Abstract: This paper addresses the problem of addiction in society. We focus on the United States specifically and limit our model to the following drugs: nicotine, marijuana, prescription drugs, alcohol. The problem is to create a model that can accurately predict the spread of nicotine. This is followed by the creation of a model that can be applied to different drugs with inputs depending on an individual's income, education level, and race.
From the information above, we conclude that the most dangerous substances are: Tobacco, Opioid-based Unprescribed Painkillers, and Alcohol, while the least dangerous is marijuana. This is deduced from a combination of its health impacts, explicit and implicit costs of using. While marijuana is the least dangerous according to our model, it still possesses significant dangers to productivity, safety, and cognitive function.
Our models functioned on several assumptions. We assumed that nationwide trends are directly applicable to all individual populations, which may not be the case. A study can be conducted to provide evidence of drug usage in specific areas across the country in order to pinpoint our data. The spread of nicotine abuse as well as the abuse of other drugs is on the rise throughout the country. This is especially alarming in the younger generation as model 2 suggests. The amount of high school seniors predicted to be using these substances indicates a societal issue that needs to be addressed in order to prevent damage to today's youth and lower these numbers for later generations. The impact of these drugs, while varied between them, signifies how abuse can quickly lead to poverty and strain on the economy that must support them.
Keywords: Addictive substances, Opioid-based Unprescribed Painkillers, Computer Modeling
Introduction: The model developed for part 1 details how the predicted growth of nicotine usage is anticipated to level off in the future as it currently is following a pattern of logistic growth. We use information provided to graph the function from 2011 to 2018. According to the data from the table, we create a logistic function (Figure 2) y = (15.1173)/(1+1111.39e^(-2.15689x)) by calculator. In order to minimize the number for y, we use 1 for 2011, 2 for 2012, 3 for 2013, and so on. Then, we plug 29 as the corresponding number for 2029 to x to find the percentage of high school students who vape for the next 10 years, which is 15.1173 percent. This number may not be correct because there is a rising number of events created dedicate to educate students to stop/prevent them from vaping.
An alteration in this model that could more accurately depict the expansion of vaping could include increased education about its dangers which would slow its growth. As seen in Figure 1, the model closely follows the data found on the high school vaping data provided in the question. The data would follow a line of best fit calculated with a logistic regression formula because the percent of users must reach a limit as it cannot exceed 100%. Figure 3 demonstrates the age demographics of the United States which we use to determine how the percentage of growth translates into sheer numbers in terms of age. For example, if 15% of individuals use nicotine for a given year, we can multiply this by the number of individuals in their age groups and get how many people use nicotine.
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