Association mining based approach to analyze COVID-19 response and case growth in the United States.

Containing the COVID-19 pandemic while balancing the economy has proven to be quite a challenge for the world. We still have limited understanding of which combination of policies have been most effective in flattening the curve; given the challenges of the dynamic and evolving nature of the pandemic, lack of quality data etc. This paper introduces a novel data mining-based approach to understand the effects of different non-pharmaceutical interventions in containing the COVID-19 infection rate. We used the association rule mining approach to perform descriptive data mining on publicly available data for 50 states in the United States to understand the similarity and differences among various policies and underlying conditions that led to transitions between different infection growth curve phases. We used a multi-peak logistic growth model to label the different phases of infection growth curve. The common trends in the data were analyzed with respect to lockdowns, face mask mandates, mobility, and infection growth. We observed that face mask mandates combined with mobility reduction through moderate stay-at-home orders were most effective in reducing the number of COVID-19 cases across various states.

Exploring the relationship between mobility and COVID- 19 infection rates for the second peak in the United States using phase-wise association.

Human mobility plays an important role in the dynamics of infectious disease spread. Evidence from the initial nationwide lockdowns for COVID- 19 indicates that restricting human mobility is an effective strategy to contain the spread. While a direct correlation was observed early on, it is not known how mobility impacted COVID- 19 infection growth rates once lockdowns are lifted, primarily due to modulation by other factors such as face masks, social distancing, and the non-linear patterns of both mobility and infection growth. This paper introduces a piece-wise approach to better explore the phase-wise association between state-level COVID- 19 incidence data and anonymized mobile phone data for various states in the United States. Prior literature analyzed the linear correlation between mobility and the number of cases during the early stages of the pandemic. However, it is important to capture the non-linear dynamics of case growth and mobility to be usable for both tracking and forecasting COVID- 19 infections, which is accomplished by the piece-wise approach. The associations between mobility and case growth rate varied widely for various phases of the epidemic curve when the stay-at-home orders were lifted. The mobility growth patterns had a strong positive association of 0.7 with the growth in the number of cases, with a lag of 5 to 7 weeks, for the fast-growth phase of the pandemic, for only 20 states that had a peak between July 1st and September 30, 2020. Overall though, mobility cannot be used to predict the rise in the number of cases after initial lockdowns have been lifted. Our analysis explores the gradual diminishing value of mobility associations in the later stage of the outbreak. Our analysis indicates that the relationship between mobility and the increase in the number of cases, once lockdowns have been lifted, is tenuous at best and there is no strong relationship between these signals. But we identify the remnants of the last associations in specific phases of the growth curve.