Antibiotic resistance is a growing crisis across the globe. With the use of antibiotics in heathcare settings in an ever-growing population, the growth of antibiotic resistance has been named a top 10 global public health threat by the World Health Organization. Through an analysis of 6 countries; Mexico, China, the United States, India, Saudi Arabia, and Ethiopia, I look at the current implementation of policy and contributing factors to the use and abuse of antibiotics within the country. Through my research, I was able to find knowledge, behaviors, and a lack of enforcement to be the main contributors to the growing antibiotic crisis. Based on the evidence, I suggested three policies that focused on treatment, prevention, or economic assistance in an effort to combat the antibiotic crisis on a global scale. With socio-economic factors in mind as well as sustainability of policy, the evidence pointed in the direction of a three-pronged approach on prevention with education, policy enforcement, and a global database to minimize the growth of antibiotic resistance as well as improve public health at a global level.

Pathogenic drug resistance is a major global health concern. Thus, there is great interest in modeling the behavior of resistant mutations–how quickly they will rise in frequency within a population, and whether they come with fitness tradeoffs that can form the basis of treatment strategies. These models often depend on precise measurements of the relative fitness advantage (s) for each mutation and the strength of the fitness tradeoff that each mutation suffers in other contexts. Precisely quantifying s helps us create better, more accurate models of how mutants act in different treatment strategies. For example, P. falciparum acquires antimalarial drug resistance through a series of mutations to a single gene. Prior work in yeast expressing this P. falciparum gene demonstrated that mutations come with tradeoffs. Computational work has demonstrated the possibility of a treatment strategy which enriches for a particular resistant mutation that then makes the population grow poorly once the drug is removed. This treatment strategy requires knowledge of s and how it changes when multiple mutants are competing across various drug concentrations. Here, we precisely quantified s in varying drug concentrations for five resistant mutants, each of which provide varying degrees of drug resistance to antimalarial drugs. DNA barcodes were used to label each strain, allowing the mutants to be pooled together for direct competition in different concentrations of drug. This will provide data that can make the models more accurate, potentially facilitating more effective drug treatments in the future.