On the use of Bayesian decision theory for issuing natural hazard warnings

Economou, Theo; Stephenson, David B.; Rougier, Jonathan; Neal, Robert A.; Mylne, Ken

doi:10.1098/rspa.2016.0295

Cited by 32 publications

(33 citation statements)

References 27 publications

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“…Therefore, this general method can greatly help warning analysts in surveillance systems to making well‐informed decisions. Furthermore, the availability of samples from the predictive distribution of the counts implies that the predictions from the proposed models can be readily utilised in a decision theoretic framework for issuing warnings (as in the work of Economou et al).…”

Section: Discussionmentioning

confidence: 99%

A modelling approach for correcting reporting delays in disease surveillance data

Bastos

Economou

Gomes

et al. 2019

Statistics in Medicine

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One difficulty for real‐time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistical problems, infrastructure difficulties, and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty. Implementation of the model is fast due to the use of the integrated nested Laplace approximation. The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and severe acute respiratory infection data in the state of Paraná, Brazil.

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Section: Discussionmentioning

confidence: 99%

A modelling approach for correcting reporting delays in disease surveillance data

Bastos

Economou

Gomes

et al. 2019

Statistics in Medicine

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“…This asymmetry is all the more notable, if we take into account the considerable historical evidence that humans are more likely to fail to respond to warnings than we are to overreact to them (Mileti & Sorensen, ). Moreover, if the concern is how people will react to false alarms, research on people's reactions to weather forecasts suggests that people are more forgiving of false alarms than of failure to warn of major events (Economou et al, ). Without evidence that people react differently when it comes to climate events than to weather and other sorts of events, climate researchers are making a claim for which they lack evidence, and which available evidence suggests may not be the case.…”

Section: Arguments Against the Storyline Approachmentioning

confidence: 99%

“…This is crucial to the recognition that the interests of scientists in protecting their reputations may not be the same as the interest of citizens in protecting themselves. One recent study (Economou et al, ), for example, shows that the decision rule for issuing natural hazard warnings, such as weather warnings, can be customized to reflect the interests of different users. Crucially, this paper shows that considering the interest of the reputation of the forecaster leads to a different decision rule than would be appropriate for a member of society who wishes to be protected from an extreme weather event.…”

Section: What Is At Stake and Why It Mattersmentioning

confidence: 99%

Climate Change Attribution: When Is It Appropriate to Accept New Methods?

Lloyd

Орескес

2018

Earth's Future

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The most common approaches to detection and attribution (D&A) of extreme weather events using fraction of attributable risk or risk ratio answer a particular form of research question, namely “What is the probability of a certain class of weather events, given global climate change, relative to a world without?” In a set of recent papers, Trenberth et al. (2015, https://doi.org/10.1038/nclimate2657) and Shepherd (2016, https://doi.org/10.1007/s40641-016-0033-y) have argued that this is not always the best tool for analyzing causes, or for communicating with the public about climate events and extremes. Instead, they promote the idea of a “storyline” approach, which asks complementary questions, such as “How much did climate change affect the severity of a given storm?” From the vantage of history and philosophy of science, a proposal to introduce a new approach or to answer different research questions—especially those of public interest—does not appear particularly controversial. However, the proposal proved highly controversial, with the majority of D&A scientists reacting in a very negative and even personal manner. Some suggested the proposed alternatives amount to a weakening of standards, or an abandonment of scientific method. Here, we address the question: Why is this such a controversial proposition? We argue that there is no “right” or “wrong” approach to D&A in any absolute sense, but rather that in different contexts, society may have a greater or lesser concern with errors of a particular type. How we view the relative risk of overestimation versus underestimation of harm is context‐dependent.

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“…These losses could be individually specified by a risk manager, or using some low‐dimensional parametrization of the loss function (e.g. Economou et al , ). Many forms of loss exist, such as the loss of profit, loss due to regret, loss of reputation or loss of life, amongst others.…”

Section: Decision Theorymentioning

confidence: 99%

Decision theory‐based detection of atmospheric natural hazards from satellite imagery using the example of volcanic ash

Western

Rougier

Watson

2018

Quart J Royal Meteoro Soc

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Atmospheric natural hazards pose a risk to people, aircraft and infrastructure. Automated algorithms can detect these hazards from satellite imagery so that the relevant advice can be issued. The transparency and adaptability of these automated algorithms is important to cater to the needs of the end user, who should be able to readily interpret the hazard warning. This means avoiding heuristic techniques. Decision theory is a statistical tool that transparently considers the risk of false positives and negatives when detecting the hazard. By assigning losses to incorrect actions, ownership of the hazard warning is shared between the scientists and risk managers. These losses are readily adaptable depending on the perceived threat of the hazard. This study demonstrates how decision theory can be applied to the detection of atmospheric natural hazards using the example of volcanic ash during an ongoing eruption. The only observations are the difference in brightness temperature between two channels on the SEVIRI sensor. We apply the method to two volcanic eruptions: the 2010 eruption of Eyjafjallajökull, Iceland, and the 2011 eruption of Puyehue-Cordón Caulle, Chile. The simple probabilistic method appears to work well and is able to distinguish volcanic ash from desert dust, which is a common false positive for volcanic ash. As is made clear, decision theory is a tool for decision support, providing transparency and adaptability, but it still requires careful input from scientists and risk managers. Effectively it provides a space where these groups of experts can meet and convert their shared understanding of a hazard into a choice of action.

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On the use of Bayesian decision theory for issuing natural hazard warnings

Cited by 32 publications

References 27 publications

A modelling approach for correcting reporting delays in disease surveillance data

A modelling approach for correcting reporting delays in disease surveillance data

Climate Change Attribution: When Is It Appropriate to Accept New Methods?

Decision theory‐based detection of atmospheric natural hazards from satellite imagery using the example of volcanic ash

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