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Tsimpida D, Tsakiridi A, Daras K, Corcoran R, Gabbay M. Unravelling the dynamics of mental health inequalities in England: A 12-year nationwide longitudinal spatial analysis of recorded depression prevalence. SSM Popul Health 2024; 26:101669. [PMID: 38708408 PMCID: PMC11066558 DOI: 10.1016/j.ssmph.2024.101669] [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: 04/20/2023] [Revised: 03/28/2024] [Accepted: 04/03/2024] [Indexed: 05/07/2024] Open
Abstract
Background Depression is one of the most significant public health issues, but evidence of geographic patterns and trends of depression is limited. We aimed to examine the spatio-temporal patterns and trends of depression prevalence among adults in a nationwide longitudinal spatial study in England and evaluate the influence of neighbourhood socioeconomic deprivation in explaining patterns. Methods Information on recorded depression prevalence was obtained from the indicator Quality and Outcomes Framework: Depression prevalence that measured the annual percentage of adults diagnosed with depression for Lower Super Output Areas (LSOA) from 2011 to 2022. We applied Cluster and Outlier Analysis using the Local Moran's I algorithm. Local effects of deprivation on depression in 2020 examined with Geographically Weighted Regression (GWR). Inequalities in recorded prevalence were presented using Prevalence Rate Ratios (PRR). Results The North West Region of England had the highest concentration of High-High clusters of depression, with 17.4% of the area having high values surrounded by high values in both space and time and the greatest percentage of areas with a high rate of increase (43.1%). Inequalities widened among areas with a high rate of increase in prevalence compared to those with a lower rate of increase, with the PRR increasing from 1.66 (99% CI 1.61-1.70) in 2011 to 1.81 (99% CI 1.76-1.85) by 2022. Deprivation explained 3%-39% of the variance in depression in 2020 across the country. Conclusions It is crucial to monitor depression's spatial patterns and trends and investigate mechanisms of mental health inequalities. Our findings can help identify priority areas and target prevention and intervention strategies in England. Evaluating mental health interventions in different geographic contexts can provide valuable insights to policymakers on the most effective and context-sensitive strategies, enabling them to allocate resources towards preventing the worsening of mental health inequalities.
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Affiliation(s)
- Dialechti Tsimpida
- Department of Public Health, Policy and Systems, University of Liverpool, UK
- Centre for Research on Ageing, University of Southampton, UK
- Department of Gerontology, University of Southampton, UK
| | | | - Konstantinos Daras
- Department of Public Health, Policy and Systems, University of Liverpool, UK
- National Institute for Health Research Applied Research Collaboration North West Coast (NIHR ARC NWC), UK
| | - Rhiannon Corcoran
- National Institute for Health Research Applied Research Collaboration North West Coast (NIHR ARC NWC), UK
- Department of Primary Care and Mental Health, University of Liverpool, UK
| | - Mark Gabbay
- National Institute for Health Research Applied Research Collaboration North West Coast (NIHR ARC NWC), UK
- Department of Primary Care and Mental Health, University of Liverpool, UK
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Sumboh JG, Laryea NA, Otchere J, Ahorlu CS, de Souza DK. Towards Understanding the Microepidemiology of Lymphatic Filariasis at the Community Level in Ghana. Trop Med Infect Dis 2024; 9:107. [PMID: 38787040 PMCID: PMC11125695 DOI: 10.3390/tropicalmed9050107] [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: 04/19/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
Studies on the distribution of lymphatic filariasis (LF) have mostly focused on reporting prevalence at the community level and distribution at the district levels. Understanding the distribution patterns at community levels may help in designing surveillance strategies. This study aimed to characterize the spatial distribution of LF infections in four hotspot communities in Ghana. The research, involving 252 participants, collected demographic data, mass drug administration (MDA) information, household GPS coordinates, and antigen detection test results. The LF prevalence varied significantly among the communities, with Asemda having the highest (33.33%) and Mempeasem having the lowest (4.44%). Females had lower odds of infection than males (OR = 2.67, p = 0.003 CI: 1.39-5.13). Spatial analysis using kernel density, Anselin Local Moran's, Getis-Ord Gi models, Ordinary Least Squares, and Geographic Weighted Regression revealed mixed patterns of spatial autocorrelation. This study identified LF hotspots, indicating clusters of high or low prevalence with some areas showing disparities between MDA coverage and LF positivity rates. Despite these hotspots, the overall distribution of LF appeared random, suggesting the importance of purposeful sampling in surveillance activities. These findings contribute valuable insights into the micro-epidemiology of LF, emphasizing the need for community-specific investigations to understand the factors influencing the effectiveness of MDA programs in controlling filarial infections. The study highlights the importance of refining surveillance strategies based on community-level distribution patterns.
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Affiliation(s)
- Jeffrey Gabriel Sumboh
- Department of Parasitology, Noguchi Memorial Institute for Medical Research, College of Health Sciences, University of Ghana, Accra P.O. Box LG 581, Ghana; (J.G.S.); (J.O.)
| | - Nii A. Laryea
- Department of Epidemiology, Noguchi Memorial Institute for Medical Research, College of Health Sciences, University of Ghana, Accra P.O. Box LG 581, Ghana; (N.A.L.); (C.S.A.)
| | - Joseph Otchere
- Department of Parasitology, Noguchi Memorial Institute for Medical Research, College of Health Sciences, University of Ghana, Accra P.O. Box LG 581, Ghana; (J.G.S.); (J.O.)
| | - Collins S. Ahorlu
- Department of Epidemiology, Noguchi Memorial Institute for Medical Research, College of Health Sciences, University of Ghana, Accra P.O. Box LG 581, Ghana; (N.A.L.); (C.S.A.)
| | - Dziedzom K. de Souza
- Department of Parasitology, Noguchi Memorial Institute for Medical Research, College of Health Sciences, University of Ghana, Accra P.O. Box LG 581, Ghana; (J.G.S.); (J.O.)
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Enoe J, Sutherland M, Davis D, Ramlal B, Griffith-Charles C, Bhola KH, Asefa EM. A conceptional model integrating geographic information systems (GIS) and social media data for disease exposure assessment. GEOSPATIAL HEALTH 2024; 19. [PMID: 38551510 DOI: 10.4081/gh.2024.1264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/28/2024] [Indexed: 04/02/2024]
Abstract
Although previous studies have acknowledged the potential of geographic information systems (GIS) and social media data (SMD) in assessment of exposure to various environmental risks, none has presented a simple, effective and user-friendly tool. This study introduces a conceptual model that integrates individual mobility patterns extracted from social media, with the geographic footprints of infectious diseases and other environmental agents utilizing GIS. The efficacy of the model was independently evaluated for selected case studies involving lead in the ground; particulate matter in the air; and an infectious, viral disease (COVID- 19). A graphical user interface (GUI) was developed as the final output of this study. Overall, the evaluation of the model demonstrated feasibility in successfully extracting individual mobility patterns, identifying potential exposure sites and quantifying the frequency and magnitude of exposure. Importantly, the novelty of the developed model lies not merely in its efficiency in integrating GIS and SMD for exposure assessment, but also in considering the practical requirements of health practitioners. Although the conceptual model, developed together with its associated GUI, presents a promising and practical approach to assessment of the exposure to environmental risks discussed here, its applicability, versatility and efficacy extends beyond the case studies presented in this study.
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Affiliation(s)
- Jerry Enoe
- Department of Geomatics Engineering and Land Management, The University of the West Indies, St. Augustine.
| | - Michael Sutherland
- Department of Geomatics Engineering and Land Management, The University of the West Indies, St. Augustine.
| | - Dexter Davis
- Department of Geomatics Engineering and Land Management, The University of the West Indies, St. Augustine.
| | - Bheshem Ramlal
- Department of Geomatics Engineering and Land Management, The University of the West Indies, St. Augustine.
| | - Charisse Griffith-Charles
- Department of Geomatics Engineering and Land Management, The University of the West Indies, St. Augustine.
| | - Keston H Bhola
- Department of Computers and Technology, School of Arts and Science, St George's University.
| | - Elsai Mati Asefa
- School of Environmental Health, College of Health and Medical Sciences, Haramaya University, Harar.
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Bayode T, Siegmund A. Identifying childhood malaria hotspots and risk factors in a Nigerian city using geostatistical modelling approach. Sci Rep 2024; 14:5445. [PMID: 38443428 PMCID: PMC10914794 DOI: 10.1038/s41598-024-55003-x] [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: 10/11/2023] [Accepted: 02/19/2024] [Indexed: 03/07/2024] Open
Abstract
Malaria ranks high among prevalent and ravaging infectious diseases in sub-Saharan Africa (SSA). The negative impacts, disease burden, and risk are higher among children and pregnant women as part of the most vulnerable groups to malaria in Nigeria. However, the burden of malaria is not even in space and time. This study explores the spatial variability of malaria prevalence among children under five years (U5) in medium-sized rapidly growing city of Akure, Nigeria using model-based geostatistical modeling (MBG) technique to predict U5 malaria burden at a 100 × 100 m grid, while the parameter estimation was done using Monte Carlo maximum likelihood method. The non-spatial logistic regression model shows that U5 malaria prevalence is significantly influenced by the usage of insecticide-treated nets-ITNs, window protection, and water source. Furthermore, the MBG model shows predicted U5 malaria prevalence in Akure is greater than 35% at certain locations while we were able to ascertain places with U5 prevalence > 10% (i.e. hotspots) using exceedance probability modelling which is a vital tool for policy development. The map provides place-based evidence on the spatial variation of U5 malaria in Akure, and direction on where intensified interventions are crucial for the reduction of U5 malaria burden and improvement of urban health in Akure, Nigeria.
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Affiliation(s)
- Taye Bayode
- Institute of Geography & Heidelberg Centre for Environment (HCE), Heidelberg University, Heidelberg, Germany.
- Department of Geography-Research Group for Earth Observation (rgeo), UNESCO Chair on World Heritage and Biosphere Reserve Observation and Education, Heidelberg University of Education, Heidelberg, Germany.
| | - Alexander Siegmund
- Institute of Geography & Heidelberg Centre for Environment (HCE), Heidelberg University, Heidelberg, Germany
- Department of Geography-Research Group for Earth Observation (rgeo), UNESCO Chair on World Heritage and Biosphere Reserve Observation and Education, Heidelberg University of Education, Heidelberg, Germany
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5
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Emecen AN, Kıran P, Çağlayan D. Influential Factors of Tuberculosis Notification Rates in Turkey: A Provincial-Level Spatial Analysis. THORACIC RESEARCH AND PRACTICE 2024; 25:68-74. [PMID: 38454202 PMCID: PMC11114173 DOI: 10.5152/thoracrespract.2024.23109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 01/07/2024] [Indexed: 03/09/2024]
Abstract
OBJECTIVE The total annual count of reported tuberculosis (TB) cases continues to decline throughout Turkey. Recognizing the regions with high and low burdens and revealing the factors affecting TB notification rates may play a role in guiding national control programs. This study aimed to analyze the spatial distribution of TB notification rates from 2005 to 2018 and evaluate the factors contributing to TB rates. MATERIAL AND METHODS In this ecological study, we used freely available open data from the Internet. We employed global and local spatial autocorrelation analysis to identify the spatial distribution and the clusters with low and high burdens. We conducted an ordinary least square regression model, spatial lag model, and spatial error model. The best-fitting model was selected via model parameters. RESULTS Throughout the study period, the provinces in West Marmara Region (Edirne, Kırklareli, Tekirdağ, Çanakkale) were consistently in a high-burden cluster. In univariate ordinary least square regression, population density, the proportion of contacts screened for TB, the proportion of TB contacts who received prophylaxis, TB dispensary count, mean particulate matter 10 levels, and gross domestic product were found to be positively associated with TB notification rate. The best-fitting multivariate spatial lag model revealed that the proportion of contacts screened for TB (β, z-value: 0.89, 2.21) positively affected TB notification rate. CONCLUSION The high TB burden in West Marmara Region should warn policymakers to maintain a focused approach to controlling TB in this area. This study showed the importance of contact tracing efforts to prevent the underdetection of TB cases.
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Affiliation(s)
- Ahmet Naci Emecen
- Dokuz Eylül University Research and Application Hospital, İzmir, Turkey
| | - Pınar Kıran
- Department of Public Health, Epidemiology Subsection, Dokuz Eylül University Faculty of Medicine, İzmir, Turkey
| | - Derya Çağlayan
- Department of Public Health, Epidemiology Subsection, Dokuz Eylül University Faculty of Medicine, İzmir, Turkey
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Pérez-Castro E, Guzmán-Martínez M, Godínez-Jaimes F, Reyes-Carreto R, Vargas-de-León C, Aguirre-Salado AI. Spatial Survival Model for COVID-19 in México. Healthcare (Basel) 2024; 12:306. [PMID: 38338191 PMCID: PMC10855302 DOI: 10.3390/healthcare12030306] [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: 10/07/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 02/12/2024] Open
Abstract
A spatial survival analysis was performed to identify some of the factors that influence the survival of patients with COVID-19 in the states of Guerrero, México, and Chihuahua. The data that we analyzed correspond to the period from 28 February 2020 to 24 November 2021. A Cox proportional hazards frailty model and a Cox proportional hazards model were fitted. For both models, the estimation of the parameters was carried out using the Bayesian approach. According to the DIC, WAIC, and LPML criteria, the spatial model was better. The analysis showed that the spatial effect influences the survival times of patients with COVID-19. The spatial survival analysis also revealed that age, gender, and the presence of comorbidities, which vary between states, and the development of pneumonia increase the risk of death from COVID-19.
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Affiliation(s)
- Eduardo Pérez-Castro
- Unidad de Investigación de Salud en el Trabajo, Centro Médico Nacional Siglo XXI, Ciudad de México 06720, Mexico;
| | - María Guzmán-Martínez
- Facultad de Matemáticas, Universidad Autónoma de Guerrero, Chilpancingo 39087, Mexico; (F.G.-J.); (R.R.-C.)
| | - Flaviano Godínez-Jaimes
- Facultad de Matemáticas, Universidad Autónoma de Guerrero, Chilpancingo 39087, Mexico; (F.G.-J.); (R.R.-C.)
| | - Ramón Reyes-Carreto
- Facultad de Matemáticas, Universidad Autónoma de Guerrero, Chilpancingo 39087, Mexico; (F.G.-J.); (R.R.-C.)
| | - Cruz Vargas-de-León
- Sección de Estudios de Posgrado, Escuela Superior de Medicina, Instituto Politécnico Nacional, Ciudad de México 11340, Mexico
- División de Investigación, Hospital Juárez de México, Ciudad de México 07760, Mexico
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Luo D, Wang L, Zhang M, Martinez L, Chen S, Zhang Y, Wang W, Wu Q, Wu Y, Liu K, Xie B, Chen B. Spatial spillover effect of environmental factors on the tuberculosis occurrence among the elderly: a surveillance analysis for nearly a dozen years in eastern China. BMC Public Health 2024; 24:209. [PMID: 38233763 PMCID: PMC10795419 DOI: 10.1186/s12889-024-17644-5] [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: 08/17/2023] [Accepted: 01/02/2024] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND In many areas of China, over 30% of tuberculosis cases occur among the elderly. We aimed to investigate the spatial distribution and environmental factors that predicted the occurence of tuberculosis in this group. METHODS Data were collected on notified pulmonary tuberculosis (PTB) cases aged ≥ 65 years in Zhejiang Province from 2010 to 2021. We performed spatial autocorrelation and spatial-temporal scan statistics to determine the clusters of epidemics. Spatial Durbin Model (SDM) analysis was used to identify significant environmental factors and their spatial spillover effects. RESULTS 77,405 cases of PTB among the elderly were notified, showing a decreasing trend in the notification rate. Spatial-temporal analysis showed clustering of epidemics in the western area of Zhejiang Province. The results of the SDM indicated that a one-unit increase in PM2.5 led to a 0.396% increase in the local notification rate. The annual mean temperature and precipitation had direct effects and spatial spillover effects on the rate, while complexity of the shape of the greenspace (SHAPE_AM) and SO2 had negative spatial spillover effects. CONCLUSION Targeted interventions among the elderly in Western Zhejiang may be more efficient than broad, province-wide interventions. Low annual mean temperature and high annual mean precipitation in local and neighboring areas tend to have higher PTB onset among the elderly.
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Affiliation(s)
- Dan Luo
- Department of Public Health, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Luyu Wang
- School of Urban Design, Wuhan University, Hubei, Wuhan, China
| | - Mengdie Zhang
- Zhejiang University School of Public Health, Hangzhou, Zhejiang, China
| | - Leonardo Martinez
- Department of Epidemiology, School of Public Health, Boston University, Boston, MA, USA
| | - Songhua Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Yu Zhang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Wei Wang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Qian Wu
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Yonghao Wu
- Department of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China, 310058
| | - Kui Liu
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.
- National Centre for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
| | - Bo Xie
- School of Urban Design, Wuhan University, Hubei, Wuhan, China.
| | - Bin Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.
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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: 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: 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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Jato-Espino D, Mayor-Vitoria F, Moscardó V, Capra-Ribeiro F, Bartolomé del Pino LE. Toward One Health: a spatial indicator system to model the facilitation of the spread of zoonotic diseases. Front Public Health 2023; 11:1215574. [PMID: 37457260 PMCID: PMC10340543 DOI: 10.3389/fpubh.2023.1215574] [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: 05/04/2023] [Accepted: 06/14/2023] [Indexed: 07/18/2023] Open
Abstract
Recurrent outbreaks of zoonotic infectious diseases highlight the importance of considering the interconnections between human, animal, and environmental health in disease prevention and control. This has given rise to the concept of One Health, which recognizes the interconnectedness of between human and animal health within their ecosystems. As a contribution to the One Health approach, this study aims to develop an indicator system to model the facilitation of the spread of zoonotic diseases. Initially, a literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to identify relevant indicators related to One Health. The selected indicators focused on demographics, socioeconomic aspects, interactions between animal and human populations and water bodies, as well as environmental conditions related to air quality and climate. These indicators were characterized using values obtained from the literature or calculated through distance analysis, geoprocessing tasks, and other methods. Subsequently, Multi-Criteria Decision-Making (MCDM) techniques, specifically the Entropy and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods, were utilized to combine the indicators and create a composite metric for assessing the spread of zoonotic diseases. The final indicators selected were then tested against recorded zoonoses in the Valencian Community (Spain) for 2021, and a strong positive correlation was identified. Therefore, the proposed indicator system can be valuable in guiding the development of planning strategies that align with the One Health principles. Based on the results achieved, such strategies may prioritize the preservation of natural landscape features to mitigate habitat encroachment, protect land and water resources, and attenuate extreme atmospheric conditions.
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Affiliation(s)
- Daniel Jato-Espino
- GREENIUS Research Group, Universidad Internacional de Valencia—VIU, Calle Pintor Sorolla, Valencia, Spain
| | - Fernando Mayor-Vitoria
- GREENIUS Research Group, Universidad Internacional de Valencia—VIU, Calle Pintor Sorolla, Valencia, Spain
| | - Vanessa Moscardó
- GREENIUS Research Group, Universidad Internacional de Valencia—VIU, Calle Pintor Sorolla, Valencia, Spain
| | - Fabio Capra-Ribeiro
- GREENIUS Research Group, Universidad Internacional de Valencia—VIU, Calle Pintor Sorolla, Valencia, Spain
- School of Architecture, College of Art and Design, Louisiana State University, Baton Rouge, LA, United States
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Akuno AO, Ramírez-Ramírez LL, Espinoza JF. Inference on a Multi-Patch Epidemic Model with Partial Mobility, Residency, and Demography: Case of the 2020 COVID-19 Outbreak in Hermosillo, Mexico. ENTROPY (BASEL, SWITZERLAND) 2023; 25:968. [PMID: 37509915 PMCID: PMC10378648 DOI: 10.3390/e25070968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/02/2023] [Accepted: 06/14/2023] [Indexed: 07/30/2023]
Abstract
Most studies modeling population mobility and the spread of infectious diseases, particularly those using meta-population multi-patch models, tend to focus on the theoretical properties and numerical simulation of such models. As such, there is relatively scant literature focused on numerical fit, inference, and uncertainty quantification of epidemic models with population mobility. In this research, we use three estimation techniques to solve an inverse problem and quantify its uncertainty for a human-mobility-based multi-patch epidemic model using mobile phone sensing data and confirmed COVID-19-positive cases in Hermosillo, Mexico. First, we utilize a Brownian bridge model using mobile phone GPS data to estimate the residence and mobility parameters of the epidemic model. In the second step, we estimate the optimal model epidemiological parameters by deterministically inverting the model using a Darwinian-inspired evolutionary algorithm (EA)-that is, a genetic algorithm (GA). The third part of the analysis involves performing inference and uncertainty quantification in the epidemic model using two Bayesian Monte Carlo sampling methods: t-walk and Hamiltonian Monte Carlo (HMC). The results demonstrate that the estimated model parameters and incidence adequately fit the observed daily COVID-19 incidence in Hermosillo. Moreover, the estimated parameters from the HMC method yield large credible intervals, improving their coverage for the observed and predicted daily incidences. Furthermore, we observe that the use of a multi-patch model with mobility yields improved predictions when compared to a single-patch model.
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Affiliation(s)
- Albert Orwa Akuno
- Departamento de Probabilidad y Estadística, Centro de Investigación en Matemáticas A.C., Jalisco s/n, Colonia Valenciana, Guanajuato C.P. 36023, Gto, Mexico
| | - L Leticia Ramírez-Ramírez
- Departamento de Probabilidad y Estadística, Centro de Investigación en Matemáticas A.C., Jalisco s/n, Colonia Valenciana, Guanajuato C.P. 36023, Gto, Mexico
| | - Jesús F Espinoza
- Departamento de Matemáticas, Universidad de Sonora, Rosales y Boulevard Luis Encinas, Hermosillo C.P. 83000, Sonora, Mexico
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Soh S, Ho SH, Seah A, Ong J, Richards DR, Gaw LYF, Dickens BS, Tan KW, Koo JR, Cook AR, Lim JT. Spatial Methods for Inferring Extremes in Dengue Outbreak Risk in Singapore. Viruses 2022; 14:v14112450. [PMID: 36366548 PMCID: PMC9695662 DOI: 10.3390/v14112450] [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: 09/02/2022] [Revised: 10/30/2022] [Accepted: 11/02/2022] [Indexed: 11/09/2022] Open
Abstract
Dengue is a major vector-borne disease worldwide. Here, we examined the spatial distribution of extreme weekly dengue outbreak risk in Singapore from 2007 to 2020. We divided Singapore into equal-sized hexagons with a circumradius of 165 m and obtained the weekly number of dengue cases and the surface characteristics of each hexagon. We accounted for spatial heterogeneity using max-stable processes. The 5-, 10-, 20-, and 30-year return levels, or the weekly dengue case counts expected to be exceeded once every 5, 10, 20, and 30 years, respectively, were determined for each hexagon conditional on their surface characteristics remaining constant over time. The return levels were higher in the country's east, with the maximum weekly dengue cases per hexagon expected to exceed 51 at least once in 30 years in many areas. The surface characteristics with the largest impact on outbreak risk were the age of public apartments and the percentage of impervious surfaces, where a 3-year and 10% increase in each characteristic resulted in a 3.8% and 3.3% increase in risk, respectively. Vector control efforts should be prioritized in older residential estates and places with large contiguous masses of built-up environments. Our findings indicate the likely scale of outbreaks in the long term.
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Affiliation(s)
- Stacy Soh
- Environmental Health Institute, National Environment Agency, Singapore 138667, Singapore
| | - Soon Hoe Ho
- Environmental Health Institute, National Environment Agency, Singapore 138667, Singapore
- Correspondence:
| | - Annabel Seah
- Environmental Health Institute, National Environment Agency, Singapore 138667, Singapore
| | - Janet Ong
- Environmental Health Institute, National Environment Agency, Singapore 138667, Singapore
| | | | - Leon Yan-Feng Gaw
- Department of Architecture, College of Design and Engineering, National University of Singapore, Singapore 117566, Singapore
| | - Borame Sue Dickens
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Ken Wei Tan
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Joel Ruihan Koo
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Alex R. Cook
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Jue Tao Lim
- Environmental Health Institute, National Environment Agency, Singapore 138667, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University Novena Campus, Singapore 639798, Singapore
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