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Xiong Y, Wang C, Zhang Y. Interacting particle models on the impact of spatially heterogeneous human behavioral factors on dynamics of infectious diseases. PLoS Comput Biol 2024; 20:e1012345. [PMID: 39116182 PMCID: PMC11335169 DOI: 10.1371/journal.pcbi.1012345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 08/20/2024] [Accepted: 07/22/2024] [Indexed: 08/10/2024] Open
Abstract
Human behaviors have non-negligible impacts on spread of contagious disease. For instance, large-scale gathering and high mobility of population could lead to accelerated disease transmission, while public behavioral changes in response to pandemics may effectively reduce contacts and suppress the peak of the outbreak. In order to understand how spatial characteristics like population mobility and clustering interplay with epidemic outbreaks, we formulate a stochastic-statistical environment-epidemic dynamic system (SEEDS) via an agent-based biased random walk model on a two-dimensional lattice. The "popularity" and "awareness" variables are taken into consideration to capture human natural and preventive behavioral factors, which are assumed to guide and bias agent movement in a combined way. It is found that the presence of the spatial heterogeneity, like social influence locality and spatial clustering induced by self-aggregation, potentially suppresses the contacts between agents and consequently flats the epidemic curve. Surprisedly, disease responses might not necessarily reduce the susceptibility of informed individuals and even aggravate disease outbreak if each individual responds independently upon their awareness. The disease control is achieved effectively only if there are coordinated public-health interventions and public compliance to these measures. Therefore, our model may be useful for quantitative evaluations of a variety of public-health policies.
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Affiliation(s)
- Yunfeng Xiong
- School of Mathematical Sciences, Beijing Normal University, Beijing, China
| | - Chuntian Wang
- Department of Mathematics, The University of Alabama, Tuscaloosa, Alabama, United States of America
| | - Yuan Zhang
- Center for Applied Statistics and School of Statistics, Renmin University of China, Bejing, China
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2
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Gerbino KR, Borin JM, Ardell SM, Lee JJ, Corbett KD, Meyer JR. Bacteriophage Φ21's receptor-binding protein evolves new functions through destabilizing mutations that generate non-genetic phenotypic heterogeneity. Virus Evol 2024; 10:veae049. [PMID: 39170727 PMCID: PMC11336670 DOI: 10.1093/ve/veae049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 06/21/2024] [Accepted: 07/10/2024] [Indexed: 08/23/2024] Open
Abstract
How viruses evolve to expand their host range is a major question with implications for predicting the next pandemic. Gain-of-function experiments have revealed that host-range expansions can occur through relatively few mutations in viral receptor-binding proteins, and the search for molecular mechanisms that explain such expansions is underway. Previous research on expansions of receptor use in bacteriophage λ has shown that mutations that destabilize λ's receptor-binding protein cause it to fold into new conformations that can utilize novel receptors but have weakened thermostability. These observations led us to hypothesize that other viruses may take similar paths to expand their host range. Here, we find support for our hypothesis by studying another virus, bacteriophage 21 (Φ21), which evolves to use two new host receptors within 2 weeks of laboratory evolution. By measuring the thermodynamic stability of Φ21 and its descendants, we show that as Φ21 evolves to use new receptors and expands its host range, it becomes less stable and produces viral particles that are genetically identical but vary in their thermostabilities. Next, we show that this non-genetic heterogeneity between particles is directly associated with receptor use innovation, as phage particles with more derived receptor-use capabilities are more unstable and decay faster. Lastly, by manipulating the expression of protein chaperones during Φ21 infection, we demonstrate that heterogeneity in receptor use of phage particles arises during protein folding. Altogether, our results provide support for the hypothesis that viruses can evolve new receptor-use tropisms through mutations that destabilize the receptor-binding protein and produce multiple protein conformers.
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Affiliation(s)
- Krista R Gerbino
- School of Biological Sciences, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, United States
| | - Joshua M Borin
- School of Biological Sciences, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, United States
| | - Sarah M Ardell
- School of Biological Sciences, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, United States
| | - Justin J Lee
- School of Biological Sciences, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, United States
| | - Kevin D Corbett
- School of Biological Sciences, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, United States
- Department of Cellular and Molecular Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, United States
| | - Justin R Meyer
- School of Biological Sciences, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, United States
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3
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Pangan G, Woodard V. A Study Examining the Impact of County-Level Demographic, Socioeconomic, and Political Affiliation Characteristics on COVID-19 Vaccination Patterns in Indiana. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:892. [PMID: 39063468 PMCID: PMC11276591 DOI: 10.3390/ijerph21070892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 06/27/2024] [Accepted: 07/05/2024] [Indexed: 07/28/2024]
Abstract
The COVID-19 vaccination campaign resulted in uneven vaccine uptake throughout the United States, particularly in rural areas, areas with socially and economically disadvantaged groups, and populations that exhibited vaccine hesitancy behaviors. This study examines how county-level sociodemographic and political affiliation characteristics differentially affected patterns of COVID-19 vaccinations in the state of Indiana every month in 2021. We linked county-level demographics from the 2016-2020 American Community Survey Five-Year Estimates and the Indiana Elections Results Database with county-level COVID-19 vaccination counts from the Indiana State Department of Health. We then created twelve monthly linear regression models to assess which variables were consistently being selected, based on the Akaike Information Criterion (AIC) and adjusted R-squared values. The vaccination models showed a positive association with proportions of Bachelor's degree-holding residents, of 40-59 year-old residents, proportions of Democratic-voting residents, and a negative association with uninsured and unemployed residents, persons living below the poverty line, residents without access to the Internet, and persons of Other Race. Overall, after April, the variables selected were consistent, with the model's high adjusted R2 values for COVID-19 cumulative vaccinations demonstrating that the county sociodemographic and political affiliation characteristics can explain most of the variation in vaccinations. Linking county-level sociodemographic and political affiliation characteristics with Indiana's COVID-19 vaccinations revealed inherent inequalities in vaccine coverage among different sociodemographic groups. Increased vaccine uptake could be improved in the future through targeted messaging, which provides culturally relevant advertising campaigns for groups less likely to receive a vaccine, and increasing access to vaccines for rural, under-resourced, and underserved populations.
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Affiliation(s)
- Giuseppe Pangan
- Department of Applied & Computational Mathematics & Statistics, University of Notre Dame, Notre Dame, IN 46556, USA;
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4
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de Melo SN, Soeiro Barbosa D, Câmara DCP, César Simões T, Buzanovsky LP, Sousa Duarte AG, Maia-Elkhoury ANS, Cardoso DT, Edel Donato L, Werneck GL, Bruhn FRP, Silva Belo V. Tegumentary leishmaniasis in Brazil: priority municipalities and spatiotemporal relative risks from 2001 to 2020. Pathog Glob Health 2024; 118:418-428. [PMID: 38904099 PMCID: PMC11338199 DOI: 10.1080/20477724.2024.2367442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2024] Open
Abstract
Understanding the distribution of tegumentary leishmaniasis (TL) in different periods enables the adequate conduction of actions at the public health level. The present study analyzes the spatiotemporal evolution of TL incidence rates in the municipalities of Brazil and identifies priority areas from 2001 to 2020. Notifications of new cases were analyzed employing space-time scan statistics and Local Indicators of Spatial Association. As TL incidence rates presented a downward trend in most Brazilian municipalities, spatiotemporal clusters of high relative risks (RR) were more frequent in the first decade of the series. There was a concentration of those clusters in the North and Northeast regions, mainly in the Legal Amazon area. More recent high-RR areas were identified in municipalities of different regions. The number of priority municipalities showed a stable trend in Brazil. There was a great concentration of such municipalities in the states of Acre, Mato Grosso, Rondônia, Pará, and Amapá, as well as large areas in Roraima, Amazonas, Maranhão, and Tocantins, and smaller areas in the states of Goiás, Ceará, Bahia, Minas Gerais, São Paulo, and Paraná. The present study contributes to the understanding of the historical evolution of TL in Brazil and subsidizes actions to combat the disease.
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Affiliation(s)
- Saulo Nascimento de Melo
- Campus Centro-Oeste Dona Lindu, Universidade Federal de São João del-Rei, Divinópolis, MG, Brazil
| | - David Soeiro Barbosa
- Departamento de Parasitologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | | | | | | | | | - Ana Nilce Silveira Maia-Elkhoury
- Communicable Diseases, Prevention, Control & Elimination (CDE) - VT, Organização Pan-Americana da Saúde, Rio de Janeiro, RJ, Brazil
| | - Diogo Tavares Cardoso
- Departamento de Parasitologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Lucas Edel Donato
- Secretaria de Vigilância em Saúde e Ambiente, Ministério da Saúde, Brasília, DF, Brazil
| | - Guilherme Loureiro Werneck
- Departamento de Epidemiologia, Instituto de Medicina Social, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Instituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | | | - Vinícius Silva Belo
- Campus Centro-Oeste Dona Lindu, Universidade Federal de São João del-Rei, Divinópolis, MG, Brazil
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5
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Foo FY, Abdul Rahman N, Shaik Abdullah FZ, Abd Naeeim NS. Spatio-temporal clustering analysis of COVID-19 cases in Johor. Infect Dis Model 2024; 9:387-396. [PMID: 38385018 PMCID: PMC10879677 DOI: 10.1016/j.idm.2024.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 11/17/2023] [Accepted: 01/28/2024] [Indexed: 02/23/2024] Open
Abstract
At the end of the year 2019, a virus named SARS-CoV-2 induced the coronavirus disease, which is very contagious and quickly spread around the world. This new infectious disease is called COVID-19. Numerous areas, such as the economy, social services, education, and healthcare system, have suffered grave consequences from the invasion of this deadly virus. Thus, a thorough understanding of the spread of COVID-19 is required in order to deal with this outbreak before it becomes an infectious disaster. In this research, the daily reported COVID-19 cases in 92 sub-districts in Johor state, Malaysia, as well as the population size associated to each sub-district, are used to study the propagation of COVID-19 disease across space and time in Johor. The time frame of this research is about 190 days, which started from August 5, 2021, until February 10, 2022. The clustering technique known as spatio-temporal clustering, which considers the spatio-temporal metric was adapted to determine the hot-spot areas of the COVID-19 disease in Johor at the sub-district level. The results indicated that COVID-19 disease does spike in the dynamic populated sub-districts such as the state's economic centre (Bandar Johor Bahru), and during the festive season. These findings empirically prove that the transmission rate of COVID-19 is directly proportional to human mobility and the presence of holidays. On the other hand, the result of this study will help the authority in charge in stopping and preventing COVID-19 from spreading and become worsen at the national level.
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Affiliation(s)
- Fong Ying Foo
- School of Mathematical Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia
| | - Nuzlinda Abdul Rahman
- School of Mathematical Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia
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Alahmari AA, Almuzaini Y, Alamri F, Alenzi R, Khan AA. Strengthening global health security through health early warning systems: A literature review and case study. J Infect Public Health 2024; 17 Suppl 1:85-95. [PMID: 38368245 DOI: 10.1016/j.jiph.2024.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 02/19/2024] Open
Abstract
Disease transmission is dependent on a variety of factors, including the characteristics of an event, such as crowding and shared accommodations, the potential of participants having prolonged exposure and close contact with infectious individuals, the type of activities, and the characteristics of the participants, such as their age and immunity to infectious agents [1-3]. Effective control of outbreaks of infectious diseases requires rapid diagnosis and intervention in high-risk settings. As a result, syndromic and event-based surveillance may be used to enhance the responsiveness of the surveillance system [1]. In public health, surveillance is collecting, analyzing, and interpreting data across time to inform decision-making and aid policy implementation [1]. In this review article we aimed to provide an overview of the principles, types, uses, advantages, and limitations of surveillance systems and to highlight the importance of early warning systems in response to the information received by disease surveillance. The study conducted a comprehensive literature search using several databases, selecting, and reviewing 78 articles that covered different types of surveillance systems, their applications, and their impact on controlling infectious diseases. The article also presents a case study from the Hajj gathering, which highlighted the development, evaluation, and impact of early warning systems on response to the information received by disease surveillance. The study concludes that ongoing disease surveillance should be accompanied by well-designed early warning and response systems, and continuous efforts should be invested in evaluating and validating these systems to minimize the risk of reporting delays and reducing the risk of outbreaks.
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Affiliation(s)
- Ahmed A Alahmari
- Global Center of Mass Gatherings Medicine, Ministry of Health, Riyadh, Saudi Arabia.
| | - Yasir Almuzaini
- Global Center of Mass Gatherings Medicine, Ministry of Health, Riyadh, Saudi Arabia
| | - Fahad Alamri
- Global Center of Mass Gatherings Medicine, Ministry of Health, Riyadh, Saudi Arabia
| | | | - Anas A Khan
- Global Center of Mass Gatherings Medicine, Ministry of Health, Riyadh, Saudi Arabia; Department of Emergency Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia
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Ramos Monserrat M, Ramón Molinas J, Fuster Truyol M, Bonet Manresa A, Planas Juan T, Montaño Moreno JJ, Pérez Martín MDLÁ, Ruíz Armengol P, Personat Labrador A, Lamilla Buades CM, Carrión García VM, Salvá Garví M, Nuñez Jiménez C, Cabeza Irigoyen E. Assessing the social impacts of the COVID-19 crisis using phone helplines. The case of the Balearic Islands, Spain. Front Public Health 2024; 12:1270906. [PMID: 38550322 PMCID: PMC10976841 DOI: 10.3389/fpubh.2024.1270906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 02/28/2024] [Indexed: 04/02/2024] Open
Abstract
Background Crises and health policies to tackle them can increase health inequalities. We explored the scope and usefulness of helplines set up during the COVID-19 crisis and characterised the vulnerability of their users. This study explored the geographic and socioeconomic effects of the telephone helplines set up by the Balearic Islands Government and aimed to characterise the vulnerability of their users. Methods Telephonic survey combined with a geographical analysis of a sample of calls made between 15th of March and 30th of June of 2020 to five helplines: COVID-19 general information; psychological, social (minimum vital income), labour (temporary employment regulation), and housing (rental assistance) helps. The questionnaire included sociodemographic and housing characteristics, type of problem, and if it was solved or not. We used multinomial regression to explore factors associated with having solved the problem. We calculated the standardised rate of calls by municipality using Chi-squared and z-test to test differences. Results 1,321 interviews from 2,678 selected (231 excluded, 608 untraceable, and 518 refusals). 63.8% of women, 48.7% were born in another country. They had no internet at home in 3.1%, only on the phone in 17.3%. The 23.5% had no income at home. The Problem was solved in 25.4%, and partly in 30.9%. Factors associated with not solving the problem were not having income at home (p = 0.021), labour (p = 0.008), economic (p = 0.000) or housing (p = 0.000) problems. People from 55 of 67 municipalities did at least one call. The highest rates of calls were from coastal tourist municipalities. Conclusion Helplines reached most of the territory of the Balearic Islands and were used mainly in tourist municipalities. It probably has not been helpful for families with more significant deprivation. Digital inequalities have emerged.
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Affiliation(s)
- Maria Ramos Monserrat
- Balearic Islands Public Health Department, Palma, Spain
- Balearic Islands Health Research Institute (IdISBa), Palma de Mallorca, Spain
- University of Balearic Islands, Palma de Mallorca, Spain
| | | | - Marta Fuster Truyol
- Balearic Islands Health Research Institute (IdISBa), Palma de Mallorca, Spain
| | - Aina Bonet Manresa
- Balearic Islands Health Research Institute (IdISBa), Palma de Mallorca, Spain
| | - Trinidad Planas Juan
- Balearic Islands Public Health Department, Palma, Spain
- Balearic Islands Health Research Institute (IdISBa), Palma de Mallorca, Spain
| | - Juan José Montaño Moreno
- Balearic Islands Health Research Institute (IdISBa), Palma de Mallorca, Spain
- University of Balearic Islands, Palma de Mallorca, Spain
| | | | | | | | | | | | | | - Catalina Nuñez Jiménez
- Balearic Islands Public Health Department, Palma, Spain
- Balearic Islands Health Research Institute (IdISBa), Palma de Mallorca, Spain
| | - Elena Cabeza Irigoyen
- Balearic Islands Public Health Department, Palma, Spain
- Balearic Islands Health Research Institute (IdISBa), Palma de Mallorca, Spain
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8
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Seamon E, Ridenhour BJ, Miller CR, Johnson-Leung J. Spatial Modeling of Sociodemographic Risk for COVID-19 Mortality. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.07.21.23292785. [PMID: 37546990 PMCID: PMC10402221 DOI: 10.1101/2023.07.21.23292785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
In early 2020, the Coronavirus Disease 19 (COVID-19) rapidly spread across the United States (US), exhibiting significant geographic variability. While several studies have examined the predictive relationships of differing factors on COVID-19 deaths, few have looked at spatiotemporal variation at refined geographic scales. The objective of this analysis is to examine this spatiotemporal variation in COVID-19 deaths with respect to association with socioeconomic, health, demographic, and political factors. We use multivariate regression applied to Health and Human Services (HHS) regions as well as nationwide county-level geographically weighted random forest (GWRF) models. Analyses were performed on data from three separate time frames which correspond to the spread of distinct viral variants in the US: pandemic onset until May 2021, May 2021 through November 2021, and December 2021 until April 2022. Multivariate regression results for all regions across three time windows suggest that existing measures of social vulnerability for disaster preparedness (SVI) are predictive of a higher degree of mortality from COVID-19. In comparison, GWRF models provide a more robust evaluation of feature importance and prediction, exposing the value of local features for prediction, such as obesity, which is obscured by coarse-grained analysis. Overall, GWRF results indicate that this more nuanced modeling strategy is useful for determining the spatial variation in the importance of sociodemographic risk factors for predicting COVID-19 mortality.
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Affiliation(s)
- Erich Seamon
- University of Idaho, Institute for Modeling, Collaboration, and Innovation, Moscow, 83843, USA
| | - Benjamin J. Ridenhour
- University of Idaho, Institute for Modeling, Collaboration, and Innovation, Moscow, 83843, USA
- University of Idaho, Department of Mathematics and Statistical Science, Moscow, 83843, USA
| | - Craig R. Miller
- University of Idaho, Institute for Modeling, Collaboration, and Innovation, Moscow, 83843, USA
- University of Idaho, Department of Biological Sciences, Moscow, 83843, USA
| | - Jennifer Johnson-Leung
- University of Idaho, Institute for Modeling, Collaboration, and Innovation, Moscow, 83843, USA
- University of Idaho, Department of Mathematics and Statistical Science, Moscow, 83843, USA
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9
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Adhikari B, Abdia Y, Ringa N, Clemens F, Mak S, Rose C, Janjua NZ, Otterstatter M, Irvine MA. Visible minority status and occupation were associated with increased COVID-19 infection in Greater Vancouver British Columbia between June and November 2020: an ecological study. Front Public Health 2024; 12:1336038. [PMID: 38481842 PMCID: PMC10935735 DOI: 10.3389/fpubh.2024.1336038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 02/16/2024] [Indexed: 05/12/2024] Open
Abstract
Background The COVID-19 pandemic has highlighted health disparities, especially among specific population groups. This study examines the spatial relationship between the proportion of visible minorities (VM), occupation types and COVID-19 infection in the Greater Vancouver region of British Columbia, Canada. Methods Provincial COVID-19 case data between June 24, 2020, and November 7, 2020, were aggregated by census dissemination area and linked with sociodemographic data from the Canadian 2016 census. Bayesian spatial Poisson regression models were used to examine the association between proportion of visible minorities, occupation types and COVID-19 infection. Models were adjusted for COVID-19 testing rates and other sociodemographic factors. Relative risk (RR) and 95% Credible Intervals (95% CrI) were calculated. Results We found an inverse relationship between the proportion of the Chinese population and risk of COVID-19 infection (RR = 0.98 95% CrI = 0.96, 0.99), whereas an increased risk was observed for the proportions of the South Asian group (RR = 1.10, 95% CrI = 1.08, 1.12), and Other Visible Minority group (RR = 1.06, 95% CrI = 1.04, 1.08). Similarly, a higher proportion of frontline workers (RR = 1.05, 95% CrI = 1.04, 1.07) was associated with higher infection risk compared to non-frontline. Conclusion Despite adjustments for testing, housing, occupation, and other social economic status variables, there is still a substantial association between the proportion of visible minorities, occupation types, and the risk of acquiring COVID-19 infection in British Columbia. This ecological analysis highlights the existing disparities in the burden of diseases among different visible minority populations and occupation types.
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Affiliation(s)
| | | | - Notice Ringa
- BC Centre for Disease Control, Vancouver, BC, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | | | - Sunny Mak
- BC Centre for Disease Control, Vancouver, BC, Canada
| | - Caren Rose
- BC Centre for Disease Control, Vancouver, BC, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Naveed Z. Janjua
- BC Centre for Disease Control, Vancouver, BC, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Michael Otterstatter
- BC Centre for Disease Control, Vancouver, BC, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Michael A. Irvine
- BC Centre for Disease Control, Vancouver, BC, Canada
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
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10
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Vandelli V, Palandri L, Coratza P, Rizzi C, Ghinoi A, Righi E, Soldati M. Conditioning factors in the spreading of Covid-19 - Does geography matter? Heliyon 2024; 10:e25810. [PMID: 38356610 PMCID: PMC10865316 DOI: 10.1016/j.heliyon.2024.e25810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 01/23/2024] [Accepted: 02/02/2024] [Indexed: 02/16/2024] Open
Abstract
There is evidence in literature that the spread of COVID-19 can be influenced by various geographic factors, including territorial features, climate, population density, socioeconomic conditions, and mobility. The objective of the paper is to provide an updated literature review on geographical studies analysing the factors which influenced COVID-19 spreading. This literature review took into account not only the geographical aspects but also the COVID-19-related outcomes (infections and deaths) allowing to discern the potential influencing role of the geographic factors per type of outcome. A total of 112 scientific articles were selected, reviewed and categorized according to subject area, aim, country/region of study, considered geographic and COVID-19 variables, spatial and temporal units of analysis, methodologies, and main findings. Our literature review showed that territorial features may have played a role in determining the uneven geography of COVID-19; for instance, a certain agreement was found regarding the direct relationship between urbanization degree and COVID-19 infections. For what concerns climatic factors, temperature was the variable that correlated the best with COVID-19 infections. Together with climatic factors, socio-demographic ones were extensively taken into account. Most of the analysed studies agreed that population density and human mobility had a significant and direct relationship with COVID-19 infections and deaths. The analysis of the different approaches used to investigate the role of geographic factors in the spreading of the COVID-19 pandemic revealed that the significance/representativeness of the outputs is influenced by the scale considered due to the great spatial variability of geographic aspects. In fact, a more robust and significant association between geographic factors and COVID-19 was found by studies conducted at subnational or local scale rather than at country scale.
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Affiliation(s)
- Vittoria Vandelli
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Lucia Palandri
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Paola Coratza
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Cristiana Rizzi
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Alessandro Ghinoi
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Elena Righi
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Mauro Soldati
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
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11
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Ribeiro M, Azevedo L, Santos AP, Pinto Leite P, Pereira MJ. Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022. PLoS One 2024; 19:e0297772. [PMID: 38300912 PMCID: PMC10833534 DOI: 10.1371/journal.pone.0297772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 01/12/2024] [Indexed: 02/03/2024] Open
Abstract
During the SARS-CoV-2 pandemic, governments and public health authorities collected massive amounts of data on daily confirmed positive cases and incidence rates. These data sets provide relevant information to develop a scientific understanding of the pandemic's spatiotemporal dynamics. At the same time, there is a lack of comprehensive approaches to describe and classify patterns underlying the dynamics of COVID-19 incidence across regions over time. This seriously constrains the potential benefits for public health authorities to understand spatiotemporal patterns of disease incidence that would allow for better risk communication strategies and improved assessment of mitigation policies efficacy. Within this context, we propose an exploratory statistical tool that combines functional data analysis with unsupervised learning algorithms to extract meaningful information about the main spatiotemporal patterns underlying COVID-19 incidence on mainland Portugal. We focus on the timeframe spanning from August 2020 to March 2022, considering data at the municipality level. First, we describe the temporal evolution of confirmed daily COVID-19 cases by municipality as a function of time, and outline the main temporal patterns of variability using a functional principal component analysis. Then, municipalities are classified according to their spatiotemporal similarities through hierarchical clustering adapted to spatially correlated functional data. Our findings reveal disparities in disease dynamics between northern and coastal municipalities versus those in the southern and hinterland. We also distinguish effects occurring during the 2020-2021 period from those in the 2021-2022 autumn-winter seasons. The results provide proof-of-concept that the proposed approach can be used to detect the main spatiotemporal patterns of disease incidence. The novel approach expands and enhances existing exploratory tools for spatiotemporal analysis of public health data.
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Affiliation(s)
- Manuel Ribeiro
- CERENA, DER, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Leonardo Azevedo
- CERENA, DER, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - André Peralta Santos
- Direção de Serviços de Informação e Análise, Direção-Geral da Saúde, Lisbon, Portugal
- NOVA National School of Public Health, Public Health Research Centre, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Pedro Pinto Leite
- Direção de Serviços de Informação e Análise, Direção-Geral da Saúde, Lisbon, Portugal
| | - Maria João Pereira
- CERENA, DER, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
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12
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Abdrabo KI, Mabrouk M, Han H, Saber M, Kantoush SA, Sumi T. Mapping COVID-19's potential infection risk based on land use characteristics: A case study of commercial activities in two Egyptian cities. Heliyon 2024; 10:e24702. [PMID: 38312664 PMCID: PMC10834811 DOI: 10.1016/j.heliyon.2024.e24702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 01/07/2024] [Accepted: 01/12/2024] [Indexed: 02/06/2024] Open
Abstract
The contagious COVID-19 has recently emerged and evolved into a world-threatening pandemic outbreak. After pursuing rigorous prophylactic measures two years ago, most activities globally reopened despite the emergence of lethal genetic strains. In this context, assessing and mapping activity characteristics-based hot spot regions facilitating infectious transmission is essential. Hence, our research question is: How can the potential hotspots of COVID-19 risk be defined intra-cities based on the spatial planning of commercial activity in particular? In our research, Zayed and October cities, Egypt, characterized by various commercial activities, were selected as testbeds. First, we analyzed each activity's spatial and morphological characteristics and potential infection risk based on the Centre for Disease Control and Prevention (CDCP) criteria and the Kriging Interpolation method. Then, using Google Mobility, previous reports, and semi-structured interviews, points of interest and population flow were defined and combined with the last step as interrelated horizontal layers for determining hotspots. A validation study compared the generated activity risk map, spatial COVID-19 cases, and land use distribution using logistic regression (LR) and Pearson coefficients (rxy). Through visual analytics, our findings indicate the central areas of both cities, including incompatible and concentrated commercial activities, have high-risk peaks (LR = 0.903, rxy = 0.78) despite the medium urban density of districts, indicating that urban density alone is insufficient for public health risk reduction. Health perspective-based spatial configuration of activities is advised as a risk assessment tool along with urban density for appropriate decision-making in shaping pandemic-resilient cities.
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Affiliation(s)
- Karim I. Abdrabo
- Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, Japan
- Faculty of Urban and Regional Planning, Cairo University, Giza, Egypt
| | - Mahmoud Mabrouk
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China
- Faculty of Urban and Regional Planning, Cairo University, Giza, Egypt
| | - Haoying Han
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China
- Faculty of Innovation and Design, City University of Macau, Macau
| | - Mohamed Saber
- Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, Japan
| | - Sameh A. Kantoush
- Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, Japan
| | - Tetsuya Sumi
- Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, Japan
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13
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Yin C, Mpofu E, Brock K, Ingman S. Nursing Home Residents' COVID-19 Infections in the United States: A Systematic Review of Personal and Contextual Factors. Gerontol Geriatr Med 2024; 10:23337214241229824. [PMID: 38370579 PMCID: PMC10870703 DOI: 10.1177/23337214241229824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 12/22/2023] [Accepted: 01/15/2024] [Indexed: 02/20/2024] Open
Abstract
Background: This mixed methods systemic review synthesizes the evidence about nursing home risks for COVID-19 infections. Methods: Four electronic databases (PubMed, Web of Science, Scopus, and Sage Journals Online) were searched between January 2020 and October 2022. Inclusion criteria were studies reported on nursing home COVID-19 infection risks by geography, demography, type of nursing home, staffing and resident's health, and COVID-19 vaccination status. The Mixed Methods Appraisal Tool (MMAT) was used to assess the levels of evidence for quality, and a narrative synthesis for reporting the findings by theme. Results: Of 579 initial articles, 48 were included in the review. Findings suggest that highly populated counties and urban locations had a higher likelihood of COVID-19 infections. Larger nursing homes with a low percentage of fully vaccinated residents also had increased risks for COVID-19 infections than smaller nursing homes. Residents with advanced age, of racial minority, and those with chronic illnesses were at higher risk for COVID-19 infections. Discussion and implications: Findings suggest that along with known risk factors for COVID-19 infections, geographic and resident demographics are also important preventive care considerations. Access to COVID-19 vaccinations for vulnerable residents should be a priority.
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Affiliation(s)
- Cheng Yin
- University of North Texas, Denton, USA
| | - Elias Mpofu
- University of North Texas, Denton, USA
- University of Sydney, Australia
- University of Johannesburg, South Africa
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14
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Zhou P, Zhang H, Liu L, Pan Y, Liu Y, Sang X, Liu C, Chen Z. Sustainable planning in Wuhan City during COVID-19: an analysis of influential factors, risk profiles, and clustered patterns. Front Public Health 2023; 11:1241029. [PMID: 38152666 PMCID: PMC10751330 DOI: 10.3389/fpubh.2023.1241029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 11/21/2023] [Indexed: 12/29/2023] Open
Abstract
The outbreak of novel coronavirus pneumonia (COVID-19) is closely related to the intra-urban environment. It is important to understand the influence mechanism and risk characteristics of urban environment on infectious diseases from the perspective of urban environment composition. In this study, we used python to collect Sina Weibo help data as well as urban multivariate big data, and The random forest model was used to measure the contribution of each influential factor within to the COVID-19 outbreak. A comprehensive risk evaluation system from the perspective of urban environment was constructed, and the entropy weighting method was used to produce the weights of various types of risks, generate the specific values of the four types of risks, and obtain the four levels of comprehensive risk zones through the K-MEANS clustering of Wuhan's central urban area for zoning planning. Based on the results, we found: ①the five most significant indicators contributing to the risk of the Wuhan COVID-19 outbreak were Road Network Density, Shopping Mall Density, Public Transport Density, Educational Facility Density, Bank Density. Floor Area Ration, Poi Functional Mix ②After streamlining five indicators such as Proportion of Aged Population, Tertiary Hospital Density, Open Space Density, Night-time Light Intensity, Number of Beds Available in Designated Hospitals, the prediction accuracy of the random forest model was the highest. ③The spatial characteristics of the four categories of new crown epidemic risk, namely transmission risk, exposure risk, susceptibility risk and Risk of Scarcity of Medical Resources, were highly differentiated, and a four-level integrated risk zone was obtained by K-MEANS clustering. Its distribution pattern was in the form of "multicenter-periphery" gradient diffusion. For the risk composition of the four-level comprehensive zones combined with the internal characteristics of the urban environment in specific zones to develop differentiated control strategies. Targeted policies were then devised for each partition, offering a practical advantage over singular COVID-19 impact factor analyses. This methodology, beneficial for future public health crises, enables the swift identification of unique risk profiles in different partitions, streamlining the formulation of precise policies. The overarching goal is to maintain regular social development, harmonizing preventive measures and economic efforts.
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Affiliation(s)
| | | | - Lanjun Liu
- School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan, China
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15
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Wang P, Huang J. A data-driven framework to assess population dynamics during novel coronavirus outbreaks: A case study on Xiamen Island, China. PLoS One 2023; 18:e0293803. [PMID: 37948384 PMCID: PMC10637684 DOI: 10.1371/journal.pone.0293803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 10/19/2023] [Indexed: 11/12/2023] Open
Abstract
The outbreak of the Coronavirus Disease 2019 (COVID-19) has profoundly influenced daily life, necessitating the understanding of the relationship between the epidemic's progression and population dynamics. In this study, we present a data-driven framework that integrates GIS-based data mining technology and a Susceptible, Exposed, Infected and Recovered (SEIR) model. This approach helps delineate population dynamics at the grid and community scales and analyze the impacts of government policies, urban functional areas, and intercity flows on population dynamics during the pandemic. Xiamen Island was selected as a case study to validate the effectiveness of the data-driven framework. The results of the high/low cluster analysis provide 99% certainty (P < 0.01) that the population distribution between January 23 and March 16, 2020, was not random, a phenomenon referred to as high-value clustering. The SEIR model predicts that a ten-day delay in implementing a lockdown policy during an epidemic can lead to a significant increase in the number of individuals infected by the virus. Throughout the epidemic prevention and control period (January 23 to February 21, 2020), residential and transportation areas housed more residents. After the resumption of regular activities, the population was mainly concentrated in residential, industrial, and transportation, as well as road facility areas. Notably, the migration patterns into and out of Xiamen were primarily centered on neighboring cities both before and after the outbreak. However, migration indices from cities outside the affected province drastically decreased and approached zero following the COVID-19 outbreak. Our findings offer new insights into the interplay between the epidemic's development and population dynamics, which enhances the prevention and control of the coronavirus epidemic.
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Affiliation(s)
- Peng Wang
- Fujian Key Laboratory of Coastal Pollution Prevention and Control, Xiamen University, Xiamen, China
| | - Jinliang Huang
- Fujian Key Laboratory of Coastal Pollution Prevention and Control, Xiamen University, Xiamen, China
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16
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Li J, Jia K, Zhao W, Yuan B, Liu Y. Natural and socio-environmental factors contribute to the transmissibility of COVID-19: evidence from an improved SEIR model. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023; 67:1789-1802. [PMID: 37561207 DOI: 10.1007/s00484-023-02539-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 06/28/2023] [Accepted: 08/01/2023] [Indexed: 08/11/2023]
Abstract
COVID-19 has ravaged Brazil, and its spread showed spatial heterogeneity. Changes in the environment have been implicated as potential factors involved in COVID-19 transmission. However, considerable research efforts have not elucidated the risk of environmental factors on COVID-19 transmission from the perspective of infectious disease dynamics. The aim of this study is to model the influence of the environment on COVID-19 transmission and to analyze how the socio-ecological factors affecting the probability of virus transmission in 10 states dramatically shifted during the early stages of the epidemic in Brazil. First, this study used a Pearson correlation to analyze the interconnection between COVID-19 morbidity and socio-ecological factors and identified factors with significant correlations as the dominant factors affecting COVID-19 transmission. Then, the time-lag effect of dominant factors on the morbidity of COVID-19 was investigated by constructing a distributed lag nonlinear model and standard two-stage meta-analytic model, and the results were considered in the improved SEIR model. Lastly, a machine learning method was introduced to explore the nonlinear relationship between the environmental propagation probability and socio-ecological factors. By analyzing the impact of environmental factors on virus transmission, it can be found that population mobility directly caused by human activities had a greater impact on virus transmission than temperature and humidity. The heterogeneity of meteorological factors can be accounted for by the diverse climate patterns in Brazil. The improved SEIR model was adopted to explore the interconnection of COVID-19 transmission and the environment, which revealed a new strategy to probe the causal links between them.
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Affiliation(s)
- Jie Li
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Kun Jia
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
| | - Wenwu Zhao
- Stake Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
- Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Bo Yuan
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Yanxu Liu
- Stake Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
- Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
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17
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Annamalai A, Karuppaiya V, Ezhumalai D, Cheruparambath P, Balakrishnan K, Venkatesan A. Nano-based techniques: A revolutionary approach to prevent covid-19 and enhancing human awareness. J Drug Deliv Sci Technol 2023; 86:104567. [PMID: 37313114 PMCID: PMC10183109 DOI: 10.1016/j.jddst.2023.104567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 04/22/2023] [Accepted: 05/13/2023] [Indexed: 06/15/2023]
Abstract
In every century of history, there are many new diseases emerged, which are not even cured by many developed countries. Today, despite of scientific development, new deadly pandemic diseases are caused by microorganisms. Hygiene is considered to be one of the best methods of avoiding such communicable diseases, especially viral diseases. Illness caused by SARS-CoV-2 was termed COVID-19 by the WHO, the acronym derived from "coronavirus disease 2019. The globe is living in the worst epidemic era, with the highest infection and mortality rate owing to COVID-19 reaching 6.89% (data up to March 2023). In recent years, nano biotechnology has become a promising and visible field of nanotechnology. Interestingly, nanotechnology is being used to cure many ailments and it has revolutionized many aspects of our lives. Several COVID-19 diagnostic approaches based on nanomaterial have been developed. The various metal NPs, it is highly anticipated that could be viable and economical alternatives for treating drug resistant in many deadly pandemic diseases in near future. This review focuses on an overview of nanotechnology's increasing involvement in the diagnosis, prevention, and therapy of COVID-19, also this review provides readers with an awareness and knowledge of importance of hygiene.
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Affiliation(s)
- Asaikkutti Annamalai
- Marine Biotechnology Laboratory, Department of Biotechnology, School of Life Sciences, Pondicherry University, Pondicherry, 605 014, Puducherry, India
| | - Vimala Karuppaiya
- Cancer Nanomedicine Laboratory, Department of Zoology, School of Life Sciences, Periyar University, Salem, 636 011, Tamil Nadu, India
| | - Dhineshkumar Ezhumalai
- Dr. Krishnamoorthi Foundation for Advanced Scientific Research, Vellore, 632 001, Tamil Nadu, India
- Manushyaa Blossom Private Limited, Chennai, 600 102, Tamil Nadu, India
| | | | - Kaviarasu Balakrishnan
- Dr. Krishnamoorthi Foundation for Advanced Scientific Research, Vellore, 632 001, Tamil Nadu, India
- Manushyaa Blossom Private Limited, Chennai, 600 102, Tamil Nadu, India
| | - Arul Venkatesan
- Marine Biotechnology Laboratory, Department of Biotechnology, School of Life Sciences, Pondicherry University, Pondicherry, 605 014, Puducherry, India
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18
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Freise D, Schiele V, Schmitz H. Housing situations and local COVID-19 infection dynamics using small-area data. Sci Rep 2023; 13:14301. [PMID: 37652980 PMCID: PMC10471764 DOI: 10.1038/s41598-023-40734-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 08/16/2023] [Indexed: 09/02/2023] Open
Abstract
Low socio-economic status is associated with higher SARS-CoV-2 incidences. In this paper we study whether this is a result of differences in (1) the frequency, (2) intensity, and/or (3) duration of local SARS-CoV-2 outbreaks depending on the local housing situations. So far, there is not clear evidence which of the three factors dominates. Using small-scale data from neighborhoods in the German city Essen and a flexible estimation approach which does not require prior knowledge about specific transmission characteristics of SARS-CoV-2, behavioral responses or other potential model parameters, we find evidence for the last of the three hypotheses. Outbreaks do not happen more often in less well-off areas or are more severe (in terms of the number of cases), but they last longer. This indicates that the socio-economic gradient in infection levels is at least in parts a result of a more sustained spread of infections in neighborhoods with worse housing conditions after local outbreaks and suggests that in case of an epidemic allocating scarce resources in containment measures to areas with poor housing conditions might have the greatest benefit.
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Affiliation(s)
| | | | - Hendrik Schmitz
- Paderborn University, Paderborn, Germany.
- RWI Essen, Essen, Germany.
- Leibniz Science Campus Ruhr, Essen, Germany.
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19
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Phang P, Labadin J, Suhaila J, Aslam S, Hazmi H. Exploration of spatiotemporal heterogeneity and socio-demographic determinants on COVID-19 incidence rates in Sarawak, Malaysia. BMC Public Health 2023; 23:1396. [PMID: 37474904 PMCID: PMC10357875 DOI: 10.1186/s12889-023-16300-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 07/12/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND In Sarawak, 252 300 coronavirus disease 2019 (COVID-19) cases have been recorded with 1 619 fatalities in 2021, compared to only 1 117 cases in 2020. Since Sarawak is geographically separated from Peninsular Malaysia and half of its population resides in rural districts where medical resources are limited, the analysis of spatiotemporal heterogeneity of disease incidence rates and their relationship with socio-demographic factors are crucial in understanding the spread of the disease in Sarawak. METHODS The spatial dependence of district-wise incidence rates is investigated using spatial autocorrelation analysis with two orders of contiguity weights for various pandemic waves. Nine determinants are chosen from 14 covariates of socio-demographic factors via elastic net regression and recursive partitioning. The relationships between incidence rates and socio-demographic factors are examined using ordinary least squares, spatial lag and spatial error models, and geographically weighted regression. RESULTS In the first 8 months of 2021, COVID-19 severely affected Sarawak's central region, which was followed by the southern region in the next 2 months. In the third wave, based on second-order spatial weights, the incidence rate in a district is most strongly influenced by its neighboring districts' rate, although the variance of incidence rates is best explained by local regression coefficient estimates of socio-demographic factors in the first wave. It is discovered that the percentage of households with garbage collection facilities, population density and the proportion of male in the population are positively associated with the increase in COVID-19 incidence rates. CONCLUSION This research provides useful insights for the State Government and public health authorities to critically incorporate socio-demographic characteristics of local communities into evidence-based decision-making for altering disease monitoring and response plans. Policymakers can make well-informed judgments and implement targeted interventions by having an in-depth understanding of the spatial patterns and relationships between COVID-19 incidence rates and socio-demographic characteristics. This will effectively help in mitigating the spread of the disease.
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Affiliation(s)
- Piau Phang
- Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, 94300, Sarawak, Malaysia.
| | - Jane Labadin
- Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, 94300, Sarawak, Malaysia
| | - Jamaludin Suhaila
- Department of Mathematical Science, Faculty of Science, Universiti Teknologi Malaysia, Skudai, 81310, Johor, Malaysia
| | - Saira Aslam
- Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, 94300, Sarawak, Malaysia
| | - Helmy Hazmi
- Faculty of Medicine and Health Science, Universiti Malaysia Sarawak, Kota Samarahan, 94300, Sarawak, Malaysia
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20
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Qiao M, Huang B. COVID-19 spread prediction using socio-demographic and mobility-related data. CITIES (LONDON, ENGLAND) 2023; 138:104360. [PMID: 37159808 PMCID: PMC10156989 DOI: 10.1016/j.cities.2023.104360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 03/24/2023] [Accepted: 05/01/2023] [Indexed: 05/11/2023]
Abstract
Studying the impacts of factors that may vary spatially and temporally as infectious disease progresses is critical for the prediction and intervention of COVID-19. This study aimed to quantitatively assess the spatiotemporal impacts of socio-demographic and mobility-related factors to predict the spread of COVID-19. We designed two different schemes that enhanced temporal and spatial features respectively, and both with the geographically and temporally weighted regression (GTWR) model adopted to consider the heterogeneity and non-stationarity problems, to reveal the spatiotemporal associations between the factors and the spread of COVID-19 pandemic. Results indicate that our two schemes are effective in facilitating the accuracy of predicting the spread of COVID-19. In particular, the temporally enhanced scheme quantifies the impacts of the factors on the temporal spreading trend of the epidemic at the city level. Simultaneously, the spatially enhanced scheme figures out how the spatial variances of the factors determine the spatial distribution of the COVID-19 cases among districts, particularly between the urban area and the surrounding suburbs. Findings provide potential policy implications in terms of dynamic and adaptive anti-epidemic.
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Affiliation(s)
- Mengling Qiao
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Bo Huang
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
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21
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Aydede Y, Ditzen J. Identifying the regional drivers of influenza-like illness in Nova Scotia, Canada, with dominance analysis. Sci Rep 2023; 13:10114. [PMID: 37344569 DOI: 10.1038/s41598-023-37184-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 06/17/2023] [Indexed: 06/23/2023] Open
Abstract
The spread of viral pathogens is inherently a spatial process. While the temporal aspects of viral spread at the epidemiological level have been increasingly well characterized, the spatial aspects of viral spread are still understudied due to a striking absence of theoretical expectations of how spatial dynamics may impact the temporal dynamics of viral populations. Characterizing the spatial transmission and understanding the factors driving it are important for anticipating local timing of disease incidence and for guiding more informed control strategies. Using a unique data set from Nova Scotia, Canada, the objective of this study is to apply a new novel method that recovers a spatial network of the influenza-like viral spread where the regions in their dominance are identified and ranked. We, then, focus on identifying regional predictors of those dominant regions. Our analysis uncovers 18 key regional drivers among 112 regions, each distinguished by unique community-level vulnerability factors such as demographic and economic characteristics. These findings offer valuable insights for implementing targeted public health interventions and allocating resources effectively.
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Affiliation(s)
| | - Jan Ditzen
- Free University of Bolzano, Bolzano, Italy
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22
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Belvis F, Aleta A, Padilla-Pozo Á, Pericàs JM, Fernández-Gracia J, Rodríguez JP, Eguíluz VM, De Santana CN, Julià M, Benach J. Key epidemiological indicators and spatial autocorrelation patterns across five waves of COVID-19 in Catalonia. Sci Rep 2023; 13:9709. [PMID: 37322048 PMCID: PMC10272129 DOI: 10.1038/s41598-023-36169-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 05/26/2023] [Indexed: 06/17/2023] Open
Abstract
This research studies the evolution of COVID-19 crude incident rates, effective reproduction number R(t) and their relationship with incidence spatial autocorrelation patterns in the 19 months following the disease outbreak in Catalonia (Spain). A cross-sectional ecological panel design based on n = 371 health-care geographical units is used. Five general outbreaks are described, systematically preceded by generalized values of R(t) > 1 in the two previous weeks. No clear regularities concerning possible initial focus appear when comparing waves. As for autocorrelation, we identify a wave's baseline pattern in which global Moran's I increases rapidly in the first weeks of the outbreak to descend later. However, some waves significantly depart from the baseline. In the simulations, both baseline pattern and departures can be reproduced when measures aimed at reducing mobility and virus transmissibility are introduced. Spatial autocorrelation is inherently contingent on the outbreak phase and is also substantially modified by external interventions affecting human behavior.
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Affiliation(s)
- Francesc Belvis
- Research Group on Health Inequalities, Environment, and Employment Conditions (GREDS-EMCONET), Department of Political and Social Sciences, Universitat Pompeu Fabra, 08005, Barcelona, Spain.
- Johns Hopkins University-Universitat Pompeu Fabra Public Policy Center (JHU-UPF PPC), 08005, Barcelona, Spain.
| | - Alberto Aleta
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018, Zaragoza, Spain
| | - Álvaro Padilla-Pozo
- Research Group on Health Inequalities, Environment, and Employment Conditions (GREDS-EMCONET), Department of Political and Social Sciences, Universitat Pompeu Fabra, 08005, Barcelona, Spain
- Johns Hopkins University-Universitat Pompeu Fabra Public Policy Center (JHU-UPF PPC), 08005, Barcelona, Spain
- Department of Sociology, Cornell University, Ithaca, New York, USA
| | - Juan-M Pericàs
- Research Group on Health Inequalities, Environment, and Employment Conditions (GREDS-EMCONET), Department of Political and Social Sciences, Universitat Pompeu Fabra, 08005, Barcelona, Spain
- Johns Hopkins University-Universitat Pompeu Fabra Public Policy Center (JHU-UPF PPC), 08005, Barcelona, Spain
- Liver Unit, Internal Medicine Department, Vall d'Hebron University Hospital, Vall d'Hebron Institute for Research, CIBERehd, 08035, Barcelona, Spain
- Infectious Disease Department, Hospital Clínic, 08036, Barcelona, Spain
| | - Juan Fernández-Gracia
- Instituto de Física Interdisciplinar Y Sistemas Complejos IFISC (CSIC-UIB), 07122, Palma de Mallorca, Spain
| | - Jorge P Rodríguez
- Instituto de Física Interdisciplinar Y Sistemas Complejos IFISC (CSIC-UIB), 07122, Palma de Mallorca, Spain
- Instituto Mediterráneo de Estudios Avanzados IMEDEA (CSIC-UIB), 07190, Esporles, Spain
| | - Víctor M Eguíluz
- Instituto de Física Interdisciplinar Y Sistemas Complejos IFISC (CSIC-UIB), 07122, Palma de Mallorca, Spain
| | - Charles Novaes De Santana
- Instituto de Física Interdisciplinar Y Sistemas Complejos IFISC (CSIC-UIB), 07122, Palma de Mallorca, Spain
| | - Mireia Julià
- Research Group on Health Inequalities, Environment, and Employment Conditions (GREDS-EMCONET), Department of Political and Social Sciences, Universitat Pompeu Fabra, 08005, Barcelona, Spain
- Johns Hopkins University-Universitat Pompeu Fabra Public Policy Center (JHU-UPF PPC), 08005, Barcelona, Spain
- ESIMar (Mar Nursing School), Parc de Salut Mar, Universitat Pompeu Fabra-Affiliated, 08003, Barcelona, Spain
- SDHEd (Social Determinants and Health Education Research Group), IMIM (Hospital del Mar Medical Research Institute), 08005, Barcelona, Spain
| | - Joan Benach
- Research Group on Health Inequalities, Environment, and Employment Conditions (GREDS-EMCONET), Department of Political and Social Sciences, Universitat Pompeu Fabra, 08005, Barcelona, Spain
- Johns Hopkins University-Universitat Pompeu Fabra Public Policy Center (JHU-UPF PPC), 08005, Barcelona, Spain
- Ecological Humanities Research Group (GHECO), Universidad Autónoma de Madrid, 28049, Madrid, Spain
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Clements ACA. Spatial and Temporal Data Visualisation for Mass Dissemination: Advances in the Era of COVID-19. Trop Med Infect Dis 2023; 8:314. [PMID: 37368732 DOI: 10.3390/tropicalmed8060314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 06/01/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
The COVID-19 pandemic is the first major pandemic of the digital age and has been characterised by unprecedented public consumption of spatial and temporal disease data, which can enable greater transparency and accountability of governments to the public for their public health decisions. A variety of state and non-state actors have collated and presented maps, charts, and plots of data related to the pandemic in both static and dynamic formats. In particular, there has been a proliferation of online dashboards presenting data related to the pandemic. The sources and types of information displayed have evolved rapidly during the pandemic, with a general trend towards providing more specialised information pertinent to specific aspects of epidemiology or disease control, as opposed simply to disease and death notifications. Limited evaluation of the quality of COVID-19 data visualisation tools has been conducted and significant effort now needs to be spent on standardisation and quality improvement of national and international data visualisation systems including developing common indicators, data quality assurance mechanisms and visualisation approaches, and building compatible electronic systems for data collection and sharing. The increasing availability of disease data for public consumption presents challenges and opportunities for government, media organisations, academic research institutions, and the general public. A key challenge is ensuring consistency and effectiveness of public health messaging to ensure a coordinated response and public trust in intervention strategies. Capitalising on opportunities for greater government accountability for public health decision-making, and more effective mobilisation of public health interventions, is predicated on the provision of accurate and timely information.
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Affiliation(s)
- Archie C A Clements
- Peninsula Medical School, University of Plymouth, Plymouth PL4 8AA, UK
- Telethon Kids Institute, Nedlands, Perth, WA 6009, Australia
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24
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Yu Z, Liu X. Spatial variations of the third and fourth COVID-19 waves in Hong Kong: A comparative study using built environment and socio-demographic characteristics. ENVIRONMENT AND PLANNING. B, URBAN ANALYTICS AND CITY SCIENCE 2023; 50:1144-1160. [PMID: 38603206 PMCID: PMC9168414 DOI: 10.1177/23998083221107019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Since the first confirmed case was reported in January 2020, Hong Kong has experienced multiple waves of COVID-19 outbreaks. Recent literature has explored the spatial patterns of disease incidence and their relationships with the built environment and demographic characteristics. Nonetheless, few studies aim at the comparative patterns of different epidemic waves occurring in the same spatial context. This study analyses spatial patterns of the third and fourth COVID-19 epidemic waves and then evaluates the spatial relationship between case incidence and built environment and socio-demographic characteristics. By collecting local-related cases, this study incorporates a two-fold analytical strategy: (1) Using rank-size distribution and log-odd ratio to depict the spatial pattern of COVID-19 incidence rates; (2) through global and local regression models, investigating incidence's associations with the urban built environment and socio-demographic characteristics. The results reveal that the two different epidemic waves have far distinct spatial tendencies to their infection risk factors, reflecting location-specific associations with the built environments and socio-demographics. Collectively, we discover that the third and fourth COVID-19 waves are likely associated with residential context and urban activities, respectively. Practical implications are discussed that would be of interest to policymakers and health professionals.
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Affiliation(s)
- Zidong Yu
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - Xintao Liu
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
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25
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Ajayakumar J, Curtis A, Curtis J. The utility of Zip4 codes in spatial epidemiological analysis. PLoS One 2023; 18:e0285552. [PMID: 37256874 DOI: 10.1371/journal.pone.0285552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 04/25/2023] [Indexed: 06/02/2023] Open
Abstract
There are many public health situations within the United States that require fine geographical scale data to effectively inform response and intervention strategies. However, a condition for accessing and analyzing such data, especially when multiple institutions are involved, is being able to preserve a degree of spatial privacy and confidentiality. Hospitals and state health departments, who are generally the custodians of these fine-scale health data, are sometimes understandably hesitant to collaborate with each other due to these concerns. This paper looks at the utility and pitfalls of using Zip4 codes, a data layer often included as it is believed to be "safe", as a source for sharing fine-scale spatial health data that enables privacy preservation while maintaining a suitable precision for spatial analysis. While the Zip4 is widely supplied, researchers seldom utilize it. Nor is its spatial characteristics known by data guardians. To address this gap, we use the context of a near-real time spatial response to an emerging health threat to show how the Zip4 aggregation preserves an underlying spatial structure making it potentially suitable dataset for analysis. Our results suggest that based on the density of urbanization, Zip4 centroids are within 150 meters of the real location almost 99% of the time. Spatial analysis experiments performed on these Zip4 data suggest a far more insightful geographic output than if using more commonly used aggregation units such as street lines and census block groups. However, this improvement in analytical output comes at a spatial privy cost as Zip4 centroids have a higher potential of compromising spatial anonymity with 73% of addresses having a spatial k anonymity value less than 5 when compared to other aggregations. We conclude that while offers an exciting opportunity to share data between organizations, researchers and analysts need to be made aware of the potential for serious confidentiality violations.
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Affiliation(s)
- Jayakrishnan Ajayakumar
- GIS Health & Hazards Lab, Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Andrew Curtis
- GIS Health & Hazards Lab, Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Jacqueline Curtis
- GIS Health & Hazards Lab, Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
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Elshehawey AM, Qian Z. A novel high-order multivariate Markov model for spatiotemporal analysis with application to COVID-19 outbreak. J Korean Stat Soc 2023; 52:1-27. [PMID: 37361424 PMCID: PMC10225786 DOI: 10.1007/s42952-023-00210-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 03/13/2023] [Indexed: 06/28/2023]
Abstract
We propose a new strategy for analyzing the evolution of random phenomena over time and space simultaneously based on the high-order multivariate Markov chains. We develop a novel Markov model of order r for m chains consisting of s possible states to gather parsimony with realism. It can capture negative and positive associations among the chains with only a reduced number of parameters, r m 2 s 2 + 2 , remarkably lower than m s r m + 1 required for the full parameterized model. Our model privileges are enhanced by a Monte Carlo simulation experiment, besides application to analyze the spatial-temporal dynamics for the risk level of a recently global pandemic (COVID-19) outbreak in world health organization (WHO) regions for predicting the risk state of epidemiological prevalence and monitoring infection control.
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Affiliation(s)
- A. M. Elshehawey
- Department of Applied, Mathematical and Actuarial Statistics, Faculty of Commerce, Damietta University, Damietta, Egypt
| | - Zhengming Qian
- Department of Statistics and Data Science, School of Economics, Xiamen University, Xiamen, China
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Ren X, Zhang S, Luo P, Zhao J, Kuang W, Ni H, Zhou N, Dai H, Hong X, Yang X, Zha W, Lv Y. Spatial heterogeneity of socio-economic determinants of typhoid/paratyphoid fever in one province in central China from 2015 to 2019. BMC Public Health 2023; 23:927. [PMID: 37217879 DOI: 10.1186/s12889-023-15738-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 04/23/2023] [Indexed: 05/24/2023] Open
Abstract
BACKGROUND Typhoid fever and paratyphoid fever are one of the most criticial public health issues worldwide, especially in developing countries. The incidence of this disease may be closely related to socio-economic factors, but there is a lack of research on the spatial level of relevant determinants of typhoid fever and paratyphoid fever. METHODS In this study, we took Hunan Province in central China as an example and collected the data on typhoid and paratyphoid incidence and socio-economic factors in 2015-2019. Firstly spatial mapping was made on the disease prevalence, and again using geographical probe model to explore the critical influencing factors of typhoid and paratyphoid, finally employing MGWR model to analysis the spatial heterogeneity of these factors. RESULTS The results showed that the incidence of typhoid and paratyphoid fever was seasonal and periodic and frequently occurred in summer. In the case of total typhoid and paratyphoid fever, Yongzhou was the most popular, followed by Xiangxi Tujia and Miao Autonomous Prefecture, Huaihua and Chenzhou generally focused on the south and west. And Yueyang, Changde and Loudi had a slight increase trend year by year from 2015 to 2019. Moreover, the significant effects on the incidence of typhoid and paratyphoid fever from strong to weak were as follows: gender ratio(q = 0.4589), students in ordinary institutions of higher learning(q = 0.2040), per capita disposable income of all residents(q = 0.1777), number of foreign tourists received(q = 0.1697), per capita GDP(q = 0.1589), and the P values for these factors were less than 0.001. According to the MGWR model, gender ratio, per capita disposable income of all residents and Number of foreign tourists received had a positive effect on the incidence of typhoid and paratyphoid fever. In contrast, students in ordinary institutions of higher learning had a negative impact, and per capita GDP shows a bipolar change. CONCLUSIONS The incidence of typhoid and paratyphoid fever in Hunan Province from 2015 to 2019 was a marked seasonality, concentrated in the south and west of Hunan Province. Attention should be paid to the prevention and control of critical periods and concentrated areas. Different socio-economic factors may show other directions and degrees of action in other prefecture-level cities. To summarize, health education, entry-exit epidemic prevention and control can be strengthened. This study may be beneficial to carry out targeted, hierarchical and focused prevention and control of typhoid fever and paratyphoid fever, and provide scientific reference for related theoretical research.
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Affiliation(s)
- Xiang Ren
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410013, Hunan, China
| | - Siyu Zhang
- Hunan Provincial Center for Disease Control and Prevention, Changsha, 410005, Hunan, China
| | - Piaoyi Luo
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410013, Hunan, China
| | - Jin Zhao
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410013, Hunan, China
- Changsha Center for Disease Control and Prevention, Changsha, 410024, Hunan, China
| | - Wentao Kuang
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410013, Hunan, China
| | - Han Ni
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410013, Hunan, China
| | - Nan Zhou
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410013, Hunan, China
| | - Haoyun Dai
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410013, Hunan, China
| | - Xiuqin Hong
- Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, 410007, Hunan, China
| | - Xuewen Yang
- Changsha Center for Disease Control and Prevention, Changsha, 410024, Hunan, China
| | - Wenting Zha
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410013, Hunan, China.
| | - Yuan Lv
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410013, Hunan, China.
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Lambio C, Schmitz T, Elson R, Butler J, Roth A, Feller S, Savaskan N, Lakes T. Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20105830. [PMID: 37239558 DOI: 10.3390/ijerph20105830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/28/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023]
Abstract
Identifying areas with high and low infection rates can provide important etiological clues. Usually, areas with high and low infection rates are identified by aggregating epidemiological data into geographical units, such as administrative areas. This assumes that the distribution of population numbers, infection rates, and resulting risks is constant across space. This assumption is, however, often false and is commonly known as the modifiable area unit problem. This article develops a spatial relative risk surface by using kernel density estimation to identify statistically significant areas of high risk by comparing the spatial distribution of address-level COVID-19 cases and the underlying population at risk in Berlin-Neukölln. Our findings show that there are varying areas of statistically significant high and low risk that straddle administrative boundaries. The findings of this exploratory analysis further highlight topics such as, e.g., Why were mostly affluent areas affected during the first wave? What lessons can be learned from areas with low infection rates? How important are built structures as drivers of COVID-19? How large is the effect of the socio-economic situation on COVID-19 infections? We conclude that it is of great importance to provide access to and analyse fine-resolution data to be able to understand the spread of the disease and address tailored health measures in urban settings.
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Affiliation(s)
- Christoph Lambio
- Geography Department, Applied Geoinformation Science Lab, Humboldt-University Berlin, 10099 Berlin, Germany
| | - Tillman Schmitz
- Geography Department, Applied Geoinformation Science Lab, Humboldt-University Berlin, 10099 Berlin, Germany
| | - Richard Elson
- UK Health Security Agency, 61, Colindale Avenue, London NW9 5EQ, UK
- School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
| | - Jeffrey Butler
- Geography Department, Applied Geoinformation Science Lab, Humboldt-University Berlin, 10099 Berlin, Germany
| | - Alexandra Roth
- Local Health Department Berlin-Neukölln, Gesundheitsamt Neukölln, Blaschkoallee 32, 12359 Berlin, Germany
| | - Silke Feller
- Local Health Department Berlin-Neukölln, Gesundheitsamt Neukölln, Blaschkoallee 32, 12359 Berlin, Germany
| | - Nicolai Savaskan
- Local Health Department Berlin-Neukölln, Gesundheitsamt Neukölln, Blaschkoallee 32, 12359 Berlin, Germany
| | - Tobia Lakes
- Geography Department, Applied Geoinformation Science Lab, Humboldt-University Berlin, 10099 Berlin, Germany
- IRI THESys, Integrative Research Institute on Transformations of Human-Environment Systems, Humboldt-Universität zu Berlin, 10099 Berlin, Germany
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29
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Umair A, Masciari E, Ullah MH. Vaccine sentiment analysis using BERT + NBSVM and geo-spatial approaches. THE JOURNAL OF SUPERCOMPUTING 2023; 79:1-31. [PMID: 37359330 PMCID: PMC10164419 DOI: 10.1007/s11227-023-05319-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 04/17/2023] [Indexed: 06/28/2023]
Abstract
Since the spread of the coronavirus flu in 2019 (hereafter referred to as COVID-19), millions of people worldwide have been affected by the pandemic, which has significantly impacted our habits in various ways. In order to eradicate the disease, a great help came from unprecedentedly fast vaccines development along with strict preventive measures adoption like lockdown. Thus, world wide provisioning of vaccines was crucial in order to achieve the maximum immunization of population. However, the fast development of vaccines, driven by the urge of limiting the pandemic caused skeptical reactions by a vast amount of population. More specifically, the people's hesitancy in getting vaccinated was an additional obstacle in fighting COVID-19. To ameliorate this scenario, it is important to understand people's sentiments about vaccines in order to take proper actions to better inform the population. As a matter of fact, people continuously update their feelings and sentiments on social media, thus a proper analysis of those opinions is an important challenge for providing proper information to avoid misinformation. More in detail, sentiment analysis (Wankhade et al. in Artif Intell Rev 55(7):5731-5780, 2022. 10.1007/s10462-022-10144-1) is a powerful technique in natural language processing that enables the identification and classification of people feelings (mainly) in text data. It involves the use of machine learning algorithms and other computational techniques to analyze large volumes of text and determine whether they express positive, negative or neutral sentiment. Sentiment analysis is widely used in industries such as marketing, customer service, and healthcare, among others, to gain actionable insights from customer feedback, social media posts, and other forms of unstructured textual data. In this paper, Sentiment Analysis will be used to elaborate on people reaction to COVID-19 vaccines in order to provide useful insights to improve the correct understanding of their correct usage and possible advantages. In this paper, a framework that leverages artificial intelligence (AI) methods is proposed for classifying tweets based on their polarity values. We analyzed Twitter data related to COVID-19 vaccines after the most appropriate pre-processing on them. More specifically, we identified the word-cloud of negative, positive, and neutral words using an artificial intelligence tool to determine the sentiment of tweets. After this pre-processing step, we performed classification using the BERT + NBSVM model to classify people's sentiments about vaccines. The reason for choosing to combine bidirectional encoder representations from transformers (BERT) and Naive Bayes and support vector machine (NBSVM ) can be understood by considering the limitation of BERT-based approaches, which only leverage encoder layers, resulting in lower performance on short texts like the ones used in our analysis. Such a limitation can be ameliorated by using Naive Bayes and Support Vector Machine approaches that are able to achieve higher performance in short text sentiment analysis. Thus, we took advantage of both BERT features and NBSVM features to define a flexible framework for our sentiment analysis goal related to vaccine sentiment identification. Moreover, we enrich our results with spatial analysis of the data by using geo-coding, visualization, and spatial correlation analysis to suggest the most suitable vaccination centers to users based on the sentiment analysis outcomes. In principle, we do not need to implement a distributed architecture to run our experiments as the available public data are not massive. However, we discuss a high-performance architecture that will be used if the collected data scales up dramatically. We compared our approach with the state-of-art methods by comparing most widely used metrics like Accuracy, Precision, Recall and F-measure. The proposed BERT + NBSVM outperformed alternative models by achieving 73% accuracy, 71% precision, 88% recall and 73% F-measure for classification of positive sentiments while 73% accuracy, 71% precision, 74% recall and 73% F-measure for classification of negative sentiments respectively. These promising results will be properly discussed in next sections. The use of artificial intelligence methods and social media analysis can lead to a better understanding of people's reactions and opinions about any trending topic. However, in the case of health-related topics like COVID-19 vaccines, proper sentiment identification could be crucial for implementing public health policies. More in detail, the availability of useful findings on user opinions about vaccines can help policymakers design proper strategies and implement ad-hoc vaccination protocols according to people's feelings, in order to provide better public service. To this end, we leveraged geospatial information to support effective recommendations for vaccination centers.
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Affiliation(s)
- Areeba Umair
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, Campania Italy
| | - Elio Masciari
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, Campania Italy
| | - Muhammad Habib Ullah
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, Campania Italy
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Wong S, Ponder CS, Melix B. Spatial and racial covid-19 disparities in U.S. nursing homes. Soc Sci Med 2023; 325:115894. [PMID: 37060641 PMCID: PMC10080861 DOI: 10.1016/j.socscimed.2023.115894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 03/16/2023] [Accepted: 04/06/2023] [Indexed: 04/17/2023]
Abstract
In many parts of the world nursing home residents have experienced a disproportionate risk of exposure to COVID-19 and have died at much higher rates than other groups. There is a critical need to identify the factors driving COVID-19 risk in nursing homes to better understand and address the conditions contributing to their vulnerability during public health crises. This study investigates the characteristics associated with COVID-19 cases and deaths among residents in U.S. nursing homes from 2020 to 2021, with a focus on geospatial and racial inequalities. Using data from the Centers for Medicare and Medicaid Services and LTCFocus, this paper uses zero-inflated negative binomial regression models, Kruskal-Wallis tests, and Local Moran's I to generate statistical and geospatial results. Our analysis reveals that majority Hispanic facilities have alarmingly high COVID-19 cases and deaths, suggesting that these facilities have the greatest need for policy improvements in staffing and financing to reduce racial inequalities in nursing home care. At the same time we also detect COVID-19 hot spots in rural areas with predominately White residents, indicating a need to rethink public messaging strategies in these areas. The top states with COVID-19 hot spots are Kentucky, Pennsylvania, Illinois, and Oklahoma. This research provides new insights into the socio-spatial contexts and inequities that contribute to the vulnerability of nursing home residents during a pandemic.
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Affiliation(s)
- Sandy Wong
- Department of Geography, Florida State University, Bellamy Building, Room 323, 113 Collegiate Loop, PO Box 3062190, Tallahassee, FL, 32306, United States.
| | - C S Ponder
- Department of Geography, Florida State University, Bellamy Building, Room 323, 113 Collegiate Loop, PO Box 3062190, Tallahassee, FL, 32306, United States
| | - Bertram Melix
- Department of Geography, Florida State University, Bellamy Building, Room 323, 113 Collegiate Loop, PO Box 3062190, Tallahassee, FL, 32306, United States
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Alves A, da Costa NM, Morgado P, da Costa EM. Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies. Int J Health Geogr 2023; 22:8. [PMID: 37024965 PMCID: PMC10078027 DOI: 10.1186/s12942-023-00329-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 03/28/2023] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND COVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection. METHODS We present a data-driven GIS-multicriteria analysis to derive a spatial-based susceptibility index to COVID-19 infection in Portugal. The cumulative incidence over 14 days was used in a stepwise multiple linear regression as the target variable along potential determinants at the municipal scale. To infer the existence of thresholds in the relationships between determinants and incidence the most relevant factors were examined using a bivariate Bayesian change point analysis. The susceptibility index was mapped based on these thresholds using a weighted linear combination. RESULTS Regression results support that COVID-19 spread in mainland Portugal had strong associations with factors related to socio-territorial specificities, namely sociodemographic, economic and mobility. Change point analysis revealed evidence of nonlinearity, and the susceptibility classes reflect spatial dependency. The spatial index of susceptibility to infection explains with accuracy previous and posterior infections. Assessing the NPI levels in relation to the susceptibility map points towards a disagreement between the severity of restrictions and the actual propensity for transmission, highlighting the need for more tailored interventions. CONCLUSIONS This article argues that NPI to contain COVID-19 spread should consider the spatial variation of the susceptibility to infection. The findings highlight the importance of customising interventions to specific geographical contexts due to the uneven distribution of COVID-19 infection determinants. The methodology has the potential for replication at other geographical scales and regions to better understand the role of health determinants in explaining spatiotemporal patterns of diseases and promoting evidence-based public health policies.
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Affiliation(s)
- André Alves
- Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, 1600-276, Lisbon, Portugal.
| | - Nuno Marques da Costa
- Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, 1600-276, Lisbon, Portugal
- Associate Laboratory TERRA, 1349-017, Lisbon, Portugal
| | - Paulo Morgado
- Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, 1600-276, Lisbon, Portugal
- Associate Laboratory TERRA, 1349-017, Lisbon, Portugal
| | - Eduarda Marques da Costa
- Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, 1600-276, Lisbon, Portugal
- Associate Laboratory TERRA, 1349-017, Lisbon, Portugal
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Rosenberg FJ, Genial C, dos Santos BC. Urban COVID-19 endemism in Petrópolis: detection of an endemic focus by spatial analysis. Rev Peru Med Exp Salud Publica 2023; 40:213-219. [PMID: 38232268 PMCID: PMC10953638 DOI: 10.17843/rpmesp.2023.402.11341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 06/21/2023] [Indexed: 01/19/2024] Open
Abstract
OBJECTIVES. Motivation for the study. During the COVID-19 pandemic, spatial analysis methodologies were used to identify the territorial determinants of its distribution and social inequalities in access to medical care. Main findings. We found a concentration of cases in a specific location of the municipality, which remained constant throughout the study period, with sporadic outbreaks in other areas. Implications. It is necessary to pay attention to possible endemic foci of viral diseases in urban settings and to take measures to eliminate them as well as to prevent their spread within and outside the area.
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Affiliation(s)
- Felix J. Rosenberg
- Fórum Itaboraí, Fiocruz, Petrópolis, Brasil.Fórum ItaboraíFiocruz, PetrópolisBrasil
| | - Caiett Genial
- Fórum Itaboraí, Fiocruz, Petrópolis, Brasil.Fórum ItaboraíFiocruz, PetrópolisBrasil
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Prado T, Rey-Benito G, Miagostovich MP, Sato MIZ, Rajal VB, Filho CRM, Pereira AD, Barbosa MRF, Mannarino CF, da Silva AS. Wastewater-based epidemiology for preventing outbreaks and epidemics in Latin America - Lessons from the past and a look to the future. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 865:161210. [PMID: 36581294 DOI: 10.1016/j.scitotenv.2022.161210] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/05/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
Wastewater-based epidemiology (WBE) is an approach with the potential to complement clinical surveillance systems. Using WBE, it is possible to carry out an early warning of a possible outbreak, monitor spatial and temporal trends of infectious diseases, produce real-time results and generate representative epidemiological information in a territory, especially in areas of social vulnerability. Despite the historical uses of this approach, particularly in the Global Polio Eradication Initiative, and for other pathogens, it was during the COVID-19 pandemic that occurred an exponential increase in environmental surveillance programs for SARS-CoV-2 in wastewater, with many experiences and developments in the field of public health using data for decision making and prioritizing actions to control the pandemic. In Latin America, WBE was applied in heterogeneous contexts and with emphasis on populations that present many socio-environmental inequalities, a condition shared by all Latin American countries. This manuscript addresses the concepts and applications of WBE in public health actions, as well as different experiences in Latin American countries, and discusses a model to implement this surveillance system at the local or national level. We emphasize the need to implement this sentinel surveillance system in countries that want to detect the early entry and spread of new pathogens and monitor outbreaks or epidemics of infectious agents in their territories as a complement of public health surveillance systems.
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Affiliation(s)
- Tatiana Prado
- Laboratory of Comparative and Environmental Virology, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Av. Brasil, 4365, Manguinhos, Rio de Janeiro, CEP 21040-360, Brazil.
| | - Gloria Rey-Benito
- Pan American Health Organization (PAHO/WHO), 525 23rd St NW, Washington, DC 20037, United States of America.
| | - Marize Pereira Miagostovich
- Laboratory of Comparative and Environmental Virology, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Av. Brasil, 4365, Manguinhos, Rio de Janeiro, CEP 21040-360, Brazil
| | - Maria Inês Zanoli Sato
- Department of Environmental Analysis, Environmental Company of the São Paulo State (CETESB), Av. Prof. Frederico Hermann Jr., 345, São Paulo CEP 05459-900, Brazil
| | - Veronica Beatriz Rajal
- Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) and Facultad de Ingeniería, UNSa, Av. Bolivia 5150, Salta 4400, Argentina; Singapore Centre for Environmental Life Science Engineering (SCELSE), Nanyang Technological University, Singapore
| | - Cesar Rossas Mota Filho
- Department of Sanitary and Environmental Engineering, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Alyne Duarte Pereira
- Department of Sanitary and Environmental Engineering, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Mikaela Renata Funada Barbosa
- Department of Environmental Analysis, Environmental Company of the São Paulo State (CETESB), Av. Prof. Frederico Hermann Jr., 345, São Paulo CEP 05459-900, Brazil
| | - Camille Ferreira Mannarino
- Sergio Arouca National School of Public Health, Oswaldo Cruz Foundation, Av. Brasil, 4365, Manguinhos, Rio de Janeiro, CEP 21040-360, Brazil
| | - Agnes Soares da Silva
- Pan American Health Organization (PAHO/WHO), 525 23rd St NW, Washington, DC 20037, United States of America.
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Shukla AK, Seth T, Muhuri PK. Artificial intelligence centric scientific research on COVID-19: an analysis based on scientometrics data. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 82:1-33. [PMID: 37362722 PMCID: PMC9978294 DOI: 10.1007/s11042-023-14642-4] [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: 02/07/2022] [Revised: 07/01/2022] [Accepted: 02/03/2023] [Indexed: 06/28/2023]
Abstract
With the spread of the deadly coronavirus disease throughout the geographies of the globe, expertise from every field has been sought to fight the impact of the virus. The use of Artificial Intelligence (AI), especially, has been the center of attention due to its capability to produce trustworthy results in a reasonable time. As a result, AI centric based research on coronavirus (or COVID-19) has been receiving growing attention from different domains ranging from medicine, virology, and psychiatry etc. We present this comprehensive study that closely monitors the impact of the pandemic on global research activities related exclusively to AI. In this article, we produce highly informative insights pertaining to publications, such as the best articles, research areas, most productive and influential journals, authors, and institutions. Studies are made on top 50 most cited articles to identify the most influential AI subcategories. We also study the outcome of research from different geographic areas while identifying the research collaborations that have had an impact. This study also compares the outcome of research from the different countries around the globe and produces insights on the same.
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Affiliation(s)
- Amit K. Shukla
- Faculty of Information Technology, University of Jyväskylä, Box 35 (Agora), Jyväskylä, 40014 Finland
| | - Taniya Seth
- Department of Computer Science, South Asian University, Akbar Bhawan, Chanakyapuri, New Delhi 110021 India
| | - Pranab K. Muhuri
- Department of Computer Science, South Asian University, Akbar Bhawan, Chanakyapuri, New Delhi 110021 India
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Nazia N, Law J, Butt ZA. Modelling the spatiotemporal spread of COVID-19 outbreaks and prioritization of the risk areas in Toronto, Canada. Health Place 2023; 80:102988. [PMID: 36791508 PMCID: PMC9922578 DOI: 10.1016/j.healthplace.2023.102988] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 12/16/2022] [Accepted: 02/09/2023] [Indexed: 02/16/2023]
Abstract
Modelling the spatiotemporal spread of a highly transmissible disease is challenging. We developed a novel spatiotemporal spread model, and the neighbourhood-level data of COVID-19 in Toronto was fitted into the model to visualize the spread of the disease in the study area within two weeks of the onset of first outbreaks from index neighbourhood to its first-order neighbourhoods (called dispersed neighbourhoods). We also model the data to classify hotspots based on the overall incidence rate and persistence of the cases during the study period. The spatiotemporal spread model shows that the disease spread to 1-4 neighbourhoods bordering the index neighbourhood within two weeks. Some dispersed neighbourhoods became index neighbourhoods and further spread the disease to their nearby neighbourhoods. Most of the sources of infection in the dispersed neighbourhood were households and communities (49%), and after excluding the healthcare institutions (40%), it becomes 82%, suggesting the expansion of transmission was from close contacts. The classification of hotspots informs high-priority areas concentrated in the northwestern and northeastern parts of Toronto. The spatiotemporal spread model along with the hotspot classification approach, could be useful for a deeper understanding of spatiotemporal dynamics of infectious diseases and planning for an effective mitigation strategy where local-level spatially enabled data are available.
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Affiliation(s)
- Nushrat Nazia
- School of Public Health Sciences, University of Waterloo, 200 University Ave W., Waterloo, ON, N2L3G1, Canada.
| | - Jane Law
- School of Public Health Sciences, University of Waterloo, 200 University Ave W., Waterloo, ON, N2L3G1, Canada; School of Planning, University of Waterloo, 200 University Ave W., Waterloo, ON, N2L3G1, Canada.
| | - Zahid Ahmad Butt
- School of Public Health Sciences, University of Waterloo, 200 University Ave W., Waterloo, ON, N2L3G1, Canada.
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Dahu BM, Alaboud K, Nowbuth AA, Puckett HM, Scott GJ, Sheets LR. The Role of Remote Sensing and Geospatial Analysis for Understanding COVID-19 Population Severity: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4298. [PMID: 36901308 PMCID: PMC10002247 DOI: 10.3390/ijerph20054298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 01/30/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
Remote sensing (RS), satellite imaging (SI), and geospatial analysis have established themselves as extremely useful and very diverse domains for research associated with space, spatio-temporal components, and geography. We evaluated in this review the existing evidence on the application of those geospatial techniques, tools, and methods in the coronavirus pandemic. We reviewed and retrieved nine research studies that directly used geospatial techniques, remote sensing, or satellite imaging as part of their research analysis. Articles included studies from Europe, Somalia, the USA, Indonesia, Iran, Ecuador, China, and India. Two papers used only satellite imaging data, three papers used remote sensing, three papers used a combination of both satellite imaging and remote sensing. One paper mentioned the use of spatiotemporal data. Many studies used reports from healthcare facilities and geospatial agencies to collect the type of data. The aim of this review was to show the use of remote sensing, satellite imaging, and geospatial data in defining features and relationships that are related to the spread and mortality rate of COVID-19 around the world. This review should ensure that these innovations and technologies are instantly available to assist decision-making and robust scientific research that will improve the population health diseases outcomes around the globe.
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Affiliation(s)
- Butros M. Dahu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Department of Health Management and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Khuder Alaboud
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- NextGen Biomedical Informatics Center, University of Missouri, Columbia, MO 65211, USA
| | - Avis Anya Nowbuth
- Pan African Organization for Health Education and Research (POHER), Manchester, MO 63011, USA
| | - Hunter M. Puckett
- Department of Health Management and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Grant J. Scott
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Lincoln R. Sheets
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Department of Health Management and Informatics, University of Missouri, Columbia, MO 65211, USA
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Choiruddin A, Hannanu FF, Mateu J, Fitriyanah V. COVID-19 transmission risk in Surabaya and Sidoarjo: an inhomogeneous marked Poisson point process approach. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2023; 37:2271-2282. [PMID: 36815869 PMCID: PMC9919753 DOI: 10.1007/s00477-023-02393-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/30/2023] [Indexed: 05/15/2023]
Abstract
Understanding the spatio-temporal dynamics of COVID-19 transmission is necessary to plan better strategies for controlling the spread of the disease. However, only a few studies explore the COVID-19 transmission risk over a fine spatial resolution while considering relevant spatial and temporal factors. To this aim, we consider an inhomogeneous marked Poisson point process model to assess COVID-19 transmission risk using data of home addresses of confirmed cases, in relation to locations of sources of crowd (enterprise, market, and place of worship) and population density in Surabaya and Sidoarjo, Indonesia. Our marked model is able to analyze how the spatial covariates are varying with time, helping authorities to evaluate the information of covariates depending on the period in which restrictions are taking place. Our results show that enterprise, place of worship, and population densities have significant impact to the transmission risk in Surabaya and Sidoarjo. We finally provide predicted risk maps which provide additional information based on the demographic-based risk analysis to help conduct more efficient testing, tracing, and vaccination programs.
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Affiliation(s)
- Achmad Choiruddin
- Department of Statistics, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, 60111 Indonesia
| | | | - Jorge Mateu
- Department of Mathematics, Universitat Jaume I, Castellon, 12071 Spain
| | - Vanda Fitriyanah
- Department of Statistics, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, 60111 Indonesia
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Ruiz‐Pérez M, Moragues A, Seguí‐Pons JM, Muncunill J, Pou Goyanes A, Colom Fernández A. Geographical Distribution and Social Justice of the COVID-19 Pandemic: The Case of Palma (Balearic Islands). GEOHEALTH 2023; 7:e2022GH000733. [PMID: 36819934 PMCID: PMC9930193 DOI: 10.1029/2022gh000733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/17/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
The spatial distribution of the COVID-19 infection rate in the city of Palma (Balearic Islands) is analyzed from the geolocation of positive cases by census tract and its relationship with socioeconomic variables is evaluated. Data on infections have been provided by the Health Service of the Ministry of Health and Consumption of the Government of the Balearic Islands. The study combines several methods of analysis: spatial autocorrelation, calculation of the Gini index and least squares regression, and weighted geographical regression. The results show that the pandemic comprised five waves in the March 2020-March 2022 period, corresponding to the months of April 2020, August 2020, December 2020, July 2021, and January 2022. Each wave shows a particular geographical distribution pattern, however, the second and third waves show higher levels of spatial concentration. In this sense, the second wave, affecting the peripheral neighborhoods of the eastern part of the city. The Gini index confirms geographical imbalances in the distribution of infections in the first waves of the pandemic. In addition, the regression models indicate that the most significant socioeconomic variables in the prediction of COVID-19 infection are average income, percentage of children under 18 years of age, average size of the household, and percentage of single-person households. The study shows that economic imbalances in the city have had a clear influence on the spatial pattern of pandemic distribution. It shows the need to implement spatial justice policies in income distribution to balance the effects of the pandemic.
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Affiliation(s)
- Maurici Ruiz‐Pérez
- Servei de SIG i TeledeteccióUniversitat de les Illes BalearsPalmaSpain
- Institut d’Investigació Sanitària de les Illes BallearsPalmaSpain
- Departament de GeografiaUniversitat de les Illes BalearsPalmaSpain
| | | | | | - Josep Muncunill
- Institut d’Investigació Sanitària de les Illes BallearsPalmaSpain
| | | | - Antoni Colom Fernández
- Institut d’Investigació Sanitària de les Illes BallearsPalmaSpain
- Departament de GeografiaUniversitat de les Illes BalearsPalmaSpain
- EpiPHAAN Research GroupSchool of Health SciencesUniversity of MálagaInstituto de Investigación Biomédica en Málaga (IBIMA)MálagaSpain
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Space-time cluster detection techniques for infectious diseases: A systematic review. Spat Spatiotemporal Epidemiol 2023; 44:100563. [PMID: 36707196 DOI: 10.1016/j.sste.2022.100563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 12/08/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Public health organizations have increasingly harnessed geospatial technologies for disease surveillance, health services allocation, and targeting place-based health promotion initiatives. METHODS We conducted a systematic review around the theme of space-time clustering detection techniques for infectious diseases using PubMed, Web of Science, and Scopus. Two reviewers independently determined inclusion and exclusion. RESULTS Of 2,887 articles identified, 354 studies met inclusion criteria, the majority of which were application papers. Studies of airborne diseases were dominant, followed by vector-borne diseases. Most research used aggregated data instead of point data, and a significant proportion of articles used a repetition of a spatial clustering method, instead of using a "true" space-time detection approach, potentially leading to the detection of false positives. Noticeably, most articles did not make their data available, limiting replicability. CONCLUSION This review underlines recent trends in the application of space-time clustering methods to the field of infectious disease, with a rapid increase during the COVID-19 pandemic.
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Han J, Yin J, Wu X, Wang D, Li C. Environment and COVID-19 incidence: A critical review. J Environ Sci (China) 2023; 124:933-951. [PMID: 36182196 PMCID: PMC8858699 DOI: 10.1016/j.jes.2022.02.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 01/27/2022] [Accepted: 02/10/2022] [Indexed: 05/19/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic is an unprecedented worldwide health crisis. Many previous research studies have found and investigated its links with one or some natural or human environmental factors. However, a review on the relationship between COVID-19 incidence and both the natural and human environment is still lacking. This review summarizes the inter-correlation between COVID-19 incidence and environmental factors. Based on keyword searching, we reviewed 100 relevant peer-reviewed articles and other research literature published since January 2020. This review is focused on three main findings. One, we found that individual environmental factors have impacts on COVID-19 incidence, but with spatial heterogeneity and uncertainty. Two, environmental factors exert interactive effects on COVID-19 incidence. In particular, the interactions of natural factors can affect COVID-19 transmission in micro- and macro- ways by impacting SARS-CoV-2 survival, as well as human mobility and behaviors. Three, the impact of COVID-19 incidence on the environment lies in the fact that COVID-19-induced lockdowns caused air quality improvement, wildlife shifts and socio-economic depression. The additional value of this review is that we recommend future research perspectives and adaptation strategies regarding the interactions of the environment and COVID-19. Future research should be extended to cover both the effects of the environment on the COVID-19 pandemic and COVID-19-induced impacts on the environment. Future adaptation strategies should focus on sustainable environmental and public policy responses.
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Affiliation(s)
- Jiatong Han
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
| | - Jie Yin
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
| | - Xiaoxu Wu
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China.
| | - Danyang Wang
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
| | - Chenlu Li
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
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Tepe E. The impact of built and socio-economic environment factors on Covid-19 transmission at the ZIP-code level in Florida. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 326:116806. [PMID: 36410149 PMCID: PMC9663736 DOI: 10.1016/j.jenvman.2022.116806] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 10/21/2022] [Accepted: 11/14/2022] [Indexed: 05/12/2023]
Abstract
Most studies have explored the Covid-19 outbreak by mainly focusing on restrictive public policies, human health, and behaviors at the macro level. However, the impacts of built and socio-economic environments, accounting for spatial effects on the spread at the local levels, have not been thoroughly studied. In this study, the relationships between the spatial spread of the virus and various indicators of the built and socio-economic environments are investigated, using Florida ZIP-code data on accumulated cases before large-scale vaccination campaigns began in 2021. Spatial regression models are used to account for the spatial dependencies and interactions that are core factors in Covid-19 spread. This study reveals both the spillover dynamics of the coronavirus spread at the ZIP code level and the existence of spatial dependencies among the unobserved variables represented by the error term. In addition, the findings show a positive association between the expected number of Covid-19 cases and specific land uses, such as education facilities and retail densities. Finally, the study highlights critical socio-economic characteristics causing a substantial increase in Covid-19 spread. Such results could help policymakers, public health experts, and urban planners design strategies to mitigate the spread of future Covid-19-like diseases.
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Affiliation(s)
- Emre Tepe
- Department of Urban and Regional Planning, University of Florida, 444 Architectural Building P.O. Box 115706, Gainesville, FL, 32611, USA.
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Schmitz T, Lakes T, Manafa G, Lambio C, Butler J, Roth A, Savaskan N. Exploration of the COVID-19 pandemic at the neighborhood level in an intra-urban setting. Front Public Health 2023; 11:1128452. [PMID: 37124802 PMCID: PMC10133460 DOI: 10.3389/fpubh.2023.1128452] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 03/24/2023] [Indexed: 05/02/2023] Open
Abstract
The COVID-19 pandemic represents a worldwide threat to health. Since its onset in 2019, the pandemic has proceeded in different phases, which have been shaped by a complex set of influencing factors, including public health and social measures, the emergence of new virus variants, and seasonality. Understanding the development of COVID-19 incidence and its spatiotemporal patterns at a neighborhood level is crucial for local health authorities to identify high-risk areas and develop tailored mitigation strategies. However, analyses at the neighborhood level are scarce and mostly limited to specific phases of the pandemic. The aim of this study was to explore the development of COVID-19 incidence and spatiotemporal patterns of incidence at a neighborhood scale in an intra-urban setting over several pandemic phases (March 2020-December 2021). We used reported COVID-19 case data from the health department of the district Berlin-Neukölln, Germany, additional socio-demographic data, and text documents and materials on implemented public health and social measures. We examined incidence over time in the context of the measures and other influencing factors, with a particular focus on age groups. We used incidence maps and spatial scan statistics to reveal changing spatiotemporal patterns. Our results show that several factors may have influenced the development of COVID-19 incidence. In particular, the far-reaching measures for contact reduction showed a substantial impact on incidence in Neukölln. We observed several age group-specific effects: school closures had an effect on incidence in the younger population (< 18 years), whereas the start of the vaccination campaign had an impact primarily on incidence among the elderly (> 65 years). The spatial analysis revealed that high-risk areas were heterogeneously distributed across the district. The location of high-risk areas also changed across the pandemic phases. In this study, existing intra-urban studies were supplemented by our investigation of the course of the pandemic and the underlying processes at a small scale over a long period of time. Our findings provide new insights for public health authorities, community planners, and policymakers about the spatiotemporal development of the COVID-19 pandemic at the neighborhood level. These insights are crucial for guiding decision-makers in implementing mitigation strategies.
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Affiliation(s)
- Tillman Schmitz
- Applied Geoinformation Science, Geography Department, Humboldt University Berlin, Berlin, Germany
- *Correspondence: Tillman Schmitz,
| | - Tobia Lakes
- Applied Geoinformation Science, Geography Department, Humboldt University Berlin, Berlin, Germany
- Integrative Research Institute on Transformations of Human Environment Systems (IRI THESys), Berlin, Germany
| | - Georgianna Manafa
- Applied Geoinformation Science, Geography Department, Humboldt University Berlin, Berlin, Germany
| | - Christoph Lambio
- Applied Geoinformation Science, Geography Department, Humboldt University Berlin, Berlin, Germany
| | - Jeffrey Butler
- Applied Geoinformation Science, Geography Department, Humboldt University Berlin, Berlin, Germany
| | - Alexandra Roth
- Department of Public Health Neukölln, District Office Neukölln, Berlin, Germany
| | - Nicolai Savaskan
- Department of Public Health Neukölln, District Office Neukölln, Berlin, Germany
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Mwiinde AM, Siankwilimba E, Sakala M, Banda F, Michelo C. Climatic and Environmental Factors Influencing COVID-19 Transmission-An African Perspective. Trop Med Infect Dis 2022; 7:433. [PMID: 36548688 PMCID: PMC9785776 DOI: 10.3390/tropicalmed7120433] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/01/2022] [Accepted: 12/03/2022] [Indexed: 12/14/2022] Open
Abstract
Since the outbreak of COVID-19 was decreed by the World Health Organization as a public health emergency of worldwide concern, the epidemic has drawn attention from all around the world. The disease has since spread globally in developed and developing countries. The African continent has not been spared from the pandemic; however, the low number of cases in Africa compared to developed countries has brought about more questions than answers. Africa is known to have a poor healthcare system that cannot sustain the emerging infectious disease pandemic. This study explored climatic and environmental elements influencing COVID-19 transmission in Africa. This study involved manuscripts and data that evaluated and investigated the climatic and environmental elements of COVID-19 in African countries. Only articles written in English were considered in the systematic review. Seventeen articles and one database were selected for manuscript write-ups after the review process. The findings indicated that there is evidence that suggests the influence of climatic and environmental elements on the spread of COVID-19 in the continent of Africa; however, the evidence needs more investigation in all six regions of Africa and at the country level to understand the role of weather patterns and environmental aspects in the transmission of COVID-19.
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Affiliation(s)
- Allan Mayaba Mwiinde
- Graduate School of Public Health, Department of Epidemiology Ridgeway Campus, University of Zambia, Lusaka P.O. Box 50516, Zambia
- Department of Public Health, Mazabuka Municipal Council, Mazabuka P.O. Box 670022, Zambia
| | - Enock Siankwilimba
- Graduate School of Business, University of Zambia, Lusaka P.O. Box 50516, Zambia
| | - Masauso Sakala
- School of Engineering, Department of Geomatic Engineering, University of Zambia, Lusaka P.O. Box 50516, Zambia
| | - Faustin Banda
- School of Engineering, Department of Geomatic Engineering, University of Zambia, Lusaka P.O. Box 50516, Zambia
- The National Remote Sensing Centre, Plot Number 15302 Airport Road, Lusaka P.O. Box 310303, Zambia
| | - Charles Michelo
- Department of Public Health, Mazabuka Municipal Council, Mazabuka P.O. Box 670022, Zambia
- Harvest Research Institute, Lusaka P.O. Box 51176, Zambia
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Takasaki Y, Abizaid C, Coomes OT. COVID-19 contagion across remote communities in tropical forests. Sci Rep 2022; 12:20727. [PMID: 36456613 PMCID: PMC9713114 DOI: 10.1038/s41598-022-25238-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 11/28/2022] [Indexed: 12/03/2022] Open
Abstract
Understanding COVID-19 contagion among poor populations is hampered by a paucity of data, and especially so in remote rural communities with limited access to transportation, communication, and health services. We report on the first study on COVID-19 contagion across rural communities without road access. We conducted telephone surveys with over 400 riverine communities in the Peruvian Amazon in the early phase of the pandemic. During the first wave (April-June, 2020), COVID-19 spread from cities to most communities through public and private river transportation according to their remoteness. The initial spread was delayed by transportation restrictions but at the same time was driven in unintended ways by government social assistance. During the second wave (August, 2020), although people's self-protective behaviors (promoted through communication access) helped to suppress the contagion, people responded to transportation restrictions and social assistance in distinct ways, leading to greater contagion among Indigenous communities than mestizo communities. As such, the spatial contagion during the early phase of the pandemic in tropical forests was shaped by river transportation and social behaviors. These novel findings have important implications for research and policies on pandemics in rural areas.
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Affiliation(s)
- Yoshito Takasaki
- Graduate School of Economics, University of Tokyo, Tokyo, Japan.
| | - Christian Abizaid
- Department of Geography and Planning and School of the Environment, University of Toronto, Toronto, ON, Canada
| | - Oliver T Coomes
- Department of Geography, McGill University, Montreal, QC, Canada
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Yang TC, Matthews SA, Sun F. Multiscale Dimensions of Spatial Process: COVID-19 Fully Vaccinated Rates in U.S. Counties. Am J Prev Med 2022; 63:954-961. [PMID: 35963747 PMCID: PMC9259504 DOI: 10.1016/j.amepre.2022.06.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/18/2022] [Accepted: 06/09/2022] [Indexed: 11/15/2022]
Abstract
INTRODUCTION This study aimed to examine the heterogeneity of the associations between social determinants and COVID-19 fully vaccinated rate. METHODS This study proposes 3 multiscale dimensions of spatial process, including level of influence (the percentage of population affected by a certain determinant across the entire area), scalability (the spatial process of a determinant into global, regional, and local process), and specificity (the determinant that has the strongest association with the fully vaccinated rate). The multiscale geographically weighted regression was applied to the COVID-19 fully vaccinated rates in U.S. counties (N=3,106) as of October 26, 2021, and the analyses were conducted in May 2022. RESULTS The results suggest the following: (1) Percentage of Republican votes in the 2020 presidential election is a primary influencer because 84% of the U.S. population lived in counties where this determinant is found the most dominant; (2) Demographic compositions (e.g., percentages of racial/ethnic minorities) play a larger role than socioeconomic conditions (e.g., unemployment) in shaping fully vaccinated rates; (3) The spatial process underlying fully vaccinated rates is largely local. CONCLUSIONS The findings challenge the 1-size-fits-all approach to designing interventions promoting COVID-19 vaccination and highlight the importance of a place-based perspective in ecological health research.
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Affiliation(s)
- Tse-Chuan Yang
- Department of Epidemiology, School of Public and Population Health, The University of Texas Medical Branch, Galveston, Texas.
| | - Stephen A Matthews
- Department of Sociology and Criminology, Pennsylvania State University, University Park, Pennsylvania; Department of Anthropology, Pennsylvania State University, University Park, Pennsylvania
| | - Feinuo Sun
- Global Aging & Community Initiative, Mount Saint Vincent University, Halifax, Nova Scotia, Canada
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Hatami F, Chen S, Paul R, Thill JC. Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192315771. [PMID: 36497846 PMCID: PMC9736132 DOI: 10.3390/ijerph192315771] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/12/2022] [Accepted: 11/24/2022] [Indexed: 05/09/2023]
Abstract
The global COVID-19 pandemic has taken a heavy toll on health, social, and economic costs since the end of 2019. Predicting the spread of a pandemic is essential to developing effective intervention policies. Since the beginning of this pandemic, many models have been developed to predict its pathways. However, the majority of these models assume homogeneous dynamics over the geographic space, while the pandemic exhibits substantial spatial heterogeneity. In addition, spatial interaction among territorial entities and variations in their magnitude impact the pandemic dynamics. In this study, we used a spatial extension of the SEIR-type epidemiological model to simulate and predict the 4-week number of COVID-19 cases in the Charlotte-Concord-Gastonia Metropolitan Statistical Area (MSA), USA. We incorporated a variety of covariates, including mobility, pharmaceutical, and non-pharmaceutical interventions, demographics, and weather data to improve the model's predictive performance. We predicted the number of COVID-19 cases for up to four weeks in the 10 counties of the studied MSA simultaneously over the time period 29 March 2020 to 13 March 2021, and compared the results with the reported number of cases using the root-mean-squared error (RMSE) metric. Our results highlight the importance of spatial heterogeneity and spatial interactions among locations in COVID-19 pandemic modeling.
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Affiliation(s)
- Faizeh Hatami
- Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Shi Chen
- Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Rajib Paul
- Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Jean-Claude Thill
- Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- Correspondence:
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Cui P, Zou P, Ju X, Liu Y, Su Y. Research Progress and Improvement Ideas of Anti-Epidemic Resilience in China's Urban Communities. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15293. [PMID: 36430012 PMCID: PMC9690367 DOI: 10.3390/ijerph192215293] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 11/14/2022] [Accepted: 11/18/2022] [Indexed: 06/16/2023]
Abstract
In the post-epidemic era, China's urban communities are at the forefront of implementing the whole chain of accurate epidemic prevention and control. However, the uncertainty of COVID-19, the loopholes in community management and people's overly optimistic judgment of the epidemic have led to the frequent rebound of the epidemic and serious consequences. Existing studies have not yet formed a panoramic framework of community anti-epidemic work under the concept of resilience. Therefore, this article first summarizes the current research progress of resilient communities from three perspectives, including ideas and perspectives, theories and frameworks and methods and means, and summarizes the gap of the current research. Then, an innovative idea on the epidemic resilience of urban communities in China is put forward: (1) the evolution mechanism of community anti-epidemic resilience is described through the change law of dynamic networks; (2) the anti-epidemic resilience of urban communities is evaluated or predicted through the measurement criteria; (3) a simulation platform based on Multi-Agent and dynamic Bayesian networks simulates the interactive relationship between "epidemic disturbance-cost constraint--epidemic resilience"; (4) the anti-epidemic strategies are output intelligently to provide community managers with decision-making opinions on community epidemic prevention and control.
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Affiliation(s)
- Peng Cui
- Department of Engineering Management, School of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
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Fuente D, Hervás D, Rebollo M, Conejero JA, Oliver N. COVID-19 outbreaks analysis in the Valencian Region of Spain in the prelude of the third wave. Front Public Health 2022; 10:1010124. [PMID: 36466513 PMCID: PMC9713945 DOI: 10.3389/fpubh.2022.1010124] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/02/2022] [Indexed: 11/19/2022] Open
Abstract
Introduction The COVID-19 pandemic has led to unprecedented social and mobility restrictions on a global scale. Since its start in the spring of 2020, numerous scientific papers have been published on the characteristics of the virus, and the healthcare, economic and social consequences of the pandemic. However, in-depth analyses of the evolution of single coronavirus outbreaks have been rarely reported. Methods In this paper, we analyze the main properties of all the tracked COVID-19 outbreaks in the Valencian Region between September and December of 2020. Our analysis includes the evaluation of the origin, dynamic evolution, duration, and spatial distribution of the outbreaks. Results We find that the duration of the outbreaks follows a power-law distribution: most outbreaks are controlled within 2 weeks of their onset, and only a few last more than 2 months. We do not identify any significant differences in the outbreak properties with respect to the geographical location across the entire region. Finally, we also determine the cluster size distribution of each infection origin through a Bayesian statistical model. Discussion We hope that our work will assist in optimizing and planning the resource assignment for future pandemic tracking efforts.
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Affiliation(s)
- David Fuente
- Instituto Universitario de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, València, Spain
| | - David Hervás
- Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, València, Spain
| | - Miguel Rebollo
- Valencia Research Institute on Artificial Intelligence, Universitat Politècnica de València, València, Spain
| | - J. Alberto Conejero
- Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, València, Spain
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Hasegawa N, Rusakevich A, Bernicker E, Teh BS, Schefler A. Comparison of Tumor Size and Gene Expression at Presentation in Uveal Melanoma Patients before and during the COVID-19 Pandemic. Ocul Oncol Pathol 2022; 8:156-160. [PMID: 36923229 PMCID: PMC9372456 DOI: 10.1159/000524918] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 04/22/2022] [Indexed: 12/04/2022] Open
Abstract
Introduction: The aim of this study was to compare the clinical and gene expression variables of uveal melanoma patients presenting before and after the start of the COVID-19 pandemic as surrogate markers in order to assess the pandemic's potential impact on care. Methods: We conducted a retrospective chart review of uveal melanoma patients at Retina Consultants of Texas and assessed tumor size, staging, and gene expression data during two time periods: May 2019 to February 2020 (Group 1: Before the COVID-19 pandemic declaration by the WHO in March 2020) and May 2020 to March 2021 (Group 2: After the start of the COVID-19 pandemic). Results: A total of 80 patients with uveal melanoma were studied (Group 1: 40 [50%] and Group 2: 40 [50%]). There was no statistically significant difference in the tumor thickness (p = 0.768), largest base dimension (p = 0.758), Collaborative Ocular Melanoma Study size class (p = 0.762), and American Joint Committee on Cancer stages (p = 0.872) between the two groups. Additionally, there was no difference in the tumors' gene expression data including gene expression profile class (p = 0.587) and PRAME expressivity (p = 0.861) between the two groups. Discussion/Conclusion: The COVID-19 pandemic had no effect on the presentation of uveal melanoma patients across all tumor characteristics including size, staging, and gene expression data, suggesting there was not a significant diagnostic delay in care for uveal melanoma patients at our center due to the pandemic.
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Affiliation(s)
- Naomi Hasegawa
- aDepartment of Ophthalmology, University of Texas at Houston, Houston, Texas, USA
| | | | - Eric Bernicker
- cHouston Methodist Cancer Center, Houston Methodist Hospital, Houston, Texas, USA
| | - Bin S Teh
- dDepartment of Radiation Oncology, Houston Methodist Hospital, Houston, Texas, USA
| | - Amy Schefler
- bRetina Consultants of Texas, Houston, Texas, USA
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Dudzińska M, Gwiaździńska-Goraj M, Jezierska-Thöle A. Social Factors as Major Determinants of Rural Development Variation for Predicting Epidemic Vulnerability: A Lesson for the Future. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13977. [PMID: 36360858 PMCID: PMC9656134 DOI: 10.3390/ijerph192113977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/20/2022] [Accepted: 10/21/2022] [Indexed: 06/16/2023]
Abstract
There have been changes in social attitudes in recent years. These changes have been a consequence of a new societal view of the common good, which manifests itself in social responsibility for a clean and healthy environment. The outbreak and spread of the COVID-19 epidemic has highlighted the socio-spatial variation across regions and countries. The epidemic necessitated restrictive measures by state authorities. In the initial period in many countries, the actions of the authorities were identical throughout the country. This was mainly due to a lack of information about the differentiation of areas in relation to the epidemic risk. The aim of the research was to present a model for classifying rural areas taking into account vulnerability to epidemic threats. The model takes into account demographic, social, economic and spatial-environmental development factors. A total of 33 indicators based on public statistics that can be used to determine the area's vulnerability to epidemic threats were identified. The study showed that for Poland, 11 indicators are statistically significant to the developed classification model. The study found that social factors were vital in determining an area's vulnerability to epidemic threats. We include factors such as average number of persons per one apartment, village centers (number), events (number), number of people per facility (cultural center, community center, club, community hall), residents of nursing homes per 1000 inhabitants, and the number of children in pre-school education establishments per 1000 children aged 3-5 years. The research area was rural areas in Poland. The results of the classification and the methods used should be made available as a resource for crisis management. This will enable a better response to threats from other epidemics in the future, and will influence the remodeling of the environment and social behavior to reduce risks at this risk, which has a significant impact on sustainable development in rural areas.
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Affiliation(s)
- Małgorzata Dudzińska
- Institute of Spatial Management and Geography, Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland
| | - Marta Gwiaździńska-Goraj
- Institute of Spatial Management and Geography, Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland
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