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The increasingly frequent occurrence of natural, man-made and environmental disasters in recent years has resulted in both a large number of casualties and widespread property damage. To mitigate the damage caused, a lot of emergency supplies are required. However, due to the limited quantity of emergency supplies, the allocation of rescue resources is extremely important. Using the March 2014 Ebola outbreak in western Africa as an example, this paper uses Dijkstra's algorithm to build a system which determines the optimal allocation of emergency resource to cities in Sierra Leone. Each city's weightage is calculated using data provided before Dijkstra's algorithm is applied.

Online social media microblogs may be a valuable resource for timely identification of critical ad hoc health-related incidents or serious epidemic outbreaks. In this paper, we explore emotion classification of Twitter microblogs related to localized public health threats, and study whether the public mood can be effectively utilized in early discovery or alarming of such events. We analyse user tweets around recent incidents of Ebola, finding differences in the expression of emotions in tweets posted prior to and after the incidents have emerged. We also analyse differences in the nature of the tweets in the immediately affected area as compared to areas remote to the events. The results of this analysis suggest that emotions in social media microblogging data (from Twitter in particular) may be utilized effectively as a source of evidence for disease outbreak detection and monitoring.