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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] [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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2
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Cortes-Ramirez J, Gatton M, Wilches-Vega JD, Mayfield HJ, Wang N, Paris-Pineda OM, Sly PD. Mapping the risk of respiratory infections using suburban district areas in a large city in Colombia. BMC Public Health 2023; 23:1400. [PMID: 37474891 PMCID: PMC10360249 DOI: 10.1186/s12889-023-16179-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 06/22/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND Acute respiratory infections (ARI) in Cúcuta -Colombia, have a comparatively high burden of disease associated with high public health costs. However, little is known about the epidemiology of these diseases in the city and its distribution within suburban areas. This study addresses this gap by estimating and mapping the risk of ARI in Cúcuta and identifying the most relevant risk factors. METHODS A spatial epidemiological analysis was designed to investigate the association of sociodemographic and environmental risk factors with the rate of ambulatory consultations of ARI in urban sections of Cúcuta, 2018. The ARI rate was calculated using a method for spatial estimation of disease rates. A Bayesian spatial model was implemented using the Integrated Nested Laplace Approximation approach and the Besag-York-Mollié specification. The risk of ARI per urban section and the hotspots of higher risk were also estimated and mapped. RESULTS A higher risk of IRA was found in central, south, north and west areas of Cúcuta after adjusting for sociodemographic and environmental factors, and taking into consideration the spatial distribution of the city's urban sections. An increase of one unit in the percentage of population younger than 15 years; the Index of Multidimensional Poverty and the rate of ARI in the migrant population was associated with a 1.08 (1.06-1.1); 1.04 (1.01-1.08) and 1.25 (1.22-1.27) increase of the ARI rate, respectively. Twenty-four urban sections were identified as hotspots of risk in central, south, north and west areas in Cucuta. CONCLUSION Sociodemographic factors and their spatial patterns are determinants of acute respiratory infections in Cúcuta. Bayesian spatial hierarchical models can be used to estimate and map the risk of these infections in suburban areas of large cities in Colombia. The methods of this study can be used globally to identify suburban areas and or specific communities at risk to support the implementation of prevention strategies and decision-making in the public and private health sectors.
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Affiliation(s)
- Javier Cortes-Ramirez
- Centre for Data Science, Queensland University of Technology, Brisbane City, Australia.
- Children's Health and Environment Program, Child Health Research Centre, The University of Queensland, St Lucia, Australia.
- Faculty of Health, University of Santander, Santander, Colombia.
- Queensland University of Technology, O Block D Wing Room D722. Ring Road, Kelvin Grove Campus, Victoria Park Road. Kelvin Grove, Kelvin Grove, QLD, 4059, Australia.
| | - Michelle Gatton
- Centre for Immunology and Infection Control, Queensland University of Technology, Brisbane City, Australia
| | | | - Helen J Mayfield
- School of Public Health, The University of Queensland, St Lucia, Australia
| | - Ning Wang
- National Centre for Chronic and Noncommunicable Disease Control and Prevention. Chinese Centre for Disease Control and Prevention, Beijing, China
| | | | - Peter D Sly
- Children's Health and Environment Program, Child Health Research Centre, The University of Queensland, St Lucia, Australia
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3
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Frota CC, Correia FGS, Alves Vasconcelos LR, de Sousa PRC, Ferreira MLDS, Saraiva SP, Mota Ferreira R, Romcy KAM, Pinheiro RF, de Oliveira RTG, Pinheiro Júnior FML, Martins AF, Sanhueza-Sanzana C, de Almeida RLF, Kendall C, Coelho ICB, Pires Neto RDJ, Aguiar ÍWO, Kerr LRFS. Positivity of dengue, chikungunya, and Zika infections in women in Northeast Brazil post-Zika epidemic. Pathog Glob Health 2023; 117:485-492. [PMID: 36316985 PMCID: PMC10262776 DOI: 10.1080/20477724.2022.2142187] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023] Open
Abstract
The state of Ceará, in the Northeast Region of Brazil, presents the simultaneous circulation of Zika (ZIKV), dengue (DENV) and chikungunya (CHIKV) viruses. In 2017 there were a high number of cases of these three arboviruses, especially CHIKV. Here, we detected the presence of arboviruses ZIKV, DENV and CHIKV and their coinfections in women in endemic regions of the city of Fortaleza, Ceará in a post-Zika epidemic year. Sociodemographic and environmental characteristics associated with arbovirus positivity were also analyzed. Women (n = 1289) between 15 and 39 years old were included. RT-qPCR was performed for virus detection and IgM antibody positivity was also analyzed. One hundred and six (8.3%) participants were positive for one or more arboviruses. Monoinfections (76; 5.9%) were distributed between 22 (1.7%) for ZIKV, 39 (3.1%) for DENV and 15 (1.2%) for CHIKV. Co-infections were detected in 30 (2.3%) of the positive participants and one case with triple infection was found. IgM positivity was found in 2.4% of ZIKV RT-qPCR, 9.6% of DENV and 16.3% of CHIKV. RT-qPCR positivity for arboviruses was associated with low socioeconomic class and presence of a water box sealing in the household. A higher positivity to the three viruses occurred in the month with the lowest wind velocity, which was also preceded by the highest peak of rain and humidity. We identified the simultaneous circulation and co-infection of ZIKV, DENV and CHIKV in Fortaleza in a post-Zika epidemic year. We also highlight the need for continuous epidemiological surveillance combined with molecular diagnostic tools.
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Affiliation(s)
| | | | | | | | | | - Susy Pereira Saraiva
- Public Health Postgraduate Program, Federal University of Ceará, Fortaleza, Brazil
| | | | | | | | | | | | | | | | | | - Carl Kendall
- School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA
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Chien LC, Chen LWA, Lin RT. Lagged meteorological impacts on COVID-19 incidence among high-risk counties in the United States-a spatiotemporal analysis. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2022; 32:774-781. [PMID: 34211113 PMCID: PMC8247626 DOI: 10.1038/s41370-021-00356-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 06/16/2021] [Accepted: 06/17/2021] [Indexed: 05/16/2023]
Abstract
BACKGROUND The associations between meteorological factors and coronavirus disease 2019 (COVID-19) have been discussed globally; however, because of short study periods, the lack of considering lagged effects, and different study areas, results from the literature were diverse and even contradictory. OBJECTIVE The primary purpose of this study is to conduct more reliable research to evaluate the lagged meteorological impacts on COVID-19 incidence by considering a relatively long study period and diversified high-risk areas in the United States. METHODS This study adopted the distributed lagged nonlinear model with a spatial function to analyze COVID-19 incidence predicted by multiple meteorological measures from March to October of 2020 across 203 high-risk counties in the United States. The estimated spatial function was further smoothed within the entire continental United States by the biharmonic spline interpolation. RESULTS Our findings suggest that the maximum temperature, minimum relative humidity, and precipitation were the best meteorological predictors. Most significantly positive associations were found from 3 to 11 lagged days in lower levels of each selected meteorological factor. In particular, a significantly positive association appeared in minimum relative humidity higher than 88.36% at 5-day lag. The spatial analysis also shows excessive risks in the north-central United States. SIGNIFICANCE The research findings can contribute to the implementation of early warning surveillance of COVID-19 by using weather forecasting for up to two weeks in high-risk counties.
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Affiliation(s)
- Lung-Chang Chien
- Department of Epidemiology and Biostatistics, School of Public Health, University of Nevada, Las Vegas, Las Vegas, NV, USA
| | - L-W Antony Chen
- Department of Environmental and Occupational Health, School of Public Health, University of Nevada, Las Vegas, Las Vegas, NV, USA
| | - Ro-Ting Lin
- Department of Occupational Safety and Health, College of Public Health, China Medical University, Taichung, Taiwan.
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5
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Baek K, Choi J, Park JT, Kwak K. Influence of temperature and precipitation on the incidence of hepatitis A in Seoul, Republic of Korea: a time series analysis using distributed lag linear and non-linear model. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2022; 66:1725-1736. [PMID: 35829753 DOI: 10.1007/s00484-022-02313-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 03/21/2022] [Accepted: 06/06/2022] [Indexed: 06/15/2023]
Abstract
This study aimed to analyze the association between temperature and precipitation and the incidence of hepatitis A in Seoul, Korea, as meteorological factors may have different effects on specific diseases depending on the lifestyle in each region. Weekly cases of hepatitis A, weekly mean daily precipitation, and temperature data from 2016 to 2020 were analyzed. Quasi-Poisson-generalized linear models with time variable adjusted by spline function were used considering 0-6-week lags. The association of each variable and hepatitis A incidence was assessed by the single lag and the constrained distributed lag model. Multivariable distributed lag linear and non-linear models were used to develop models with significant independent variables. Weekly mean of daily mean temperature (Tmean) and maximum temperature (Tmax) were negatively associated with hepatitis A in the 6-week lag. Precipitation was negatively associated with hepatitis A in the 5- and 6-week lags. The multivariable model showed the negative association of Tmax, precipitation and hepatitis A in the 5- and 6-week lags. In the non-linear models, the incidence rate ratio (IRR) was the highest at a Tmax of 11 °C and decreased thereafter. IRR was the highest at 12 mm of precipitation and showed decrease pattern to 25 mm and then gradually increased in the 5- and 6-week lags. Identifying the impact of climate factors on hepatitis A incidence would help in the development of strategies to prevent diseases and indirectly estimate the impact of climate change on hepatitis A epidemiology.
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Affiliation(s)
- Kiook Baek
- Department of Occupational and Environmental Medicine, Yeungnam University Hospital, Daegu, Republic of Korea
| | - Jonghyuk Choi
- Department of Preventive Medicine, College of Medicine, Dankook University, Cheonan, Republic of Korea
| | - Jong-Tae Park
- Department of Occupational and Environmental Medicine, Korea University Ansan Hospital, Ansan, Republic of Korea
| | - Kyeongmin Kwak
- Department of Occupational and Environmental Medicine, Korea University Ansan Hospital, Ansan, Republic of Korea.
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Keating P, Murray J, Schenkel K, Merson L, Seale A. Electronic data collection, management and analysis tools used for outbreak response in low- and middle-income countries: a systematic review and stakeholder survey. BMC Public Health 2021; 21:1741. [PMID: 34560871 PMCID: PMC8464108 DOI: 10.1186/s12889-021-11790-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 08/29/2021] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Use of electronic data collection, management and analysis tools to support outbreak response is limited, especially in low income countries. This can hamper timely decision-making during outbreak response. Identifying available tools and assessing their functions in the context of outbreak response would support appropriate selection and use, and likely more timely data-driven decision-making during outbreaks. METHODS We conducted a systematic review and a stakeholder survey of the Global Outbreak Alert and Response Network and other partners to identify and describe the use of, and technical characteristics of, electronic data tools used for outbreak response in low- and middle-income countries. Databases included were MEDLINE, EMBASE, Global Health, Web of Science and CINAHL with publications related to tools for outbreak response included from January 2010-May 2020. Software tool websites of identified tools were also reviewed. Inclusion and exclusion criteria were applied and counts, and proportions of data obtained from the review or stakeholder survey were calculated. RESULTS We identified 75 electronic tools including for data collection (33/75), management (13/75) and analysis (49/75) based on data from the review and survey. Twenty-eight tools integrated all three functionalities upon collection of additional information from the tool developer websites. The majority were open source, capable of offline data collection and data visualisation. EpiInfo, KoBoCollect and Open Data Kit had the broadest use, including for health promotion, infection prevention and control, and surveillance data capture. Survey participants highlighted harmonisation of data tools as a key challenge in outbreaks and the need for preparedness through training front-line responders on data tools. In partnership with the Global Health Network, we created an online interactive decision-making tool using data derived from the survey and review. CONCLUSIONS Many electronic tools are available for data -collection, -management and -analysis in outbreak response, but appropriate tool selection depends on knowledge of tools' functionalities and capabilities. The online decision-making tool created to assist selection of the most appropriate tool(s) for outbreak response helps by matching requirements with functionality. Applying the tool together with harmonisation of data formats, and training of front-line responders outside of epidemic periods can support more timely data-driven decision making in outbreaks.
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Affiliation(s)
- Patrick Keating
- London School of Hygiene and Tropical Medicine, London, UK. .,United Kingdom Public Health Rapid Support Team, London, UK.
| | - Jillian Murray
- London School of Hygiene and Tropical Medicine, London, UK
| | | | | | - Anna Seale
- London School of Hygiene and Tropical Medicine, London, UK.,United Kingdom Public Health Rapid Support Team, London, UK
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7
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Jonkmans N, D'Acremont V, Flahault A. Scoping future outbreaks: a scoping review on the outbreak prediction of the WHO Blueprint list of priority diseases. BMJ Glob Health 2021; 6:e006623. [PMID: 34531189 PMCID: PMC8449939 DOI: 10.1136/bmjgh-2021-006623] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 09/01/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND The WHO's Research and Development Blueprint priority list designates emerging diseases with the potential to generate public health emergencies for which insufficient preventive solutions exist. The list aims to reduce the time to the availability of resources that can avert public health crises. The current SARS-CoV-2 pandemic illustrates that an effective method of mitigating such crises is the pre-emptive prediction of outbreaks. This scoping review thus aimed to map and identify the evidence available to predict future outbreaks of the Blueprint diseases. METHODS We conducted a scoping review of PubMed, Embase and Web of Science related to the evidence predicting future outbreaks of Ebola and Marburg virus, Zika virus, Lassa fever, Nipah and Henipaviral disease, Rift Valley fever, Crimean-Congo haemorrhagic fever, Severe acute respiratory syndrome, Middle East respiratory syndrome and Disease X. Prediction methods, outbreak features predicted and implementation of predictions were evaluated. We conducted a narrative and quantitative evidence synthesis to highlight prediction methods that could be further investigated for the prevention of Blueprint diseases and COVID-19 outbreaks. RESULTS Out of 3959 articles identified, we included 58 articles based on inclusion criteria. 5 major prediction methods emerged; the most frequent being spatio-temporal risk maps predicting outbreak risk periods and locations through vector and climate data. Stochastic models were predominant. Rift Valley fever was the most predicted disease. Diseases with complex sociocultural factors such as Ebola were often predicted through multifactorial risk-based estimations. 10% of models were implemented by health authorities. No article predicted Disease X outbreaks. CONCLUSIONS Spatiotemporal models for diseases with strong climatic and vectorial components, as in River Valley fever prediction, may currently best reduce the time to the availability of resources. A wide literature gap exists in the prediction of zoonoses with complex sociocultural and ecological dynamics such as Ebola, COVID-19 and especially Disease X.
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Affiliation(s)
- Nils Jonkmans
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Valérie D'Acremont
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
- Swiss Tropical and Public Health Institute, Basel, Switzerland
| | - Antoine Flahault
- Institute of Global Health, Faculty of Medicine, Université de Genève, Geneva, Switzerland
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Hussain-Alkhateeb L, Rivera Ramírez T, Kroeger A, Gozzer E, Runge-Ranzinger S. Early warning systems (EWSs) for chikungunya, dengue, malaria, yellow fever, and Zika outbreaks: What is the evidence? A scoping review. PLoS Negl Trop Dis 2021; 15:e0009686. [PMID: 34529649 PMCID: PMC8445439 DOI: 10.1371/journal.pntd.0009686] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Early warning systems (EWSs) are of increasing importance in the context of outbreak-prone diseases such as chikungunya, dengue, malaria, yellow fever, and Zika. A scoping review has been undertaken for all 5 diseases to summarize existing evidence of EWS tools in terms of their structural and statistical designs, feasibility of integration and implementation into national surveillance programs, and the users' perspective of their applications. METHODS Data were extracted from Cochrane Database of Systematic Reviews (CDSR), Google Scholar, Latin American and Caribbean Health Sciences Literature (LILACS), PubMed, Web of Science, and WHO Library Database (WHOLIS) databases until August 2019. Included were studies reporting on (a) experiences with existing EWS, including implemented tools; and (b) the development or implementation of EWS in a particular setting. No restrictions were applied regarding year of publication, language or geographical area. FINDINGS Through the first screening, 11,710 documents for dengue, 2,757 for Zika, 2,706 for chikungunya, 24,611 for malaria, and 4,963 for yellow fever were identified. After applying the selection criteria, a total of 37 studies were included in this review. Key findings were the following: (1) a large number of studies showed the quality performance of their prediction models but except for dengue outbreaks, only few presented statistical prediction validity of EWS; (2) while entomological, epidemiological, and social media alarm indicators are potentially useful for outbreak warning, almost all studies focus primarily or exclusively on meteorological indicators, which tends to limit the prediction capacity; (3) no assessment of the integration of the EWS into a routine surveillance system could be found, and only few studies addressed the users' perspective of the tool; (4) almost all EWS tools require highly skilled users with advanced statistics; and (5) spatial prediction remains a limitation with no tool currently able to map high transmission areas at small spatial level. CONCLUSIONS In view of the escalating infectious diseases as global threats, gaps and challenges are significantly present within the EWS applications. While some advanced EWS showed high prediction abilities, the scarcity of tool assessments in terms of integration into existing national surveillance systems as well as of the feasibility of transforming model outputs into local vector control or action plans tends to limit in most cases the support of countries in controlling disease outbreaks.
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Affiliation(s)
- Laith Hussain-Alkhateeb
- Global Health, School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | | | - Axel Kroeger
- Centre for Medicine and Society, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany
| | | | - Silvia Runge-Ranzinger
- Centre for Medicine and Society, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany
- Heidelberg Institute of Global Health, University of Heidelberg, Heidelberg, Germany
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9
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Lee SA, Jarvis CI, Edmunds WJ, Economou T, Lowe R. Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions. J R Soc Interface 2021; 18:20210096. [PMID: 34034534 PMCID: PMC8150046 DOI: 10.1098/rsif.2021.0096] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 04/26/2021] [Indexed: 12/14/2022] Open
Abstract
Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.
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Affiliation(s)
- Sophie A. Lee
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Christopher I. Jarvis
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - W. John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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10
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Li SL, Messina JP, Pybus OG, Kraemer MUG, Gardner L. A review of models applied to the geographic spread of Zika virus. Trans R Soc Trop Med Hyg 2021; 115:956-964. [PMID: 33570155 PMCID: PMC8417088 DOI: 10.1093/trstmh/trab009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 12/13/2020] [Accepted: 01/08/2021] [Indexed: 11/13/2022] Open
Abstract
In recent years, Zika virus (ZIKV) has expanded its geographic range and in 2015–2016 caused a substantial epidemic linked to a surge in developmental and neurological complications in newborns. Mathematical models are powerful tools for assessing ZIKV spread and can reveal important information for preventing future outbreaks. We reviewed the literature and retrieved modelling studies that were developed to understand the spatial epidemiology of ZIKV spread and risk. We classified studies by type, scale, aim and applications and discussed their characteristics, strengths and limitations. We examined the main objectives of these models and evaluated the effectiveness of integrating epidemiological and phylogeographic data, along with socioenvironmental risk factors that are known to contribute to vector–human transmission. We also assessed the promising application of human mobility data as a real-time indicator of ZIKV spread. Lastly, we summarised model validation methods used in studies to ensure accuracy in models and modelled outcomes. Models are helpful for understanding ZIKV spread and their characteristics should be carefully considered when developing future modelling studies to improve arbovirus surveillance.
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Affiliation(s)
- Sabrina L Li
- School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK
| | - Jane P Messina
- School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK.,School of Global and Area Studies, University of Oxford, 12 Bevington Road, Oxford, OX2 6LH, UK
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, 11a Mansfield Rd, Oxford, OX1 3SZ, UK
| | - Moritz U G Kraemer
- Department of Zoology, University of Oxford, 11a Mansfield Rd, Oxford, OX1 3SZ, UK
| | - Lauren Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218-2682, USA.,Center for Systems Science and Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218-2682, USA
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11
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Liang R, Lu Y, Qu X, Su Q, Li C, Xia S, Liu Y, Zhang Q, Cao X, Chen Q, Niu B. Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data. Transbound Emerg Dis 2019; 67:935-946. [PMID: 31738013 DOI: 10.1111/tbed.13424] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 11/01/2019] [Accepted: 11/07/2019] [Indexed: 12/28/2022]
Abstract
African swine fever (ASF) is a virulent infectious disease of pigs. As there is no effective vaccine and treatment method at present, it poses a great threat to the pig industry once it breaks out. In this paper, we used ASF outbreak data and the WorldClim database meteorological data and selected the CfsSubset Evaluator-Best First feature selection method combined with the random forest algorithms to construct an African swine fever outbreak prediction model. Subsequently, we also established a test set for data other than modelling, and the accuracy accuracy value range of the model on the independent test set was 76.02%-84.64%, which indicated that the modelling effect was better and the prediction accuracy was higher than previous estimates. In addition, logistic regression analysis was conducted on 12 features used for modelling and the ROC curves were drawn. The results showed that the bio14 features (precipitation of driest month) had the largest contribution to the outbreak of ASF, and it was speculated that the outbreak of the epidemic was significantly related to precipitation. Finally, we used this qualitative prediction model to build a global online prediction system for ASF outbreaks, in the hope that this study will help to decision-makers who can then take the relevant prevention and control measures in order to prevent the further spread of future epidemics of the disease.
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Affiliation(s)
- Ruirui Liang
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Yi Lu
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Xiaosheng Qu
- National Engineering Laboratory of Southwest Endangered Medicinal Resources Development, Guangxi Botanical Garden of Medicinal Plants, Nanning, China
| | - Qiang Su
- School of Life Sciences, Shanghai University, Shanghai, China.,Computing Center of Guangxi, Nanning, China.,Guangxi Institute for Food and Drug Control, Nanning, China
| | - Chunxia Li
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Sijing Xia
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Yongxin Liu
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Qiang Zhang
- Technical Center For Animal, Plant and Food Inspection and Quarantine of Shanghai Customs, Shanghai, China
| | - Xin Cao
- Institute of Clinical Science, Shanghai Medical College, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qin Chen
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Bing Niu
- School of Life Sciences, Shanghai University, Shanghai, China
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12
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Chien LC, Sy F, Pérez A. Identifying high risk areas of Zika virus infection by meteorological factors in Colombia. BMC Infect Dis 2019; 19:888. [PMID: 31651247 PMCID: PMC6814059 DOI: 10.1186/s12879-019-4499-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 09/23/2019] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Several Zika virus (ZIKV) outbreaks have occurred since October 2015. Because there is no effective treatment for ZIKV infection, developing an effective surveillance and warning system is currently a high priority to prevent ZIKV infection. Despite Aedes mosquitos having been known to spread ZIKV, the calculation approach is diverse, and only applied to local areas. This study used meteorological measurements to monitor ZIKV infection due to the high correlation between climate change and Aedes mosquitos and the convenience to obtain meteorological data from weather monitoring stations. METHODS This study applied the Bayesian structured additive regression modeling approach to include spatial interactive terms with meteorological factors and a geospatial function in a zero-inflated Poisson model. The study area contained 32 administrative departments in Colombia from October 2015 to December 2017. Weekly ZIKV infection cases and daily meteorological measurements were collected. Mapping techniques were adopted to visualize spatial findings. A series of model selections determined the best combinations of meteorological factors in the same model. RESULTS When multiple meteorological factors are considered in the same model, both total rainfall and average temperature can best assess the geographic disparities of ZIKV infection. Meanwhile, a 1-in. increase in rainfall is associated with an increase in the logarithm of relative risk (logRR) of ZIKV infection of at most 1.66 (95% credible interval [CI] = 1.09, 2.15) as well as a 1 °F increase in average temperature is significantly associated with at most 0.79 (95% CI = 0.12, 1.22) increase in the logRR of ZIKV. Moreover, after controlling rainfall and average temperature, an independent geospatial function in the model results in two departments with an excessive ZIKV risk which may be explained by unobserved factors other than total rainfall and average temperature. CONCLUSION Our study found that meteorological factors are significantly associated with ZIKV infection across departments. The study determined both total rainfall and average temperature as the best meteorological factors to identify high risk departments of ZIKV infection. These findings can help governmental agencies monitor at risk areas according to meteorological measurements, and develop preventions in those at risk areas in priority.
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Affiliation(s)
- Lung-Chang Chien
- Department of Environmental and Occupational Health, University of Nevada, Las Vegas, School of Public Health, 4505 S. Maryland Parkway, Las Vegas, NV, 89154, USA.
| | - Francisco Sy
- Department of Environmental and Occupational Health, University of Nevada, Las Vegas, School of Public Health, 4505 S. Maryland Parkway, Las Vegas, NV, 89154, USA
| | - Adriana Pérez
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston-UTHealth, School of Public Health, Austin Campus, 1616 Guadalupe, Suite 6.300, Austin, TX, 78071, USA
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13
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Martínez-Bello DA, López-Quílez A, Prieto AT. Joint Estimation of Relative Risk for Dengue and Zika Infections, Colombia, 2015-2016. Emerg Infect Dis 2019; 25:1118-1126. [PMID: 31107226 PMCID: PMC6537708 DOI: 10.3201/eid2506.180392] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
We jointly estimated relative risk for dengue and Zika virus disease (Zika) in Colombia, establishing the spatial association between them at the department and city levels for October 2015-December 2016. Cases of dengue and Zika were allocated to the 87 municipalities of 1 department and the 293 census sections of 1 city in Colombia. We fitted 8 hierarchical Bayesian Poisson joint models of relative risk for dengue and Zika, including area- and disease-specific random effects accounting for several spatial patterns of disease risk (clustered or uncorrelated heterogeneity) within and between both diseases. Most of the dengue and Zika high-risk municipalities varied in their risk distribution; those for Zika were in the northern part of the department and dengue in the southern to northeastern parts. At city level, spatially clustered patterns of dengue high-risk census sections indicated Zika high-risk areas. This information can be used to inform public health decision making.
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Martínez-Bello DA, López-Quílez A, Torres Prieto A. Spatio-Temporal Modeling of Zika and Dengue Infections within Colombia. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:ijerph15071376. [PMID: 29966348 PMCID: PMC6068969 DOI: 10.3390/ijerph15071376] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 06/23/2018] [Accepted: 06/26/2018] [Indexed: 12/14/2022]
Abstract
The aim of this study is to estimate the parallel relative risk of Zika virus disease (ZVD) and dengue using spatio-temporal interaction effects models for one department and one city of Colombia during the 2015–2016 ZVD outbreak. We apply the integrated nested Laplace approximation (INLA) for parameter estimation, using the epidemiological week (EW) as a time measure. At the departmental level, the best model showed that the dengue or ZVD risk in one municipality was highly associated with risk in the same municipality during the preceding EWs, while at the city level, the final model selected established that the high risk of dengue or ZVD in one census sector was highly associated not only with its neighboring census sectors in the same EW, but also with its neighboring sectors in the preceding EW. The spatio-temporal models provided smoothed risk estimates, credible risk intervals, and estimation of the probability of high risk of dengue and ZVD by area and time period. We explore the intricacies of the modeling process and interpretation of the results, advocating for the use of spatio-temporal models of the relative risk of dengue and ZVD in order to generate highly valuable epidemiological information for public health decision making.
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Affiliation(s)
- Daniel Adyro Martínez-Bello
- Department of Statistics and Operations Research, Faculty of Mathematics, University of Valencia, 46100 Valencia, Spain.
| | - Antonio López-Quílez
- Department of Statistics and Operations Research, Faculty of Mathematics, University of Valencia, 46100 Valencia, Spain.
| | - Alexander Torres Prieto
- Epidemiologic Monitoring Office, Secretary of Health of the Department of Santander, Cl. 45 11-52 Bucaramanga, Colombia.
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