Another challenge when developing mathematical models with respect to data usage is the accessibility of specific data pertaining to the topic of simulation. Often times data necessary to simulate certain scenarios, such as number of deaths by a certain disease, is not accessible to the general public or researchers alike. This makes it impossible to accurately model certain scenarios, of which solutions could be offered with mathematical modeling. The WHO has proposed that “Raw data need to be made publicly accessible for research purposes. National health equity surveillance data need to be reported to, among others, national policymakers and WHO. Global health equity surveillance data need to be reported to the Economic and Social Council, other international bodies, and back to national governments” This would allow mathematical models to be more consistent, accurate, and faster and easier to develop. In turn, modeling done more efficiently allows for interventions to be made much sooner, and problems from the community to global level would be resolved relatively quicker.
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Solutions to prevent this loss of important data have been explored by communities around the globe. An example of this includes community-based monitoring, in which communities that are not in countries where health data is routinely collected record their own data. This has been seen in India, where a pattern in sex determination and abortion rates was observed after community-based data was collected. When the sex of the child was determined as female, it was seen that many women chose to terminate these pregnancies. This potentially detrimental pattern to the population would not have been caught without this data collection and analysis. Based on this, mathematical models can then be used to simulate interventions and policies to offer the best solution to this problem.
It has been scientifically demonstrated that the use of mathematical models can be beneficial in predicting outcomes of epidemic, policy implementation, and other public health related endeavors. These models can depict scenarios at a community, country, or even global scale. However, there are caveats to the usage of these models. An especially important one to consider is the data of which these models are based on. Models rely on the inputs from equations, which are formulated on the basis of data collected that pertains to the event being modeled. If there is inaccurate collection of data of which the model is based on, then it is likely that the model will not accurately predict the events that it is meant to model. This can lead to detrimental and costly results, as the action taken based on the model could fail or have adverse effects.
Developed or undeveloped, inequities in healthcare are seen all over the globe in all different sectors of society. It is vital as a global community that these inequities are recognized, and that change is enacted in order to establish healthcare reform in unequitable areas. For this to occur, it is important that substantial evidence and data is collected so that reasons behind inequities in health and wellness resources can be detected. Both developed and undeveloped countries lack proper data collection methods and important accessibility and maintenance of data collection. Improvement of this is necessary in order to meet the goals and standards of the WHO (World Health Organization) to identify and solve problems with inequities in heath care across the globe.
Social determinants play a large role in accessibility of resources that promote health and wellness. These include circumstances such as place of birth and residency, occupation, race, ethnicity, age, sexual orientation, gender, and disabilities. It has been seen in the past that specific social groups are not provided with the same medical treatment that people belonging to other social groups are provided with. An example of this is homosexuals affected with HIV/AIDS during the HIV/AIDS epidemic in the United States during the 1980s and early 1990s, in which homosexuals were denied medical counsel from majority of doctors due to fear and homophobia. Another example includes black and white segregation, in which people of color were not allowed to achieve the same education, shop at the same stores, and overall were unable to live at a higher standard of living due to civic restrictions, fostering decreased mental and physical health. In developed countries, it is often stigma, fear, discrimination and social segregation and condemnation that lead to inequities in access to healthcare resources.
Health inequities at the community, city, country and global level have a large impact on the overall wellness and quality of life of all populations – large and small – around the world. These differences in health among certain populations can be caused by a variety social, governmental, and economical factors that put select groups of people at an inherent disadvantage in gaining access to important healthcare and wellness resources. These inequities, although greatly rooted socially and systemically within our society, are avoidable and require extensive and important measures in order to circumvent them. In order enact change and avoid detrimental outcomes from health inequity, we must investigate where these differences in health arise from, from social, scientific and civic perspectives.
There are many issues regarding food safety and the current methods and infrastructure in place to track food products through the journey from farm to store shelf are not sufficient. There is no standardized system for tracking food and many processors handle it differently. When there are issues with contamination, lengthy and resource intensive traceback investigations must occur. Some distributors keep records in closed databases or on paper meaning they are not readily accessible or publicly available. Frank Yiannas, Walmart VP of food safety, mentioned in an interview that after giving staff a randomly selected package of mangoes, it required almost a full seven days to trace them back to the source. In seven days, hundreds of thousands of people can be significantly harmed by a contaminated product.
Foodborne illness carries substantial health and economic consequences. The World Health Organization (WHO) estimates that food related illness is responsible for 600 million illness and 400,000 deaths annually. The Center for Disease Control and Prevention (CDC) believes that foodborne disease affects 179 million Americans annually. Additionally, Robert Scharff, an economist for the Food and Drug Administration (FDA), estimates that foodborne illness costs the US fifty to ninety billion dollars each year. This is an ongoing problem that has not been appropriately addressed and we see outbreaks every year such as the papaya salmonella instance this year and Chipotle's E.coli outbreak two years ago.
It is possible rate of cellular movement will follow the predictions made in our hypothesis, or that it will not. The average rate at which cells migrate in order to heal the wound could be roughly the same throughout the entire process. Additionally, there could be a large discrepancy in rates of cells based on their position relative to the wound, in which case studying average rate of movement over time would not be an effective way to capture cellular movement rate.
Displacement patterns in cellular movement across the wound could follow the predictions made in our hypothesis, or could demonstrate large differences in total displacement over time, based on a cells location relative to the wound. It is possible that cells closer to the wound will move a greater distance over time than cells further away from the wound.
LLC-Pk1 parental cells will be cultured and plated in dishes. These cells will then be allowed to grow and divide until they are at confluent levels. Once the cells are confluent, a scratch assay will be conducted in which a small portion of the cells are scraped from the plate, creating a small and consistent gap between two large patches of cells. The cells are then washed twice with HBS and submerged in non-CO2 media. They are then observed under phase microscopy at 10X magnification in a time-lapse fashion. The migration of the cells into the “wound” will then be captured and analyzed. Rate of cellular migration and length of time it took for the wound to close will be parameters analyzed.