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

Katragadda, Satya; Gottumukkala, Raju; Bhupatiraju, Ravi Teja; Kamal, Azmyin Md.; Raghavan, Vijay V.; Chu, Henry; Kolluru, Ramesh; Ashkar, Ziad

doi:10.1038/s41598-021-96912-5

Cited by 8 publications

(11 citation statements)

References 42 publications

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“…In the COVID-19 crisis, many nations, including Thailand, have a highest priority to save lives and protect their economies. A previous study using ARM for mining COVID-19 data to analyze factors related to COVID-19 situation management showed 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 in United State [ 24 ]. In this study, the ARM technique was used to analyze and identify factors related to the length of stay and prognosis of COVID-19 patients and found that the top five factors related to hospital stays longer than 14 days consisted of healthcare workers uncommon underlying diseases such as thalassemia, thyroid diseases, gout and G6PD deficiency, pregnant patients, dyslipidemia and signs of pneumonia in chest x-rays.…”

Section: Discussionmentioning

confidence: 99%

“…In this work, we present a study to combine time series analysis and association analysis to forecast the COVID-19 admitted cases as well as to analyze their potential factors and characteristics. To estimate the number of new cases and to predict the prognosis for better understanding of the current situation and progression of COVID-19, we exploited the autoregressive integrated moving average (ARIMA) model and its subclasses (i.e., AR, MA, ARMA) [ 12 , 17 , 26 ], and association rule mining (ARM) [ 21 , 24 ] as tools for investigation (Fig. 1 ).…”

Section: Methodsmentioning

confidence: 99%

“…Besides the time-series analysis, association rule mining (ARM) can be used as a multivariate analysis to help us understand the correlation among factors [ 24 ]. Given a dataset containing a collection of records or transactions, each record comprises a set of categorical attributes.…”

Section: Methodsmentioning

confidence: 99%

“…ARM was demonstrated to be an effective model for mining the frequent symptom pattern for COVID-19 patients, which could assist clinicians in decision making [ 23 ]. Another study used ARM to analyze the patterns of different non-pharmaceutical interventions to manage the infection growth rate in the United States [ 24 ]. Even though there are many advanced data-driven time series methods used to predict the future number of COVID-19 patients, a new and more accurate prediction model is important in the pandemic crisis.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting COVID-19 cases using time series modeling and association rule mining

Somyanonthanakul

Warin

Amasiri

et al. 2022

BMC Med Res Methodol

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Abstracts Background The aim of this study was to evaluate the most effective combination of autoregressive integrated moving average (ARIMA), a time series model, and association rule mining (ARM) techniques to identify meaningful prognostic factors and predict the number of cases for efficient COVID-19 crisis management. Methods The 3685 COVID-19 patients admitted at Thailand’s first university field hospital following the four waves of infections from March 2020 to August 2021 were analyzed using the autoregressive integrated moving average (ARIMA), its derivative to exogenous variables (ARIMAX), and association rule mining (ARM). Results The ARIMA (2, 2, 2) model with an optimized parameter set predicted the number of the COVID-19 cases admitted at the hospital with acceptable error scores (R2 = 0.5695, RMSE = 29.7605, MAE = 27.5102). Key features from ARM (symptoms, age, and underlying diseases) were selected to build an ARIMAX (1, 1, 1) model, which yielded better performance in predicting the number of admitted cases (R2 = 0.5695, RMSE = 27.7508, MAE = 23.4642). The association analysis revealed that hospital stays of more than 14 days were related to the healthcare worker patients and the patients presented with underlying diseases. The worsening cases that required referral to the hospital ward were associated with the patients admitted with symptoms, pregnancy, metabolic syndrome, and age greater than 65 years old. Conclusions This study demonstrated that the ARIMAX model has the potential to predict the number of COVID-19 cases by incorporating the most associated prognostic factors identified by ARM technique to the ARIMA model, which could be used for preparation and optimal management of hospital resources during pandemics.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forecasting COVID-19 cases using time series modeling and association rule mining

Somyanonthanakul

Warin

Amasiri

et al. 2022

BMC Med Res Methodol

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“…This study does not consider the many mitigating factors imposed by local authorities to curb the spread of the virus; these include: mask usage, lockdowns, social distancing guidelines, and public compliance with health regulations that could have had an effect on the number of cases. The impact of these measures are studied in other works on COVID 34 . Second, the models have been generated on the data aggregated at a daily level of granularity.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Examining the COVID-19 case growth rate due to visitor vs. local mobility in the United States using machine learning

Katragadda

Bhupatiraju

Raghavan

et al. 2022

Sci Rep

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Travel patterns and mobility affect the spread of infectious diseases like COVID-19. However, we do not know to what extent local vs. visitor mobility affects the growth in the number of cases. This study evaluates the impact of state-level local vs. visitor mobility in understanding the growth with respect to the number of cases for COVID spread in the United States between March 1, 2020, and December 31, 2020. Two metrics, namely local and visitor transmission risk, were extracted from mobility data to capture the transmission potential of COVID-19 through mobility. A combination of the three factors: the current number of cases, local transmission risk, and the visitor transmission risk, are used to model the future number of cases using various machine learning models. The factors that contribute to better forecast performance are the ones that impact the number of cases. The statistical significance of the forecasts is also evaluated using the Diebold–Mariano test. Finally, the performance of models is compared for three waves across all 50 states. The results show that visitor mobility significantly impacts the case growth by improving the prediction accuracy by 33.78%. We also observe that the impact of visitor mobility is more pronounced during the first peak, i.e., March–June 2020.

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Discovering associations between radiological features and COVID‐19 patients' deterioration

Ahmadinejad

Ayyoubzadeh

Zeinalkhani

et al. 2023

Health Science Reports

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Background and AimsData mining methods are effective and well‐known tools for developing predictive models and extracting useful information from various data of patients. The present study aimed to predict the severity of patients with COVID‐19 by applying the rule mining method using characteristics of medical images.MethodsThis retrospective study has analyzed the radiological data from 104 COVID‐19 hospitalized patients diagnosed with COVID‐19 in a hospital in Iran. A data set containing 75 binary features was generated. Apriori method is utilized for association rule mining on this data set. Only rules with confidence equal to one were generated. The performance of rules is calculated by support, coverage, and lift indexes.ResultsTen rules were extracted with only X‐ray‐related features on cases referred to ICU. The Support and Coverage index of all of these rules was 0.087, and the Lift index of them was 1.58. Thirteen rules were extracted from only CT scan‐related features on cases referred to ICU. The CXR_Pleural effusion feature has appeared in all the rules. The CXR_Left upper zone feature appears in 9 rules out of 10. The Support and Coverage index of all rules was 0.15, and the Lift index of all rules was 1.63. the CT_Adjacent pleura thickening feature has appeared in all rules, and the CT_Right middle lobe appeared in 9 rules out of 13.ConclusionThis study could reveal the application and efficacy of CXR and CT scan imaging modalities in predicting ICU admission to a major COVID‐19 infection via data mining methods. The findings of this study could help data scientists, radiologists, and clinicians in the future development and implementation of these methods in similar conditions and timely and appropriately save patients from adverse disease outcomes.

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

Cited by 8 publications

References 42 publications

Forecasting COVID-19 cases using time series modeling and association rule mining

Forecasting COVID-19 cases using time series modeling and association rule mining

Examining the COVID-19 case growth rate due to visitor vs. local mobility in the United States using machine learning

Discovering associations between radiological features and COVID‐19 patients' deterioration

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