Unsupervised learning for county-level typological classification for COVID-19 research

Lai, Yuan; Charpignon, Marie-Laure; Ebner, Daniel K.; Celi, Leo Anthony

doi:10.1016/j.ibmed.2020.100002

Cited by 10 publications

(15 citation statements)

References 10 publications

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“…In this review we identify those works that used it to perform clustering as part of the process of a spatial analysis. For example, Lai, Charpignon, Ebner, and Celi ( 2020 ) used it to group US counties based on sociodemographic characteristics and COVID‐19 data; and Abdallah, Khafagy, and Omara ( 2020 ) for GPS location data. SIR models can add explicit geographical variables to study epidemic dynamics (Geng et al., 2020 ; O'Sullivan, Gahegan, Exter, & Adams, 2020 ; Thomas et al., 2020 ).…”

Section: Resultsmentioning

confidence: 99%

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A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

Franch-Pardo

Desjardins

Barea-Navarro

et al. 2021

Transactions in GIS

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COVID‐19 has infected over 163 million people and has resulted in over 3.9 million deaths. Regarding the tools and strategies to research the ongoing pandemic, spatial analysis has been increasingly utilized to study the impacts of COVID‐19. This article provides a review of 221 scientific articles that used spatial science to study the pandemic published from June 2020 to December 2020. The main objectives are: to identify the tools and techniques used by the authors; to review the subjects addressed and their disciplines; and to classify the studies based on their applications. This contribution will facilitate comparisons with the body of work published during the first half of 2020, revealing the evolution of the COVID‐19 phenomenon through the lens of spatial analysis. Our results show that there was an increase in the use of both spatial statistical tools (e.g., geographically weighted regression, Bayesian models, spatial regression) applied to socioeconomic variables and analysis at finer spatial and temporal scales. We found an increase in remote sensing approaches, which are now widely applied in studies around the world. Lockdowns and associated changes in human mobility have been extensively examined using spatiotemporal techniques. Another dominant topic studied has been the relationship between pollution and COVID‐19 dynamics, which enhance the impact of human activities on the pandemic's evolution. This represents a shift from the first half of 2020, when the research focused on climatic and weather factors. Overall, we have seen a vast increase in spatial tools and techniques to study COVID‐19 transmission and the associated risk factors.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

Franch-Pardo

Desjardins

Barea-Navarro

et al. 2021

Transactions in GIS

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“…We quantified variation in excess mortality across geographies and demographics, and further proposed a preliminary attribution of excess deaths indirectly related to SARS-CoV-2 infection across acute and chronic conditions. The analysis of sub-state level data pinpoints local differences that are often overlooked at an aggregated level [34]. It also provides a quantitative foundation for more targeted policy responses, including implications for directing state and county health budgets towards the most impacted population subgroups.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of County-Level Heterogeneity in Excess Mortality in Colorado from March to September 2020

Chandra

Charpignon

et al. 2021

Preprint

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Importance: Tracking the direct and indirect impact of the coronavirus disease 2019 (COVID-19) pandemic on all-cause mortality in the United States has been hindered by the lack of testing and by reporting delays. Evaluating excess mortality, or the number of deaths above what is expected in a given time period, provides critical insights into the true burden of the COVID-19 pandemic caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Stratifying mortality data by demographics such as age, sex, race, ethnicity, and geography helps quantify how subgroups of the population have been differentially affected. Similarly, stratifying mortality data by cause of death reveals the public health effects of the pandemic in terms of other acute and chronic diseases. Objective: To provide stratified estimates of excess mortality in Colorado from March to September 2020. Design, Setting, and Population: This study evaluated the number of excess deaths both directly due to SARS-CoV-2 infection and from all other causes between March and September 2020 at the county level in Colorado. Data were obtained from the Vital Statistics Program at the Colorado Department of Public Health and Environment. These estimates of excess mortality were derived by comparing population- adjusted mortality rates in 2020 with rates in the same months from 2015 to 2019. Results: We found evidence of excess mortality in Colorado between March and September 2020. Two peaks in excess deaths from all causes were recorded in the state, one mid-April and the other at the end of June. Since the first documented SARS-CoV-2 infection on March 5th, we estimated that the excess mortality rate in Colorado was two times higher than the officially reported COVID-19 mortality rate. State-level cumulative excess mortality from all causes reached 71 excess deaths per 100k residents (~4000 excess deaths in the state); in contrast, 35 deaths per 100k directly due to SARS-CoV-2 were recorded in the same period (~1980 deaths. Excess mortality occurred in 52 of 64 counties, accounting for 99% of the state's population. Most excess deaths recorded from March to September 2020 were associated with acute events (estimated at 44 excess deaths per 100k residents and at 9 after excluding deaths directly due to SARS-CoV-2) rather than with chronic conditions (~21 excess deaths per 100k). Among Coloradans aged 14-44, 1.4 times more deaths occurred in those months than during the same period in the five previous years. Hispanic White males died of COVID-19 at the highest rate during this time (~90 deaths from COVID-19 per 100k residents); however, Non-Hispanic Black/African American males were the most affected in terms of overall excess mortality (~204 excess deaths per 100k). Beyond inequalities in COVID-19 mortality per se, these findings signal considerable regional and racial-ethnic disparities in excess all-cause mortality that need to be addressed for a just recovery and in future public health crises.

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“…By using a population-based cohort of more than 700,000 patient-level cases, this is probably the largest cluster analysis about coronavirus patient-level cases to date. Other studies proposed unsupervised ML methods for aggregated population data 12 , CT image analyses 13,14 , molecular-level clustering 15 , or coronavirus-related scientific texts 16 . Several studies provided to date results from unsupervised ML on patient-level epidemiological data 17,18,19,20,21,22,23,24 .…”

Section: Introductionmentioning

confidence: 99%

Subphenotyping of COVID-19 patients at pre-admission towards anticipated severity stratification: an analysis of 778 692 Mexican patients through an age-gender unbiased meta-clustering technique

Zhou¹,

Romero²,

Martı́nez-Miranda

et al. 2021

Preprint

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Objective To describe COVID-19 subphenotypes regarding severity patterns including prognostic, ICU and morbimortality outcomes, through stratification based on gender and age groups, as described by inter-patient variability patterns in clinical phenotypes and demographic features. Materials and methods We used the COVID-19 open data from the Mexican Government including patient-level epidemiological and clinical data from 778 692 SARS-CoV-2 patients from January 13, 2020 to September 30, 2020. Inter-patient variability was analyzed by combining dimensionality reduction and hierarchical clustering methods. We produced cluster analyses for all combinations of gender and age groups (<18, 18-49, 50-64, and >64). For each group, the optimum number of clusters was selected combining a quantitative approach using the Silhouette coefficient, and a qualitative approach through a subgroup expert inspection via visual analytics. Using the features of the resultant age-gender clusters, we performed a meta-clustering analysis to provide an overall description of the population. Results We observed a total of 56 age-gender clusters, grouped in 11 clinically distinguishable meta-clusters with different outcomes. Meta-clusters 1 to 3 showed the highest recovery rates (90.27-95.22%). These clusters include: (1) healthy patients of all ages, (2) children with comorbidities who had priority in medical resources, (3) young patients with obesity and smoking habit. Meta-clusters 4 and 5 showed moderate recovery rates (81.3-82.81%): (4) patients with hypertension or diabetes of all ages, (5) typical obese patients with three highly correlated conditions, namely, pneumonia, hypertension and diabetes. Meta-clusters 6 to 11 had very low recovery rates (53.96-66.94%) which include: (6) immunosuppressed patients with the highest comorbidity rate in many diseases, (7) CKD patients with the worse survival length and recovery, (8) elderly smoker with mild COPD, (9) severe diabetic elderly with hypertension, (10, 11) oldest obese smokers with severe COPD and mild cardiovascular disease with the latter (11) showing a relatively higher age and smoke rate, severe COPD and shorter survival length, reinforcing a high correlation between smoking habit and COPD among elderly. Additionally, the source Mexican state and type of clinical institution proved to be an important factor for heterogeneity in severity. Discussion The proposed unsupervised learning approach successfully uncovered discriminative COVID-19 severity patterns for both genders and all age groups from clinical phenotypes and demographic features. A careful read of group outcomes showed consistent results regarding recent literature. Regarding the Mexican population, our results suggest that habits and comorbidities may play a key role in predicting mortality in older patients. Centenarians tended to fall in the groups with better outcomes repeatedly. Additionally, immunosuppression was not found as a relevant factor for severity alone but did when present along with chronic kidney disease. Further useful correlations could be found by evaluating the duration of unhealthy habits, demographic features, comorbidities, the time since diagnosis, recovery progress, readmission record, and the effect of source variability. Conclusion The resultant eleven meta-clusters provide bases to comprehend the classification of patients with COVID-19 based on comorbidities, habits, demographic characteristics, geographic data and type of clinical institutions, as well as revealing the correlations between the above characteristics thereby help to anticipate the possible clinical outcomes for every specifically characterized patient. These subphenotypes can establish target groups for automated stratification or triage systems to provide personalized therapies or treatments. Code available at: https://github.com/bdslab-upv/covid19-metaclustering Dynamic results visualization at: http://covid19sdetool.upv.es/?tab=mexicoGov

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Unsupervised learning for county-level typological classification for COVID-19 research

Cited by 10 publications

References 10 publications

A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

Evaluation of County-Level Heterogeneity in Excess Mortality in Colorado from March to September 2020

Subphenotyping of COVID-19 patients at pre-admission towards anticipated severity stratification: an analysis of 778 692 Mexican patients through an age-gender unbiased meta-clustering technique

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