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01:12Hindsight Biases
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01:04Random Error
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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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00:59Propagation of Uncertainty from Random Error
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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02:24Predicting Reaction Outcomes
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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01:10Propagation of Uncertainty from Systematic Error
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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01:03Prediction Intervals
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
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Predicting the Unpredictable - Harder than Expected.
Anneli Eriksson1, Martin Gerdin Wärnberg1, Thorkild Tylleskär2
1Centre for Research on Health Care in Disasters, Health Systems and Policy Research Group, Department of Global Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.
Prehospital and Disaster Medicine
|February 22, 2020
Summary
Existing disaster risk indexes failed to predict the scale of needs following earthquakes. This research highlights the limitations of current tools in guiding rapid humanitarian funding decisions after seismic events.
Area of Science:
- Disaster Management and Humanitarian Aid
- Geosciences and Seismology
- Risk Assessment and Prediction
Background:
- Earthquakes create urgent humanitarian needs requiring swift international assistance and funding.
- Data scarcity in the acute phase of disasters delays needs-based funding decisions.
- Existing disaster risk indexes, combining hazard and vulnerability data, are proposed as rapid tools for prioritizing funding.
Purpose of the Study:
- To determine the extent to which indicators within commonly used disaster indexes can predict the scale of disaster needs after earthquakes.
- To identify reliable predictors for the scale of humanitarian needs following seismic events.
Main Methods:
- Assessed the predictive performance of vulnerability and outcome indicators from four disaster risk and severity indexes.
- Employed linear regression analysis to evaluate indicators individually and in combination.
- Utilized data on deaths and affected individuals from the Emergency Events Database (EM-DAT) (2007-2016) as the scale of needs outcome variable, measured by Root Mean Square Error (RMSE).
Main Results:
- The assessed vulnerability and outcome indicators demonstrated a lack of predictive power for the scale of disaster needs.
- Attempts to develop a multivariable model using the lowest RMSE indicators did not yield substantially improved predictive performance.
- No significant correlation was found between the indicators in the assessed indexes and the actual scale of needs post-earthquake.
Conclusions:
- Current indicators within the four assessed disaster indexes are insufficient to accurately predict the scale of needs following earthquakes.
- The findings indicate a critical gap in tools for rapid, needs-based humanitarian funding allocation after seismic disasters.
- Further research is needed to develop more effective predictive models for disaster needs assessment.