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This project utilizes nonparametric density and regression to construct a technique that can accurately and consistently model different countries cumulative growth curve through phase divisions, while also employing clustering tools to determine which variable derived from the phase divisions is the most important and successful predictor of the different phases each country undergoes. 11 different countries, United States, Italy, South Korea, Taiwan, India, Brazil, Russia, Sweden, Peru, Spain, and South Africa are analyzed. These countries are chosen using stratified sampling based on how many cases and deaths they have, how they are handling their infection curve, and the overall situation of the pandemic in their country. To begin, the first and second nonparametric densities are utilized to divide up the infection curve into separate phases, which are then modeled using regression. The model type (linear, quadratic, or logarithmic), duration, slope, and r2 for the first three phases for every country are further analyzed using multivariate correlation, clustering variables, and hierarchical clustering. The authors draw links from the analysis to real life events in order to help predict the COVID-19 model of similar countries based on the most important parameter and decisions.
This project uses nonparametric density and regression to construct a technique that can accurately and consistently model different countries’ cumulative growth curve through phase divisions. Previous outbreaks (SARS, MERS, and the 1918 flu pandemic) and existing models (SIR and logistic/exponential) were initially consulted to help model the growth, but the unique replication and circumstances of COVID-19 are unlike any other. Additionally, different countries have different approaches to the pandemic, and using one prediction line for the whole curve will not model the growth patterns accurately. This paper utilizes the first and second nonparametric densities to divide up the graph into separate phases and then model each phase using regression. Although each phase already provides a general picture of the different stages of the COVID-19 pandemic, South Korea’s and Italy’s graphs were further studied and compared to uncover other underlying patterns. The importance of factors such as strictness and timing of government regulations, an abundance of healthcare resources, the presence of a local outbreak, testing availability, and a working contact tracing system are all reflected in the slopes and durations of each country’s models. This tool can be further applied across other nations that have reached farther phases in the outbreak to predict the duration and slopes for countries that are still trying to control the outbreak.
This project uses nonparametric density and regression to construct a technique that can accurately and consistently model different countries’ cumulative growth curve through phase divisions. Previous outbreaks (SARS, MERS, and the 1918 flu pandemic) and existing models (SIR and logistic/exponential) were initially consulted to help model the growth, but the unique replication and circumstances of COVID-19 is unlike any other. Additionally, different countries have different approaches to the pandemic, and using one prediction line for the whole curve will not model the growth patterns accurately. This project utilizes the first and second nonparametric densities to divide up the graph into separate phases and then model each phase using regression. Although each phase already provides a general picture of the different stages of the COVID-19 pandemic, Taiwan’s and United States’ graphs were further studied and compared to uncover other underlying patterns. The importance of factors such as strictness and timing of government regulations, testing availability, and a working contact tracing system are all reflected in the slopes and durations of each country’s models.
This project uses nonparametric density and regression to construct a technique that can accurately and consistently model different countries’ cumulative growth curve through phase divisions. Previous outbreaks (SARS, MERS, and the 1918 flu pandemic) and existing models (SIR and logistic/exponential) were initially consulted to help model the growth, but the unique replication and circumstances of COVID-19 is unlike any other. Additionally, different countries have different approaches to the pandemic, and using one prediction line for the whole curve will not model the growth patterns accurately. This paper utilizes the first and second nonparametric densities to divide up the graph into separate phases and then model each phase using regression. Although each phase already provides a general picture of the different stages of the COVID-19 pandemic, South Korea’s graph was further studied to uncover other underlying patterns. The importance of factors such as strictness and timing of government regulations, abundancy of healthcare resources, the presence of a local outbreak, testing availability, and a working contact tracing system are all reflected in the slopes and durations of each country’s models. This tool can be further applied across other nations that have reached farther phases in the outbreak to predict the duration and slopes for countries that are still trying to control the outbreak.
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