Search results

This paper aims to design a vulnerability assessment model considering the multidimensional and systematic approach to disaster risk and vulnerability. This model serves to both risk mitigation and disaster preparedness phases of humanitarian logistics.

A survey of 27,218 households in Pueblo Rico and Dosquebradas was conducted to obtain information about disaster risk for landslides, floods and collapses. We adopted a cross entropy-based approach for the measure of disaster vulnerability (Kullback–Leibler divergence), and a maximum-entropy estimation for the reconstruction of risk a priori categorization (logistic regression). The capabilities approach of Sen supported theoretically our multidimensional assessment of disaster vulnerability.

Disaster vulnerability is shaped by economic, such as physical attributes of households, and health indicators, which are in specific morbidity indicators that seem to affect vulnerability outputs. Vulnerability is heterogeneous between communities/districts according to formal comparisons of Kullback–Leibler divergence. Nor social dimension, neither chronic illness indicators seem to shape vulnerability, at least for Pueblo Rico and Dosquebradas.

The results need a qualitative or case study validation at the community/district level.

We discuss how risk mitigation policies and disaster preparedness strategies can be driven by empirical results. For example, the type of stock to preposition can vary according to the disaster or the kind of alternative policies that can be formulated on the basis of the strong relationship between morbidity and disaster risk.

Entropy-based metrics are not widely used in humanitarian logistics literature, as well as empirical data-driven techniques.

This paper aims to present the internalization process by which Venezuelan migrants and refugees are resettled. Using system dynamics, the authors model a Brazilian humanitarian operation (“Acolhida” – Welcome), simulate the internalization process, propose policies and provide lessons learned for future migratory operations.

Using system dynamics simulation, the authors use Acolhida Operation’s historical data to recreate the reception and resettlement process of Venezuelan migrants and refugees. The authors identify the main bottlenecks in the system and propose policies to respond to scenarios according to the number of internalization vacancies, that is, available places in Brazil where migrants and refugees can be resettled. Finally, based on interviews with former decision-makers, the model represents a first attempt to convert the pressure of public opinion on authorities into temporary shelters as a way of reducing the number of unassisted people.

The results confirm that internalization vacancies are the main constraint when resettling Venezuelan migrants and refugees. Had the internalization program been promoted since the operation’s beginning, there would have been fewer unassisted people in Roraima and fewer shelters. The pressure-converting mechanism presented in this study, although incipient, constitutes a first attempt to support decision-makers in determining when to build temporary shelters.

This study can be useful to public authorities and humanitarian organizations when developing policies to enhance resettlement in migratory crises. In Acolhida’s case, the internalization program should continue to be the operation’s priority and can be enhanced by investing more resources to create internalization vacancies while maintaining logistical capacities.

The authors suggest policies to improve the Acolhida internalization program: give more people the choice to relocate in other cities, increase turnover in shelters and provide a more efficient and effective response to Venezuelan migration in Roraima.

Although a number of studies have applied system dynamics to humanitarian operations, few models have focused on migratory emergencies, such as those occurring in northern Brazil. The model is applied to the largest humanitarian operation carried out in the Brazilian territory and provides decision-makers with valuable insights and alternatives for better implementation in the future. Furthermore, this study narrows the gap between the social sciences and modeling and simulation techniques by proposing ways of predicting migratory implications in the construction of shelters and resettlement policies.