The perception of security refers to the subjective evaluation of risks related to security events and the magnitude of their consequences. Negative perceptions have severe implications in society. These feelings are commonly quantified by citizen's surveys, which are time-consuming and do not adapt well to the changing dynamics linked to security. Recently, Twitter social network emerged as an alternative to quantify some of these feelings dynamically. However, most of these approaches focused on counting the amount of content related to crime, which is only one of the relevant factors determining the perception of security. In addition, these models do not account for the polarity and tone underlying the social network content. This work introduces a model for quantification of Perception of Security on Twitter based on sentiment analysis and studies its relationship with actual crime. The model relies on an automatic strategy for filtering content related to security based on a support vector machine classifier and quantifying the sentiment in the posts using a multinomial naive Bayes classifier. These measures of sentiment allow computing estimates of perception of security at a daily scale. This model studied more than 1.700.000 tweets in Bogotá (Colombia), collected during more than one year (March 18 of 2019 -April 28 of 2020). Results suggest that machine learning-based approaches may outperform previous security content filtering strategies by 21% in F1-score and provide quantifications on the sentiments of about 40% on accuracy. Trained models were used to provide daily estimates of perception of security, which are different from those captured by counting the number of posts related to security. Furthermore, the associations between perceptions and actual crimes exhibited monthly variations and showed that in some months, perceptions were more related to robbery, a crime with high incidence in the city. These results may help decisionmakers devise strategies to reduce the impact of the negative perception on citizens.
An agent-based model was developed to assess the use of Genetically Modified Mosquitoes (GMMs) as a control strategy for the Malaria epidemic. Mosquitoes responsible for the transmission of Malaria (vectors) have been modified genetically so that the probability of transmitting the parasite causing the disease when biting a human being is reduced with respect to wild type vectors. Our model represents the population dynamics of the introduction of a transgenic strain of malaria vectors of the species Anopheles Gambiae. In the model three different types of agents were included: wild type, homozygous and heterozygous transgenic mosquitoes. Each agent is characterized by a fitness parameter that represents a reproduction rate, relative to the wild type population. The model considers specific biological processes such as: gonotrophic cycle (the average interval between successive blood meals), egg maturation time and life cycle of the vector. Additionally, some spatial aspects such as: biting zones (human settlements) and water zones (breeding places) were included in the model to consider the influence of environmental conditions. Through simulations it was observed that the model represents adequately the dynamics of Malaria vectors. These results may be used to evaluate different control strategies considering spatial and environmental features.
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