Medical resource preparation and allocation for humanitarian assistance based on module organization.

BACKGROUND: This research aims to associate the allocation of medical resources with the function of the modular organization and the possible needs for humanitarian assistance missions. METHODS: The overseas humanitarian medical assistance mission, which was sent after a disaster on the hospital ship Peace Ark, part of China's People's Liberation Army (PLA) Navy, was considered as study model. The cases used for clustering and matching sample formation were randomly selected from the existing information related to Peace Ark's mission. RESULTS: Categories of the reusable resources clustered by this research met the requirement of the actual consumption almost completely (more than 95%) and the categories of non-reusable resources met the requirement by more than 80%. In the mission's original resource preparing plan, more than 30% of the non-reusable resource categories remained unused during the mission. In the original resource preparing plan, some key non-reusable resources inventories were completely exhausted at the end of the mission, while 5% to 30% of non-reusable resources remained in the resource allocation plan generated by this research at the end of the mission. CONCLUSIONS: The medical resource allocation plan generated here can enhance the supporting level for the humanitarian assistance mission. This research could lay the foundation for an assistant decision-making system for humanitarian assistance mission.

Effect of human movement on airborne disease transmission in an airplane cabin: study using numerical modeling and quantitative risk analysis.

BACKGROUND: Airborne transmission of respiratory infectious disease in indoor environment (e.g. airplane cabin, conference room, hospital, isolated room and inpatient ward) may cause outbreaks of infectious diseases, which may lead to many infection cases and significantly influences on the public health. This issue has received more and more attentions from academics. This work investigates the influence of human movement on the airborne transmission of respiratory infectious diseases in an airplane cabin by using an accurate human model in numerical simulation and comparing the influences of different human movement behaviors on disease transmission. METHODS: The Eulerian-Lagrangian approach is adopted to simulate the dispersion and deposition of the expiratory aerosols. The dose-response model is used to assess the infection risks of the occupants. The likelihood analysis is performed as a hypothesis test on the input parameters and different human movement pattern assumptions. An in-flight SARS outbreak case is used for investigation. A moving person with different moving speeds is simulated to represent the movement behaviors. A digital human model was used to represent the detailed profile of the occupants, which was obtained by scanning a real thermal manikin using the 3D laser scanning system. RESULTS: The analysis results indicate that human movement can strengthen the downward transport of the aerosols, significantly reduce the overall deposition and removal rate of the suspended aerosols and increase the average infection risk in the cabin. The likelihood estimation result shows that the risk assessment results better fit the outcome of the outbreak case when the movements of the seated passengers are considered. The intake fraction of the moving person is significantly higher than most of the seated passengers. CONCLUSIONS: The infection risk distribution in the airplane cabin highly depends on the movement behaviors of the passengers and the index patient. The walking activities of the crew members and the seated passengers can significantly increase their personal infection risks. Taking the influence of the movement of the seated passengers and the index patient into consideration is necessary and important. For future studies, investigations on the behaviors characteristics of the passengers during flight will be useful and helpful for infection control.

Simulation of the spread of infectious diseases in a geographical environment.

The study of mathematical models for the spread of infectious diseases is an important issue in epidemiology. Given the fact that most existing models cannot comprehensively depict heterogeneities (e.g., the population heterogeneity and the distribution heterogeneity) and complex contagion patterns (which are mostly caused by the human interaction induced by modern transportation) in the real world, a theoretical model of the spread of infectious diseases is proposed. It employs geo-entity based cellular automata to simulate the spread of infectious diseases in a geographical environment. In the model, physical geographical regions are defined as cells. The population within each cell is divided into three classes: Susceptible, Infective, and Recovered, which are further divided into some subclasses by states of individuals. The transition rules, which determine the changes of proportions of those subclasses and reciprocal transformation formulas among them, are provided. Through defining suitable spatial weighting functions, the model is applied to simulate the spread of the infectious diseases with not only local contagion but also global contagion. With some cases of simulation, it has been shown that the results are reasonably consistent with the spread of infectious diseases in the real world. The model is supposed to model dynamics of infectious diseases on complex networks, which is nearly impossible to be achieved with differential equations because of the complexity of the problem. The cases of simulation also demonstrate that efforts of all kinds of interventions can be visualized and explored, and then the model is able to provide decision-making support for prevention and control of infectious diseases.