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Aboelnaga S, Czech K, Wielechowski M, Kotyza P, Smutka L, Ndue K. COVID-19 resilience index in European Union countries based on their risk and readiness scale. PLoS One 2023; 18:e0289615. [PMID: 37540717 PMCID: PMC10403121 DOI: 10.1371/journal.pone.0289615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 07/22/2023] [Indexed: 08/06/2023] Open
Abstract
Addressing risks and pandemics at a country level is a complex task that requires transdisciplinary approaches. The paper aims to identify groups of the European Union countries characterized by a similar COVID-19 Resilience Index (CRI). Developed in the paper CRI index reflects the countries' COVID-19 risk and their readiness for a crisis situation, including a pandemic. Moreover, the study detects the factors that significantly differentiate the distinguished groups. According to our research, Bulgaria, Hungary, Malta, and Poland have the lowest COVID-19 Resilience Index score, with Croatia, Greece, Czechia, and Slovakia following close. At the same time, Ireland and Scandinavian countries occupy the top of the leader board, followed by Luxemburg. The Kruskal-Wallis test results indicate four COVID-19 risk indicators that significantly differentiate the countries in the first year of the COVID-19 pandemic. Among the significant factors are not only COVID-19-related factors, i.e., the changes in residential human mobility, the stringency of anti-COVID-19 policy, but also strictly environmental factors, namely pollution and material footprint. It indicates that the most critical global environmental issues might be crucial in the phase of a future pandemic. Moreover, we detect eight readiness factors that significantly differentiate the analysed country groups. Among the significant factors are the economic indicators such as GDP per capita and labour markets, the governance indicators such as Rule of Law, Access to Information, Implementation and Adaptability measures, and social indicators such as Tertiary Attainment and Research, Innovation, and Infrastructure.
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Affiliation(s)
- Somaya Aboelnaga
- Department of Urban Regional Development, Faculty of Urban and Regional Planning, Cairo University, Giza, Egypt
| | - Katarzyna Czech
- Department of Econometrics and Statistics, Institute of Economics and Finance, Warsaw University of Life Sciences, Warszawa, Poland
| | - Michał Wielechowski
- Department of Economics and Economic Policy, Institute of Economics and Finance, Warsaw University of Life Sciences, Warszawa, Poland
| | - Pavel Kotyza
- Department of Economics, The Czech University of Life Sciences, Prague, Czechia
| | - Lubos Smutka
- Department of Trade and Finance, The Czech University of Life Sciences, Prague, Czechia
| | - Kennedy Ndue
- Institute of Agricultural Economics, Budapest, Hungary
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Zhang X, Yin R, Zheng M, Kong D, Chen W. Impact of COVID-19 on health services utilization in mainland China and its different regions based on S-ARIMA predictions. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0001044. [PMID: 36962843 PMCID: PMC10021243 DOI: 10.1371/journal.pgph.0001044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 11/28/2022] [Indexed: 06/18/2023]
Abstract
Global health services are disrupted by the COVID-19 pandemic. We evaluated extent and duration of impacts of the pandemic on health services utilization in different economically developed regions of mainland China. Based on monthly health services utilization data in China, we used Seasonal Autoregressive Integrated Moving Average (S-ARIMA) models to predict outpatient and emergency department visits to hospitals (OEH visits) per capita without pandemic. The impacts were evaluated by three dimensions:1) absolute instant impacts were evaluated by difference between predicted and actual OEH visits per capita in February 2020 and relative instant impacts were the ratio of absolute impacts to baseline OEH visits per capita; 2) absolute and relative accumulative impacts from February 2020 to March 2021; 3) duration of impacts was estimated by time that actual OEH visits per capita returned to its predicted value. From February 2020 to March 2021, the COVID-19 pandemic reduced OEH visits by 0.4676 per capita, equivalent to 659,453,647 visits, corresponding to a decrease of 15.52% relative to the pre-pandemic average annual level in mainland China. The instant impacts in central, northeast, east and west China were 0.1279, 0.1265, 0.1215, and 0.0986 visits per capita, respectively; and corresponding relative impacts were 77.63%, 66.16%, 44.39%, and 50.57%, respectively. The accumulative impacts in northeast, east, west and central China were up to 0.5898, 0.4459, 0.3523, and 0.3324 visits per capita, respectively; and corresponding relative impacts were 23.72%, 12.53%, 13.91%, and 16.48%, respectively. The OEH visits per capita has returned back to predicted values within the first 2, 6, 9, 9 months for east, central, west and northeast China, respectively. Less economically developed areas were affected for a longer time. Safe and equitable access to health services, needs paying great attention especially for undeveloped areas.
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Affiliation(s)
- Xiangliang Zhang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Center for Migrant Health Policy, Sun Yat-sen University, Guangzhou, China
| | - Rong Yin
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Center for Migrant Health Policy, Sun Yat-sen University, Guangzhou, China
| | - Meng Zheng
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Center for Migrant Health Policy, Sun Yat-sen University, Guangzhou, China
| | - Di Kong
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Center for Migrant Health Policy, Sun Yat-sen University, Guangzhou, China
| | - Wen Chen
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Center for Migrant Health Policy, Sun Yat-sen University, Guangzhou, China
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Frisina Doetter L, Frisina PG, Preuß B. Pandemic Meets Endemic: The Role of Social Inequalities and Failing Public Health Policies as Drivers of Disparities in COVID-19 Mortality among White, Black, and Hispanic Communities in the United States of America. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:14961. [PMID: 36429679 PMCID: PMC9690946 DOI: 10.3390/ijerph192214961] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 11/04/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
The COVID-19 pandemic placed the United States of America (U.S.) under enormous strain, leaving it with higher deaths during the first wave of the outbreak compared to all other advanced economies. Blacks and Hispanics were among those hardest hit by the virus-a fact attributed to enduring problems related to the social determinants of health adversely affecting Communities of Color (CoC). In this study, we ask which distinct factors relating to policy stringency and community vulnerability influenced COVID-19 mortality among Whites, Blacks, and Hispanics during the first year of the pandemic. To address this question, we utilized a mix of correlational and regression analyses. Findings point to the highly divergent impact of public policy and vulnerability on COVID-19 mortality. Specifically, we observed that state-led measures aimed at controlling the spread of the virus only improved mortality for Whites. However, pre-existing social determinants of health (i.e., population density, epidemiological and healthcare system factors) played a significant role in determining COVID-19 outcomes for CoC, even in the face of stringent containment measures by states. This suggests that state-led policy to address present and/or future public health crises need to account for the particular nature of vulnerability affecting Blacks and Hispanics in the U.S.
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Affiliation(s)
- Lorraine Frisina Doetter
- Collaborative Research Centre (CRC) 1342 & Research Center on Inequality and Social Policy (SOCIUM), The University of Bremen, 28359 Bremen, Germany
| | | | - Benedikt Preuß
- Research Center on Inequality and Social Policy (SOCIUM), The University of Bremen, 28359 Bremen, Germany
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Lamarca AP, de Almeida LGP, Francisco RDS, Cavalcante L, Brustolini O, Gerber AL, Guimarães APDC, de Oliveira TH, dos Santos Nascimento ÉR, Policarpo C, de Souza IV, de Carvalho EM, Ribeiro MS, Carvalho S, Dias da Silva F, de Oliveira Garcia MH, de Souza LM, Da Silva CG, Ribeiro CLP, Cavalcanti AC, de Mello CMB, Tanuri A, Vasconcelos ATRD. Phylodynamic analysis of SARS-CoV-2 spread in Rio de Janeiro, Brazil, highlights how metropolitan areas act as dispersal hubs for new variants. Microb Genom 2022; 8. [DOI: 10.1099/mgen.0.000859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
During the first semester of 2021, all of Brazil has suffered an intense wave of COVID-19 associated with the Gamma variant. In July, the first cases of Delta variant were detected in the state of Rio de Janeiro. In this work, we have employed phylodynamic methods to analyse more than 1 600 genomic sequences of Delta variant collected until September in Rio de Janeiro to reconstruct how this variant has surpassed Gamma and dispersed throughout the state. After the introduction of Delta, it has initially spread mostly in the homonymous city of Rio de Janeiro, the most populous of the state. In a second stage, dispersal occurred to mid- and long-range cities, which acted as new close-range hubs for spread. We observed that the substitution of Gamma by Delta was possibly caused by its higher viral load, a proxy for transmissibility. This variant turnover prompted a new surge in cases, but with lower lethality than was observed during the peak caused by Gamma. We reason that high vaccination rates in the state of Rio de Janeiro were possibly what prevented a higher number of deaths.
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Affiliation(s)
- Alessandra Pavan Lamarca
- Laboratório de Bioinformática, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | - Luiz G. P. de Almeida
- Laboratório de Bioinformática, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | | | - Liliane Cavalcante
- Laboratório de Bioinformática, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | - Otávio Brustolini
- Laboratório de Bioinformática, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | - Alexandra L. Gerber
- Laboratório de Bioinformática, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | | | - Thiago Henrique de Oliveira
- Departamento de Genética, Instituto de Biologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Cintia Policarpo
- Departamento de Genética, Instituto de Biologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | | | | | | | - Silvia Carvalho
- Secretaria Estadual de Saúde do Rio de Janeiro, Rio de Janeiro, Brazil
| | | | | | | | | | | | - Andréa Cony Cavalcanti
- Departamento de Virologia, Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
- Laboratório Central de Saúde Pública Noel Nutels, Rio de Janeiro, Brazil
| | | | - Amilcar Tanuri
- Departamento de Genética, Instituto de Biologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
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Heltberg ML, Michelsen C, Martiny ES, Christensen LE, Jensen MH, Halasa T, Petersen TC. Spatial heterogeneity affects predictions from early-curve fitting of pandemic outbreaks: a case study using population data from Denmark. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220018. [PMID: 36117868 PMCID: PMC9470254 DOI: 10.1098/rsos.220018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
The modelling of pandemics has become a critical aspect in modern society. Even though artificial intelligence can help the forecast, the implementation of ordinary differential equations which estimate the time development in the number of susceptible, (exposed), infected and recovered (SIR/SEIR) individuals is still important in order to understand the stage of the pandemic. These models are based on simplified assumptions which constitute approximations, but to what extent this are erroneous is not understood since many factors can affect the development. In this paper, we introduce an agent-based model including spatial clustering and heterogeneities in connectivity and infection strength. Based on Danish population data, we estimate how this impacts the early prediction of a pandemic and compare this to the long-term development. Our results show that early phase SEIR model predictions overestimate the peak number of infected and the equilibrium level by at least a factor of two. These results are robust to variations of parameters influencing connection distances and independent of the distribution of infection rates.
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Affiliation(s)
- Mathias L. Heltberg
- Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, Copenhagen E 2100, Denmark
- Laboratoire de Physique, Ecole Normale Superieure, Rue Lhomond 15, Paris 07505, France
- Infektionsberedskab, Statens Serum Institute, Artillerivej, Copenhagen S 2300, Denmark
| | - Christian Michelsen
- Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, Copenhagen E 2100, Denmark
| | - Emil S. Martiny
- Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, Copenhagen E 2100, Denmark
| | - Lasse Engbo Christensen
- DTU Compute, Section for Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Anker Engelunds Vej 101A, Kongens Lyngby 2800, Denmark
| | - Mogens H. Jensen
- Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, Copenhagen E 2100, Denmark
| | - Tariq Halasa
- Animal Welfare and Disease Control, University of Copenhagen, Gronnegårdsvej 8, Frederiksberg C 1870, Denmark
| | - Troels C. Petersen
- Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, Copenhagen E 2100, Denmark
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Pinto ADS, Rodrigues CA, Nascimento Sobrinho CL, Cruz LAD, Santos Junior EGD, Nunes PC, Costa MGR, Rocha MODC. COVID-19 epidemic curve in Brazil: a sum of multiple epidemics, whose inequality and population density in the states are correlated with growth rate and daily acceleration. An ecological study. Rev Soc Bras Med Trop 2022; 55:e0118. [PMID: 35239897 PMCID: PMC8909413 DOI: 10.1590/0037-8682-0118-2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 11/12/2021] [Indexed: 12/30/2022] Open
Abstract
Background: The epidemic curve has been obtained based on the 7-day moving average of the events. Although it facilitates the visualization of discrete variables, it does not allow the calculation of the absolute variation rate. Recently, we demonstrated that the polynomial interpolation method can be used to accurately calculate the daily acceleration of cases and deaths due to COVID-19. This study aimed to measure the diversity of epidemic curves and understand the importance of socioeconomic variables in the acceleration, peak cases, and deaths due to COVID-19 in Brazilian states. Methods: Epidemiological data for COVID-19 from federative units in Brazil were obtained from the Ministry of Health’s website from February 25 to July 11, 2020. Socioeconomic data were obtained from the Instituto Brasileiro de Geografia e Estatística (https://www.ibge.gov.br/). Using the polynomial interpolation methods, daily cases, deaths and acceleration were calculated. Moreover, the correlation coefficient between the epidemic curve data and socioeconomic data was determined. Results: The combination of daily data and case acceleration determined that Brazilian states were in different stages of the epidemic. Maximum case acceleration, peak of cases, maximum death acceleration, and peak of deaths were associated with the Gini index of the gross domestic product of Brazilian states and population density but did not correlate with the per capita gross domestic product of Brazilian states. Conclusions: Brazilian states showed heterogeneous data curves. Population density and socioeconomic inequality were correlated with a more rapid exponential growth in new cases and deaths.
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Martins-Filho PR. Relationship between population density and COVID-19 incidence and mortality estimates: A county-level analysis. J Infect Public Health 2021; 14:1087-1088. [PMID: 34245973 PMCID: PMC8253654 DOI: 10.1016/j.jiph.2021.06.018] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 06/27/2021] [Indexed: 11/26/2022] Open
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