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Stocco CS, Zubiolo TFM, Beltrame MHA, Dell'Agnolo CM. Sexual violence against children and adolescents in Paraná State: geospatial analysis and main socioeconomic indicators. J Pediatr (Rio J) 2024; 100:498-504. [PMID: 38703786 DOI: 10.1016/j.jped.2024.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 03/10/2024] [Accepted: 03/12/2024] [Indexed: 05/06/2024] Open
Abstract
OBJECTIVE Child sexual violence is a multidimensional problem of many contemporary societies, affecting people of all sexes, social stratum and age groups. Offenses involving children and adolescents are more serious, given their total or partial dependence on parents and caregivers. Information on child sexual violence in Brazil is found in raw form and without detail. The objective was to compare the information with social and economic data in the state of Paraná. METHODS The authors conducted a retrospective study of secondary data from the Notifiable Diseases Information System (SINAN) on cases of sexual violence involving victims aged 0 to 19 years. Results are presented according to notification characteristics. The authors applied exploratory spatial data analysis to assess spatial autocorrelations and investigated relationships by the ordinary least squares regression model. RESULTS Between 2017 and 2021, there were 13,403 reports of child sexual violence in Paraná State, Brazil. Most victims (82.8%) were female and aged between 10 and 14 years. The majority of sexual violence cases (67.8%) occurred in the home environment. The highest rates on a population basis were observed in the North Central and Greater Curitiba regions, mainly in cities with higher population density and with higher rates of other types of violence. CONCLUSION The results provide data that can promote a broader understanding of the distribution of sexual violence and the state and associated variations. It is expected to improve the provision of care for victims of child sexual violence and assist in strategic planning to prevent future offenses.
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Affiliation(s)
| | | | - Matheus Henrique Arruda Beltrame
- Universidade Estadual de Maringá, Departamento de Medicina, Maringá, PR, Brazil; Universidade Estadual de Maringá, Grupo de Estudos em Tecnologias Digitais e Geoprocessamento em Saúde (GETS), Maringá, PR, Brazil
| | - Cátia Millene Dell'Agnolo
- Universidade Estadual de Maringá, Departamento de Medicina, Programa de Pós-Graduação em Gestão, Tecnologia e Inovação em Urgência e Emergência, Maringá, PR, Brazil
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Agarwal SK, Mishra S. Health impact evaluation of Aspirational Districts Program in India: Evidence from National Family Health Survey. ECONOMICS AND HUMAN BIOLOGY 2024; 54:101411. [PMID: 39018957 DOI: 10.1016/j.ehb.2024.101411] [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: 09/11/2023] [Revised: 06/22/2024] [Accepted: 07/01/2024] [Indexed: 07/19/2024]
Abstract
The Aspirational District Program (ADP) is a unique initiative of Government of India launched in 2018 that aims to reduce inter-district multidimensional inequality. ADP aims to bring the least developed districts to catch up with the rest of the other districts in the country. The program is comprehensive in its scope as it targets improvement of several key development indicators spanning health and nutrition, education, agriculture and water resources, financial inclusion and skill development and basic infrastructure indicators. Aspirational districts (ADs) are eligible for enhanced funding and priority allocation of various initiatives undertaken by the central and the state governments. Our research estimates the causal impact of ADP on the targeted health and nutrition indicators using a combination of propensity score matching and difference-in-differences (PSM-DID). We use the fourth and fifth rounds of National Family Health Survey (NFHS) data collected in 2015-16 and 2019-21 respectively which serve as the pre and post-treatment data for our analysis. Moreover, we take advantage of the transparent mechanism outlined for the identification of ADs under ADP, which we use for propensity score matching for our PSM-DID. While we observe negative impact of ADP on early initiation of breastfeeding, we believe that the impact is confounded with the effects of Covid-19 since part of NFHS-5 data was collected during the pandemic. However, the negative impact of ADP on early initiation of breastfeeding disappears when we only use pre-covid data (i.e. data for districts from states surveyed before the pandemic). Additionally, using pre-covid data we find a reduction in the prevalence of underweight children younger than 5 years to an extent of 2 to 4 percentage points in ADs as an impact of ADP, which is robust across multiple specification. We do not find evidence of a positive or a negative impact of ADP on any other health and nutrition indicators. Future research efforts should be made towards impact evaluation of all the targeted indicators in order to get a comprehensive unbiased evaluation of ADP.
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Affiliation(s)
- Sandip K Agarwal
- Department of Economic Sciences, Indian Institute of Science Education and Research Bhopal, India.
| | - Shubham Mishra
- Department of Economic Sciences, Indian Institute of Science Education and Research Bhopal, India
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Silva M, Viana CM, Betco I, Nogueira P, Roquette R, Rocha J. Spatiotemporal dynamics of epidemiology diseases: mobility based risk and short-term prediction modeling of COVID-19. Front Public Health 2024; 12:1359167. [PMID: 39022425 PMCID: PMC11251998 DOI: 10.3389/fpubh.2024.1359167] [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: 12/20/2023] [Accepted: 06/17/2024] [Indexed: 07/20/2024] Open
Abstract
Nowadays, epidemiological modeling is applied to a wide range of diseases, communicable and non-communicable, namely AIDS, Ebola, influenza, Dengue, Malaria, Zika. More recently, in the context of the last pandemic declared by the World Health Organization (WHO), several studies applied these models to SARS-CoV-2. Despite the increasing number of researches using spatial analysis, some constraints persist that prevent more complex modeling such as capturing local epidemiological dynamics or capturing the real patterns and dynamics. For example, the unavailability of: (i) epidemiological information such as the frequency with which it is made available; (ii) sociodemographic and environmental factors (e.g., population density and population mobility) at a finer scale which influence the evolution patterns of infectious diseases; or (iii) the number of cases information that is also very dependent on the degree of testing performed, often with severe territorial disparities and influenced by context factors. Moreover, the delay in case reporting and the lack of quality control in epidemiological information is responsible for biases in the data that lead to many results obtained being subject to the ecological fallacy, making it difficult to identify causal relationships. Other important methodological limitations are the control of spatiotemporal dependence, management of non-linearity, ergodicy, among others, which can impute inconsistencies to the results. In addition to these issues, social contact, is still difficult to quantify in order to be incorporated into modeling processes. This study aims to explore a modeling framework that can overcome some of these modeling methodological limitations to allow more accurate modeling of epidemiological diseases. Based on Geographic Information Systems (GIS) and spatial analysis, our model is developed to identify group of municipalities where population density (vulnerability) has a stronger relationship with incidence (hazard) and commuting movements (exposure). Specifically, our framework shows how to operate a model over data with no clear trend or seasonal pattern which is suitable for a short-term predicting (i.e., forecasting) of cases based on few determinants. Our tested models provide a good alternative for when explanatory data is few and the time component is not available, once they have shown a good fit and good short-term forecast ability.
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Affiliation(s)
- Melissa Silva
- Associated Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, Lisbon, Portugal
| | - Cláudia M. Viana
- Associated Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, Lisbon, Portugal
| | - Iuria Betco
- Associated Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, Lisbon, Portugal
| | - Paulo Nogueira
- Associated Laboratory TERRA, Nursing Research, Innovation and Development Centre of Lisbon (CIDNUR), Nursing School of Lisbon, Lisbon, Portugal
- Instituto de Saúde Ambiental (ISAMB), Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
- Escola Nacional de Saúde Pública, ENSP, Centro de Investigação em Saúde Pública, CISP, Comprehensive Health Research Center, CHRC, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Rita Roquette
- NOVA IMS Information Management School, NOVA University of Lisbon, Lisbon, Portugal
| | - Jorge Rocha
- Associated Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, Lisbon, Portugal
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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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Balasubramani K, Ravichandran V, Prasad KA, Ramkumar M, Shekhar S, James MM, Kodali NK, Behera SK, Gopalan N, Sharma RK, Sarma DK, Santosh M, Dash AP, Balabaskaran Nina P. Spatio-temporal epidemiology and associated indicators of COVID-19 (wave-I and II) in India. Sci Rep 2024; 14:220. [PMID: 38167962 PMCID: PMC10761923 DOI: 10.1038/s41598-023-50363-2] [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: 08/13/2022] [Accepted: 12/19/2023] [Indexed: 01/05/2024] Open
Abstract
The spatio-temporal distribution of COVID-19 across India's states and union territories is not uniform, and the reasons for the heterogeneous spread are unclear. Identifying the space-time trends and underlying indicators influencing COVID-19 epidemiology at micro-administrative units (districts) will help guide public health strategies. The district-wise daily COVID-19 data of cases and deaths from February 2020 to August 2021 (COVID-19 waves-I and II) for the entire country were downloaded and curated from public databases. The COVID-19 data normalized with the projected population (2020) and used for space-time trend analysis shows the states/districts in southern India are the worst hit. Coastal districts and districts adjoining large urban regions of Mumbai, Chennai, Bengaluru, Goa, and New Delhi experienced > 50,001 cases per million population. Negative binomial regression analysis with 21 independent variables (identified through multicollinearity analysis, with VIF < 10) covering demography, socio-economic status, environment, and health was carried out for wave-I, wave-II, and total (wave-I and wave-II) cases and deaths. It shows wealth index, derived from household amenities datasets, has a high positive risk ratio (RR) with COVID-19 cases (RR: 3.577; 95% CI: 2.062-6.205) and deaths (RR: 2.477; 95% CI: 1.361-4.506) across the districts. Furthermore, socio-economic factors such as literacy rate, health services, other workers' rate, alcohol use in men, tobacco use in women, overweight/obese women, and rainfall have a positive RR and are significantly associated with COVID-19 cases/deaths at the district level. These positively associated variables are highly interconnected in COVID-19 hotspot districts. Among these, the wealth index, literacy rate, and health services, the key indices of socio-economic development within a state, are some of the significant indicators associated with COVID-19 epidemiology in India. The identification of district-level space-time trends and indicators associated with COVID-19 would help policymakers devise strategies and guidelines during public health emergencies.
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Affiliation(s)
- Karuppusamy Balasubramani
- Department of Geography, School of Earth Sciences, Central University of Tamil Nadu, Thiruvarur, 610005, India
| | - Venkatesh Ravichandran
- Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, India
| | - Kumar Arun Prasad
- Department of Geography, School of Earth Sciences, Central University of Tamil Nadu, Thiruvarur, 610005, India
| | - Mu Ramkumar
- Department of Geology, Periyar University, Salem, India
| | - Sulochana Shekhar
- Department of Geography, School of Earth Sciences, Central University of Tamil Nadu, Thiruvarur, 610005, India
| | - Meenu Mariya James
- Department of Epidemiology and Public Health, School of Life Sciences, Central University of Tamil Nadu, Thiruvarur, 610005, India
| | - Naveen Kumar Kodali
- Department of Epidemiology and Public Health, School of Life Sciences, Central University of Tamil Nadu, Thiruvarur, 610005, India
| | - Sujit Kumar Behera
- Department of Epidemiology and Public Health, School of Life Sciences, Central University of Tamil Nadu, Thiruvarur, 610005, India
| | - Natarajan Gopalan
- Department of Epidemiology and Public Health, School of Life Sciences, Central University of Tamil Nadu, Thiruvarur, 610005, India
| | - Rakesh Kumar Sharma
- Shree Guru Gobind Singh Tricentenary University, Gurugram, New-Delhi-NCR, 122505, India
| | - Devojit Kumar Sarma
- ICMR- National Institute for Research in Environmental Health, Bhopal Bypass Road, Bhouri, Bhopal, Madhya Pradesh, India
| | - M Santosh
- School of Earth Sciences and Resources, China University of Geosciences, Beijing, People's Republic of China
- Department of Earth Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Aditya Prasad Dash
- Asian Institute of Public Health University, Phulnakhara, Cuttack, Odisha, 754001, India
| | - Praveen Balabaskaran Nina
- Department of Public Health and Community Medicine, Central University of Kerala, Kasaragod, Kerala, 671316, India.
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6
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Wang L, Hu Z, Zhou K, Kwan MP. Identifying spatial heterogeneity of COVID-19 transmission clusters and their built-environment features at the neighbourhood scale. Health Place 2023; 84:103130. [PMID: 37801805 DOI: 10.1016/j.healthplace.2023.103130] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 08/03/2023] [Accepted: 09/25/2023] [Indexed: 10/08/2023]
Abstract
The identification of high-risk areas for infectious disease transmission and its built-environment features are crucial for targeted surveillance and early prevention efforts. While previous research has explored the association between infectious disease incidence and urban built environment, the investigation of spatial heterogeneity of built-environment features in high-risk areas has been insufficient. This paper aims to address this gap by analysing the spatial heterogeneity of COVID-19 clusters in Shanghai at the neighbourhood scale and examining associated built-environment features. Using a spatiotemporal clustering algorithm, the study analysed 1395 reported cases in Shanghai from March 6 to March 17, 2022. Both global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) models were applied to examine the association between built-environment variables and the size of COVID-19 clusters. Our findings suggest that larger COVID-19 clusters emerging in the suburbs compared with the downtown and multiple built-environment features are significantly associated with this pattern. Specifically, neighbourhoods with a higher proportion of commercial, public service and industrial land, higher centrality of metro stations, and proximity to hospitals are positively associated with larger COVID-19 clusters, while neighbourhoods with higher land use mix and green/open spaces density are associated with smaller COVID-19 clusters. Moreover, we identified that metro stations with high centrality present the highest risk in the downtown, while commercial and public service places exhibit the highest risk in the suburbs. By highlighting the overlooked spatial heterogeneity of built-environment features for high-risk areas, this study aims to provide valuable guidance for public health departments in implementing place-based interventions to effectively prevent the spread of potential epidemics.
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Affiliation(s)
- Lan Wang
- College of Architecture and Urban Planning, Tongji University, China.
| | - Zhanzhan Hu
- College of Architecture and Urban Planning, Tongji University, China
| | - Kaichen Zhou
- College of Architecture and Urban Planning, Tongji University, China
| | - Mei-Po Kwan
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China
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7
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Bhadola P, Chaudhary V, Markandan K, Talreja RK, Aggarwal S, Nigam K, Tahir M, Kaushik A, Rustagi S, Khalid M. Analysing role of airborne particulate matter in abetting SARS-CoV-2 outbreak for scheming regional pandemic regulatory modalities. ENVIRONMENTAL RESEARCH 2023; 236:116646. [PMID: 37481054 DOI: 10.1016/j.envres.2023.116646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/04/2023] [Accepted: 07/11/2023] [Indexed: 07/24/2023]
Abstract
The mutating SARS-CoV-2 necessitates gauging the role of airborne particulate matter in the COVID-19 outbreak for designing area-specific regulation modalities based on the environmental state-of-affair. To scheme the protocols, the hotspots of air pollutants such as PM2.5, PM10, NH3, NO, NO2, SO2, and and environmental factors including relative humidity (RH), and temperature, along with COVID-19 cases and mortality from January 2020 till December 2020 from 29 different ground monitoring stations spanning Delhi, are mapped. Spearman correlation coefficients show a positive relationship between SARS-COV-2 with particulate matter (PM2.5 with r > 0.36 and PM10 with r > 0.31 and p-value <0·001). Besides, SARS-COV-2 transmission showed a substantial correlation with NH3 (r = 0.41), NO2 (r = 0.36), and NO (r = 0.35) with a p-value <0.001, which is highly indicative of their role in SARS-CoV-2 transmission. These outcomes are associated with the source of PM and its constituent trace elements to understand their overtone with COVID-19. This strongly validates temporal and spatial variation in COVID-19 dependence on air pollutants as well as on environmental factors. Besides, the bottlenecks of missing latent data, monotonous dependence of variables, and the role air pollutants with secondary environmental variables are discussed. The analysis set the foundation for strategizing regional-based modalities considering environmental variables (i.e., pollutant concentration, relative humidity, temperature) as well as urban and transportation planning for efficient control and handling of future public health emergencies.
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Affiliation(s)
- Pradeep Bhadola
- Centre for Theoretical Physics & Natural Philosophy, Mahidol University, Nakhonsawan 60130, Thailand
| | - Vishal Chaudhary
- Department of Physics, Bhagini Nivedita College, University of Delhi, Delhi 110072, India.
| | - Kalaimani Markandan
- Department of Chemical & Petroleum Engineering, Faculty of Engineering, Technology and Built Environment, UCSI University, Cheras 56000, Kuala Lumpur, Malaysia
| | - Rishi Kumar Talreja
- Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi 110029, India
| | - Sumit Aggarwal
- Division of Epidemiology and Communicable Diseases (ECD), Indian Council of Medical Research (ICMR)-Headquaters, New Delhi 110029, India
| | - Kuldeep Nigam
- Division of Epidemiology and Communicable Diseases (ECD), Indian Council of Medical Research (ICMR)-Headquaters, New Delhi 110029, India
| | - Mohammad Tahir
- Department of Computing, University of Turku, FI-20014, Turun Yliopisto, Finland
| | - Ajeet Kaushik
- NanoBio Tech Laboratory, Department of Environmental Engineering, Florida Polytechnic University, Lakeland, FL, 33805, USA; School of Engineering, University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand, India
| | - Sarvesh Rustagi
- School of Applied and Life Sciences, Uttaranchal University, Dehradun, Uttrakhand, India
| | - Mohammad Khalid
- Sunway Centre for Electrochemical Energy and Sustainable Technology (SCEEST), School of Engineering and Technology, Sunway University, No. 5, Jalan University, Bandar Sunway, 47500, Petaling Jaya, Selangor, Malaysia; Division of Research and Development, Lovely Professional University, Phagwara, 144411, Punjab, India; School of Engineering and Technology, Sharda University, Greater Noida, 201310, India.
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8
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Li R, Huang Y. COVID-19 pandemic and minority health disparities in New York City: A spatial and temporal perspective. ENVIRONMENT AND PLANNING. B, URBAN ANALYTICS AND CITY SCIENCE 2023; 50:1194-1211. [PMID: 38603324 PMCID: PMC9475371 DOI: 10.1177/23998083221126525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
New York City (NYC) was the epicenter of COVID-19 pandemic for a long time, and the government introduced a city-wide lockdown policy to mitigate the spread of virus. Minority communities, however, suffered disproportionally high percentage of infection and mortality rates, a disturbing phenomenon that deserves scrutiny. Adopting a spatial and temporal perspective, this study aims to investigate health disparities in this pandemic by focusing on mobility in the city. Considering both public transit and the lockdown policy essential factors that impact infection and mortality, this study introduced a measure indicating mobility-restricted transit as the spatial factor. Additional factors include ethnic minorities based on their nativity and three categories of social vulnerability: socioeconomic status, household composition, and housing type. This study selects eight phases, each of which consists of 2 weeks to derive infection and mortality rates to investigate the impacts of those factors. As infection and mortality data are published based on ZIP code, this study further estimates the infection and mortality rates at a finer level of census tract through spatial apportionment. Results reveal the significant impact of mobility-restricted transit on both infection and mortality and show certain clusters of neighborhoods being highly impacted. In addition, this study identifies neighborhoods where native-born and foreign-born of each ethnic minority (Blacks, Hispanics, and Asians) have high risk of infection and mortality. Through a spatial and temporal perspectives, this study identifies the complexity of patterns in minority health disparities in COVID-19 pandemic, which can inform policy makers for localized support to vulnerable neighborhoods to alleviate minority health disparities.
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Affiliation(s)
- Rui Li
- University at Albany, Albany, USA
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Bhaskar A, Chandra J, Hashemi H, Butler K, Bennett L, Cellini J, Braun D, Dominici F. A Literature Review of the Effects of Air Pollution on COVID-19 Health Outcomes Worldwide: Statistical Challenges and Data Visualization. Annu Rev Public Health 2023; 44:1-20. [PMID: 36542771 DOI: 10.1146/annurev-publhealth-071521-120424] [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] [Indexed: 12/24/2022]
Abstract
Several peer-reviewed papers and reviews have examined the relationship between exposure to air pollution and COVID-19 spread and severity. However, many of the existing reviews on this topic do not extensively present the statistical challenges associated with this field, do not provide comprehensive guidelines for future researchers, and review only the results of a relatively small number of papers. We reviewed 139 papers, 127 of which reported a statistically significant positive association between air pollution and adverse COVID-19 health outcomes. Here, we summarize the evidence, describe the statistical challenges, and make recommendations for future research. To summarize the 139 papers with data from geographical locations around the world, we also present anopen-source data visualization tool that summarizes these studies and allows the research community to contribute evidence as new research papers are published.
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Affiliation(s)
- A Bhaskar
- Department of Government, Harvard University, Cambridge, Massachusetts, USA
| | - J Chandra
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - H Hashemi
- Environmental Systems Research Institute, Redlands, California, USA
| | - K Butler
- Environmental Systems Research Institute, Redlands, California, USA
| | - L Bennett
- Environmental Systems Research Institute, Redlands, California, USA
| | - Jacqueline Cellini
- Countway Library of Medicine, Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - Danielle Braun
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA;
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Francesca Dominici
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA;
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Coker ES, Molitor J, Liverani S, Martin J, Maranzano P, Pontarollo N, Vergalli S. Bayesian profile regression to study the ecologic associations of correlated environmental exposures with excess mortality risk during the first year of the Covid-19 epidemic in lombardy, Italy. ENVIRONMENTAL RESEARCH 2023; 216:114484. [PMID: 36220446 PMCID: PMC9547389 DOI: 10.1016/j.envres.2022.114484] [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: 05/16/2022] [Revised: 09/23/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Many countries, including Italy, have experienced significant social and spatial inequalities in mortality during the Covid-19 pandemic. This study applies a multiple exposures framework to investigate how joint place-based factors influence spatial inequalities of excess mortality during the first year of the Covid -19 pandemic in the Lombardy region of Italy. For the Lombardy region, we integrated municipality-level data on all-cause mortality between 2015 and 2020 with 13 spatial covariates, including 5-year average concentrations of six air pollutants, the average temperature in 2020, and multiple socio-demographic factors, and health facilities per capita. Using the clustering algorithm Bayesian profile regression, we fit spatial covariates jointly to identify clusters of municipalities with similar exposure profiles and estimated associations between clusters and excess mortality in 2020. Cluster analysis resulted in 13 clusters. Controlling for spatial autocorrelation of excess mortality and health-protective agency, two clusters had significantly elevated excess mortality than the rest of Lombardy. Municipalities in these highest-risk clusters are in Bergamo, Brescia, and Cremona provinces. The highest risk cluster (C11) had the highest long-term particulate matter air pollution levels (PM2.5 and PM10) and significantly elevated NO2 and CO air pollutants, temperature, proportion ≤18 years, and male-to-female ratio. This cluster is significantly lower for income and ≥65 years. The other high-risk cluster, Cluster 10 (C10), is elevated significantly for ozone but significantly lower for other air pollutants. Covariates with elevated levels for C10 include proportion 65 years or older and a male-to-female ratio. Cluster 10 is significantly lower for income, temperature, per capita health facilities, ≤18 years, and population density. Our results suggest that joint built, natural, and socio-demographic factors influenced spatial inequalities of excess mortality in Lombardy in 2020. Studies must apply a multiple exposures framework to guide policy decisions addressing the complex and multi-dimensional nature of spatial inequalities of Covid-19-related mortality.
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Affiliation(s)
- Eric S Coker
- Department of Environmental and Global Health, University of Florida, 1225 Center Dr, Gainesville, FL, 32610, United States.
| | - John Molitor
- College of Public Health and Human Sciences, Oregon State University, Milam Hall 157, 2520 SW Campus Way, Corvallis, OR, 97331, United States.
| | - Silvia Liverani
- School of Mathematical Sciences, Queen Mary University of London, Mile End Road London E1 4NS, United Kingdom.
| | - James Martin
- Department of Environmental and Global Health, University of Florida, 1225 Center Dr, Gainesville, FL, 32610, United States
| | - Paolo Maranzano
- Department of Economics, Management and Statistics of the University of Milano-Bicocca (UniMiB), Piazza Dell'Ateneo Nuovo, 1 - 20126, Milano, Italy.
| | - Nicola Pontarollo
- Department of Economics and Management, Università Degli Studi di Brescia, Brescia, Via S. Faustino 74/B, 25122, Brescia, Italy.
| | - Sergio Vergalli
- Department of Agricultural Economics, Università Cattolica Del Sacro Cuore, Piacenza, Via Emilia Parmense, 29122, Piacenza PC, Italy.
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11
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McGowan VJ, Bambra C. COVID-19 mortality and deprivation: pandemic, syndemic, and endemic health inequalities. Lancet Public Health 2022; 7:e966-e975. [PMID: 36334610 PMCID: PMC9629845 DOI: 10.1016/s2468-2667(22)00223-7] [Citation(s) in RCA: 83] [Impact Index Per Article: 41.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 08/23/2022] [Accepted: 08/23/2022] [Indexed: 11/06/2022]
Abstract
COVID-19 has exacerbated endemic health inequalities resulting in a syndemic pandemic of higher mortality and morbidity rates among the most socially disadvantaged. We did a scoping review to identify and synthesise published evidence on geographical inequalities in COVID-19 mortality rates globally. We included peer-reviewed studies, from any country, written in English that showed any area-level (eg, neighbourhood, town, city, municipality, or region) inequalities in mortality by socioeconomic deprivation (ie, measured via indices of multiple deprivation: the percentage of people living in poverty or proxy factors including the Gini coefficient, employment rates, or housing tenure). 95 papers from five WHO global regions were included in the final synthesis. A large majority of the studies (n=86) found that COVID-19 mortality rates were higher in areas of socioeconomic disadvantage than in affluent areas. The subsequent discussion reflects on how the unequal nature of the pandemic has resulted from a syndemic of COVID-19 and endemic inequalities in chronic disease burden.
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Affiliation(s)
- Victoria J McGowan
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK; Fuse-The Centre for Translational Research in Public Health, Newcastle Upon Tyne, UK
| | - Clare Bambra
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK; Fuse-The Centre for Translational Research in Public Health, Newcastle Upon Tyne, UK.
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12
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Nazia N, Law J, Butt ZA. Spatiotemporal clusters and the socioeconomic determinants of COVID-19 in Toronto neighbourhoods, Canada. Spat Spatiotemporal Epidemiol 2022; 43:100534. [PMID: 36460444 PMCID: PMC9411108 DOI: 10.1016/j.sste.2022.100534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 07/19/2022] [Accepted: 08/24/2022] [Indexed: 12/15/2022]
Abstract
The aim of this study is to identify spatiotemporal clusters and the socioeconomic drivers of COVID-19 in Toronto. Geographical, epidemiological, and socioeconomic data from the 140 neighbourhoods in Toronto were used in this study. We used local and global Moran's I, and space-time scan statistic to identify spatial and spatiotemporal clusters of COVID-19. We also used global (spatial regression models), and local geographically weighted regression (GWR) and Multiscale Geographically weighted regression (MGWR) models to identify the globally and locally varying socioeconomic drivers of COVID-19. The global regression model identified a lower percentage of educated people and a higher percentage of immigrants in the neighbourhoods as significant predictors of COVID-19. MGWR shows the best fit model to explain the variables affecting COVID-19. The findings imply that a single intervention package for the entire area would not be an effective strategy for controlling COVID-19; a locally adaptable intervention package would be beneficial.
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Affiliation(s)
- Nushrat Nazia
- School of Public Health Sciences, University of Waterloo, 200 University Ave W., Waterloo, ON N2L3G1, Canada,Corresponding author at: 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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13
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Pandey B, Gu J, Ramaswami A. Characterizing COVID-19 waves in urban and rural districts of India. NPJ URBAN SUSTAINABILITY 2022; 2:26. [PMID: 37521776 PMCID: PMC9613454 DOI: 10.1038/s42949-022-00071-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 09/23/2022] [Indexed: 05/03/2023]
Abstract
Understanding spatial determinants, i.e., social, infrastructural, and environmental features of a place, which shape infectious disease is critically important for public health. We present an exploration of the spatial determinants of reported COVID-19 incidence across India's 641 urban and rural districts, comparing two waves (2020-2021). Three key results emerge using three COVID-19 incidence metrics: cumulative incidence proportion (aggregate risk), cumulative temporal incidence rate, and severity ratio. First, in the same district, characteristics of COVID-19 incidences are similar across waves, with the second wave over four times more severe than the first. Second, after controlling for state-level effects, urbanization (urban population share), living standards, and population age emerge as positive determinants of both risk and rates across waves. Third, keeping all else constant, lower shares of workers working from home correlate with greater infection risk during the second wave. While much attention has focused on intra-urban disease spread, our findings suggest that understanding spatial determinants across human settlements is also important for managing current and future pandemics.
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Affiliation(s)
- Bhartendu Pandey
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08540 USA
| | - Jianyu Gu
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08540 USA
- National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401 USA
| | - Anu Ramaswami
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08540 USA
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14
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Middya AI, Roy S. Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models. TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2022; 183:121911. [PMID: 35938066 PMCID: PMC9345394 DOI: 10.1016/j.techfore.2022.121911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 06/28/2022] [Accepted: 07/21/2022] [Indexed: 06/15/2023]
Abstract
Deep learning methods have become the state of the art for spatio-temporal predictive analysis in a wide range of fields, including environmental management, public health, urban planning, pollution monitoring, and so on. Despite the fact that a variety of powerful deep learning-based models can address various problem-specific issues in different research domain, it has been found that no single optimal model can outperform everywhere. Now, in the last two years, various deep learning-based studies have provided a variety of best-performing techniques for predicting COVID-19 health outcomes. In this context, this study attempts to perform a case study that investigates the spatio-temporal variation in the performance of deep-learning-based methods for predicting COVID-19 health outcomes in India. Various widely applied deep learning models namely CNN (convolutional neural network), RNN (recurrent neural network), Vanilla LSTM (long short-term memory), LSTM Autoencoder, and Bidirectional LSTM are considered to investigate their spatio-temporal performance variation. The effectiveness of the models is assessed using various metrics based on COVID-19 mortality time-series from 36 states and union territories of India.
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Affiliation(s)
- Asif Iqbal Middya
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Sarbani Roy
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
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15
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Chaudhary V, Bhadola P, Kaushik A, Khalid M, Furukawa H, Khosla A. Assessing temporal correlation in environmental risk factors to design efficient area-specific COVID-19 regulations: Delhi based case study. Sci Rep 2022; 12:12949. [PMID: 35902653 PMCID: PMC9333075 DOI: 10.1038/s41598-022-16781-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 07/15/2022] [Indexed: 12/12/2022] Open
Abstract
Amid ongoing devastation due to Serve-Acute-Respiratory-Coronavirus2 (SARS-CoV-2), the global spatial and temporal variation in the pandemic spread has strongly anticipated the requirement of designing area-specific preventive strategies based on geographic and meteorological state-of-affairs. Epidemiological and regression models have strongly projected particulate matter (PM) as leading environmental-risk factor for the COVID-19 outbreak. Understanding the role of secondary environmental-factors like ammonia (NH3) and relative humidity (RH), latency of missing data structuring, monotonous correlation remains obstacles to scheme conclusive outcomes. We mapped hotspots of airborne PM2.5, PM10, NH3, and RH concentrations, and COVID-19 cases and mortalities for January, 2021-July,2021 from combined data of 17 ground-monitoring stations across Delhi. Spearmen and Pearson coefficient correlation show strong association (p-value < 0.001) of COVID-19 cases and mortalities with PM2.5 (r > 0.60) and PM10 (r > 0.40), respectively. Interestingly, the COVID-19 spread shows significant dependence on RH (r > 0.5) and NH3 (r = 0.4), anticipating their potential role in SARS-CoV-2 outbreak. We found systematic lockdown as a successful measure in combatting SARS-CoV-2 outbreak. These outcomes strongly demonstrate regional and temporal differences in COVID-19 severity with environmental-risk factors. The study lays the groundwork for designing and implementing regulatory strategies, and proper urban and transportation planning based on area-specific environmental conditions to control future infectious public health emergencies.
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Affiliation(s)
- Vishal Chaudhary
- Research Cell and Department of Physics, Bhagini Nivedita College, University of Delhi, New Delhi, 110043, India.
| | - Pradeep Bhadola
- Centre for Theoretical Physics and Natural Philosophy, Nakhonsawan Studiorum for Advanced Studies, Mahidol University, Nakhonsawan, 60130, Thailand.
| | - Ajeet Kaushik
- NanoBioTech Laboratory, Health System Engineering, Department of Environmental Engineering, Florida Polytechnic University, Lakeland, FL, 33805, USA.,School of Engineering, University of Petroleum and Energy Studies (UPES) , Dehradun, Uttarakhand, India
| | - Mohammad Khalid
- Graphene and Advanced 2D Materials Research Group (GAMRG), School of Engineering and Technology, Sunway University, No. 5, Jalan University, Bandar Sunway, 47500, Petaling Jaya, Selangor, Malaysia.,Sunway Materials Smart Science & Engineering (SMS2E) Research Cluster, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500, Petaling Jaya, Selangor, Malaysia
| | - Hidemitsu Furukawa
- Department of Mechanical Systems Engineering, Graduate School of Science and Engineering, Yamagata University, Yonezawa, Yamagata, 992-8510, Japan
| | - Ajit Khosla
- School of Advanced Materials and Nanotechnology, Xidian University, Xi'an, 710126, People's Republic of China.
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16
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Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19148267. [PMID: 35886114 PMCID: PMC9324591 DOI: 10.3390/ijerph19148267] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/01/2022] [Accepted: 07/04/2022] [Indexed: 02/04/2023]
Abstract
The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.
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17
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Salvatore M, Purkayastha S, Ganapathi L, Bhattacharyya R, Kundu R, Zimmermann L, Ray D, Hazra A, Kleinsasser M, Solomon S, Subbaraman R, Mukherjee B. Lessons from SARS-CoV-2 in India: A data-driven framework for pandemic resilience. SCIENCE ADVANCES 2022; 8:eabp8621. [PMID: 35714183 PMCID: PMC9205583 DOI: 10.1126/sciadv.abp8621] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
India experienced a massive surge in SARS-CoV-2 infections and deaths during April to June 2021 despite having controlled the epidemic relatively well during 2020. Using counterfactual predictions from epidemiological disease transmission models, we produce evidence in support of how strengthening public health interventions early would have helped control transmission in the country and significantly reduced mortality during the second wave, even without harsh lockdowns. We argue that enhanced surveillance at district, state, and national levels and constant assessment of risk associated with increased transmission are critical for future pandemic responsiveness. Building on our retrospective analysis, we provide a tiered data-driven framework for timely escalation of future interventions as a tool for policy-makers.
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Affiliation(s)
- Maxwell Salvatore
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | | | - Lakshmi Ganapathi
- Division of Infectious Diseases, Boston Children’s Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Ritoban Kundu
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Lauren Zimmermann
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI, USA
| | - Debashree Ray
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Aditi Hazra
- Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Sunil Solomon
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ramnath Subbaraman
- Department of Public Health and Community Medicine and Center for Global Public Health, Tufts University School of Medicine, Boston, MA, USA
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
- Corresponding author.
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18
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Impact of urbanisation and environmental factors on spatial distribution of COVID-19 cases during the early phase of epidemic in Singapore. Sci Rep 2022; 12:9758. [PMID: 35697756 PMCID: PMC9191550 DOI: 10.1038/s41598-022-12941-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 04/22/2022] [Indexed: 11/26/2022] Open
Abstract
Geographical weighted regression (GWR) can be used to explore the COVID-19 transmission pattern between cases. This study aimed to explore the influence from environmental and urbanisation factors, and the spatial relationship between epidemiologically-linked, unlinked and imported cases during the early phase of the epidemic in Singapore. Spatial relationships were evaluated with GWR modelling. Community COVID-19 cases with residential location reported from 21st January 2020 till 17th March 2020 were considered for analyses. Temperature, relative humidity, population density and urbanisation are the variables used as exploratory variables for analysis. ArcGIS was used to process the data and perform geospatial analyses. During the early phase of COVID-19 epidemic in Singapore, significant but weak correlation of temperature with COVID-19 incidence (significance 0.5–1.5) was observed in several sub-zones of Singapore. Correlations between humidity and incidence could not be established. Across sub-zones, high residential population density and high levels of urbanisation were associated with COVID-19 incidence. The incidence of COVID-19 case types (linked, unlinked and imported) within sub-zones varied differently, especially those in the western and north-eastern regions of Singapore. Areas with both high residential population density and high levels of urbanisation are potential risk factors for COVID-19 transmission. These findings provide further insights for directing appropriate resources to enhance infection prevention and control strategies to contain COVID-19 transmission.
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19
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de Souza APG, Mota CMDM, Rosa AGF, de Figueiredo CJJ, Candeias ALB. A spatial-temporal analysis at the early stages of the COVID-19 pandemic and its determinants: The case of Recife neighborhoods, Brazil. PLoS One 2022; 17:e0268538. [PMID: 35580093 PMCID: PMC9113566 DOI: 10.1371/journal.pone.0268538] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 04/30/2022] [Indexed: 12/11/2022] Open
Abstract
The outbreak of COVID-19 has led to there being a worldwide socio-economic crisis, with major impacts on developing countries. Understanding the dynamics of the disease and its driving factors, on a small spatial scale, might support strategies to control infections. This paper explores the impact of the COVID-19 on neighborhoods of Recife, Brazil, for which we examine a set of drivers that combines socio-economic factors and the presence of non-stop services. A three-stage methodology was conducted by conducting a statistical and spatial analysis, including clusters and regression models. COVID-19 data were investigated concerning ten dates between April and July 2020. Hotspots of the most affected regions and their determinant effects were highlighted. We have identified that clusters of confirmed cases were carried from a well-developed neighborhood to socially deprived areas, along with the emergence of hotspots of the case-fatality rate. The influence of age-groups, income, level of education, and the access to essential services on the spread of COVID-19 was also verified. The recognition of variables that influence the spatial spread of the disease becomes vital for pinpointing the most vulnerable areas. Consequently, specific prevention actions can be developed for these places, especially in heterogeneous cities.
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Affiliation(s)
| | - Caroline Maria de Miranda Mota
- Programa de Pós-graduação em Engenharia de Produção, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil
- Departamento de Engenharia de Produção, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil
- * E-mail:
| | - Amanda Gadelha Ferreira Rosa
- Programa de Pós-graduação em Engenharia de Produção, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil
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20
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Spatiotemporal clustering patterns and sociodemographic determinants of COVID-19 (SARS-CoV-2) infections in Helsinki, Finland. Spat Spatiotemporal Epidemiol 2022; 41:100493. [PMID: 35691637 PMCID: PMC8817446 DOI: 10.1016/j.sste.2022.100493] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 01/21/2022] [Accepted: 02/04/2022] [Indexed: 12/22/2022]
Abstract
This study aims to elucidate the variations in spatiotemporal patterns and sociodemographic determinants of SARS-CoV-2 infections in Helsinki, Finland. Global and local spatial autocorrelation were inspected with Moran's I and LISA statistics, and Getis-Ord Gi* statistics was used to identify the hot spot areas. Space-time statistics were used to detect clusters of high relative risk and regression models were implemented to explain sociodemographic determinants for the clusters. The findings revealed the presence of spatial autocorrelation and clustering of COVID-19 cases. High–high clusters and high relative risk areas emerged primarily in Helsinki's eastern neighborhoods, which are socioeconomically vulnerable, with a few exceptions revealing local outbreaks in other areas. The variation in COVID-19 rates was largely explained by median income and the number of foreign citizens in the population. Furthermore, the use of multiple spatiotemporal analysis methods are recommended to gain deeper insights into the complex spatiotemporal clustering patterns and sociodemographic determinants of the COVID-19 cases.
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21
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Saha P, Nath P, Middya AI, Roy S. Improving temporal predictions through time-series labeling using matrix profile and motifs. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06744-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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22
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Nian F, Shi Y, Cao J. COVID-19 Propagation Model Based on Economic Development and Interventions. WIRELESS PERSONAL COMMUNICATIONS 2022; 122:2355-2365. [PMID: 34421225 PMCID: PMC8371428 DOI: 10.1007/s11277-021-08998-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/08/2021] [Indexed: 05/03/2023]
Abstract
In order to understand the influencing factors affecting the COVID-19 propagation, and analyze the development trend of the epidemic situation in the world, COVID-19 propagation model to simulate the COVID-19 propagation in the population is proposed in this paper. First of all, this paper analyzes the economic factors and interventions affecting the COVID-19 propagation in various different countries. Then, the touch number for COVID-19 High-risk Population Dynamic Network in this paper was redefined, and it predicts and analyzes the development trend of the epidemic situation in different countries. The simulation data and the published confirmed data by the world health organization could fit well, which also verified the reliability of the model. Finally, this paper also analyzes the impact of public awareness of prevention on the control of the epidemic. The analysis shows that increasing the awareness of prevention, timely and early adoption of protective measures such as wearing masks, and reducing travel can greatly reduce the risk of infection and the outbreak scale.
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Affiliation(s)
- Fuzhong Nian
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050 China
| | - Yayong Shi
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050 China
| | - Jun Cao
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050 China
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