned a conditional autoregressive prior distribution, which is a special case of a Gaussian Markov random field. The autocorrelation structure implied by this model depends on a binary neighbourhood matrix, where two random effects are assumed to be partially autocorrelated if their areal units share a common border, and are conditionally independent otherwise. This paper proposes a novel graph-based optimisation algorithm for estimating either a static or a temporally varying neighbourhood matrix for the data that better represents its spatial correlation structure, by viewing the areal units as the vertices of a graph and the neighbour relations as the set of edges. The improved estimation performance of our methodology compared to the commonly used border sharing rule is evidenced by simulation, before the method is applied to a new respiratory disease surveillance study in Scotland between 2011 and 2017.

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101. Animal disease surveillance in Scotland 2002

Research in Veterinary Science ◽

10.1016/s0034-5288(02)90105-0 ◽

2002 ◽

Vol 72 ◽

pp. 36-37

Author(s):

G.B.B. Mitchell ◽

D.K. Somerville

Keyword(s):

Disease Surveillance ◽

Animal Disease

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The Potential for Big Data in Animal Disease Surveillance in Ireland

Frontiers in Veterinary Science ◽

10.3389/fvets.2017.00150 ◽

2017 ◽

Vol 4 ◽

Cited By ~ 4

Author(s):

Damien Barrett

Keyword(s):

Big Data ◽

Disease Surveillance ◽

Animal Disease

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Small animal disease surveillance: pruritus and Pseudomonas skin infections

Veterinary Record ◽

10.1136/vr.k3462 ◽

2018 ◽

Vol 183 (6) ◽

pp. 182-187 ◽

Cited By ~ 3

Author(s):

Elena Arsevska ◽

David A. Singleton ◽

Christopher Jewell ◽

Susan Paterson ◽

Philip H. Jones ◽

...

Keyword(s):

Disease Surveillance ◽

Small Animal ◽

Skin Infections ◽

Animal Disease

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Animal Disease Surveillance in the 21st Century: Applications and Robustness of Phylodynamic Methods in Recent U.S. Human-Like H3 Swine Influenza Outbreaks

Frontiers in Veterinary Science ◽

10.3389/fvets.2020.00176 ◽

2020 ◽

Vol 7 ◽

Author(s):

Moh A. Alkhamis ◽

Chong Li ◽

Montserrat Torremorell

Keyword(s):

21St Century ◽

Disease Surveillance ◽

Swine Influenza ◽

Animal Disease ◽

Influenza Outbreaks

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Inconsistencies in reporting risk information: a pilot analysis of online news coverage of West Nile Virus

Global Health Promotion ◽

10.1177/1757975915594603 ◽

2015 ◽

Vol 24 (3) ◽

pp. 14-22 ◽

Cited By ~ 3

Author(s):

Kristina Birnbrauer ◽

Dennis Owen Frohlich ◽

Debbie Treise

Keyword(s):

West Nile Virus ◽

Disease Surveillance ◽

News Coverage ◽

Disease Outbreaks ◽

Online News ◽

Risk Information ◽

West Nile ◽

News Stories ◽

The Us ◽

Action Frame

West Nile Virus (WNV) has been reported as one of the worst epidemics in US history. This study sought to understand how WNV news stories were framed and how risk information was portrayed from its 1999 arrival in the US through the year 2012. The authors conducted a quantitative content analysis of online news articles obtained through Google News ( N = 428). The results of this analysis were compared to the CDC’s ArboNET surveillance system. The following story frames were identified in this study: action, conflict, consequence, new evidence, reassurance and uncertainty, with the action frame appearing most frequently. Risk was communicated quantitatively without context in the majority of articles, and only in 2006, the year with the third-highest reported deaths, was risk reported with statistical accuracy. The results from the analysis indicated that at-risk communities were potentially under-informed as accurate risks were not communicated. This study offers evidence about how disease outbreaks are covered in relation to actual disease surveillance data.

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