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Picinini Freitas L, Douwes-Schultz D, Schmidt AM, Ávila Monsalve B, Salazar Flórez JE, García-Balaguera C, Restrepo BN, Jaramillo-Ramirez GI, Carabali M, Zinszer K. Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space-time Markov switching model. Sci Rep 2024; 14:10003. [PMID: 38693192 PMCID: PMC11063144 DOI: 10.1038/s41598-024-59976-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 04/17/2024] [Indexed: 05/03/2024] Open
Abstract
Zika, a viral disease transmitted to humans by Aedes mosquitoes, emerged in the Americas in 2015, causing large-scale epidemics. Colombia alone reported over 72,000 Zika cases between 2015 and 2016. Using national surveillance data from 1121 municipalities over 70 weeks, we identified sociodemographic and environmental factors associated with Zika's emergence, re-emergence, persistence, and transmission intensity in Colombia. We fitted a zero-state Markov-switching model under the Bayesian framework, assuming Zika switched between periods of presence and absence according to spatially and temporally varying probabilities of emergence/re-emergence (from absence to presence) and persistence (from presence to presence). These probabilities were assumed to follow a series of mixed multiple logistic regressions. When Zika was present, assuming that the cases follow a negative binomial distribution, we estimated the transmission intensity rate. Our results indicate that Zika emerged/re-emerged sooner and that transmission was intensified in municipalities that were more densely populated, at lower altitudes and/or with less vegetation cover. Warmer temperatures and less weekly-accumulated rain were also associated with Zika emergence. Zika cases persisted for longer in more densely populated areas with more cases reported in the previous week. Overall, population density, elevation, and temperature were identified as the main contributors to the first Zika epidemic in Colombia. We also estimated the probability of Zika presence by municipality and week, and the results suggest that the disease circulated undetected by the surveillance system on many occasions. Our results offer insights into priority areas for public health interventions against emerging and re-emerging Aedes-borne diseases.
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Affiliation(s)
- Laís Picinini Freitas
- Université de Montréal, École de Santé Publique, Montreal, H3N 1X9, Canada.
- Centre de Recherche en Santé Publique, Montreal, H3N 1X9, Canada.
| | - Dirk Douwes-Schultz
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, H3A 1G1, Canada.
| | - Alexandra M Schmidt
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, H3A 1G1, Canada
| | - Brayan Ávila Monsalve
- Universidad Cooperativa de Colombia, Faculty of Medicine, Villavicencio, 500003, Colombia
| | - Jorge Emilio Salazar Flórez
- Instituto Colombiano de Medicina Tropical, Universidad CES, Medellín, 055450, Colombia
- Infectious and Chronic Diseases Study Group (GEINCRO), San Martín University Foundation, Medellín, 050031, Colombia
| | - César García-Balaguera
- Universidad Cooperativa de Colombia, Faculty of Medicine, Villavicencio, 500003, Colombia
| | - Berta N Restrepo
- Instituto Colombiano de Medicina Tropical, Universidad CES, Medellín, 055450, Colombia
| | | | - Mabel Carabali
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, H3A 1G1, Canada
| | - Kate Zinszer
- Université de Montréal, École de Santé Publique, Montreal, H3N 1X9, Canada
- Centre de Recherche en Santé Publique, Montreal, H3N 1X9, Canada
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Luo W, Liu Q, Zhou Y, Ran Y, Liu Z, Hou W, Pei S, Lai S. Spatiotemporal variations of "triple-demic" outbreaks of respiratory infections in the United States in the post-COVID-19 era. BMC Public Health 2023; 23:2452. [PMID: 38062417 PMCID: PMC10704638 DOI: 10.1186/s12889-023-17406-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 12/04/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND The US confronted a "triple-demic" of influenza, respiratory syncytial virus (RSV), and COVID-19 in the winter of 2022, leading to increased respiratory infections and a higher demand for medical supplies. It is urgent to analyze these epidemics and their spatial-temporal co-occurrence, identifying hotspots and informing public health strategies. METHODS We employed retrospective and prospective space-time scan statistics to assess the situations of COVID-19, influenza, and RSV in 51 US states from October 2021 to February 2022, and from October 2022 to February 2023, respectively. This enabled monitoring of spatiotemporal variations for each epidemic individually and collectively. RESULTS Compared to winter 2021, COVID-19 cases decreased while influenza and RSV infections significantly increased in winter 2022. We found a high-risk cluster of influenza and COVID-19 (not all three) in winter 2021. In late November 2022, a large high-risk cluster of triple-demic emerged in the central US. The number of states at high risk for multiple epidemics increased from 15 in October 2022 to 21 in January 2023. CONCLUSIONS Our study offers a novel spatiotemporal approach that combines both univariate and multivariate surveillance, as well as retrospective and prospective analyses. This approach offers a more comprehensive and timely understanding of how the co-occurrence of COVID-19, influenza, and RSV impacts various regions within the United States. Our findings assist in tailor-made strategies to mitigate the effects of these respiratory infections.
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Affiliation(s)
- Wei Luo
- GeoSpatialX Lab, Department of Geography, National University of Singapore, 1 Arts Link, #04-32 Block AS2, Singapore, 117570, Singapore.
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
| | - Qianhuang Liu
- GeoSpatialX Lab, Department of Geography, National University of Singapore, 1 Arts Link, #04-32 Block AS2, Singapore, 117570, Singapore
| | - Yuxuan Zhou
- Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China
| | - Yiding Ran
- GeoSpatialX Lab, Department of Geography, National University of Singapore, 1 Arts Link, #04-32 Block AS2, Singapore, 117570, Singapore
| | - Zhaoyin Liu
- GeoSpatialX Lab, Department of Geography, National University of Singapore, 1 Arts Link, #04-32 Block AS2, Singapore, 117570, Singapore
| | - Weitao Hou
- Department Of Biological Sciences, National University of Singapore, Singapore, Singapore
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, USA
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK.
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Gardini Sanches Palasio R, Marques Moralejo Bermudi P, Luiz de Lima Macedo F, Reis Santana LM, Chiaravalloti-Neto F. Zika, chikungunya and co-occurrence in Brazil: space-time clusters and associated environmental-socioeconomic factors. Sci Rep 2023; 13:18026. [PMID: 37865641 PMCID: PMC10590386 DOI: 10.1038/s41598-023-42930-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 09/16/2023] [Indexed: 10/23/2023] Open
Abstract
Chikungunya and Zika have been neglected as emerging diseases. This study aimed to analyze the space-time patterns of their occurrence and co-occurrence and their associated environmental and socioeconomic factors. Univariate (individually) and multivariate (co-occurrence) scans were analyzed for 608,388 and 162,992 cases of chikungunya and Zika, respectively. These occurred more frequently in the summer and autumn. The clusters with the highest risk were initially located in the northeast, dispersed to the central-west and coastal areas of São Paulo and Rio de Janeiro (2018-2021), and then increased in the northeast (2019-2021). Chikungunya and Zika demonstrated decreasing trends of 13% and 40%, respectively, whereas clusters showed an increasing trend of 85% and 57%, respectively. Clusters with a high co-occurrence risk have been identified in some regions of Brazil. High temperatures are associated with areas at a greater risk of these diseases. Chikungunya was associated with low precipitation levels, more urbanized environments, and places with greater social inequalities, whereas Zika was associated with high precipitation levels and low sewage network coverage. In conclusion, to optimize the surveillance and control of chikungunya and Zika, this study's results revealed high-risk areas with increasing trends and priority months and the role of socioeconomic and environmental factors.
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Affiliation(s)
- Raquel Gardini Sanches Palasio
- Laboratory of Spatial Analysis in Health (LAES), Department of Epidemiology, School of Public Health, University of São Paulo (FSP/USP), São Paulo, SP, Brazil.
| | - Patricia Marques Moralejo Bermudi
- Laboratory of Spatial Analysis in Health (LAES), Department of Epidemiology, School of Public Health, University of São Paulo (FSP/USP), São Paulo, SP, Brazil
| | - Fernando Luiz de Lima Macedo
- Epidemiological Surveillance Center (CVE) Prof. Alexandre Vranjac, Coordination of Disease Control, Health Department of the State of São Paulo, São Paulo, SP, Brazil
| | - Lidia Maria Reis Santana
- Epidemiological Surveillance Center (CVE) Prof. Alexandre Vranjac, Coordination of Disease Control, Health Department of the State of São Paulo, São Paulo, SP, Brazil
- Federal University of Sao Paulo (Unifesp), São Paulo, SP, Brazil
| | - Francisco Chiaravalloti-Neto
- Laboratory of Spatial Analysis in Health (LAES), Department of Epidemiology, School of Public Health, University of São Paulo (FSP/USP), São Paulo, SP, Brazil
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Coyle CR, Desjardins MR, Curriero FC, Rudolph J, Astemborski J, Falade-Nwulia O, Kirk GD, Thomas DL, Mehta SH, Genberg BL. Geographic variation in HCV treatment penetration among people who inject drugs in Baltimore, MD. J Viral Hepat 2023; 30:810-818. [PMID: 37382024 PMCID: PMC10527489 DOI: 10.1111/jvh.13864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/06/2023] [Accepted: 06/09/2023] [Indexed: 06/30/2023]
Abstract
We evaluated geographic heterogeneity in hepatitis C virus (HCV) treatment penetration among people who inject drug (PWID) across Baltimore, MD since the advent of direct-acting antivirals (DAAs) using space-time clusters of HCV viraemia. Using data from a community-based cohort of PWID, the AIDS Linked to the IntraVenous Experience (ALIVE) study, we identified space-time clusters with higher-than-expected rates of HCV viraemia between 2015 and 2019 using scan statistics. We used Poisson regression to identify covariates associated with HCV viraemia and used the regression-fitted values to detect adjusted space-time clusters of HCV viraemia in Baltimore city. Overall, in the cohort, HCV viraemia fell from 77% in 2015 to 64%, 49%, 39% and 36% from 2016 to 2019. In Baltimore city, the percentage of census tracts where prevalence of HCV viraemia was ≥85% dropped from 57% to 34%, 25%, 22% and 10% from 2015 to 2019. We identified two clusters of higher-than-expected HCV viraemia in the unadjusted analysis that lasted from 2015 to 2017 in East and West Baltimore and one adjusted cluster of HCV viraemia in West Baltimore from 2015 to 2016. Neither differences in age, sex, race, HIV status, nor neighbourhood deprivation were able to explain the significant space-time clusters. However, residing in a cluster with higher-than-expected viraemia was associated with age, sex, educational attainment and higher levels of neighbourhood deprivation. Nearly 4 years after DAAs became available, HCV treatment has penetrated all PWID communities across Baltimore city. While nearly all census tracts experienced improvements, change was more gradual in areas with higher levels of poverty.
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Affiliation(s)
- Catelyn R. Coyle
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Merck & Co. Inc., Rahway, NJ
| | - Michael R. Desjardins
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Frank C. Curriero
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Jacqueline Rudolph
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Jacquie Astemborski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Oluwaseun Falade-Nwulia
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Gregory D. Kirk
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - David L. Thomas
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Shruti H. Mehta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Becky L. Genberg
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
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Almeida PMPD, Câmara DCP, Nobre AA, Ayllón T, Ribeiro MS, Dias CMG, Peixoto EM, Rocha MMD, Carvalho S, Honório NA. Spatio-Temporal Cluster Detection of Dengue, Chikungunya, and Zika Viruses' Infection in Rio de Janeiro State from 2010 to 2019. Viruses 2023; 15:1496. [PMID: 37515183 PMCID: PMC10384805 DOI: 10.3390/v15071496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/24/2023] [Accepted: 06/27/2023] [Indexed: 07/30/2023] Open
Abstract
Dengue (DENV), chikungunya (CHIKV), and Zika (ZIKV) virus infections are widespread throughout the Rio de Janeiro state. The co-circulation of these emergent arboviruses constitutes a serious public health problem, resulting in outbreaks that can spatially and temporally overlap. Environmental conditions favor the presence, maintenance, and expansion of Aedes aegypti, the primary vector of these urban arboviruses. This study assessed the detection of clusters of urban arboviruses in the Rio de Janeiro state from 2010 to 2019. Notified cases of dengue, chikungunya, and Zika were grouped by year according to the onset of symptoms and their municipality of residence. The study period recorded the highest number of dengue epidemics in the state along with the simultaneous circulation of chikungunya and Zika viruses. The analyzes showed that the central municipalities of the metropolitan regions were associated with higher risk areas. Central municipalities in metropolitan regions were the first most likely clusters for dengue and Zika, and the second most likely cluster for chikungunya. Furthermore, the northwest and north regions were comprised clusters with the highest relative risk for the three arboviruses, underscoring the impact of these arboviruses in less densely populated regions of Brazil. The identification of high-risk areas over time highlights the need for effective control measures, targeted prevention and control interventions for these urban arboviral diseases.
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Affiliation(s)
- Paula Maria Pereira de Almeida
- Laboratório das Interações Vírus Hospedeiros, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro 210400-900, RJ, Brazil
- Núcleo Operacional Sentinela de Mosquitos Vetores-Nosmove, Fundação Oswaldo Cruz, Rio de Janeiro 21400-900, RJ, Brazil
- Secretaria de Estado de Saúde do Rio de Janeiro, Rio de Janeiro 20031-142, RJ, Brazil
| | - Daniel Cardoso Portela Câmara
- Laboratório das Interações Vírus Hospedeiros, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro 210400-900, RJ, Brazil
- Núcleo Operacional Sentinela de Mosquitos Vetores-Nosmove, Fundação Oswaldo Cruz, Rio de Janeiro 21400-900, RJ, Brazil
| | - Aline Araújo Nobre
- Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro 210400-900, RJ, Brazil
| | - Tania Ayllón
- Núcleo Operacional Sentinela de Mosquitos Vetores-Nosmove, Fundação Oswaldo Cruz, Rio de Janeiro 21400-900, RJ, Brazil
- Department of Genetics, Physiology and Microbiology, Faculty of Biological Sciences, University Complutense of Madrid, 28040 Madrid, Spain
| | - Mário Sérgio Ribeiro
- Secretaria de Estado de Saúde do Rio de Janeiro, Rio de Janeiro 20031-142, RJ, Brazil
| | | | | | - Maíra Mendonça da Rocha
- Secretaria de Estado de Saúde do Rio de Janeiro, Rio de Janeiro 20031-142, RJ, Brazil
- Escola Nacional de Saúde Pública, Fundação Oswaldo Cruz, Rio de Janeiro 210400-900, RJ, Brazil
| | - Silvia Carvalho
- Secretaria de Estado de Saúde do Rio de Janeiro, Rio de Janeiro 20031-142, RJ, Brazil
| | - Nildimar Alves Honório
- Laboratório das Interações Vírus Hospedeiros, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro 210400-900, RJ, Brazil
- Núcleo Operacional Sentinela de Mosquitos Vetores-Nosmove, Fundação Oswaldo Cruz, Rio de Janeiro 21400-900, RJ, Brazil
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Tessema ZT, Tesema GA, Ahern S, Earnest A. A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research. Int J Environ Res Public Health 2023; 20:6277. [PMID: 37444123 PMCID: PMC10341419 DOI: 10.3390/ijerph20136277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 06/26/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
Advancements in Bayesian spatial and spatio-temporal modelling have been observed in recent years. Despite this, there are unresolved issues about the choice of appropriate spatial unit and adjacency matrix in disease mapping. There is limited systematic review evidence on this topic. This review aimed to address these problems. We searched seven databases to find published articles on this topic. A modified quality assessment tool was used to assess the quality of studies. A total of 52 studies were included, of which 26 (50.0%) were on infectious diseases, 10 (19.2%) on chronic diseases, 8 (15.5%) on maternal and child health, and 8 (15.5%) on other health-related outcomes. Only 6 studies reported the reasons for using the specified spatial unit, 8 (15.3%) studies conducted sensitivity analysis for prior selection, and 39 (75%) of the studies used Queen contiguity adjacency. This review highlights existing variation and limitations in the specification of Bayesian spatial and spatio-temporal models used in health research. We found that majority of the studies failed to report the rationale for the choice of spatial units, perform sensitivity analyses on the priors, or evaluate the choice of neighbourhood adjacency, all of which can potentially affect findings in their studies.
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Affiliation(s)
- Zemenu Tadesse Tessema
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
- Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar P.O. Box 196, Ethiopia
| | - Getayeneh Antehunegn Tesema
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
- Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar P.O. Box 196, Ethiopia
| | - Susannah Ahern
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Arul Earnest
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
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Luo W, Liu Q, Zhou Y, Ran Y, Liu Z, Hou W, Pei S, Lai S. Spatiotemporal Variations of "Triple-demic" Outbreaks of Respiratory Infections in the United States in the Post-COVID-19 Era. medRxiv 2023:2023.05.23.23290387. [PMID: 37293024 PMCID: PMC10246133 DOI: 10.1101/2023.05.23.23290387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Objectives The United States confronted a "triple-demic" of influenza, respiratory syncytial virus, and COVID-19 in the winter of 2022, resulting in increased respiratory infections and a higher demand for medical supplies. It is urgent to analyze each epidemic and their co-occurrence in space and time to identify hotspots and provide insights for public health strategy. Methods We used retrospective space-time scan statistics to retrospect the situation of COVID-19, influenza, and RSV in 51 US states from October 2021 to February 2022, and then applied prospective space-time scan statistics to monitor spatiotemporal variations of each individual epidemic, respectively and collectively from October 2022 to February 2023. Results Our analysis indicated that compared to the winter of 2021, COVID-19 cases decreased while influenza and RSV infections increased significantly during the winter of 2022. We revealed that a twin-demic high-risk cluster of influenza and COVID-19 but no triple-demic clusters emerged during the winter of 2021. We further identified a large high-risk cluster of triple-demic in the central US from late November, with COVID-19, influenza, and RSV having relative risks of 1.14, 1.90, and 1.59, respectively. The number of states at high risk for multiple-demic increased from 15 in October 2022 to 21 in January 2023. Conclusion Our study provides a novel spatiotemporal perspective to explore and monitor the transmission patterns of the triple epidemic, which could inform public health authorities' resource allocation to mitigate future outbreaks.
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Tesema GA, Tessema ZT, Heritier S, Stirling RG, Earnest A. A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research. Int J Environ Res Public Health 2023; 20:5295. [PMID: 37047911 PMCID: PMC10094468 DOI: 10.3390/ijerph20075295] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/13/2023] [Accepted: 03/24/2023] [Indexed: 06/19/2023]
Abstract
With the advancement of spatial analysis approaches, methodological research addressing the technical and statistical issues related to joint spatial and spatiotemporal models has increased. Despite the benefits of spatial modelling of several interrelated outcomes simultaneously, there has been no published systematic review on this topic, specifically when such models would be useful. This systematic review therefore aimed at reviewing health research published using joint spatial and spatiotemporal models. A systematic search of published studies that applied joint spatial and spatiotemporal models was performed using six electronic databases without geographic restriction. A search with the developed search terms yielded 4077 studies, from which 43 studies were included for the systematic review, including 15 studies focused on infectious diseases and 11 on cancer. Most of the studies (81.40%) were performed based on the Bayesian framework. Different joint spatial and spatiotemporal models were applied based on the nature of the data, population size, the incidence of outcomes, and assumptions. This review found that when the outcome is rare or the population is small, joint spatial and spatiotemporal models provide better performance by borrowing strength from related health outcomes which have a higher prevalence. A framework for the design, analysis, and reporting of such studies is also needed.
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Affiliation(s)
- Getayeneh Antehunegn Tesema
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
- Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar 196, Ethiopia
| | - Zemenu Tadesse Tessema
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
- Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar 196, Ethiopia
| | - Stephane Heritier
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Rob G. Stirling
- Department of Respiratory Medicine, Alfred Health, Melbourne, VIC 3004, Australia
- Faculty of Medicine, Nursing and Health Sciences, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia
| | - Arul Earnest
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
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Lan Y, Delmelle E. Space-time cluster detection techniques for infectious diseases: A systematic review. Spat Spatiotemporal Epidemiol 2023; 44:100563. [PMID: 36707196 DOI: 10.1016/j.sste.2022.100563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 12/08/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Public health organizations have increasingly harnessed geospatial technologies for disease surveillance, health services allocation, and targeting place-based health promotion initiatives. METHODS We conducted a systematic review around the theme of space-time clustering detection techniques for infectious diseases using PubMed, Web of Science, and Scopus. Two reviewers independently determined inclusion and exclusion. RESULTS Of 2,887 articles identified, 354 studies met inclusion criteria, the majority of which were application papers. Studies of airborne diseases were dominant, followed by vector-borne diseases. Most research used aggregated data instead of point data, and a significant proportion of articles used a repetition of a spatial clustering method, instead of using a "true" space-time detection approach, potentially leading to the detection of false positives. Noticeably, most articles did not make their data available, limiting replicability. CONCLUSION This review underlines recent trends in the application of space-time clustering methods to the field of infectious disease, with a rapid increase during the COVID-19 pandemic.
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Guchhait S, Das S, Das N, Patra T. Mapping of space-time patterns of infectious disease using spatial statistical models: a case study of COVID-19 in India. Infect Dis (Lond) 2023; 55:27-43. [PMID: 36199164 DOI: 10.1080/23744235.2022.2129778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Mapping of infectious diseases like COVID-19 is the foremost importance for diseases control and prevention. This study attempts to identify the spatio-temporal pattern and evolution trend of COVID-19 at the district level in India using spatial statistical models. MATERIALS AND METHODS Active cases of eleven time-stamps (30 March-2 December, 2020) with an approximately 20-day interval are considered. The study reveals applications of spatial statistical tools, i.e. optimised hotspot and outlier analysis (which follow Gi* and Moran I statistics) and emerging hotspot with the base of space time cube, are effective for the spatio-temporal evolution of disease clusters. RESULTS The result shows the overall increasing trend of COVID-19 infection with a Mann-Kendall trend score of 2.95 (p = 0.0031). The spatial clusters of high infection (hotspots) and low infection (coldspots) change their location over time but are limited to the districts of the south-western states (Kerala, Karnataka, Andhra Pradesh, Maharashtra, Gujarat) and the north-eastern states (West Bengal, Jharkhand, Assam, Tripura, Manipur, etc.) respectively. CONCLUSIONS A total of eight types of patterns are identified, but the most concerning types are consecutive (7.24% of districts), intensifying (15.13% districts) and persistent (24.34% of districts) which will help health policy makers and the government to prioritize-based resource allocation and control measures.
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Affiliation(s)
- Santu Guchhait
- Department of Geography, Panskura Banamali College, Purba Medinipur, India
| | - Subhrangsu Das
- Department of Geography, Utkal University, Bhubaneswar, India
| | - Nirmalya Das
- Department of Geography, Panskura Banamali College, Purba Medinipur, India
| | - Tanmay Patra
- Department of Geography, Panskura Banamali College, Purba Medinipur, India
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Varga C, McDonald P, Brown WM, Shelton P, Roca AL, Novakofski JE, Mateus‐Pinilla NE. Evaluating the ability of a locally focused culling program in removing chronic wasting disease infected free-ranging white-tailed deer in Illinois, USA, 2003-2020. Transbound Emerg Dis 2022; 69:2867-2878. [PMID: 34953169 PMCID: PMC9786818 DOI: 10.1111/tbed.14441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 12/13/2021] [Accepted: 12/22/2021] [Indexed: 12/30/2022]
Abstract
In northern Illinois, chronic wasting disease (CWD) was first identified in free-ranging white-tailed deer (Odocoileus virginianus; hereafter referred to as "deer") in 2002. To reduce CWD transmission rates in Illinois, wildlife biologists have conducted locally focussed culling of deer since 2003 in areas where CWD has been detected. We used retrospective spatial, temporal and space-time scan statistical models to identify areas and periods where culling removed higher than expected numbers of CWD-positive deer. We included 490 Public Land Survey "sections" (∼2.59 km2 ) from 15 northern Illinois counties in which at least one deer tested positive for CWD between 2003 and 2020. A negative binomial regression model compared the proportion of CWD positive cases removed from sections with at least one CWD case detected in the previous years, "local area 1 (L1)," to the proportion of CWD cases in adjacent sections-L2, L3, and L4-designated by their increasing distance from L1. Of the 14,661 deer removed and tested via culling, 325 (2.22 %) were CWD-positive. A single temporal CWD cluster occurred in 2020. Three spatial clusters were identified, with a primary cluster located at the border of Boone and Winnebago counties. Four space-time clusters were identified with a primary cluster in the northern portion of the study area from 2003 to 2005 that overlapped with the spatial cluster. The proportion of CWD cases removed from L1 (3.92, 95% CI, 2.56-6.01) and L2 (2.32, 95% CI, 1.50-3.59) were significantly higher compared to L3. Focussing culling efforts on accessible properties closest to L1 areas results in more CWD-infected deer being removed, which highlights the value of collaborations among landowners, hunters, and wildlife management agencies to control CWD. Continuous evaluation and updating of the culling and surveillance programs are essential to mitigate the health burden of CWD on deer populations in Illinois.
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Affiliation(s)
- Csaba Varga
- Department of PathobiologyCollege of Veterinary MedicineUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA,Carl R. Woese Institute for Genomic BiologyUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
| | - Patrick McDonald
- Illinois Department of Natural ResourcesDivision of Wildlife ResourcesSpringfieldIllinoisUSA
| | - William M. Brown
- Department of PathobiologyCollege of Veterinary MedicineUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
| | - Paul Shelton
- Illinois Department of Natural ResourcesDivision of Wildlife ResourcesSpringfieldIllinoisUSA
| | - Alfred L. Roca
- Carl R. Woese Institute for Genomic BiologyUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA,Illinois Natural History Survey‐Prairie Research InstituteUniversity of Illinois Urbana‐ChampaignChampaignIllinoisUSA,Department of Animal SciencesUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
| | - Jan E. Novakofski
- Illinois Natural History Survey‐Prairie Research InstituteUniversity of Illinois Urbana‐ChampaignChampaignIllinoisUSA,Department of Animal SciencesUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
| | - Nohra E. Mateus‐Pinilla
- Department of PathobiologyCollege of Veterinary MedicineUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA,Illinois Natural History Survey‐Prairie Research InstituteUniversity of Illinois Urbana‐ChampaignChampaignIllinoisUSA,Department of Animal SciencesUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
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12
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Freitas LP, Carabali M, Yuan M, Jaramillo-Ramirez GI, Balaguera CG, Restrepo BN, Zinszer K. Spatio-temporal clusters and patterns of spread of dengue, chikungunya, and Zika in Colombia. PLoS Negl Trop Dis 2022; 16:e0010334. [PMID: 35998165 PMCID: PMC9439233 DOI: 10.1371/journal.pntd.0010334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 09/02/2022] [Accepted: 07/12/2022] [Indexed: 12/03/2022] Open
Abstract
Background Colombia has one of the highest burdens of arboviruses in South America. The country was in a state of hyperendemicity between 2014 and 2016, with co-circulation of several Aedes-borne viruses, including a syndemic of dengue, chikungunya, and Zika in 2015. Methodology/Principal findings We analyzed the cases of dengue, chikungunya, and Zika notified in Colombia from January 2014 to December 2018 by municipality and week. The trajectory and velocity of spread was studied using trend surface analysis, and spatio-temporal high-risk clusters for each disease in separate and for the three diseases simultaneously (multivariate) were identified using Kulldorff’s scan statistics. During the study period, there were 366,628, 77,345 and 74,793 cases of dengue, chikungunya, and Zika, respectively, in Colombia. The spread patterns for chikungunya and Zika were similar, although Zika’s spread was accelerated. Both chikungunya and Zika mainly spread from the regions on the Atlantic coast and the south-west to the rest of the country. We identified 21, 16, and 13 spatio-temporal clusters of dengue, chikungunya and Zika, respectively, and, from the multivariate analysis, 20 spatio-temporal clusters, among which 7 were simultaneous for the three diseases. For all disease-specific analyses and the multivariate analysis, the most-likely cluster was identified in the south-western region of Colombia, including the Valle del Cauca department. Conclusions/Significance The results further our understanding of emerging Aedes-borne diseases in Colombia by providing useful evidence on their potential site of entry and spread trajectory within the country, and identifying spatio-temporal disease-specific and multivariate high-risk clusters of dengue, chikungunya, and Zika, information that can be used to target interventions. Dengue, chikungunya, and Zika are diseases transmitted to humans by the bite of infected Aedes mosquitoes. Between 2014 and 2016 chikungunya and Zika viruses started causing outbreaks in Colombia, one of the countries historically most affected by dengue. We used case counts of the diseases by municipality and week to study the spread trajectory of chikungunya and Zika within Colombia’s territory, and to identify space-time high-risk clusters, i.e., the areas and time periods that dengue, chikungunya, and Zika were more present. Chikungunya and Zika spread similarly in Colombia, but Zika spread faster. The Atlantic coast, a famous touristic destination in the country, was likely the place of entry of chikungunya and Zika in Colombia. The south-western region was identified as a high-risk cluster for all three diseases in separate and simultaneously. This region has a favorable climate for the Aedes mosquitoes and other characteristics that facilitate the diseases’ transmission, such as social deprivation and high population mobility. Our results provide useful information on the locations that should be prioritized for interventions to prevent the entry of new diseases transmitted by Aedes and to reduce the burden of dengue, chikungunya and Zika where they are established.
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Affiliation(s)
- Laís Picinini Freitas
- School of Public Health, University of Montreal, Montreal, Quebec, Canada
- Centre de Recherche en Santé Publique, Montreal, Quebec, Canada
| | - Mabel Carabali
- School of Public Health, University of Montreal, Montreal, Quebec, Canada
- Centre de Recherche en Santé Publique, Montreal, Quebec, Canada
| | - Mengru Yuan
- School of Public Health, University of Montreal, Montreal, Quebec, Canada
| | | | | | - Berta N. Restrepo
- Instituto Colombiano de Medicina Tropical, Universidad CES, Medellín, Colombia
| | - Kate Zinszer
- School of Public Health, University of Montreal, Montreal, Quebec, Canada
- Centre de Recherche en Santé Publique, Montreal, Quebec, Canada
- * E-mail:
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13
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Luo W, Liu Z, Zhou Y, Zhao Y, Li YE, Masrur A, Yu M. Investigating Linkages Between Spatiotemporal Patterns of the COVID-19 Delta Variant and Public Health Interventions in Southeast Asia: Prospective Space-Time Scan Statistical Analysis Method. JMIR Public Health Surveill 2022; 8:e35840. [PMID: 35861674 PMCID: PMC9364972 DOI: 10.2196/35840] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 05/19/2022] [Accepted: 07/19/2022] [Indexed: 12/18/2022] Open
Abstract
Background The COVID-19 Delta variant has presented an unprecedented challenge to countries in Southeast Asia (SEA). Its transmission has shown spatial heterogeneity in SEA after countries have adopted different public health interventions during the process. Hence, it is crucial for public health authorities to discover potential linkages between epidemic progression and corresponding interventions such that collective and coordinated control measurements can be designed to increase their effectiveness at reducing transmission in SEA. Objective The purpose of this study is to explore potential linkages between the spatiotemporal progression of the COVID-19 Delta variant and nonpharmaceutical intervention (NPI) measures in SEA. We detected the space-time clusters of outbreaks of COVID-19 and analyzed how the NPI measures relate to the propagation of COVID-19. Methods We collected district-level daily new cases of COVID-19 from June 1 to October 31, 2021, and district-level population data in SEA. We adopted prospective space-time scan statistics to identify the space-time clusters. Using cumulative prospective space-time scan statistics, we further identified variations of relative risk (RR) across each district at a half-month interval and their potential public health intervention linkages. Results We found 7 high-risk clusters (clusters 1-7) of COVID-19 transmission in Malaysia, the Philippines, Thailand, Vietnam, and Indonesia between June and August, 2021, with an RR of 5.45 (P<.001), 3.50 (P<.001), 2.30 (P<.001), 1.36 (P<.001), 5.62 (P<.001), 2.38 (P<.001), 3.45 (P<.001), respectively. There were 34 provinces in Indonesia that have successfully mitigated the risk of COVID-19, with a decreasing range between –0.05 and –1.46 due to the assistance of continuous restrictions. However, 58.6% of districts in Malaysia, Singapore, Thailand, and the Philippines saw an increase in the infection risk, which is aligned with their loosened restrictions. Continuous strict interventions were effective in mitigating COVID-19, while relaxing restrictions may exacerbate the propagation risk of this epidemic. Conclusions The analyses of space-time clusters and RRs of districts benefit public health authorities with continuous surveillance of COVID-19 dynamics using real-time data. International coordination with more synchronized interventions amidst all SEA countries may play a key role in mitigating the progression of COVID-19.
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Affiliation(s)
- Wei Luo
- Department of Geography, National University of Singapore, Singapore, Singapore
| | - Zhaoyin Liu
- Department of Geography, National University of Singapore, Singapore, Singapore
| | - Yuxuan Zhou
- Department of Geography, National University of Singapore, Singapore, Singapore
| | - Yumin Zhao
- Department of Civil and Environmental Engineering, National University of Singapore, Singapore, Singapore
| | - Yunyue Elita Li
- Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN, United States
| | - Arif Masrur
- Department of Geography, Pennsylvania State University, State College, PA, United States
| | - Manzhu Yu
- Department of Geography, Pennsylvania State University, State College, PA, United States
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Moutinho S, Rocha J, Gomes A, Gomes B, Ribeiro AI. Spatial Analysis of Mosquito-Borne Diseases in Europe: A Scoping Review. Sustainability 2022; 14:8975. [DOI: 10.3390/su14158975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Mosquito-borne infections are increasing in endemic areas and previously unaffected regions. In 2020, the notification rate for Dengue was 0.5 cases per 100,000 population, and for Chikungunya <0.1/100,000. In 2019, the rate for Malaria was 1.3/100,000, and for West Nile Virus, 0.1/100,000. Spatial analysis is increasingly used in surveillance and epidemiological investigation, but reviews about their use in this research topic are scarce. We identify and describe the methodological approaches used to investigate the distribution and ecological determinants of mosquito-borne infections in Europe. Relevant literature was extracted from PubMed, Scopus, and Web of Science from inception until October 2021 and analysed according to PRISMA-ScR protocol. We identified 110 studies. Most used geographical correlation analysis (n = 50), mainly applying generalised linear models, and the remaining used spatial cluster detection (n = 30) and disease mapping (n = 30), mainly conducted using frequentist approaches. The most studied infections were Dengue (n = 32), Malaria (n = 26), Chikungunya (n = 26), and West Nile Virus (n = 24), and the most studied ecological determinants were temperature (n = 39), precipitation (n = 24), water bodies (n = 14), and vegetation (n = 11). Results from this review may support public health programs for mosquito-borne disease prevention and may help guide future research, as we recommended various good practices for spatial epidemiological studies.
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Mucaki EJ, Shirley BC, Rogan PK. Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada. F1000Res 2022; 10:1312. [PMID: 35646330 PMCID: PMC9130760 DOI: 10.12688/f1000research.75891.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/19/2022] [Indexed: 11/20/2022] Open
Abstract
Introduction: This study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. Methods: COVID-19 cases and locations were curated for geostatistical analyses from March 2020 through June 2021, corresponding to the first, second, and third waves of infections. Daily cases were aggregated according to designated forward sortation area (FSA), and postal codes (PC) in municipal regions Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, and Windsor/Essex county. Hotspots were identified with area-to-area tests including Getis-Ord Gi*, Global Moran’s I spatial autocorrelation, and Local Moran’s I asymmetric clustering and outlier analyses. Case counts were also interpolated across geographic regions by Empirical Bayesian Kriging, which localizes high concentrations of COVID-19 positive tests, independent of FSA or PC boundaries. The
Geostatistical Disease Epidemiology Toolbox, which is freely-available software, automates the identification of these regions and produces digital maps for public health professionals to assist in pandemic management of contact tracing and distribution of other resources. Results: This study provided indicators in real-time of likely, community-level disease transmission through innovative geospatial analyses of COVID-19 incidence data. Municipal and provincial results were validated by comparisons with known outbreaks at long-term care and other high density residences and on farms. PC-level analyses revealed hotspots at higher geospatial resolution than public reports of FSAs, and often sooner. Results of different tests and kriging were compared to determine consistency among hotspot assignments. Concurrent or consecutive hotspots in close proximity suggested potential community transmission of COVID-19 from cluster and outlier analysis of neighboring PCs and by kriging. Results were also stratified by population based-categories (sex, age, and presence/absence of comorbidities). Conclusions: Earlier recognition of hotspots could reduce public health burdens of COVID-19 and expedite contact tracing.
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Affiliation(s)
- Eliseos J. Mucaki
- Department of Biochemistry, University of Western Ontario, London, Ontario, N6A 5C1, Canada
- CytoGnomix Inc, London, Ontario, N5X 3X5, Canada
| | | | - Peter K. Rogan
- Department of Biochemistry, University of Western Ontario, London, Ontario, N6A 5C1, Canada
- CytoGnomix Inc, London, Ontario, N5X 3X5, Canada
- Department of Oncology, University of Western Ontario, London, Ontario, N6A 5C1, Canada
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16
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Abstract
The COVID-19 pandemic has drawn greater attention to social determinants of health and associated health inequities, which disproportionately affect vulnerable populations and places in the U.S. In this study, we explored geographic patterns of local-level COVID-19 vulnerability and associations with social and health determinants across Colorado. To conceptualize social and health determinants and how together they generate risk and exposure, we integrated the concepts of social vulnerability and syndemic to situate COVID-19 vulnerability within a broader hazards of place framework. Using geospatial statistics and GIS, we estimated census tract-level rates of COVID-19, which are not yet available in Colorado, and mapped areas of high and low incidence risk. We also developed composite indices that characterized social and health vulnerabilities to measure multivariate associations with COVID-19 rates. The findings revealed hotspots of persistent risk in mountain communities since the pandemic emerged in Colorado, as well as clusters of risk in the Urban Front Range's central and southern counties, and across many parts of eastern Colorado. Vulnerability analyses indicate that COVID-19 rates were associated with mental health and chronic conditions along with social determinants that represent inequities in education, income, healthcare access, and race/ethnicity (minority percent of population), which may have disproportionately exposed some communities more than others to infection and severe health outcomes. Overall, the findings provide geographic health information about COVID-19 and vulnerability context, which may better inform local decision-making for interventions and policies that support equity of social determinants of health.Supplemental data for this article is available online at https://doi.org/10.1080/08964289.2021.2021382 .
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Affiliation(s)
- Jieun Lee
- Department of Geography, GIS and Sustainability, University of Northern Colorado
| | - Ivan J Ramírez
- Department of Health and Behavioral Sciences, University of Colorado Denver
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17
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Näslund J, Ahlm C, Islam K, Evander M, Bucht G, Lwande OW. Emerging Mosquito-Borne Viruses Linked to Aedes aegypti and Aedes albopictus: Global Status and Preventive Strategies. Vector Borne Zoonotic Dis 2021; 21:731-746. [PMID: 34424778 DOI: 10.1089/vbz.2020.2762] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Emerging mosquito-borne viruses continue to cause serious health problems and economic burden among billions of people living in and near the tropical belt of the world. The highly invasive mosquito species Aedes aegypti and Aedes albopictus have successively invaded and expanded their presence as key vectors of Chikungunya virus, dengue virus, yellow fever virus, and Zika virus, and that has consecutively led to frequent outbreaks of the corresponding viral diseases. Of note, these two mosquito species have gradually adapted to the changing weather and environmental conditions leading to a shift in the epidemiology of the viral diseases, and facilitated their establishment in new ecozones inhabited by immunologically naive human populations. Many abilities of Ae. aegypti and Ae. albopictus, as vectors of significant arbovirus pathogens, may affect the infection and transmission rates after a bloodmeal, and may influence the vector competence for either virus. We highlight that many collaborating risk factors, for example, the global transportation systems may result in sporadic and more local outbreaks caused by mosquito-borne viruses related to Ae. aegypti and/or Ae. albopictus. Those local outbreaks could in synergy grow and produce larger epidemics with pandemic characters. There is an urgent need for improved surveillance of vector populations, human cases, and reliable prediction models. In summary, we recommend new and innovative strategies for the prevention of these types of infections.
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Affiliation(s)
- Jonas Näslund
- Swedish Defence Research Agency, CBRN, Defence and Security, Umeå, Sweden
| | - Clas Ahlm
- Department of Clinical Microbiology, Umeå University, Umea, Sweden.,Arctic Research Centre at Umeå University, Umea, Sweden
| | - Koushikul Islam
- Department of Clinical Microbiology, Umeå University, Umea, Sweden
| | - Magnus Evander
- Department of Clinical Microbiology, Umeå University, Umea, Sweden.,Arctic Research Centre at Umeå University, Umea, Sweden
| | - Göran Bucht
- Department of Clinical Microbiology, Umeå University, Umea, Sweden
| | - Olivia Wesula Lwande
- Department of Clinical Microbiology, Umeå University, Umea, Sweden.,Arctic Research Centre at Umeå University, Umea, Sweden
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Dass S, Ngui R, Gill BS, Chan YF, Wan Sulaiman WY, Lim YAL, Mudin RN, Chong CK, Sulaiman LH, Sam IC. Spatiotemporal spread of chikungunya virus in Sarawak, Malaysia. Trans R Soc Trop Med Hyg 2021; 115:922-931. [PMID: 33783526 DOI: 10.1093/trstmh/trab053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 12/11/2020] [Accepted: 03/10/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND We studied the spatiotemporal spread of a chikungunya virus (CHIKV) outbreak in Sarawak state, Malaysia, during 2009-2010. METHODS The residential addresses of 3054 notified CHIKV cases in 2009-2010 were georeferenced onto a base map of Sarawak with spatial data of rivers and roads using R software. The spatiotemporal spread was determined and clusters were detected using the space-time scan statistic with SaTScan. RESULTS Overall CHIKV incidence was 127 per 100 000 population (range, 0-1125 within districts). The average speed of spread was 70.1 km/wk, with a peak of 228 cases/wk and the basic reproduction number (R0) was 3.1. The highest age-specific incidence rate was 228 per 100 000 in adults aged 50-54 y. Significantly more cases (79.4%) lived in rural areas compared with the general population (46.2%, p<0.0001). Five CHIKV clusters were detected. Likely spread was mostly by road, but a fifth of rural cases were spread by river travel. CONCLUSIONS CHIKV initially spread quickly in rural areas mainly via roads, with lesser involvement of urban areas. Delayed spread occurred via river networks to more isolated areas in the rural interior. Understanding the patterns and timings of arboviral outbreak spread may allow targeted vector control measures at key transport hubs or in large transport vehicles.
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Affiliation(s)
- Sarat Dass
- School of Mathematical & Computer Sciences, Heriot-Watt University Malaysia, 62200 Putrajaya, Malaysia
| | - Romano Ngui
- Department of Parasitology, Faculty of Medicine, University Malaya, 50603 Kuala Lumpur
| | | | - Yoke Fun Chan
- Department of Medical Microbiology, Faculty of Medicine, University Malaya, 50603 Kuala Lumpur, Malaysia
| | | | - Yvonne Ai Lian Lim
- Department of Parasitology, Faculty of Medicine, University Malaya, 50603 Kuala Lumpur
| | - Rose Nani Mudin
- Vector Borne Disease Sector, Disease Control Division, Ministry of Health Malaysia, Pusat Pentadbiran Kerajaan Persekutuan, 62590 Putrajaya, Malaysia
| | - Chee Kheong Chong
- Office of the Deputy Director General of Health (Public Health), Ministry of Health Malaysia, Pusat Pentadbiran Kerajaan Persekutuan, 62590 Putrajaya
| | - Lokman Hakim Sulaiman
- Department of Community Medicine, School of Medicine, International Medical University, Bukit Jalil, 57000 Kuala Lumpur, Malaysia.,Institute for Research, Development and Innovation, International Medical University, Bukit Jalil, 57000 Kuala Lumpur, Malaysia
| | - I-Ching Sam
- Department of Medical Microbiology, Faculty of Medicine, University Malaya, 50603 Kuala Lumpur, Malaysia
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Kan Z, Kwan MP, Wong MS, Huang J, Liu D. Identifying the space-time patterns of COVID-19 risk and their associations with different built environment features in Hong Kong. Sci Total Environ 2021; 772:145379. [PMID: 33578150 PMCID: PMC7839428 DOI: 10.1016/j.scitotenv.2021.145379] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 01/11/2021] [Accepted: 01/19/2021] [Indexed: 05/07/2023]
Abstract
Identifying the space-time patterns of areas with a higher risk of transmission and the associated built environment and demographic characteristics during the COVID-19 pandemic is critical for developing targeted intervention measures in response to the pandemic. This study aims to identify areas with a higher risk of COVID-19 transmission in different periods in Hong Kong and analyze the associated built environment and demographic factors using data of individual confirmed cases. We detect statistically significant space-time clusters of COVID-19 at the Large Street Block Group (LSBG) level in Hong Kong between January 23 and April 14, 2020. Two types of high-risk areas are identified (residences of and places visited by confirmed cases) and two types of cases (imported and local cases) are considered. The demographic and built environment features for the identified high-risk areas are further examined. The results indicate that high transport accessibility, dense and high-rise buildings, a higher density of commercial land and higher land-use mix are associated with a higher risk for places visited by confirmed cases. More green spaces, higher median household income, lower commercial land density are linked to a higher risk for the residences of confirmed cases. The results in this study not only can inform policymakers to improve resource allocation and intervention strategies but also can provide guidance to the public to avoid conducting high-risk activities and visiting high-risk places.
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Affiliation(s)
- Zihan Kan
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, 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.
| | - Man Sing Wong
- Department of Land Surveying and Geo-Informatics, & Research Institute for Sustainable Urban Development, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
| | - Jianwei Huang
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Dong Liu
- Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, 1301 W Green St, Urbana, IL 61801, United States
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20
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Rosillo N, Del-Águila-Mejía J, Rojas-Benedicto A, Guerrero-Vadillo M, Peñuelas M, Mazagatos C, Segú-Tell J, Ramis R, Gómez-Barroso D. Real time surveillance of COVID-19 space and time clusters during the summer 2020 in Spain. BMC Public Health 2021; 21:961. [PMID: 34016076 PMCID: PMC8137313 DOI: 10.1186/s12889-021-10961-z] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 04/28/2021] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND On June 21st de-escalation measures and state-of-alarm ended in Spain after the COVID-19 first wave. New surveillance and control strategy was set up to detect emerging outbreaks. AIM To detect and describe the evolution of COVID-19 clusters and cases during the 2020 summer in Spain. METHODS A near-real time surveillance system to detect active clusters of COVID-19 was developed based on Kulldorf's prospective space-time scan statistic (STSS) to detect daily emerging active clusters. RESULTS Analyses were performed daily during the summer 2020 (June 21st - August 31st) in Spain, showing an increase of active clusters and municipalities affected. Spread happened in the study period from a few, low-cases, regional-located clusters in June to a nationwide distribution of bigger clusters encompassing a higher average number of municipalities and total cases by end-August. CONCLUSION STSS-based surveillance of COVID-19 can be of utility in a low-incidence scenario to help tackle emerging outbreaks that could potentially drive a widespread transmission. If that happens, spatial trends and disease distribution can be followed with this method. Finally, cluster aggregation in space and time, as observed in our results, could suggest the occurrence of community transmission.
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Affiliation(s)
- Nicolás Rosillo
- Servicio de Medicina Preventiva. Centro de Actividades Ambulatorias, 6ª planta, Bloque C, Hospital Universitario 12 de Octubre. Avenida de Córdoba, s/n, 28041, Madrid, Spain.,Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029, Madrid, Spain
| | - Javier Del-Águila-Mejía
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029, Madrid, Spain.,Servicio de Medicina Preventiva, Hospital Universitario de Móstoles, Calle Río Júcar, s/n, 28935, Móstoles, Spain
| | - Ayelén Rojas-Benedicto
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029, Madrid, Spain.,Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Calle Monforte de Lemos 3-5, 28029, Madrid, Spain
| | - María Guerrero-Vadillo
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029, Madrid, Spain
| | - Marina Peñuelas
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029, Madrid, Spain
| | - Clara Mazagatos
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029, Madrid, Spain.,Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Calle Monforte de Lemos 3-5, 28029, Madrid, Spain
| | - Jordi Segú-Tell
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029, Madrid, Spain.,Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Calle Monforte de Lemos 3-5, 28029, Madrid, Spain
| | - Rebeca Ramis
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029, Madrid, Spain. .,Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Calle Monforte de Lemos 3-5, 28029, Madrid, Spain.
| | - Diana Gómez-Barroso
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029, Madrid, Spain.,Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Calle Monforte de Lemos 3-5, 28029, Madrid, Spain
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21
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Varga C, John P, Cooke M, Majowicz SE. Area-Level Clustering of Shiga Toxin-Producing Escherichia coli Infections and Their Socioeconomic and Demographic Factors in Ontario, Canada: An Ecological Study. Foodborne Pathog Dis 2021; 18:438-447. [PMID: 33978473 DOI: 10.1089/fpd.2020.2918] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Shiga toxin-producing Escherichia coli (STEC) infections are an important health burden for human populations in Ontario and worldwide. We assessed 452 STEC cases that were reported to Ontario's reportable disease surveillance system between 2015 and 2017. A retrospective scan statistic using a Poisson model was used to detect high-rate STEC clusters at the forward sortation area (FSA; the first three digits of a postal code) level. A significant spatial cluster in the southwest region of Ontario was identified. A case-case logistic regression analysis was applied to compare FSA-level socioeconomic and demographic characteristics among STEC cases included inside the spatial cluster with cases outside of the cluster. Cases included in the spatial cluster had higher odds of living in FSAs with a low median family income, low proportion of lone-parent families, and low proportion of the visible minority population. In addition, STEC cases inside the cluster had higher odds of coming from rural FSAs. Our study demonstrated that STEC cases were spatially clustered in Ontario and their clustering was associated with FSA-level socioeconomic and demographic determinants of cases.
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Affiliation(s)
- Csaba Varga
- Department of Pathobiology, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,School of Public Health and Health Systems, University of Waterloo, Waterloo, Canada
| | - Patience John
- School of Public Health and Health Systems, University of Waterloo, Waterloo, Canada
| | - Martin Cooke
- School of Public Health and Health Systems, University of Waterloo, Waterloo, Canada.,Department of Sociology and Legal Studies, University of Waterloo, Waterloo, Canada
| | - Shannon E Majowicz
- School of Public Health and Health Systems, University of Waterloo, Waterloo, Canada
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22
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Güemes A, Ray S, Aboumerhi K, Desjardins MR, Kvit A, Corrigan AE, Fries B, Shields T, Stevens RD, Curriero FC, Etienne-Cummings R. A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States. Sci Rep 2021; 11:4660. [PMID: 33633250 DOI: 10.1101/2020.08.18.20177295] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 02/12/2021] [Indexed: 05/25/2023] Open
Abstract
Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.
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Affiliation(s)
- Amparo Güemes
- Department of Electrical and Computer Engineering, Johns Hopkins Whiting School of Engineering, The Johns Hopkins University, 3400 N. Charles Street, 105 Barton Hall, Baltimore, MD, 21218, USA.
| | - Soumyajit Ray
- Department of Electrical and Computer Engineering, Johns Hopkins Whiting School of Engineering, The Johns Hopkins University, 3400 N. Charles Street, 105 Barton Hall, Baltimore, MD, 21218, USA
| | - Khaled Aboumerhi
- Department of Electrical and Computer Engineering, Johns Hopkins Whiting School of Engineering, The Johns Hopkins University, 3400 N. Charles Street, 105 Barton Hall, Baltimore, MD, 21218, USA
| | - Michael R Desjardins
- Department of Epidemiology, Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Anton Kvit
- Department of Epidemiology, Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Anne E Corrigan
- Department of Epidemiology, Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Brendan Fries
- Department of Epidemiology, Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Timothy Shields
- Department of Epidemiology, Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Robert D Stevens
- Department of Anesthesiology and Critical Care Medicine, Neurology, Neurosurgery and Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Frank C Curriero
- Department of Epidemiology, Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Ralph Etienne-Cummings
- Department of Electrical and Computer Engineering, Johns Hopkins Whiting School of Engineering, The Johns Hopkins University, 3400 N. Charles Street, 105 Barton Hall, Baltimore, MD, 21218, USA
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23
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Güemes A, Ray S, Aboumerhi K, Desjardins MR, Kvit A, Corrigan AE, Fries B, Shields T, Stevens RD, Curriero FC, Etienne-Cummings R. A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States. Sci Rep 2021; 11:4660. [PMID: 33633250 PMCID: PMC7907397 DOI: 10.1038/s41598-021-84145-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 02/12/2021] [Indexed: 11/13/2022] Open
Abstract
Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.
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Affiliation(s)
- Amparo Güemes
- Department of Electrical and Computer Engineering, Johns Hopkins Whiting School of Engineering, The Johns Hopkins University, 3400 N. Charles Street, 105 Barton Hall, Baltimore, MD, 21218, USA.
| | - Soumyajit Ray
- Department of Electrical and Computer Engineering, Johns Hopkins Whiting School of Engineering, The Johns Hopkins University, 3400 N. Charles Street, 105 Barton Hall, Baltimore, MD, 21218, USA
| | - Khaled Aboumerhi
- Department of Electrical and Computer Engineering, Johns Hopkins Whiting School of Engineering, The Johns Hopkins University, 3400 N. Charles Street, 105 Barton Hall, Baltimore, MD, 21218, USA
| | - Michael R Desjardins
- Department of Epidemiology, Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Anton Kvit
- Department of Epidemiology, Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Anne E Corrigan
- Department of Epidemiology, Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Brendan Fries
- Department of Epidemiology, Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Timothy Shields
- Department of Epidemiology, Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Robert D Stevens
- Department of Anesthesiology and Critical Care Medicine, Neurology, Neurosurgery and Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Frank C Curriero
- Department of Epidemiology, Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Ralph Etienne-Cummings
- Department of Electrical and Computer Engineering, Johns Hopkins Whiting School of Engineering, The Johns Hopkins University, 3400 N. Charles Street, 105 Barton Hall, Baltimore, MD, 21218, USA
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24
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Martines MR, Ferreira RV, Toppa RH, Assunção LM, Desjardins MR, Delmelle EM. Detecting space-time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities. J Geogr Syst 2021; 23:7-36. [PMID: 33716567 PMCID: PMC7938278 DOI: 10.1007/s10109-020-00344-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Accepted: 12/15/2020] [Indexed: 05/19/2023]
Abstract
The first case of COVID-19 in South America occurred in Brazil on February 25, 2020. By July 20, 2020, there were 2,118,646 confirmed cases and 80,120 confirmed deaths. To assist with the development of preventive measures and targeted interventions to combat the pandemic in Brazil, we present a geographic study to detect "active" and "emerging" space-time clusters of COVID-19. We document the relationship between relative risk of COVID-19 and mortality, inequality, socioeconomic vulnerability variables. We used the prospective space-time scan statistic to detect daily COVID-19 clusters and examine the relative risk between February 25-June 7, 2020, and February 25-July 20, 2020, in 5570 Brazilian municipalities. We apply a Generalized Linear Model (GLM) to assess whether mortality rate, GINI index, and social inequality are predictors for the relative risk of each cluster. We detected 7 "active" clusters in the first time period, being one in the north, two in the northeast, two in the southeast, one in the south, and one in the capital of Brazil. In the second period, we found 9 clusters with RR > 1 located in all Brazilian regions. The results obtained through the GLM showed that there is a significant positive correlation between the predictor variables in relation to the relative risk of COVID-19. Given the presence of spatial autocorrelation in the GLM residuals, a spatial lag model was conducted that revealed that spatial effects, and both GINI index and mortality rate were strong predictors in the increase in COVID-19 relative risk in Brazil. Our research can be utilized to improve COVID-19 response and planning in all Brazilian states. The results from this study are particularly salient to public health, as they can guide targeted intervention measures, lowering the magnitude and spread of COVID-19. They can also improve resource allocation such as tests and vaccines (when available) by informing key public health officials about the highest risk areas of COVID-19.
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Affiliation(s)
- M. R. Martines
- Department of Geography, Tourism and Humanities, Research Group: Center for Studies in Landscape Ecology and Conservation, Federal University of São Carlos, Sorocaba, SP Brazil
| | - R. V. Ferreira
- Department of Geography, Research Group: Center for Studies in Landscape Ecology and Conservation, Federal University of Triângulo Mineiro, Uberaba Campus, State of Minas Gerais Brazil
| | - R. H. Toppa
- Department of Environmental Sciences, Research Group: Center for Studies in Landscape Ecology and Conservation, Federal University of São Carlos, Sorocaba, SP Brazil
| | - L. M. Assunção
- Faculty of Law, State University of Minas Gerais, Ituiutaba Campus, Brazil
| | - M. R. Desjardins
- Department of Epidemiology, Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205 USA
| | - E. M. Delmelle
- Department of Geography and Earth Sciences, Center for Applied Geographic Information Science, University of North Carolina at Charlotte, Charlotte, NC 28223 USA
- Department of Geographical and Historical Studies, University of Eastern Finland, 80101 Joensuu, Finland
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25
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Yoneoka D, Tanoue Y, Kawashima T, Nomura S, Shi S, Eguchi A, Ejima K, Taniguchi T, Sakamoto H, Kunishima H, Gilmour S, Nishiura H, Miyata H. Large-scale epidemiological monitoring of the COVID-19 epidemic in Tokyo. Lancet Reg Health West Pac 2020; 3:100016. [PMID: 34173599 DOI: 10.1016/j.lanwpc.2020.100016] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 08/05/2020] [Accepted: 08/19/2020] [Indexed: 12/23/2022]
Abstract
Background On April 7, 2020, the Japanese government declared a state of emergency regarding the novel coronavirus (COVID-19). Given the nation-wide spread of the coronavirus in major Japanese cities and the rapid increase in the number of cases with untraceable infection routes, large-scale monitoring for capturing the current epidemiological situation of COVID-19 in Japan is urgently required. Methods A chatbot-based healthcare system named COOPERA (COvid-19: Operation for Personalized Empowerment to Render smart prevention And AN care seeking) was developed to surveil the Japanese epidemiological situation in real-time. COOPERA asked questions regarding personal information, location, preventive actions, COVID-19 related symptoms and their residence. Empirical Bayes estimates of the age-sex-standardized incidence rate and disease mapping approach using scan statistics were utilized to identify the geographical distribution of the symptoms in Tokyo and their spatial correlation r with the identified COVID-19 cases. Findings We analyzed 353,010 participants from Tokyo recruited from 27th March to 6th April 2020. The mean (SD) age of participants was 42.7 (12.3), and 63.4%, 36.4% or 0.2% were female, male, or others, respectively. 95.6% of participants had no subjective symptoms. We identified several geographical clusters with high spatial correlation (r = 0.9), especially in downtown areas in central Tokyo such as Shibuya and Shinjuku. Interpretation With the global spread of COVID-19, medical resources are being depleted. A new system to monitor the epidemiological situation, COOPERA, can provide insights to assist political decision to tackle the epidemic. In addition, given that Japan has not had a strong lockdown policy to weaken the spread of the infection, our result would be useful for preparing for the second wave in other countries during the next flu season without a strong lockdown. Funding The present work was supported in part by a grant from the Ministry of Health, Labour and Welfare of Japan (H29-Gantaisaku-ippan-009).
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26
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Kazazian L, Lima Neto AS, Sousa GS, do Nascimento OJ, Castro MC. Spatiotemporal transmission dynamics of co-circulating dengue, Zika, and chikungunya viruses in Fortaleza, Brazil: 2011-2017. PLoS Negl Trop Dis 2020; 14:e0008760. [PMID: 33104708 PMCID: PMC7644107 DOI: 10.1371/journal.pntd.0008760] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 11/05/2020] [Accepted: 08/30/2020] [Indexed: 12/16/2022] Open
Abstract
The mosquito-borne viruses dengue (DENV), Zika (ZIKV), and chikungunya (CHIKV), now co-endemic in the Americas, pose growing threats to health worldwide. However, it remains unclear whether there exist interactions between these viruses that could shape their epidemiology. This study advances knowledge by assessing the transmission dynamics of co-circulating DENV, ZIKV, and CHIKV in the city of Fortaleza, Brazil. Spatiotemporal transmission dynamics of DENV, ZIKV, and CHIKV were analyzed using georeferenced data on over 210,000 reported cases from 2011 to 2017 in Fortaleza, Brazil. Local spatial clustering tests and space-time scan statistics were used to compare transmission dynamics across all years. The transmission of co-circulating viruses in 2016 and 2017 was evaluated at fine spatial and temporal scales using a measure of spatiotemporal dependence, the τ-statistic. Results revealed differences in the diffusion of CHIKV compared to previous DENV epidemics and spatially distinct transmission of DENV/ZIKV and CHIKV during the period of their co-circulation. Significant spatial clustering of viruses of the same type was observed within 14-day time intervals at distances of up to 6.8 km (p<0.05). These results suggest that arbovirus risk is not uniformly distributed within cities during co-circulation. Findings may guide outbreak preparedness and response efforts by highlighting the clustered nature of transmission of co-circulating arboviruses at the neighborhood level. The potential for competitive interactions between the arboviruses should be further investigated.
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Affiliation(s)
- Lilit Kazazian
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Antonio S. Lima Neto
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Health Surveillance Department, Fortaleza Municipal Health Secretariat (SMS-Fortaleza), Joaquim Távora, Fortaleza, Ceará, Brazil
- Health Sciences Center, University of Fortaleza (UNIFOR), Edson Queiroz, Fortaleza, Ceará, Brazil
| | - Geziel S. Sousa
- Health Surveillance Department, Fortaleza Municipal Health Secretariat (SMS-Fortaleza), Joaquim Távora, Fortaleza, Ceará, Brazil
| | - Osmar José do Nascimento
- Health Surveillance Department, Fortaleza Municipal Health Secretariat (SMS-Fortaleza), Joaquim Távora, Fortaleza, Ceará, Brazil
| | - Marcia C. Castro
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
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27
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Pham NT, Nguyen CT, Pineda-Cortel MRB. Time-series modelling of dengue incidence in the Mekong Delta region of Viet Nam using remote sensing data. Western Pac Surveill Response J 2020; 11:13-21. [PMID: 32963887 DOI: 10.5365/wpsar.2018.9.2.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Objective This study aims to enhance the capacity of dengue prediction by investigating the relationship of dengue incidence with climate and environmental factors in the Mekong Delta region (MDR) of Viet Nam by using remote sensing data. Methods To produce monthly data sets for each province, we extracted and aggregated precipitation data from the Global Satellite Mapping of Precipitation project and land surface temperatures and normalized difference vegetation indexes from the Moderate Resolution Imaging Spectroradiometer satellite observations. Monthly data sets from 2000 to 2016 were used to construct autoregressive integrated moving average (ARIMA) models to predict dengue incidence for 12 provinces across the study region. Results The final models were able to predict dengue incidence from January to December 2016 that concurred with the observation that dengue epidemics occur mostly in rainy seasons. As a result, the obtained model presents a good fit at a regional level with the correlation value of 0.65 between predicted and reported dengue cases; nevertheless, its performance declines at the subregional scale. Conclusion We demonstrated the use of remote sensing data in time-series to develop a model of dengue incidence in the MDR of Viet Nam. Results indicated that this approach could be an effective method to predict regional dengue incidence and its trends.
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28
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Desjardins MR, Eastin MD, Paul R, Casas I, Delmelle EM. Space-Time Conditional Autoregressive Modeling to Estimate Neighborhood-Level Risks for Dengue Fever in Cali, Colombia. Am J Trop Med Hyg 2020; 103:2040-2053. [PMID: 32876013 PMCID: PMC7646775 DOI: 10.4269/ajtmh.20-0080] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Vector-borne diseases affect more than 1 billion people a year worldwide, causing more than 1 million deaths, and cost hundreds of billions of dollars in societal costs. Mosquitoes are the most common vectors responsible for transmitting a variety of arboviruses. Dengue fever (DENF) has been responsible for nearly 400 million infections annually. Dengue fever is primarily transmitted by female Aedes aegypti and Aedes albopictus mosquitoes. Because both Aedes species are peri-domestic and container-breeding mosquitoes, dengue surveillance should begin at the local level—where a variety of local factors may increase the risk of transmission. Dengue has been endemic in Colombia for decades and is notably hyperendemic in the city of Cali. For this study, we use weekly cases of DENF in Cali, Colombia, from 2015 to 2016 and develop space–time conditional autoregressive models to quantify how DENF risk is influenced by socioeconomic, environmental, and accessibility risk factors, and lagged weather variables. Our models identify high-risk neighborhoods for DENF throughout Cali. Statistical inference is drawn under Bayesian paradigm using Markov chain Monte Carlo techniques. The results provide detailed insight about the spatial heterogeneity of DENF risk and the associated risk factors (such as weather, proximity to Aedes habitats, and socioeconomic classification) at a fine level, informing public health officials to motivate at-risk neighborhoods to take an active role in vector surveillance and control, and improving educational and surveillance resources throughout the city of Cali.
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Affiliation(s)
- Michael R Desjardins
- Department of Epidemiology, Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Matthew D Eastin
- Department of Geography and Earth Sciences, Center for Applied Geographic Information Science, University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Rajib Paul
- Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Irene Casas
- School of History and Social Sciences, Louisiana Tech University, Ruston, Louisiana
| | - Eric M Delmelle
- Department of Geography and Earth Sciences, Center for Applied Geographic Information Science, University of North Carolina at Charlotte, Charlotte, North Carolina
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29
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Nomura S, Yoneoka D, Shi S, Tanoue Y, Kawashima T, Eguchi A, Matsuura K, Makiyama K, Ejima K, Taniguchi T, Sakamoto H, Kunishima H, Gilmour S, Nishiura H, Miyata H. An assessment of self-reported COVID-19 related symptoms of 227,898 users of a social networking service in Japan: Has the regional risk changed after the declaration of the state of emergency? Lancet Reg Health West Pac 2020; 1:100011. [PMID: 34173594 PMCID: PMC7453215 DOI: 10.1016/j.lanwpc.2020.100011] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 07/10/2020] [Accepted: 07/19/2020] [Indexed: 01/10/2023]
Abstract
Background In the absence of widespread testing, symptomatic monitoring efforts may allow for understanding the epidemiological situation of the spread of coronavirus disease 2019 (COVID-19) in Japan. We obtained data from a social networking service (SNS) messaging application that monitors self-reported COVID-19 related symptoms in real time in Fukuoka Prefecture, Japan. We aimed at not only understanding the epidemiological situation of COVID-19 in the prefecture, but also highlighting the usefulness of symptomatic monitoring approaches that rely on self-reporting using SNS during a pandemic, and informing the assessment of Japan's emergency declaration over COVID-19. Methods We analysed symptoms data (fever over 37.5° and a strong feeling of weariness or shortness of breath), reported voluntarily via SNS chatbot by 227,898 residents of Fukuoka Prefecture during March 27 to May 3, 2020, including April 7, when a state of emergency was declared. We estimated the spatial correlation coefficient between the number of the self-reported cases of COVID-19 related symptoms and the number of PCR confirmed COVID-19 cases in the period (obtained from the prefecture website); and estimated the empirical Bayes age- and sex-standardised incidence ratio (EBSIR) of the symptoms in the period, compared before and after the declaration. The number of symptom cases was weighted by age and sex to reflect the regional population distribution according to the 2015 national census. Findings Of the participants, 3.47% reported symptoms. There was a strong spatial correlation of 0.847 (p < 0.001) at municipality level between the weighted number of self-reported symptoms and the number of COVID-19 cases for both symptoms. The EBSIR at post-code level was not likely to change remarkably before and after the declaration of the emergency, but the gap in EBSIR between high-risk and low-risk areas appeared to have increased after the declaration. Interpretation While caution is necessary as the data was limited to SNS users, the self-reported COVID-19 related symptoms considered in the study had high epidemiological evaluation ability. In addition, though based on visual assessment, after the declaration of the emergency, regional containment of the infection risk might have strengthened to some extent. SNS, which can provide a high level of real-time, voluntary symptom data collection, can be used to assess the epidemiology of a pandemic, as well as to assist in policy assessments such as emergency declarations. Funding The present work was supported in part by a grant from the Ministry of Health, Labour and Welfare of Japan (H29-Gantaisaku-ippan-009).
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Affiliation(s)
- Shuhei Nomura
- Department of Health Policy and Management, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan.,Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Daisuke Yoneoka
- Department of Health Policy and Management, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan.,Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Graduate School of Public Health, St. Luke's International University, Tokyo, Japan
| | - Shoi Shi
- Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
| | - Yuta Tanoue
- Institute for Business and Finance, Waseda University, Tokyo, Japan
| | - Takayuki Kawashima
- Department of Mathematical and Computing Science, Tokyo Institute of Technology, Tokyo, Japan
| | - Akifumi Eguchi
- Center for Preventive Medical Sciences, Chiba University, Chiba, Japan
| | - Kentaro Matsuura
- Department of Management Science, Graduate School of Engineering, Tokyo University of Science, Tokyo, Japan.,HOXO-M Inc., Tokyo, Japan
| | - Koji Makiyama
- HOXO-M Inc., Tokyo, Japan.,Yahoo Japan Corporation, Tokyo, Japan
| | - Keisuke Ejima
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, USA
| | | | - Haruka Sakamoto
- Department of Health Policy and Management, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan.,Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hiroyuki Kunishima
- Department of Infectious Diseases, St. Marianna University School of Medicine, Kanagawa, Japan
| | - Stuart Gilmour
- Graduate School of Public Health, St. Luke's International University, Tokyo, Japan
| | - Hiroshi Nishiura
- Graduate School of Medicine, Hokkaido University, Hokkaido, Japan
| | - Hiroaki Miyata
- Department of Health Policy and Management, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan
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30
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Masrur A, Yu M, Luo W, Dewan A. Space-Time Patterns, Change, and Propagation of COVID-19 Risk Relative to the Intervention Scenarios in Bangladesh. Int J Environ Res Public Health 2020; 17:E5911. [PMID: 32824030 PMCID: PMC7569836 DOI: 10.3390/ijerph17165911] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 08/12/2020] [Accepted: 08/13/2020] [Indexed: 01/08/2023]
Abstract
The novel coronavirus (COVID-19) pandemic continues to be a significant public health threat worldwide, particularly in densely populated countries such as Bangladesh with inadequate health care facilities. While early detection and isolation were identified as important non-pharmaceutical intervention (NPI) measures for containing the disease spread, this may not have been pragmatically implementable in developing countries due to social and economic reasons (i.e., poor education, less public awareness, massive unemployment). Hence, to elucidate COVID-19 transmission dynamics with respect to the NPI status-e.g., social distancing-this study conducted spatio-temporal analysis using the prospective scanning statistic at district and sub-district levels in Bangladesh and its capital, Dhaka city, respectively. Dhaka megacity has remained the highest-risk "active" cluster since early April. Lately, the central and south eastern regions in Bangladesh have been exhibiting a high risk of COVID-19 transmission. The detected space-time progression of COVID-19 infection suggests that Bangladesh has experienced a community-level transmission at the early phase (i.e., March, 2020), primarily introduced by Bangladeshi citizens returning from coronavirus epicenters in Europe and the Middle East. Potential linkages exist between the violation of NPIs and the emergence of new higher-risk clusters over the post-incubation periods around Bangladesh. Novel insights into the COVID-19 transmission dynamics derived in this study on Bangladesh provide important policy guidelines for early preparations and pragmatic NPI measures to effectively deal with infectious diseases in resource-scarce countries worldwide.
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Affiliation(s)
- Arif Masrur
- The GeoVISTA Center, The Pennsylvania State University, University Park, PA 16802, USA;
| | - Manzhu Yu
- The GeoVISTA Center, The Pennsylvania State University, University Park, PA 16802, USA;
| | - Wei Luo
- 10 Akron Street, Cambridge, MA 02138, USA;
| | - Ashraf Dewan
- School of Earth and Planetary Sciences, Curtin University, Perth, WA 6102, Australia;
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31
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Hohl A, Delmelle EM, Desjardins MR, Lan Y. Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States. Spat Spatiotemporal Epidemiol 2020; 34:100354. [PMID: 32807396 PMCID: PMC7320856 DOI: 10.1016/j.sste.2020.100354] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 06/08/2020] [Accepted: 06/18/2020] [Indexed: 01/04/2023]
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first discovered in late 2019 in Wuhan City, China. The virus may cause novel coronavirus disease 2019 (COVID-19) in symptomatic individuals. Since December of 2019, there have been over 7,000,000 confirmed cases and over 400,000 confirmed deaths worldwide. In the United States (U.S.), there have been over 2,000,000 confirmed cases and over 110,000 confirmed deaths. COVID-19 case data in the United States has been updated daily at the county level since the first case was reported in January of 2020. There currently lacks a study that showcases the novelty of daily COVID-19 surveillance using space-time cluster detection techniques. In this paper, we utilize a prospective Poisson space-time scan statistic to detect daily clusters of COVID-19 at the county level in the contiguous 48 U.S. and Washington D.C. As the pandemic progresses, we generally find an increase of smaller clusters of remarkably steady relative risk. Daily tracking of significant space-time clusters can facilitate decision-making and public health resource allocation by evaluating and visualizing the size, relative risk, and locations that are identified as COVID-19 hotspots.
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Affiliation(s)
- Alexander Hohl
- Department of Geography, The University of Utah, 260 S Campus Dr., Rm 4625, Salt Lake City, UT 84112, USA.
| | - Eric M Delmelle
- Department of Geography and Earth Sciences, Center for Applied Geographic Information Science, University of North Carolina at Charlotte, Charlotte, NC 28223,, USA
| | - Michael R Desjardins
- Department of Epidemiology, Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Yu Lan
- Department of Geography and Earth Sciences, Center for Applied Geographic Information Science, University of North Carolina at Charlotte, Charlotte, NC 28223,, USA
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32
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Ye J, Moreno-Madriñán MJ. Comparing different spatio-temporal modeling methods in dengue fever data analysis in Colombia during 2012-2015. Spat Spatiotemporal Epidemiol 2020; 34:100360. [PMID: 32807397 DOI: 10.1016/j.sste.2020.100360] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 07/02/2020] [Accepted: 07/14/2020] [Indexed: 02/06/2023]
Abstract
In this paper, we compare a variety of spatio-temporal conditional autoregressive models to a dengue fever dataset in Colombia, and incorporate an innovative data transformation method in the data analysis. In order to gain a better understanding on the effects of different niche variables in the epidemiological process, we explore Poisson-lognormal and binomial models with different Bayesian spatio-temporal modeling methods in this paper. Our results show that the selected model can well capture the variations of the data. The population density, elevation, daytime and night land surface temperatures are among the contributory variables to identify potential dengue outbreak regions; precipitation and vegetation variables are not significant in the selected spatio-temporal mixed effects model. The generated dengue fever probability maps from the model show a geographic distribution of risk that apparently coincides with the elevation gradient. The results in the paper provide the most benefits for future work in dengue studies.
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33
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Varga C, John P, Cooke M, Majowicz SE. Spatial and space-time clustering and demographic characteristics of human nontyphoidal Salmonella infections with major serotypes in Toronto, Canada. PLoS One 2020; 15:e0235291. [PMID: 32609730 DOI: 10.1371/journal.pone.0235291] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 06/11/2020] [Indexed: 01/04/2023] Open
Abstract
Nontyphoidal Salmonella enterica (NTS) causes a substantial health burden to human populations in Canada and worldwide. Exposure sources and demographic factors vary by location and can therefore have a major impact on salmonellosis clustering. We evaluated major NTS serotypes: S. Enteritidis (n = 620), S. Typhimurium (n = 150), S. Thompson (n = 138), and S. Heidelberg (n = 136) reported in the city of Toronto, Canada, between January 1, 2015, and December 31, 2017. Cases were analyzed at the forward sortation area (FSA)—level (an area indicated by the first three characters of the postal code). Serotype-specific global and local clustering of infections were evaluated using the Moran's I method. Spatial and space-time clusters were investigated using Poisson and multinomial scan statistic models. Case-case analyses using a multinomial logistic regression model was conducted to compare seasonal and demographic factors among the different serotypes. High infection rate FSAs clustered in the central region of Toronto for S. Enteritidis, in the south-central region for S. Typhimurium, in north-west region for S. Thompson, and in the south-east region for S. Heidelberg. The relative risk ratio of S. Enteritidis infections was significantly higher in cases who reported travel outside of Ontario. The relative risk ratio of infections was significantly higher in summer for S. Typhimurium, and in fall for S. Thompson. The relative risk ratio of infection was highest for the 0–9 age group for S. Typhimurium, and the 20–39 age group for S. Heidelberg. Our study will aid public health stakeholders in designing serotype-specific geographically targeted disease prevention programs.
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34
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Desjardins MR, Hohl A, Delmelle EM. Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: Detecting and evaluating emerging clusters. Appl Geogr 2020; 118:102202. [PMID: 32287518 PMCID: PMC7139246 DOI: 10.1016/j.apgeog.2020.102202] [Citation(s) in RCA: 185] [Impact Index Per Article: 46.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 03/29/2020] [Accepted: 03/29/2020] [Indexed: 05/03/2023]
Abstract
Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China in December 2019, and is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is a pandemic with an estimated death rate between 1% and 5%; and an estimatedR 0 between 2.2 and 6.7 according to various sources. As of March 28th, 2020, there were over 649,000 confirmed cases and 30,249 total deaths, globally. In the United States, there were over 115,500 cases and 1891 deaths and this number is likely to increase rapidly. It is critical to detect clusters of COVID-19 to better allocate resources and improve decision-making as the outbreaks continue to grow. Using daily case data at the county level provided by Johns Hopkins University, we conducted a prospective spatial-temporal analysis with SaTScan. We detect statistically significant space-time clusters of COVID-19 at the county level in the U.S. between January 22nd-March 9th, 2020, and January 22nd-March 27th, 2020. The space-time prospective scan statistic detected "active" and emerging clusters that are present at the end of our study periods - notably, 18 more clusters were detected when adding the updated case data. These timely results can inform public health officials and decision makers about where to improve the allocation of resources, testing sites; also, where to implement stricter quarantines and travel bans. As more data becomes available, the statistic can be rerun to support timely surveillance of COVID-19, demonstrated here. Our research is the first geographic study that utilizes space-time statistics to monitor COVID-19 in the U.S.
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Affiliation(s)
- M R Desjardins
- Department of Epidemiology & Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - A Hohl
- Department of Geography, The University of Utah, Salt Lake City, UT, 84112, USA
| | - E M Delmelle
- Department of Geography and Earth Sciences & Center for Applied Geographic Information Science, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA
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Desjardins M, Casas I, Victoria A, Carbonell D, Dávalos D, Delmelle E. Knowledge, attitudes, and practices regarding dengue, chikungunya, and Zika in Cali, Colombia. Health Place 2020; 63:102339. [DOI: 10.1016/j.healthplace.2020.102339] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 03/05/2020] [Accepted: 04/06/2020] [Indexed: 11/29/2022]
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36
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Lippi CA, Stewart-Ibarra AM, Romero M, Lowe R, Mahon R, Van Meerbeeck CJ, Rollock L, Gittens-St Hilaire M, Trotman AR, Holligan D, Kirton S, Borbor-Cordova MJ, Ryan SJ. Spatiotemporal Tools for Emerging and Endemic Disease Hotspots in Small Areas: An Analysis of Dengue and Chikungunya in Barbados, 2013-2016. Am J Trop Med Hyg 2020; 103:149-156. [PMID: 32342853 DOI: 10.4269/ajtmh.19-0919] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Dengue fever and other febrile mosquito-borne diseases place considerable health and economic burdens on small island nations in the Caribbean. Here, we used two methods of cluster detection to find potential hotspots of transmission of dengue and chikungunya in Barbados, and to assess the impact of input surveillance data and methodology on observed patterns of risk. Using Moran's I and spatial scan statistics, we analyzed the geospatial and temporal distribution of disease cases and rates across Barbados for dengue fever in 2013-2016, and a chikungunya outbreak in 2014. During years with high numbers of dengue cases, hotspots for cases were found with Moran's I in the south and central regions in 2013 and 2016, respectively. Using smoothed disease rates, clustering was detected in all years for dengue. Hotspots suggesting higher rates were not detected via spatial scan statistics, but coldspots suggesting lower than expected rates of disease activity were found in southwestern Barbados during high case years of dengue. No significant spatiotemporal structure was found in cases during the chikungunya outbreak. Spatial analysis of surveillance data is useful in identifying outbreak hotspots, potentially complementing existing early warning systems. We caution that these methods should be used in a manner appropriate to available data and reflecting explicit public health goals-managing for overall case numbers or targeting anomalous rates for further investigation.
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Affiliation(s)
- Catherine A Lippi
- Emerging Pathogens Institutue, University of Florida, Gainesville, Florida.,Department of Geography, Quantitative Disease Ecology and Conservation (QDEC) Lab Group, University of Florida, Gainesville, Florida
| | | | - Moory Romero
- Department of Environmental Studies, State University of New York College of Environmental Science and Forestry (SUNY ESF), Syracuse, New York
| | - Rachel Lowe
- Department of Infectious Disease Epidemiology, Centre on Climate Change and Planetary Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.,Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
| | - Roché Mahon
- The Caribbean Institute for Meteorology and Hydrology, St. James, Barbados
| | | | | | | | - Adrian R Trotman
- The Caribbean Institute for Meteorology and Hydrology, St. James, Barbados
| | - Dale Holligan
- Ministry of Health and Wellness, St. Michael, Barbados
| | - Shane Kirton
- Ministry of Health and Wellness, St. Michael, Barbados
| | - Mercy J Borbor-Cordova
- Facultad de Ingeniería Marítima y Ciencias del Mar, Escuela Superior Politécnica del Litoral (ESPOL), Guayaquil, Ecuador
| | - Sadie J Ryan
- Emerging Pathogens Institutue, University of Florida, Gainesville, Florida.,Department of Geography, Quantitative Disease Ecology and Conservation (QDEC) Lab Group, University of Florida, Gainesville, Florida
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37
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Ullah S, Daud H, Dass SC, Fanaee-T H, Kausarian H, Khalil A. Space-Time Clustering Characteristics of Tuberculosis in Khyber Pakhtunkhwa Province, Pakistan, 2015-2019. Int J Environ Res Public Health 2020; 17:ijerph17041413. [PMID: 32098247 PMCID: PMC7068355 DOI: 10.3390/ijerph17041413] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Revised: 01/15/2020] [Accepted: 01/21/2020] [Indexed: 12/23/2022]
Abstract
The number of tuberculosis (TB) cases in Pakistan ranks fifth in the world. The National TB Control Program (NTP) has recently reported more than 462,920 TB patients in Khyber Pakhtunkhwa province, Pakistan from 2002 to 2017. This study aims to identify spatial and space-time clusters of TB cases in Khyber Pakhtunkhwa province Pakistan during 2015-2019 to design effective interventions. The spatial and space-time cluster analyses were conducted at the district-level based on the reported TB cases from January 2015 to April 2019 using space-time scan statistics (SaTScan). The most likely spatial and space-time clusters were detected in the northern rural part of the province. Additionally, two districts in the west were detected as the secondary space-time clusters. The most likely space-time cluster shows a tendency of spread toward the neighboring districts in the central part, and the most likely spatial cluster shows a tendency of spread toward the neighboring districts in the south. Most of the space-time clusters were detected at the start of the study period 2015-2016. The potential TB clusters in the remote rural part might be associated to the dry-cool climate and lack of access to the healthcare centers in the remote areas.
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Affiliation(s)
- Sami Ullah
- Department of Fundamental & Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia;
- Correspondence:
| | - Hanita Daud
- Department of Fundamental & Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia;
| | - Sarat C. Dass
- School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia, Putrajaya 62200, Malaysia;
| | - Hadi Fanaee-T
- Center for Applied Intelligent Systems Research (CAISR), Halmstad University, SE-301 18 Halmstad, Sweden;
| | - Husnul Kausarian
- Department of Geological Engineering, Universitas Islam Riau, Pekanbaru 28284, Indonesia;
| | - Alamgir Khalil
- Department of Statistics, University of Peshawar, Peshawar 25120, Pakistan;
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Saran S, Singh P, Kumar V, Chauhan P. Review of Geospatial Technology for Infectious Disease Surveillance: Use Case on COVID-19. J Indian Soc Remote Sens 2020; 48. [PMCID: PMC7433774 DOI: 10.1007/s12524-020-01140-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
This paper discusses on the increasing relevancy of geospatial technologies such as geographic information system (GIS) in the public health domain, particularly for the infectious disease surveillance and modelling strategies. Traditionally, the disease mapping tasks have faced many challenges—(1) authors rarely documented the evidence that were used to create map, (2) before evolution of GIS, many errors aroused in mapping tasks which were expanded extremely at global scales, and (3) there were no fidelity assessment of maps which resulted in inaccurate precision. This study on infectious diseases geo-surveillance is divided into four broad sections with emphasis on handling geographical and temporal issues to help in public health decision-making and planning policies: (1) geospatial mapping of diseases using its spatial and temporal information to understand their behaviour across geography; (2) the citizen’s involvement as volunteers in giving health and disease data to assess the critical situation for disease’s spread and prevention in neighbourhood effect; (3) scientific analysis of health-related behaviour using mathematical epidemiological and geo-statistical approaches with (4) capacity building program. To illustrate each theme, recent case studies are cited and case studies are performed on COVID-19 to demonstrate selected models.
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Affiliation(s)
- Sameer Saran
- Indian Institute of Remote Sensing, Indian Space Research Organisation, #4, Kalidas Road, Dehradun, 248001 India
| | - Priyanka Singh
- Indian Institute of Remote Sensing, Indian Space Research Organisation, #4, Kalidas Road, Dehradun, 248001 India
| | - Vishal Kumar
- Indian Institute of Remote Sensing, Indian Space Research Organisation, #4, Kalidas Road, Dehradun, 248001 India
| | - Prakash Chauhan
- Indian Institute of Remote Sensing, Indian Space Research Organisation, #4, Kalidas Road, Dehradun, 248001 India
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Assmuth T, Chen X, Degeling C, Haahtela T, Irvine KN, Keune H, Kock R, Rantala S, Rüegg S, Vikström S. Integrative concepts and practices of health in transdisciplinary social ecology. ACTA ACUST UNITED AC 2020; 2:71-90. [DOI: 10.1007/s42532-019-00038-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
AbstractIncreasing recognition of interdependencies of the health of humans, other organisms and ecosystems, and of their importance to socio-ecological systems, necessitates application of integrative concepts such as One Health and EcoHealth. These concepts open new perspectives for research and practice but also generate confusion and divergent opinion, prompting new theories, and call for empirical clarification and evaluation. Through a semi-systematic evaluation of knowledge generation in scientific publications (comprised of literature reviews, conceptual models and analyses of communities of practice), we show how integrative concepts and approaches to health evolve and are adopted. Our findings indicate that while their contexts, goals and rationales vary, integrative concepts of health essentially arise from shared interests in living systems. Despite recent increased attention to ecological and societal aspects of health including broader sustainability issues, the focus remains anthropocentric and oriented towards biomedicine. Practices reflect and in turn transform these concepts, which together with practices also influence ways of integration. Overarching narratives vary between optimism and pessimism towards integrated health and knowledge. We conclude that there is an urgent need for better, coherent and more deeply integrative health concepts, approaches and practices to foster the well-being of humans, other animals and ecosystems. Consideration of these concepts and practices has methodological and political importance, as it will transform thinking and action on both society and nature and specifically can enrich science and practice, expanding their scope and linking them better. Transdisciplinary efforts are crucial to developing such concepts and practices to properly address the multiple facets of health and to achieve their appropriate integration for the socio-ecological systems at stake. We propose the term “transdisciplinary health” to denote the new approaches needed.
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Freitas LP, Cruz OG, Lowe R, Sá Carvalho M. Space-time dynamics of a triple epidemic: dengue, chikungunya and Zika clusters in the city of Rio de Janeiro. Proc Biol Sci 2019; 286:20191867. [PMID: 31594497 PMCID: PMC6790786 DOI: 10.1098/rspb.2019.1867] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Dengue, an arboviral disease transmitted by Aedes mosquitoes, has been endemic in Brazil for decades. However, vector-control strategies have not led to a significant reduction in the disease burden and have not been sufficient to prevent chikungunya and Zika entry and establishment in the country. In Rio de Janeiro city, the first Zika and chikungunya epidemics were detected between 2015 and 2016, coinciding with a dengue epidemic. Understanding the behaviour of these diseases in a triple epidemic scenario is a necessary step for devising better interventions for prevention and outbreak response. We applied scan statistics analysis to detect spatio-temporal clustering for each disease separately and for all three simultaneously. In general, clusters were not detected in the same locations and time periods, possibly owing to competition between viruses for host resources, depletion of susceptible population, different introduction times and change in behaviour of the human population (e.g. intensified vector-control activities in response to increasing cases of a particular arbovirus). Simultaneous clusters of the three diseases usually included neighbourhoods with high population density and low socioeconomic status, particularly in the North region of the city. The use of space–time cluster detection can guide intensive interventions to high-risk locations in a timely manner, to improve clinical diagnosis and management, and pinpoint vector-control measures.
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Affiliation(s)
- Laís Picinini Freitas
- Escola Nacional de Saúde Pública Sergio Arouca (ENSP), Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Oswaldo Gonçalves Cruz
- Programa de Computação Científica (PROCC), Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Rachel Lowe
- Centre on Climate Change and Planetary Health, London School of Hygiene and Tropical Medicine, London, UK.,Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.,Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
| | - Marilia Sá Carvalho
- Programa de Computação Científica (PROCC), Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
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Whiteman A, Desjardins MR, Eskildsen GA, Loaiza JR. Detecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance data. PLoS Negl Trop Dis 2019; 13:e0007266. [PMID: 31545819 PMCID: PMC6776363 DOI: 10.1371/journal.pntd.0007266] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 10/03/2019] [Accepted: 09/04/2019] [Indexed: 01/04/2023] Open
Abstract
Long term surveillance of vectors and arboviruses is an integral aspect of disease prevention and control systems in countries affected by increasing risk. Yet, little effort has been made to adjust space-time risk estimation by integrating disease case counts with vector surveillance data, which may result in inaccurate risk projection when several vector species are present, and when little is known about their likely role in local transmission. Here, we integrate 13 years of dengue case surveillance and associated Aedes occurrence data across 462 localities in 63 districts to estimate the risk of infection in the Republic of Panama. Our exploratory space-time modelling approach detected the presence of five clusters, which varied by duration, relative risk, and spatial extent after incorporating vector species as covariates. The Ae. aegypti model contained the highest number of districts with more dengue cases than would be expected given baseline population levels, followed by the model accounting for both Ae. aegypti and Ae. albopictus. This implies that arbovirus case surveillance coupled with entomological surveillance can affect cluster detection and risk estimation, potentially improving efforts to understand outbreak dynamics at national scales. Dengue cases have increased in tropical regions worldwide owing to urbanization, globalization, and climate change facilitating the spread of Aedes mosquito vectors. National surveillance programs monitor trends in dengue fever and inform the public about epidemiological scenarios where outbreak preventive actions are most needed. Yet, most estimations of dengue risk so far derive only from disease case data, ignoring Aedes occurrence as a key aspect of dengue transmission dynamic. Here we illustrate how incorporating vector presence and absence as a model covariate can considerably alter the characteristics of space-time cluster estimations of dengue cases.
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Affiliation(s)
- Ari Whiteman
- Smithsonian Tropical Research Institute, Balboa Ancón, Republic of Panama
- Department of Geography and Earth Sciences, Center for Applied Geographic Information Science, University of North Carolina at Charlotte, Charlotte, NC, United States of America
- * E-mail:
| | - Michael R. Desjardins
- Department of Geography and Earth Sciences, Center for Applied Geographic Information Science, University of North Carolina at Charlotte, Charlotte, NC, United States of America
| | | | - Jose R. Loaiza
- Smithsonian Tropical Research Institute, Balboa Ancón, Republic of Panama
- Instituto de Investigaciones Científicas y Servicios de Alta Tecnología, Panama City, Republic of Panama
- Programa Centroamericano de Maestría en Entomología, Universidad de Panamá, Panama City, Republic of Panama
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Casas I, Delmelle E. Landscapes of healthcare utilization during a dengue fever outbreak in an urban environment of Colombia. Environ Monit Assess 2019; 191:279. [PMID: 31254116 DOI: 10.1007/s10661-019-7415-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 03/20/2019] [Indexed: 06/09/2023]
Abstract
The well-being of a population and its health are influenced by a myriad of socioeconomic and environmental factors that interact across a wide range of scales, from the individual to the national and global levels. One of these factors is the provision of health services, which is regulated by both demand and supply. Although an adequate provision can significantly improve health outcomes of a population, lopsided flow of patients to specific health centers can result in serious disparities and potentially delay the timeliness of a diagnosis. In this paper, utilization patterns during an epidemic of dengue fever in the city of Cali, Colombia for the year 2010 are investigated. Specifically, the objectives are to (1) identify health facilities that exhibit patterns of over- and underutilization, (2) determine where patients who are being diagnosed at a particular facility originate from, and (3) whether patients are traveling to their closest facility and hence (4) estimate how far patients are willing to travel to be diagnosed and treated for dengue fever. Analysis is further decomposed by age group and by gender, in an attempt to test whether utilization patterns drastically change according to these variables. Answers to these questions can help health authorities plan for future epidemics, for instance, by providing guidelines as to which facilities require more resources and by improving the organization of health prevention campaigns to direct population seeking health assistance to use facilities that are underutilized.
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Affiliation(s)
- Irene Casas
- Louisiana Tech University, Ruston, LA, 71272, USA
| | - Eric Delmelle
- University of North Carolina at Charlotte, Charlotte, NC, 28223, USA.
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43
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Lu Y, Deng X, Chen J, Wang J, Chen Q, Niu B. Risk analysis of African swine fever in Poland based on spatio-temporal pattern and Latin hypercube sampling, 2014-2017. BMC Vet Res 2019; 15:160. [PMID: 31118049 PMCID: PMC6532167 DOI: 10.1186/s12917-019-1903-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 05/09/2019] [Indexed: 01/04/2023] Open
Abstract
Background African swine fever (ASF) is a devastating infectious disease of pigs. ASF poses a potential threat to the world pig industry, due to the lack of vaccines and treatments. In this study, the Geographic Information System (GIS) spatial analysis was applied to analyze the distribution, dispersion of the epidemic and clustering of ASF in Poland. Results The results show that the center of the epidemic moved gradually towards the southwest, and the distribution of the epidemic changed from south-north to east-west. Through space-time scan statistical analysis, the 3 clusters major of wild boar cases involve longer time spans and larger radii, while the other five with higher relative risks involved in domestic pigs. And then, a quantitative model was constructed to analyse the risk of releasing African swine fever virus (ASFV) from Poland by the legal export of pork and pork products. The Latin hypercube sampling results show that the probability is relatively low (the average value is 4.577 × 10− 7). Conclusions All the identification of the spatio-temporal patterns of the epidemic and the risk analysis model would give a further understanding of the dynamics of disease transmission and help to design corresponding measures to minimize the catastrophic consequences of potential ASFV introduction.
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Affiliation(s)
- Yi Lu
- School of Life Sciences, Shanghai University, Shanghai, 200444, China
| | - Xiaojun Deng
- Technology Center for Animal, Plant and Food Inspection and Quarantine, Shanghai Entry Exit Inspect and Quarantine Bur, Shanghai, 200135, China
| | - Jiahui Chen
- School of Life Sciences, Shanghai University, Shanghai, 200444, China
| | - Jianying Wang
- School of Life Sciences, Shanghai University, Shanghai, 200444, China
| | - Qin Chen
- School of Life Sciences, Shanghai University, Shanghai, 200444, China.
| | - Bing Niu
- School of Life Sciences, Shanghai University, Shanghai, 200444, China.
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Nielsen CC, Amrhein CG, Shah PS, Aziz K, Osornio-Vargas AR. Spatiotemporal Patterns of Small for Gestational Age and Low Birth Weight Births and Associations With Land Use and Socioeconomic Status. Environ Health Insights 2019; 13:1178630219869922. [PMID: 31488949 PMCID: PMC6709433 DOI: 10.1177/1178630219869922] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 07/23/2019] [Indexed: 05/22/2023]
Abstract
In addition to small for gestational age (SGA) and low birth weight at term (LBWT), critically ill cases of SGA/LBWT are significant events from outcomes and economic perspectives that require further understanding of risk factors. We aimed to assess the spatiotemporal distribution of locations where there were consistently higher numbers of critically ill SGA/LBWT (hot spots) in comparison with all SGA/LBWT and all births. We focused on Edmonton (2008-2010) and Calgary (2006-2010), Alberta, and used a geographical information system to apply emerging hot spot analysis, as a new approach for understanding SGA, LBWT, and the critically ill counterparts (ciSGA or ciLBWT). We also compared the resulting aggregated categorical patterns with proportions of land use and socioeconomic status (SES) using Spearman correlation and logistic regression. There was an overall increasing trend in all space-time clusters. Whole period emerging hot spot patterns among births and SGA generally coincided, but SGA with ciSGA and LBWT with ciLBWT did not. Regression coefficients were highest for low SES with SGA and LBWT, but not with ciSGA and ciLBWT. Open areas and industrial land use were most associated with ciLBWT but not with ciSGA, SGA, or LBWT. Differences in the space-time hot spot patterns and the associations with ciSGA and ciLBWT indicate further need to research the interplay of maternal and environmental influences. We demonstrated the novel application of emerging hot spot analysis for small newborns and spatially related them to the surrounding environment.
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Affiliation(s)
- Charlene C Nielsen
- Department of Pediatrics, University of
Alberta, Edmonton, AB, Canada
- Department of Earth and Atmospheric
Sciences, University of Alberta, Edmonton, AB, Canada
| | - Carl G Amrhein
- Department of Earth and Atmospheric
Sciences, University of Alberta, Edmonton, AB, Canada
- Faculty of Arts and Sciences, The Aga
Khan University, Nairobi, Kenya
- Faculty of Arts and Sciences, The Aga
Khan University, Karachi, Pakistan
| | - Prakesh S Shah
- Department of Pediatrics and Institute
of Health Policy, Management, and Evaluation, University of Toronto, Mount Sinai
Hospital, The Canadian Neonatal Network, Toronto, ON, Canada
| | - Khalid Aziz
- Department of Pediatrics, University of
Alberta, Edmonton, AB, Canada
| | - Alvaro R Osornio-Vargas
- Department of Pediatrics, University of
Alberta, Edmonton, AB, Canada
- Alvaro R. Osornio-Vargas, Department of
Pediatrics, University of Alberta, 3-591 ECHA, 11405 87th Avenue, Edmonton, AB
T6G 1C9, Canada.
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45
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Liu K, Sun J, Liu X, Li R, Wang Y, Lu L, Wu H, Gao Y, Xu L, Liu Q. Spatiotemporal patterns and determinants of dengue at county level in China from 2005-2017. Int J Infect Dis 2018; 77:96-104. [PMID: 30218814 DOI: 10.1016/j.ijid.2018.09.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2018] [Revised: 09/03/2018] [Accepted: 09/05/2018] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE To identify the high risk spatiotemporal clusters of dengue cases and explore the associated risk factors. METHODS Monthly indigenous dengue cases in 2005-2017 were aggregated at county level. Spatiotemporal cluster analysis was used to explore dengue distribution features using SaTScan9.4.4 and Arcgis10.3.0. In addition, the influential factors and potential high risk areas of dengue outbreaks were analyzed using ecological niche models in Maxent 3.3.1 software. RESULTS We found a heterogeneous spatial and temporal distribution pattern of dengue cases. The identified high risk region in the primary cluster covered 13 counties in Guangdong Province and in the secondary clusters included 14 counties in Yunnan Province. Additionally, there was a nonlinear association between meteorological and environmental factors and dengue outbreaks, with 8.5%-57.1%, 6.7%-38.3% and 3.2%-40.4% contribution from annual average minimum temperature, land cover and annual average precipitation, respectively. CONCLUSIONS The high risk areas of dengue outbreaks mainly are located in Guangdong and Yunnan Provinces, which are significantly shaped by environmental and meteorological factors, such as temperature, precipitation and land cover.
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Affiliation(s)
- Keke Liu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jimin Sun
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China; Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Xiaobo Liu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Ruiyun Li
- Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, N-0316 Oslo, Norway
| | - Yiguan Wang
- School of Biological Sciences, University of Queensland, QLD 4072, Australia
| | - Liang Lu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Haixia Wu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuan Gao
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Lei Xu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China; Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, N-0316 Oslo, Norway.
| | - Qiyong Liu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
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