Estimating the welfare loss to households from natural disasters in developing countries: a contingent valuation study of flooding in Vietnam

Background: Natural disasters have severe impacts on the health and well-being of affected households. However, we find evidence that official damage cost assessments for floods and other natural disasters in Vietnam, where households have little or no insurance, clearly underestimate the total economic damage costs of these events as they do not include the welfare loss from mortality, morbidity and reduced well-being experienced by the households affected by the floods. This should send a message to the local communities and national authorities that higher investments in flood alleviation, reduction and adaptive measures can be justified since the social benefits of these measures in terms of avoided damage costs are higher than previously thought. Methods: We pioneer the use of the contingent valuation (CV) approach of willingness-to-contribute (WTC) labour to a flood prevention program, as a measure of the welfare loss experienced by household due to a flooding event. In a face-to-face household survey of 706 households in the Quang Nam province in Central Vietnam, we applied this approach together with reported direct physical damage in order to shed light of the welfare loss experienced by the households. We asked about households’ WTC labour and multiplied their WTC person-days of labour by an estimate for their opportunity cost of time in order to estimate the welfare loss to households from the 2007 floods. Results: The results showed that this contingent valuation (CV) approach of asking about willingness-to-pay in-kind avoided the main problems associated with applying CV in developing countries. Conclusion: Thus, the CV approach of WTC labour instead of money is promising in terms of capturing the total welfare loss of natural disasters to households, and promising in terms of further application in other developing countries and for other types of natural disasters

A practical approach to parameter estimation applied to model predicting heart rate regulation

Mathematical models have long been used for prediction of dynamics in biological systems. Recently, several efforts have been made to render these models patient specific. One way to do so is to employ techniques to estimate parameters that enable model based prediction of observed quantities. Knowledge of variation in parameters within and between groups of subjects have potential to provide insight into biological function. Often it is not possible to estimate all parameters in a given model, in particular if the model is complex and the data is sparse. However, it may be possible to estimate a subset of model parameters reducing the complexity of the problem. In this study, we compare three methods that allow identification of parameter subsets that can be estimated given a model and a set of data. These methods will be used to estimate patient specific parameters in a model predicting baroreceptor feedback regulation of heart rate during head-up tilt. The three methods include: structured analysis of the correlation matrix, analysis via singular value decomposition followed by QR factorization, and identification of the subspace closest to the one spanned by eigenvectors of the model Hessian. Results showed that all three methods facilitate identification of a parameter subset. The ”best” subset was obtained using the structured correlation method, though this method was also the most computationally intensive. Subsets obtained using the other two methods were easier to compute, but analysis revealed that the final subsets contained correlated parameters. In conclusion, to avoid lengthy computations, these three methods may be combined for efficient identification of parameter subsets