Understanding Economic and Health Factors Impacting the Spread of COVID-19 Disease

Farseev, Aleksandr; Chu-Farseeva, Y; Yang, Qimeng; Loo, Daron Benjamin

doi:10.1101/2020.04.10.20058222

Cited by 18 publications

(19 citation statements)

References 30 publications

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“…Another work by Farseev et al . [11], covers similar economic and health factors for COVID-19 spread. The study unveils significant relationships between COVID-19 and other national statistics.…”

Section: Related Workmentioning

confidence: 99%

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Clustering of Countries for COVID-19 Cases based on Disease Prevalence, Health Systems and Environmental Indicators

Rizvi¹,

Umair

Cheema

2021

Preprint

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The coronavirus has a high basic reproduction number (R0) and has caused the global COVID-19 pandemic. Governments are implementing lockdowns that are leading to economic fallout in many countries. Policy makers can take better decisions if provided with the indicators connected with the disease spread. This study is aimed to cluster the countries using social, economic, health and environmental related metrics affecting the disease spread so as to implement the policies to control the widespread of disease. Thus, countries with similar factors can take proactive steps to fight against the pandemic. The data is acquired for 79 countries and 18 different feature variables (the factors that are associated with COVID-19 spread) are selected. Pearson Product Moment Correlation Analysis is performed between all the feature variables with cumulative death cases and cumulative confirmed cases individually to get an insight of relation of these factors with the spread of COVID-19. Unsupervised k-means algorithm is used and the feature set includes economic, environmental indicators and disease prevalence along with COVID-19 variables. The learning model is able to group the countries into 4 clusters on the basis of relation with all 18 feature variables. We also present an analysis of correlation between the selected feature variables, and COVID-19 confirmed cases and deaths. Prevalence of underlying diseases shows strong correlation with COVID-19 whereas environmental health indicators are weakly correlated with COVID-19.

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Section: Related Workmentioning

confidence: 99%

“…Another work by Farseev et al . [11] is the study of economic and health factors impacting the COVID-19 disease spread resulting in the formation of four country clusters. Siddiqui et al .…”

Section: Introductionmentioning

confidence: 99%

Clustering of Countries for COVID-19 Cases based on Disease Prevalence, Health Systems and Environmental Indicators

Rizvi¹,

Umair

Cheema

2021

Preprint

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“…Initial reports suggested a negative correlation between BCG vaccination coverage and COVID mortality and infection rates [6], [7], however, these observations were negated subsequently due to significant bias from many confounders such as demographics, testing rates and the stage of the pandemic in each country [8], [9], [10], [11]. Several other studies have shown inconsistent or weak correlations and have not sufficiently addressed interactions or confounding [5], [12], [13], [14]. Therefore, an integrated approach to decipher the network of determinants of COVID-19 burden is needed.…”

Section: Introductionmentioning

confidence: 99%

A Counterfactual Graphical Model Reveals Economic and Sociodemographic Variables as Key Determinants of Country-Wise COVID-19 Burden

Sethi

Kedia

Awasthi

et al. 2020

Preprint

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Importance: Insights into the country-wise differences in COVID-19 burden can impact the policies being developed to control disease spread. Objective: Present study evaluated the possible socio-economic and health related factors (and their temporal consistency) determining the disease burden of COVID-19. Design: A retrospective analysis for identifying associations of COVID-19 burden. Setting: Data on COVID-19 statistics (number of cases, tests and deaths per million) was extracted from the website https://www.worldometers.info/coronavirus/ on 10th April and 12th May. Variables obtained to estimate the possible determinants for COVID-19 burden included economic- gross domestic product; socio-demographic- Sustainable Development Goals, SDGs indicators related to health systems, percentage Chinese diaspora; and COVID-19 trajectory- date of first case in each country, days between first reported case and 10th April, days between 100th and 1000th case, and government response stringency index (GRSI). Main outcomes and Measures: COVID-19 burden was modeled using economic and socio-demographic determinants. Consistency of inferences for two time points at three levels of increasing statistical rigor using (i) Spearman correlations, (ii) Bayesian probabilistic graphical model, and (iii) counterfactual impact was evaluated. Results: Countries economy (reflected by GDP), mainly through the testing rates, was the major and temporally consistent determinant of COVID-19 burden in the model. Reproduction number of COVID-19 was lower where mortality due to water, sanitation, and hygiene (WaSH) was higher, thus strengthening the hygiene hypothesis. There was no association between vaccination status or tuberculosis incidence and COVID burden, refuting the claims over BCG vaccination as a possible factor against COVID-19 trajectory. Conclusion and Relevance: Countries economy, through testing power, was the major determinant of COVID-19 burden. There was weak evidence for hygiene hypothesis as a protective factor against COVID-19.

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“…The effects of the different factors and intervention measures on the spread of COVID-19 vary. Identifying key factors that most contribute to the rapid spread of COVID-19, and accurately estimating the potential impact of different non-pharmaceutical measures for containing COVID-19 are crucial for planning the most effective interventions to curb the spread of Covid-19 (Farseev et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Causal Analysis of Health Interventions and Environments for Influencing the Spread of COVID-19 in the United States of America

Zhang

et al. 2020

Preprint

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As of August 27, 2020, the number of cumulative cases of COVID-19 in the US exceeded 5,863,363 and included 180,595 deaths, thus causing a serious public health crisis. Curbing the spread of Covid-19 is still urgently needed. Given the lack of potential vaccines and effective medications, non-pharmaceutical interventions are the major option to curtail the spread of COVID-19. An accurate estimate of the potential impact of different non-pharmaceutical measures on containing, and identify risk factors influencing the spread of COVID-19 is crucial for planning the most effective interventions to curb the spread of COVID-19 and to reduce the deaths. Additive model-based bivariate causal discovery for scalar factors and multivariate Granger causality tests for time series factors are applied to the surveillance data of lab-confirmed Covid-19 cases in the US, University of Maryland Data (UMD) data, and Google mobility data from March 5, 2020 to August 25, 2020 in order to evaluate the contributions of social-biological factors, economics, the Google mobility indexes, and the rate of the virus test to the number of the new cases and number of deaths from COVID-19. We found that active cases/1000 people, workplaces, tests done/1000 people, imported COVID-19 cases, unemployment rate and unemployment claims/1000 people, mobility trends for places of residence (residential), retail and test capacity were the most significant risk factor for the new cases of COVID-19 in 23, 7, 6, 5, 4, 2, 1 and 1 states, respectively, and that active cases/1000 people, workplaces, residential, unemployment rate, imported COVID cases, unemployment claims/1000 people, transit stations, mobility trends (transit) , tests done/1000 people, grocery, testing capacity, retail, percentage of change in consumption, percentage of working from home were the most significant risk factor for the deaths of COVID-19 in 17, 10, 4, 4, 3, 2, 2, 2, 1, 1, 1, 1 states, respectively. We observed that no metrics showed significant evidence in mitigating the COVID-19 epidemic in FL and only a few metrics showed evidence in reducing the number of new cases of COVID-19 in AZ, NY and TX. Our results showed that the majority of non-pharmaceutical interventions had a large effect on slowing the transmission and reducing deaths, and that health interventions were still needed to contain COVID-19.

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Understanding Economic and Health Factors Impacting the Spread of COVID-19 Disease

Cited by 18 publications

References 30 publications

Clustering of Countries for COVID-19 Cases based on Disease Prevalence, Health Systems and Environmental Indicators

Clustering of Countries for COVID-19 Cases based on Disease Prevalence, Health Systems and Environmental Indicators

A Counterfactual Graphical Model Reveals Economic and Sociodemographic Variables as Key Determinants of Country-Wise COVID-19 Burden

Causal Analysis of Health Interventions and Environments for Influencing the Spread of COVID-19 in the United States of America

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