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Del-Águila-Mejía J, Morilla F, Donado-Campos JDM. A system dynamics modelling and analytical framework for imported dengue outbreak surveillance and risk mapping. Acta Trop 2024; 257:107304. [PMID: 38942132 DOI: 10.1016/j.actatropica.2024.107304] [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: 02/22/2024] [Revised: 06/24/2024] [Accepted: 06/25/2024] [Indexed: 06/30/2024]
Abstract
System Dynamics (SD) models have been used to understand complex, multi-faceted dengue transmission dynamics, but a gap persists between research and actionable public health tools for decision-making. Spain is an at-risk country of imported dengue outbreaks, but only qualitative assessments are available to guide public health action and control. We propose a modular SD model combining temperature-dependent vector population, transmission parameters, and epidemiological interactions to simulate outbreaks from imported cases accounting for heterogeneous local climate-related transmission patterns. Under our assumptions, 15 provinces sustain vector populations capable of generating outbreaks from imported cases, with heterogeneous risk profiles regarding seasonality, magnitude and risk window shifting from late Spring to early Autum. Results being relative to given vector-to-human populations allow flexibility when translating outcomes between geographic scales. The model and the framework are meant to serve public health by incorporating transmission dynamics and quantitative-qualitative input to the evidence-based decision-making chain. It is a flexible tool that can easily adapt to changing contexts, parametrizations and epidemiological settings thanks to the modular approach.
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Affiliation(s)
- Javier Del-Águila-Mejía
- Departamento de Medicina Preventiva y Salud Pública y Microbiología, Facultad de Medicina, Universidad Autónoma de Madrid, C. Arzobispo Morcillo, 4, Madrid 28029, Spain; Servicio de Medicina Preventiva, Hospital Universitario de Móstoles, C. Dr. Luis Montes s/n, Madrid, Móstoles 28935, Spain.
| | - Fernando Morilla
- Departamento de Informática y Automática, Universidad Nacional de Educación a Distancia, Juan del Rosal 16, Madrid 28040, Spain
| | - Juan de Mata Donado-Campos
- Departamento de Medicina Preventiva y Salud Pública y Microbiología, Facultad de Medicina, Universidad Autónoma de Madrid, C. Arzobispo Morcillo, 4, Madrid 28029, Spain; Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Calle Monforte de Lemos 3-5, Madrid 28029, Spain; Departamento de Medicina, Facultad de Ciencias Biomédicas y de la Salud, Universidad Europea de Madrid, C. Tajo, s/n, Madrid, Villaviciosa de Odón 28670, Spain; Instituto de Investigación Sanitaria del Hospital Universitario La Paz (IdiPAZ), Universidad Autónoma de Madrid, C. Arzobispo Morcillo 4, Madrid 28029, Spain
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Knoblauch S, Su Yin M, Chatrinan K, de Aragão Rocha AA, Haddawy P, Biljecki F, Lautenbach S, Resch B, Arifi D, Jänisch T, Morales I, Zipf A. High-resolution mapping of urban Aedes aegypti immature abundance through breeding site detection based on satellite and street view imagery. Sci Rep 2024; 14:18227. [PMID: 39107395 PMCID: PMC11303731 DOI: 10.1038/s41598-024-67914-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 07/17/2024] [Indexed: 08/10/2024] Open
Abstract
Identification of Aedes aegypti breeding hotspots is essential for the implementation of targeted vector control strategies and thus the prevention of several mosquito-borne diseases worldwide. Training computer vision models on satellite and street view imagery in the municipality of Rio de Janeiro, we analyzed the correlation between the density of common breeding grounds and Aedes aegypti infestation measured by ovitraps on a monthly basis between 2019 and 2022. Our findings emphasized the significance (p ≤ 0.05) of micro-habitat proxies generated through object detection, allowing to explain high spatial variance in urban abundance of Aedes aegypti immatures. Water tanks, non-mounted car tires, plastic bags, potted plants, and storm drains positively correlated with Aedes aegypti egg and larva counts considering a 1000 m mosquito flight range buffer around 2700 ovitrap locations, while dumpsters, small trash bins, and large trash bins exhibited a negative association. This complementary application of satellite and street view imagery opens the pathway for high-resolution interpolation of entomological surveillance data and has the potential to optimize vector control strategies. Consequently it supports the mitigation of emerging infectious diseases transmitted by Aedes aegypti, such as dengue, chikungunya, and Zika, which cause thousands of deaths each year.
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Affiliation(s)
- Steffen Knoblauch
- GIScience Chair, Heidelberg University, 69120, Heidelberg, Germany.
- Interdisciplinary Center of Scientific Computing, Heidelberg University, 69120, Heidelberg, Germany.
- Heidelberg Institute for Geoinformation Technology, 69118, Heidelberg, Germany.
| | - Myat Su Yin
- Faculty of ICT, Mahidol University, 73170, Nakhon Pathom, Thailand
| | | | | | - Peter Haddawy
- Faculty of ICT, Mahidol University, 73170, Nakhon Pathom, Thailand
- Bremen Spatial Cognition Center, University of Bremen, 28359, Bremen, Germany
| | - Filip Biljecki
- Department of Architecture, National University of Singapore, 117566, Singapore, Singapore
- Department of Real Estate, National University of Singapore, 119245, Singapore, Singapore
| | - Sven Lautenbach
- Heidelberg Institute for Geoinformation Technology, 69118, Heidelberg, Germany
| | - Bernd Resch
- Geo-social Analytics Lab, Paris Lodron University of Salzburg, 5020, Salzburg, Austria
- Center for Geographic Analysis, Harvard University, 02138, Cambridge, USA
| | - Dorian Arifi
- Geo-social Analytics Lab, Paris Lodron University of Salzburg, 5020, Salzburg, Austria
| | - Thomas Jänisch
- Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 80045, Aurora, USA
- Heidelberg Institute of Global Health, Heidelberg University Hospital, 69120, Heidelberg, Germany
| | - Ivonne Morales
- Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 80045, Aurora, USA
| | - Alexander Zipf
- GIScience Chair, Heidelberg University, 69120, Heidelberg, Germany
- Interdisciplinary Center of Scientific Computing, Heidelberg University, 69120, Heidelberg, Germany
- Heidelberg Institute for Geoinformation Technology, 69118, Heidelberg, Germany
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Sarker I, Karim MR, E‐Barket S, Hasan M. Dengue fever mapping in Bangladesh: A spatial modeling approach. Health Sci Rep 2024; 7:e2154. [PMID: 38812714 PMCID: PMC11130545 DOI: 10.1002/hsr2.2154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 05/10/2024] [Accepted: 05/13/2024] [Indexed: 05/31/2024] Open
Abstract
Background Epidemics of the dengue virus can trigger widespread morbidity and mortality along with no specific treatment. Examining the spatial autocorrelation and variability of dengue prevalence throughout Bangladesh's 64 districts was the focus of this study. Methods The spatial autocorrelation is evaluated with the help of Moran I and Geary C . Local Moran I was used to detect hotspots and cold spots, whereas local Getis Ord G was used to identify only spatial hotspots. The spatial heterogeneity has been detected using various conventional and spatial models, including the Poisson-Gamma model, the Poisson-Lognormal Model, the Conditional Autoregressive (CAR) model, the Convolution model, and the BYM2 model, respectively. These models are implemented using Gibbs sampling and other Bayesian hierarchical approaches to analyze the posterior distribution effectively, enabling inference within a Bayesian context. Results The study's findings show that Moran I and Geary C analysis provides a substantial clustering pattern of positive spatial autocorrelation of dengue fever (DF) rates between surrounding districts at a 90% confidence interval. The Local Indicators of Spatial Autocorrelation cluster mapped spatial clusters and outliers based on prevalence rates, while the local Getis-Ord G displayed a thorough breakdown of high or low rates, omitting outliers. Although Chattogram had the most dengue cases (15,752), Khulna district had a higher prevalence rate (133.636) than Chattogram (104.796). The BYM2 model, determined to be well-fitted based on the lowest Deviance Information Criterion value (527.340), explains a significant association between spatial heterogeneity and prevalence rates. Conclusion This research pinpoints the district with the highest prevalence rate for dengue and the neighboring districts that also have high risk, allowing government agencies and communities to take the necessary precautions to mollify the risk effect of DF.
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Affiliation(s)
- Indrani Sarker
- Department of Statistics and Data ScienceJahangirnagar UniversityDhakaBangladesh
| | - Md. Rezaul Karim
- Department of Statistics and Data ScienceJahangirnagar UniversityDhakaBangladesh
| | - Sefat E‐Barket
- Department of Statistics and Data ScienceJahangirnagar UniversityDhakaBangladesh
| | - Mehedi Hasan
- Department of Statistics and Data ScienceJahangirnagar UniversityDhakaBangladesh
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Leandro ADS, Pires-Vieira LH, Lopes RD, Rivas AV, Amaral C, Silva I, Maciel-de-Freitas R, Chiba de Castro WA. Optimising the surveillance of Aedes aegypti in Brazil by selecting smaller representative areas within an endemic city. Trop Med Int Health 2024; 29:414-423. [PMID: 38469931 DOI: 10.1111/tmi.13985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
OBJECTIVES Arboviruses, such as dengue (DENV), zika (ZIKV), and chikungunya (CHIKV), constitute a growing urban public health threat. Focusing on Aedes aegypti mosquitoes, their primary vectors, is crucial for mitigation. While traditional immature-stage mosquito surveillance has limitations, capturing adult mosquitoes through traps yields more accurate data on disease transmission. However, deploying traps presents logistical and financial challenges, demonstrating effective temporal predictions but lacking spatial accuracy. Our goal is to identify smaller representative areas within cities to enhance the early warning system for DENV outbreaks. METHODS We created Sentinel Geographic Units (SGUs), smaller areas of 1 km2 within each stratum, larger areas, with the aim of aligning the Trap Positivity Index (TPI) and Adult Density Index (ADI) with their respective strata. We conducted a two-step evaluation of SGUs. First, we examined the equivalence of TPI and ADI between SGUs and strata from January 2017 to July 2022. Second, we assessed the ability of SGU's TPI and ADI to predict DENV outbreaks in comparison to Foz do Iguaçu's Early-Warning System, which forecasts outbreaks up to 4 weeks ahead. Spatial and temporal analyses were carried out, including data interpolation and model selection based on Akaike information criteria (AIC). RESULTS Entomological indicators produced in small SGUs can effectively replace larger sentinel areas to access dengue outbreaks. Based on historical data, the best predictive capability is achieved 2 weeks after infestation verification. Implementing the SGU strategy with more frequent sampling can provide more precise space-time estimates and enhance dengue control. CONCLUSIONS The implementation of SGUs offers an efficient way to monitor mosquito populations, reducing the need for extensive resources. This approach has the potential to improve dengue transmission management and enhance the public health response in endemic cities.
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Affiliation(s)
- André de Souza Leandro
- Centro de Controle de Zoonoses de Foz do Iguaçu, Secretaria Municipal de Saúde, Foz do Iguaçu, Paraná, Brazil
- Laboratório de Mosquitos Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
| | | | - Renata Defante Lopes
- Centro de Controle de Zoonoses de Foz do Iguaçu, Secretaria Municipal de Saúde, Foz do Iguaçu, Paraná, Brazil
- Universidade Federal da Integração Latino-Americana, Instituto Latino-Americano de Ciências da Vida e da Natureza, Foz do Iguaçu, Paraná, Brazil
| | - Açucena Veleh Rivas
- Laboratory of Clinical Analysis at Hospital Ministro Costa Cavalcanti, Itaiguapy Foundation, Foz do Iguaçu, Paraná, Brazil
| | - Caroline Amaral
- Centro de Controle de Zoonoses de Foz do Iguaçu, Secretaria Municipal de Saúde, Foz do Iguaçu, Paraná, Brazil
| | - Isaac Silva
- Centro de Controle de Zoonoses de Foz do Iguaçu, Secretaria Municipal de Saúde, Foz do Iguaçu, Paraná, Brazil
| | - Rafael Maciel-de-Freitas
- Laboratório de Mosquitos Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
- Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany
| | - Wagner A Chiba de Castro
- Universidade Federal da Integração Latino-Americana, Instituto Latino-Americano de Ciências da Vida e da Natureza, Foz do Iguaçu, Paraná, Brazil
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Leandro AS, Chiba de Castro WA, Garey MV, Maciel-de-Freitas R. Spatial analysis of dengue transmission in an endemic city in Brazil reveals high spatial structuring on local dengue transmission dynamics. Sci Rep 2024; 14:8930. [PMID: 38637572 PMCID: PMC11026424 DOI: 10.1038/s41598-024-59537-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 04/11/2024] [Indexed: 04/20/2024] Open
Abstract
In the last decades, dengue has become one of the most widespread mosquito-borne arboviruses in the world, with an increasing incidence in tropical and temperate regions. The mosquito Aedes aegypti is the dengue primary vector and is more abundant in highly urbanized areas. Traditional vector control methods have showing limited efficacy in sustaining mosquito population at low levels to prevent dengue virus outbreaks. Considering disease transmission is not evenly distributed in the territory, one perspective to enhance vector control efficacy relies on identifying the areas that concentrate arbovirus transmission within an endemic city, i.e., the hotspots. Herein, we used a 13-month timescale during the SARS-Cov-2 pandemic and its forced reduction in human mobility and social isolation to investigate the spatiotemporal association between dengue transmission in children and entomological indexes based on adult Ae. aegypti trapping. Dengue cases and the indexes Trap Positive Index (TPI) and Adult Density Index (ADI) varied seasonally, as expected: more than 51% of cases were notified on the first 2 months of the study, and higher infestation was observed in warmer months. The Moran's Eigenvector Maps (MEM) and Generalized Linear Models (GLM) revealed a strong large-scale spatial structuring in the positive dengue cases, with an unexpected negative correlation between dengue transmission and ADI. Overall, the global model and the purely spatial model presented a better fit to data. Our results show high spatial structure and low correlation between entomological and epidemiological data in Foz do Iguaçu dengue transmission dynamics, suggesting the role of human mobility might be overestimated and that other factors not evaluated herein could be playing a significant role in governing dengue transmission.
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Affiliation(s)
- André S Leandro
- Laboratório de Mosquitos Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
- Centro de Controle de Zoonoses, Secretaria Municipal de Saúde de Foz do Iguaçu, Foz do Iguaçu, Brazil
| | | | | | - Rafael Maciel-de-Freitas
- Laboratório de Mosquitos Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil.
- Department of Arbovirology, Bernhard-Nocht Institute for Tropical Medicine, Hamburg, Germany.
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Paz-Bailey G, Adams LE, Deen J, Anderson KB, Katzelnick LC. Dengue. Lancet 2024; 403:667-682. [PMID: 38280388 DOI: 10.1016/s0140-6736(23)02576-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 11/01/2023] [Accepted: 11/15/2023] [Indexed: 01/29/2024]
Abstract
Dengue, caused by four closely related viruses, is a growing global public health concern, with outbreaks capable of overwhelming health-care systems and disrupting economies. Dengue is endemic in more than 100 countries across tropical and subtropical regions worldwide, and the expanding range of the mosquito vector, affected in part by climate change, increases risk in new areas such as Spain, Portugal, and the southern USA, while emerging evidence points to silent epidemics in Africa. Substantial advances in our understanding of the virus, immune responses, and disease progression have been made within the past decade. Novel interventions have emerged, including partially effective vaccines and innovative mosquito control strategies, although a reliable immune correlate of protection remains a challenge for the assessment of vaccines. These developments mark the beginning of a new era in dengue prevention and control, offering promise in addressing this pressing global health issue.
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Affiliation(s)
| | - Laura E Adams
- Centers for Disease Control and Prevention, San Juan, Puerto Rico
| | - Jacqueline Deen
- Institute of Child Health and Human Development, National Institutes of Health, University of the Philippines, Manila, Philippines
| | - Kathryn B Anderson
- Department of Microbiology and Immunology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Leah C Katzelnick
- Viral Epidemiology and Immunity Unit, Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
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Rotejanaprasert C, Chinpong K, Lawson AB, Chienwichai P, Maude RJ. Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand. BMC Med Res Methodol 2024; 24:14. [PMID: 38243198 PMCID: PMC10797994 DOI: 10.1186/s12874-023-02135-9] [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: 06/18/2023] [Accepted: 12/21/2023] [Indexed: 01/21/2024] Open
Abstract
BACKGROUND Dengue is a mosquito-borne disease that causes over 300 million infections worldwide each year with no specific treatment available. Effective surveillance systems are needed for outbreak detection and resource allocation. Spatial cluster detection methods are commonly used, but no general guidance exists on the most appropriate method for dengue surveillance. Therefore, a comprehensive study is needed to assess different methods and provide guidance for dengue surveillance programs. METHODS To evaluate the effectiveness of different cluster detection methods for dengue surveillance, we selected and assessed commonly used methods: Getis Ord [Formula: see text], Local Moran, SaTScan, and Bayesian modeling. We conducted a simulation study to compare their performance in detecting clusters, and applied all methods to a case study of dengue surveillance in Thailand in 2019 to further evaluate their practical utility. RESULTS In the simulation study, Getis Ord [Formula: see text] and Local Moran had similar performance, with most misdetections occurring at cluster boundaries and isolated hotspots. SaTScan showed better precision but was less effective at detecting inner outliers, although it performed well on large outbreaks. Bayesian convolution modeling had the highest overall precision in the simulation study. In the dengue case study in Thailand, Getis Ord [Formula: see text] and Local Moran missed most disease clusters, while SaTScan was mostly able to detect a large cluster. Bayesian disease mapping seemed to be the most effective, with adaptive detection of irregularly shaped disease anomalies. CONCLUSIONS Bayesian modeling showed to be the most effective method, demonstrating the best accuracy in adaptively identifying irregularly shaped disease anomalies. In contrast, SaTScan excelled in detecting large outbreaks and regular forms. This study provides empirical evidence for the selection of appropriate tools for dengue surveillance in Thailand, with potential applicability to other disease control programs in similar settings.
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Affiliation(s)
- Chawarat Rotejanaprasert
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
| | - Kawin Chinpong
- Chulabhorn Learning and Research Centre, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Andrew B Lawson
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Peerut Chienwichai
- Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Richard J Maude
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Open University, Milton Keynes, UK
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Katzelnick LC, Quentin E, Colston S, Ha TA, Andrade P, Eisenberg JNS, Ponce P, Coloma J, Cevallos V. Increasing transmission of dengue virus across ecologically diverse regions of Ecuador and associated risk factors. PLoS Negl Trop Dis 2024; 18:e0011408. [PMID: 38295108 PMCID: PMC10861087 DOI: 10.1371/journal.pntd.0011408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 02/12/2024] [Accepted: 01/15/2024] [Indexed: 02/02/2024] Open
Abstract
The distribution and intensity of viral diseases transmitted by Aedes aegypti mosquitoes, including dengue, have rapidly increased over the last century. Here, we study dengue virus (DENV) transmission across the ecologically and demographically distinct regions or Ecuador. We analyzed province-level age-stratified dengue incidence data from 2000-2019 using catalytic models to estimate the force of infection of DENV over eight decades. We found that provinces established endemic DENV transmission at different time periods. Coastal provinces with the largest and most connected cities had the earliest and highest increase in DENV transmission, starting around 1980 and continuing to the present. In contrast, remote and rural areas with reduced access, like the northern coast and the Amazon regions, experienced a rise in DENV transmission and endemicity only in the last 10 to 20 years. The newly introduced chikungunya and Zika viruses have age-specific distributions of hospital-seeking cases consistent with recent emergence across all provinces. To evaluate factors associated with geographic differences in DENV transmission potential, we modeled DENV vector risk using 11,693 Aedes aegypti presence points to the resolution of 1 hectare. In total, 56% of the population of Ecuador, including in provinces identified as having increasing DENV transmission in our models, live in areas with high risk of Aedes aegypti, with population size, trash collection, elevation, and access to water as important determinants. Our investigation serves as a case study of the changes driving the expansion of DENV and other arboviruses globally and suggest that control efforts should be expanded to semi-urban and rural areas and to historically isolated regions to counteract increasing dengue outbreaks.
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Affiliation(s)
- Leah C. Katzelnick
- Viral Epidemiology and Immunity Unit, Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Emmanuelle Quentin
- Centro de Investigación en Salud Pública y Epidemiología Clínica (CISPEC), Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador
| | - Savannah Colston
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Thien-An Ha
- Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America
| | - Paulina Andrade
- Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America
| | - Joseph N. S. Eisenberg
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Patricio Ponce
- Centro de Investigación en Enfermedades Infeciosas y Vectoriales (CIREV), Instituto Nacional de Investigación en Salud Pública (INSPI), Quito, Ecuador
| | - Josefina Coloma
- Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America
| | - Varsovia Cevallos
- Centro de Investigación en Enfermedades Infeciosas y Vectoriales (CIREV), Instituto Nacional de Investigación en Salud Pública (INSPI), Quito, Ecuador
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Pakaya R, Daniel D, Widayani P, Utarini A. Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review. BMC Public Health 2023; 23:2448. [PMID: 38062404 PMCID: PMC10701958 DOI: 10.1186/s12889-023-17185-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 11/08/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Creating a spatial model of dengue fever risk is challenging duet to many interrelated factors that could affect dengue. Therefore, it is crucial to understand how these critical factors interact and to create reliable predictive models that can be used to mitigate and control the spread of dengue. METHODS This scoping review aims to provide a comprehensive overview of the important predictors, and spatial modelling tools capable of producing Dengue Haemorrhagic Fever (DHF) risk maps. We conducted a methodical exploration utilizing diverse sources, i.e., PubMed, Scopus, Science Direct, and Google Scholar. The following data were extracted from articles published between January 2011 to August 2022: country, region, administrative level, type of scale, spatial model, dengue data use, and categories of predictors. Applying the eligibility criteria, 45 out of 1,349 articles were selected. RESULTS A variety of models and techniques were used to identify DHF risk areas with an arrangement of various multiple-criteria decision-making, statistical, and machine learning technique. We found that there was no pattern of predictor use associated with particular approaches. Instead, a wide range of predictors was used to create the DHF risk maps. These predictors may include climatology factors (e.g., temperature, rainfall, humidity), epidemiological factors (population, demographics, socio-economic, previous DHF cases), environmental factors (land-use, elevation), and relevant factors. CONCLUSIONS DHF risk spatial models are useful tools for detecting high-risk locations and driving proactive public health initiatives. Relying on geographical and environmental elements, these models ignored the impact of human behaviour and social dynamics. To improve the prediction accuracy, there is a need for a more comprehensive approach to understand DHF transmission dynamics.
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Affiliation(s)
- Ririn Pakaya
- Doctoral Program in Public Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia.
- Department of Public Health, Public Health Faculty, Universitas Gorontalo, Gorontalo, Indonesia.
| | - D Daniel
- Department of Health Behaviour, Environment and Social Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Prima Widayani
- Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Adi Utarini
- Doctoral Program in Public Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
- Department of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
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Lim AY, Jafari Y, Caldwell JM, Clapham HE, Gaythorpe KAM, Hussain-Alkhateeb L, Johansson MA, Kraemer MUG, Maude RJ, McCormack CP, Messina JP, Mordecai EA, Rabe IB, Reiner RC, Ryan SJ, Salje H, Semenza JC, Rojas DP, Brady OJ. A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk. BMC Infect Dis 2023; 23:708. [PMID: 37864153 PMCID: PMC10588093 DOI: 10.1186/s12879-023-08717-8] [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: 06/14/2023] [Accepted: 10/16/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. METHODS We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). RESULTS We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. CONCLUSIONS Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping.
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Affiliation(s)
- Ah-Young Lim
- Department of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
- Centre for Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
| | - Yalda Jafari
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jamie M Caldwell
- High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA
| | - Hannah E Clapham
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Laith Hussain-Alkhateeb
- School of Public Health and Community Medicine, Sahlgrenska Academy, Institute of Medicine, Global Health, University of Gothenburg, Gothenburg, Sweden
- Population Health Research Section, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | - Michael A Johansson
- Dengue Branch, Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico, USA
| | | | - Richard J Maude
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Clare P McCormack
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Jane P Messina
- School of Geography and the Environment, University of Oxford, Oxford, UK
- Oxford School of Global and Area Studies, University of Oxford, Oxford, UK
| | - Erin A Mordecai
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Ingrid B Rabe
- Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Sadie J Ryan
- Department of Geography and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Jan C Semenza
- Department of Public Health and Clinical Medicine, Section of Sustainable Health, Umeå University, Umeå, Sweden
| | - Diana P Rojas
- Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland
| | - Oliver J Brady
- Department of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
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11
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Ramadona AL, Tozan Y, Wallin J, Lazuardi L, Utarini A, Rocklöv J. Predicting the dengue cluster outbreak dynamics in Yogyakarta, Indonesia: a modelling study. THE LANCET REGIONAL HEALTH. SOUTHEAST ASIA 2023; 15:100209. [PMID: 37614350 PMCID: PMC10442971 DOI: 10.1016/j.lansea.2023.100209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/23/2022] [Accepted: 04/25/2023] [Indexed: 08/25/2023]
Abstract
Background Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia. Methods We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model. Findings When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale. Interpretation The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue. Funding Swedish Research Council Formas, Umeå Centre for Global Health Research, Swedish Council for Working Life and Social Research, Swedish research council VINNOVA and Alexander von Humboldt Foundation (Germany).
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Affiliation(s)
- Aditya Lia Ramadona
- Department of Epidemiology and Global Health, Umeå University, Umeå, 90187, Sweden
- Department of Public Health and Clinical Medicine, Units: Section of Sustainable Health, Umeå University, Umeå, 90187, Sweden
- Department of Health Behavior, Environment and Social Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia
| | - Yesim Tozan
- School of Global Public Health, New York University, New York, 10003, United States
| | - Jonas Wallin
- Department of Statistics, Lund University, Lund, 22363, Sweden
| | - Lutfan Lazuardi
- Department of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia
| | - Adi Utarini
- Department of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia
| | - Joacim Rocklöv
- Department of Public Health and Clinical Medicine, Units: Section of Sustainable Health, Umeå University, Umeå, 90187, Sweden
- Heidelberg Institute of Public Health & Heidelberg Interdisciplinary Centre for Scientific Computing, Heidelberg University, Heidelberg, 69120, Germany
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12
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Baldoquín Rodríguez W, Mirabal M, Van der Stuyft P, Gómez Padrón T, Fonseca V, Castillo RM, Monteagudo Díaz S, Baetens JM, De Baets B, Toledo Romaní ME, Vanlerberghe V. The Potential of Surveillance Data for Dengue Risk Mapping: An Evaluation of Different Approaches in Cuba. Trop Med Infect Dis 2023; 8:tropicalmed8040230. [PMID: 37104355 PMCID: PMC10143650 DOI: 10.3390/tropicalmed8040230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/03/2023] [Accepted: 04/11/2023] [Indexed: 04/28/2023] Open
Abstract
To better guide dengue prevention and control efforts, the use of routinely collected data to develop risk maps is proposed. For this purpose, dengue experts identified indicators representative of entomological, epidemiological and demographic risks, hereafter called components, by using surveillance data aggregated at the level of Consejos Populares (CPs) in two municipalities of Cuba (Santiago de Cuba and Cienfuegos) in the period of 2010-2015. Two vulnerability models (one with equally weighted components and one with data-derived weights using Principal Component Analysis), and three incidence-based risk models were built to construct risk maps. The correlation between the two vulnerability models was high (tau > 0.89). The single-component and multicomponent incidence-based models were also highly correlated (tau ≥ 0.9). However, the agreement between the vulnerability- and the incidence-based risk maps was below 0.6 in the setting with a prolonged history of dengue transmission. This may suggest that an incidence-based approach does not fully reflect the complexity of vulnerability for future transmission. The small difference between single- and multicomponent incidence maps indicates that in a setting with a narrow availability of data, simpler models can be used. Nevertheless, the generalized linear mixed multicomponent model provides information of covariate-adjusted and spatially smoothed relative risks of disease transmission, which can be important for the prospective evaluation of an intervention strategy. In conclusion, caution is needed when interpreting risk maps, as the results vary depending on the importance given to the components involved in disease transmission. The multicomponent vulnerability mapping needs to be prospectively validated based on an intervention trial targeting high-risk areas.
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Affiliation(s)
| | - Mayelin Mirabal
- Unidad de Información y Biblioteca, Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
| | | | - Tania Gómez Padrón
- Centro Provincial de Higiene Epidemiología y Microbiología, Dirección Provincial de Salud, Santiago de Cuba 90100, Cuba
| | - Viviana Fonseca
- Centro Provincial de Higiene Epidemiología y Microbiología, Dirección Provincial de Salud, Santiago de Cuba 90100, Cuba
| | - Rosa María Castillo
- Unidad Provincial de Vigilancia y Lucha Antivectorial, Dirección Provincial de Salud, Santiago de Cuba 90100, Cuba
| | - Sonia Monteagudo Díaz
- Centro Provincial de Higiene Epidemiología y Microbiología, Dirección Provincial de Salud, Cienfuegos 55100, Cuba
| | - Jan M Baetens
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
| | - Bernard De Baets
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
| | | | - Veerle Vanlerberghe
- Public Health Department, Institute of Tropical Medicine, Nationalestraat 155, 2000 Antwerp, Belgium
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13
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Yin S, Ren C, Shi Y, Hua J, Yuan HY, Tian LW. A Systematic Review on Modeling Methods and Influential Factors for Mapping Dengue-Related Risk in Urban Settings. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192215265. [PMID: 36429980 PMCID: PMC9690886 DOI: 10.3390/ijerph192215265] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/11/2022] [Accepted: 11/17/2022] [Indexed: 05/12/2023]
Abstract
Dengue fever is an acute mosquito-borne disease that mostly spreads within urban or semi-urban areas in warm climate zones. The dengue-related risk map is one of the most practical tools for executing effective control policies, breaking the transmission chain, and preventing disease outbreaks. Mapping risk at a small scale, such as at an urban level, can demonstrate the spatial heterogeneities in complicated built environments. This review aims to summarize state-of-the-art modeling methods and influential factors in mapping dengue fever risk in urban settings. Data were manually extracted from five major academic search databases following a set of querying and selection criteria, and a total of 28 studies were analyzed. Twenty of the selected papers investigated the spatial pattern of dengue risk by epidemic data, whereas the remaining eight papers developed an entomological risk map as a proxy for potential dengue burden in cities or agglomerated urban regions. The key findings included: (1) Big data sources and emerging data-mining techniques are innovatively employed for detecting hot spots of dengue-related burden in the urban context; (2) Bayesian approaches and machine learning algorithms have become more popular as spatial modeling tools for predicting the distribution of dengue incidence and mosquito presence; (3) Climatic and built environmental variables are the most common factors in making predictions, though the effects of these factors vary with the mosquito species; (4) Socio-economic data may be a better representation of the huge heterogeneity of risk or vulnerability spatial distribution on an urban scale. In conclusion, for spatially assessing dengue-related risk in an urban context, data availability and the purpose for mapping determine the analytical approaches and modeling methods used. To enhance the reliabilities of predictive models, sufficient data about dengue serotyping, socio-economic status, and spatial connectivity may be more important for mapping dengue-related risk in urban settings for future studies.
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Affiliation(s)
- Shi Yin
- Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- School of Architecture, South China University of Technology, Guangzhou 510641, China
| | - Chao Ren
- Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Correspondence:
| | - Yuan Shi
- Department of Geography and Planning, University of Liverpool, Liverpool L69 3BX, UK
| | - Junyi Hua
- School of International Affairs and Public Administration, Ocean University of China, Qingdao 266100, China
| | - Hsiang-Yu Yuan
- Department of Biomedical Sciences, City University of Hong Kong, Kowloon, Hong Kong SAR, China
| | - Lin-Wei Tian
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
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14
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Spatial Analysis of Mosquito-Borne Diseases in Europe: A Scoping Review. SUSTAINABILITY 2022. [DOI: 10.3390/su14158975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [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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15
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Sekarrini CE, Sumarmi, Bachri S, Taryana D, Giofandi EA. The application of geographic information system for dengue epidemic in Southeast Asia: A review on trends and opportunity. J Public Health Res 2022; 11:22799036221104170. [PMID: 35911430 PMCID: PMC9335475 DOI: 10.1177/22799036221104170] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 03/30/2022] [Indexed: 11/17/2022] Open
Abstract
The infectious disease dengue hemorrhagic fever remains an unresolved global problem, with climatic conditions and the location of areas located at the equator more often infected with dengue fever. Various modeling approaches have been employed for the development of a dengue risk map. The geographic information system approach was used as an instrument in applying mathematical algorithms to process field vector data into a preventive objective which is studied, then the application of remote sensing provides spatial-temporal data related to land use/land cover data sources as other variable categories. Map of hotspots for dengue fever cases is used to identify the risk of dengue fever areas by applying various complex methodologies, analysis, and visualization of advanced data are needed for its application in public health. In the last 10 years, the increase in the publication of dengue hemorrhagic fever in Southeast Asia in reputable international journals has increased significantly.
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Affiliation(s)
- Cipta Estri Sekarrini
- Program Doctoral of Geography Education, Faculty of Social Science, State University of Malang, Malang, East Java, Indonesia
| | - Sumarmi
- Department of Geography Education, Faculty of Social Science, State University of Malang, Malang, East Java, Indonesia
| | - Syamsul Bachri
- Department of Geography Education, Faculty of Social Science, State University of Malang, Malang, East Java, Indonesia
| | - Didik Taryana
- Department of Geography Education, Faculty of Social Science, State University of Malang, Malang, East Java, Indonesia
| | - Eggy Arya Giofandi
- Department of Geography, Faculty of Social Science, State University of Padang, Padang, West Sumatera, Indonesia
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16
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Cloud-based applications for accessing satellite Earth observations to support malaria early warning. Sci Data 2022; 9:208. [PMID: 35577816 PMCID: PMC9110363 DOI: 10.1038/s41597-022-01337-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 04/11/2022] [Indexed: 11/25/2022] Open
Abstract
Malaria epidemics can be triggered by fluctuations in temperature and precipitation that influence vector mosquitoes and the malaria parasite. Identifying and monitoring environmental risk factors can thus provide early warning of future outbreaks. Satellite Earth observations provide relevant measurements, but obtaining these data requires substantial expertise, computational resources, and internet bandwidth. To support malaria forecasting in Ethiopia, we developed software for Retrieving Environmental Analytics for Climate and Health (REACH). REACH is a cloud-based application for accessing data on land surface temperature, spectral indices, and precipitation using the Google Earth Engine (GEE) platform. REACH can be implemented using the GEE code editor and JavaScript API, as a standalone web app, or as package with the Python API. Users provide a date range and data for 852 districts in Ethiopia are automatically summarized and downloaded as tables. REACH was successfully used in Ethiopia to support a pilot malaria early warning project in the Amhara region. The software can be extended to new locations and modified to access other environmental datasets through GEE.
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17
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Baharom M, Ahmad N, Hod R, Abdul Manaf MR. Dengue Early Warning System as Outbreak Prediction Tool: A Systematic Review. Healthc Policy 2022; 15:871-886. [PMID: 35535237 PMCID: PMC9078425 DOI: 10.2147/rmhp.s361106] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/16/2022] [Indexed: 12/01/2022] Open
Abstract
Early warning system (EWS) for vector-borne diseases is incredibly complex due to numerous factors originating from human, environmental, vector and the disease itself. Dengue EWS aims to collect data that leads to prompt decision-making processes that trigger disease intervention strategies to minimize the impact on a specific population. Dengue EWS may have a similar structural design, functions, and analytical approaches but different performance and ability to predict outbreaks. Hence, this review aims to summarise and discuss the evidence of different EWSs, their performance, and their ability to predict dengue outbreaks. A systematic literature search was performed of four primary databases: Scopus, Web of Science, Ovid MEDLINE, and EBSCOhost. Eligible articles were evaluated using a checklist for assessing the quality of the studies. A total of 17 studies were included in this systematic review. All EWS models demonstrated reasonably good predictive abilities to predict dengue outbreaks. However, the accuracy of their predictions varied greatly depending on the model used and the data quality. The reported sensitivity ranged from 50 to 100%, while specificity was 74 to 94.7%. A range between 70 to 96.3% was reported for prediction model accuracy and 43 to 86% for PPV. Overall, meteorological alarm indicators (temperatures and rainfall) were the most frequently used and displayed the best performing indicator. Other potential alarm indicators are entomology (female mosquito infection rate), epidemiology, population and socioeconomic factors. EWS is an essential tool to support district health managers and national health planners to mitigate or prevent disease outbreaks. This systematic review highlights the benefits of integrating several epidemiological tools focusing on incorporating climatic, environmental, epidemiological and socioeconomic factors to create an early warning system. The early warning system relies heavily on the country surveillance system. The lack of timely and high-quality data is critical for developing an effective EWS.
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Affiliation(s)
- Mazni Baharom
- Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur, 56000, Malaysia
| | - Norfazilah Ahmad
- Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur, 56000, Malaysia
- Correspondence: Norfazilah Ahmad, Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur, 56000, Malaysia, Tel +60391458781, Fax +60391456670, Email
| | - Rozita Hod
- Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur, 56000, Malaysia
| | - Mohd Rizal Abdul Manaf
- Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur, 56000, Malaysia
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18
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Fang H, Xin S, Pang H, Xu F, Gui Y, Sun Y, Yang N. Evaluating the effectiveness and efficiency of risk communication for maps depicting the hazard of COVID-19. TRANSACTIONS IN GIS : TG 2022; 26:1158-1181. [PMID: 34512105 PMCID: PMC8420161 DOI: 10.1111/tgis.12814] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
COVID-19 maps convey hazard and risk information to the public, which play an important role in the risk communication for individual protection. The aim of this study is to improve the effectiveness and efficiency of communicating the specific risk of COVID-19 maps. By testing 71 subjects from Wuhan, China, this study explored how color schemes (cool, warm, and mixed colors) and data presentation forms (choropleth maps, graduated symbol maps) influence visual cognition patterns, risk perception, comprehension, and subjective satisfaction. The results indicated that the warm scheme (yellow/red) has significant strengths in visual cognition and understanding, and the choropleth map (vs. the graduated symbol map) has significant strengths in risk expression. On subjective satisfaction, the combination of the mixed scheme (blue/yellow/red) and the choropleth map scored highest mean value. These results have implications for enhancing the focused functions of COVID-19 maps that fit different terms: in the early and medium terms of disease transmission, choropleth maps with warm or cool colors should be considered as a priority design for their better risk perception. When the epidemic conditions are on the upturn, a better reading experience combination of choropleth maps with mixed colors can be considered.
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Affiliation(s)
- Hao Fang
- School of Art and CommunicationChina University of GeosciencesWuhanChina
| | - Shiwei Xin
- School of Art and CommunicationChina University of GeosciencesWuhanChina
| | - Huishan Pang
- School of Educational SciencesMinnan Normal UniversityZhangzhouChina
| | - Fan Xu
- School of Geography and Information EngineeringChina University of GeosciencesWuhanChina
| | - Yuhui Gui
- School of Art and CommunicationChina University of GeosciencesWuhanChina
| | - Yan Sun
- School of Art and CommunicationChina University of GeosciencesWuhanChina
| | - Nai Yang
- School of Geography and Information EngineeringChina University of GeosciencesWuhanChina
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19
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Joy T, Chen M, Arnbrister J, Williamson D, Li S, Nair S, Brophy M, Garcia VM, Walker K, Ernst K, Gouge DH, Carrière Y, Riehle MA. Assessing Near-Infrared Spectroscopy (NIRS) for Evaluation of Aedes aegypti Population Age Structure. INSECTS 2022; 13:insects13040360. [PMID: 35447802 PMCID: PMC9029691 DOI: 10.3390/insects13040360] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/03/2022] [Accepted: 04/05/2022] [Indexed: 02/04/2023]
Abstract
Given that older Aedes aegypti (L.) mosquitoes typically pose the greatest risk of pathogen transmission, the capacity to age grade wild Ae. aegypti mosquito populations would be a valuable tool in monitoring the potential risk of arboviral transmission. Here, we compared the effectiveness of near-infrared spectroscopy (NIRS) to age grade field-collected Ae. aegypti with two alternative techniques—parity analysis and transcript abundance of the age-associated gene SCP1. Using lab-reared mosquitoes of known ages from three distinct populations maintained as adults under laboratory or semi-field conditions, we developed and validated four NIRS models for predicting the age of field-collected Ae. aegypti. To assess the accuracy of these models, female Ae. aegypti mosquitoes were collected from Maricopa County, AZ, during the 2017 and 2018 monsoon season, and a subset were age graded using the three different age-grading techniques. For both years, each of the four NIRS models consistently graded parous mosquitoes as significantly older than nulliparous mosquitoes. Furthermore, a significant positive linear association occurred between SCP1 and NIRS age predictions in seven of the eight year/model combinations, although considerable variation in the predicted age of individual mosquitoes was observed. Our results suggest that although the NIRS models were not adequate in determining the age of individual field-collected mosquitoes, they have the potential to quickly and cost effectively track changes in the age structure of Ae. aegypti populations across locations and over time.
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Affiliation(s)
- Teresa Joy
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Minhao Chen
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Joshua Arnbrister
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Daniel Williamson
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Shujuan Li
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Shakunthala Nair
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Maureen Brophy
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Valerie Madera Garcia
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ 85724, USA; (V.M.G.); (K.E.)
| | - Kathleen Walker
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Kacey Ernst
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ 85724, USA; (V.M.G.); (K.E.)
| | - Dawn H. Gouge
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Yves Carrière
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Michael A. Riehle
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
- Correspondence: ; Tel.: +1-520-626-8500
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Carabali M, Schmidt AM, Restrepo BN, Kaufman JS. A joint spatial marked point process model for dengue and severe dengue in Medellin, Colombia. Spat Spatiotemporal Epidemiol 2022; 41:100495. [DOI: 10.1016/j.sste.2022.100495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 01/19/2022] [Accepted: 02/28/2022] [Indexed: 11/16/2022]
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Singh G, Mitra A, Soman B. Development and Use of a Reproducible Framework for Spatiotemporal Climatic Risk Assessment and its Association with Decadal Trend of Dengue in India. Indian J Community Med 2022; 47:50-54. [PMID: 35368491 PMCID: PMC8971875 DOI: 10.4103/ijcm.ijcm_862_21] [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: 06/02/2021] [Accepted: 11/30/2021] [Indexed: 11/22/2022] Open
Abstract
Introduction: The study aimed to develop a reproducible, open-source, and scalable framework for extracting climate data from satellite imagery, understanding dengue's decadal trend in India, and estimating the relationship between dengue occurrence and climatic factors. Materials and Methods: A framework was developed in the Open Source Software, and it was empirically tested using reported annual dengue occurrence data in India during 2010–2019. Census 2011 and population projections were used to calculate incidence rates. Zonal statistics were performed to extract climate parameters. Correlation coefficients were calculated to estimate the relationship of dengue with the annual average of daily mean and minimum temperature and rainy days. Results: Total 818,973 dengue cases were reported from India, with median annual incidence of 6.57 per lakh population; it was high in 2019 and 2017 (11.80 and 11.55 per lakh) and the Southern region (8.18 per lakh). The highest median annual dengue incidence was observed in Punjab (24.49 per lakh). Daily climatic data were extracted from 1164 coordinate locations across the country for the decadal period (4,249,734 observations). The annual average of daily temperature and rainy days positively correlated with dengue in India (r = 0.31 and 0.06, at P < 0.01 and 0.30, respectively). Conclusion: The study provides a reproducible algorithm for bulk climatic data extraction from research-level satellite imagery. Infectious disease models can be used to understand disease epidemiology and strengthen disease surveillance in the country.
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Affiliation(s)
- Gurpreet Singh
- Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India
| | - Arun Mitra
- Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India
| | - Biju Soman
- Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India
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Singh G, Soman B. Spatiotemporal epidemiology and forecasting of dengue in the state of Punjab, India: Study protocol. Spat Spatiotemporal Epidemiol 2021; 39:100444. [PMID: 34774263 DOI: 10.1016/j.sste.2021.100444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 07/02/2021] [Accepted: 07/21/2021] [Indexed: 11/30/2022]
Abstract
Dengue burden in India is a major public health problem. The present study has been designed to understand mechanisms by which routine data generate evidence. Secondary data analysis of routine datasets to understand spatiotemporal epidemiology and forecast dengue will be conducted. Data science approach will be adopted to generate a reproducible framework in the R environment. The lab-confirmed dengue reported by the state health authorities from 01 January 2015 to 31 December 2019 will be included. Multiple climatic variables from satellite imagery, climatic models, vegetation and built-up indices, and sociodemographic variables will be explored as risk factors. Exploratory data analysis followed by statistical analysis and machine learning will be performed. Data analysis will include geospatial information analysis, time series analysis, and spatiotemporal analysis. The study will provide value addition to the existing disease surveillance mechanisms by developing a framework for incorporating multiple routine data sources available in the country.
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Affiliation(s)
- Gurpreet Singh
- Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India
| | - Biju Soman
- Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India..
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Hoyos W, Aguilar J, Toro M. Dengue models based on machine learning techniques: A systematic literature review. Artif Intell Med 2021; 119:102157. [PMID: 34531010 DOI: 10.1016/j.artmed.2021.102157] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 05/08/2021] [Accepted: 08/17/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND Dengue modeling is a research topic that has increased in recent years. Early prediction and decision-making are key factors to control dengue. This Systematic Literature Review (SLR) analyzes three modeling approaches of dengue: diagnostic, epidemic, intervention. These approaches require models of prediction, prescription and optimization. This SLR establishes the state-of-the-art in dengue modeling, using machine learning, in the last years. METHODS Several databases were selected to search the articles. The selection was made based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Sixty-four articles were obtained and analyzed to describe their strengths and limitations. Finally, challenges and opportunities for research on machine-learning for dengue modeling were identified. RESULTS Logistic regression was the most used modeling approach for the diagnosis of dengue (59.1%). The analysis of the epidemic approach showed that linear regression (17.4%) is the most used technique within the spatial analysis. Finally, the most used intervention modeling is General Linear Model with 70%. CONCLUSIONS We conclude that cause-effect models may improve diagnosis and understanding of dengue. Models that manage uncertainty can also be helpful, because of low data-quality in healthcare. Finally, decentralization of data, using federated learning, may decrease computational costs and allow model building without compromising data security.
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Affiliation(s)
- William Hoyos
- Grupo de Investigaciones Microbiológicas y Biomédicas de Córdoba, Universidad de Córdoba, Montería, Colombia; Grupo de Investigación en I+D+i en TIC, Universidad EAFIT, Medellín, Colombia.
| | - Jose Aguilar
- Grupo de Investigación en I+D+i en TIC, Universidad EAFIT, Medellín, Colombia; Centro de Estudios en Microelectrónica y Sistemas Distribuidos, Universidad de Los Andes, Mérida, Venezuela; Universidad de Alcalá, Depto. de Automática, Alcalá de Henares, Spain
| | - Mauricio Toro
- Grupo de Investigación en I+D+i en TIC, Universidad EAFIT, Medellín, Colombia
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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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25
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Monnaka VU, Oliveira CACD. Google Trends correlation and sensitivity for outbreaks of dengue and yellow fever in the state of São Paulo. EINSTEIN-SAO PAULO 2021; 19:eAO5969. [PMID: 34346987 PMCID: PMC8302225 DOI: 10.31744/einstein_journal/2021ao5969] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Accepted: 03/04/2021] [Indexed: 02/06/2023] Open
Abstract
Objective To assess Google Trends accuracy for epidemiological surveillance of dengue and yellow fever, and to compare the incidence of these diseases with the popularity of its terms in the state of São Paulo. Methods Retrospective cohort. Google Trends survey results were compared to the actual incidence of diseases, obtained from Centro de Vigilância Epidemiológica “Prof. Alexandre Vranjac”, in São Paulo, Brazil, in periods between 2017 and 2019. The correlation was calculated by Pearson’s coefficient and cross-correlation function. The accuracy was analyzed by sensitivity and specificity values. Results There was a statistically significant correlation between the variables studied for both diseases, Pearson coefficient of 0.91 for dengue and 0.86 for yellow fever. Correlation with up to 4 weeks of anticipation for time series was identified. Sensitivity was 87% and 90%, and specificity 69% and 78% for dengue and yellow fever, respectively. Conclusion The incidence of dengue and yellow fever in the State of São Paulo showed a strong correlation with the popularity of its terms measured by Google Trends in weekly periods. Google Trends tool provided early warning, with high sensitivity, for the detection of outbreaks of these diseases.
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Affiliation(s)
- Vitor Ulisses Monnaka
- Faculdade Israelita de Ciências da Saúde Albert Einstein, Hospital Israelita Albert Einstein, São Paulo, SP, Brazil
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26
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Su Yin M, Bicout DJ, Haddawy P, Schöning J, Laosiritaworn Y, Sa-angchai P. Added-value of mosquito vector breeding sites from street view images in the risk mapping of dengue incidence in Thailand. PLoS Negl Trop Dis 2021; 15:e0009122. [PMID: 33684130 PMCID: PMC7971869 DOI: 10.1371/journal.pntd.0009122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 03/18/2021] [Accepted: 01/11/2021] [Indexed: 11/19/2022] Open
Abstract
Dengue is an emerging vector-borne viral disease across the world. The primary dengue mosquito vectors breed in containers with sufficient water and nutrition. Outdoor containers can be detected from geotagged images using state-of-the-art deep learning methods. In this study, we utilize such container information from street view images in developing a risk mapping model and determine the added value of including container information in predicting dengue risk. We developed seasonal-spatial models in which the target variable dengue incidence was explained using weather and container variable predictors. Linear mixed models with fixed and random effects are employed in our models to account for different characteristics of containers and weather variables. Using data from three provinces of Thailand between 2015 and 2018, the models are developed at the sub-district level resolution to facilitate the development of effective targeted intervention strategies. The performance of the models is evaluated with two baseline models: a classic linear model and a linear mixed model without container information. The performance evaluated with the correlation coefficients, R-squared, and AIC shows the proposed model with the container information outperforms both baseline models in all three provinces. Through sensitivity analysis, we investigate the containers that have a high impact on dengue risk. Our findings indicate that outdoor containers identified from street view images can be a useful data source in building effective dengue risk models and that the resulting models have potential in helping to target container elimination interventions.
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Affiliation(s)
- Myat Su Yin
- Faculty of ICT, Mahidol University, Nakhon Pathom, Thailand
| | - Dominique J. Bicout
- Biomathematics and Epidemiology, EPSP-TIMC, UMR CNRS 5525, Grenoble-Alpes University, VetAgro Sup, Grenoble, France
- Laue–Langevin Institute, Theory group, Grenoble, France
| | - Peter Haddawy
- Faculty of ICT, Mahidol University, Nakhon Pathom, Thailand
- Bremen Spatial Cognition Center, University of Bremen, Bremen, Germany
| | - Johannes Schöning
- Bremen Spatial Cognition Center, University of Bremen, Bremen, Germany
| | - Yongjua Laosiritaworn
- Information Technology Center, Department of Disease Control, Ministry of Public Health, Bangkok, Thailand
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Joshi A, Miller C. Review of machine learning techniques for mosquito control in urban environments. ECOL INFORM 2021. [DOI: 10.1016/j.ecoinf.2021.101241] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Withanage GP, Gunawardana M, Viswakula SD, Samaraweera K, Gunawardena NS, Hapugoda MD. Multivariate spatio-temporal approach to identify vulnerable localities in dengue risk areas using Geographic Information System (GIS). Sci Rep 2021; 11:4080. [PMID: 33602959 PMCID: PMC7892844 DOI: 10.1038/s41598-021-83204-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 02/01/2021] [Indexed: 12/04/2022] Open
Abstract
Dengue is one of the most important vector-borne infection in Sri Lanka currently leading to vast economic and social burden. Neither a vaccine nor drug is still not being practiced, vector controlling is the best approach to control disease transmission in the country. Therefore, early warning systems are imminent requirement. The aim of the study was to develop Geographic Information System (GIS)-based multivariate analysis model to detect risk hotspots of dengue in the Gampaha District, Sri Lanka to control diseases transmission. A risk model and spatial Poisson point process model were developed using separate layers for patient incidence locations, positive breeding containers, roads, total buildings, public places, land use maps and elevation in four high risk areas in the district. Spatial correlations of each study layer with patient incidences was identified using Kernel density and Euclidean distance functions with minimum allowed distance parameter. Output files of risk model indicate that high risk localities are in close proximity to roads and coincide with vegetation coverage while the Poisson model highlighted the proximity of high intensity localities to public places and possibility of artificial reservoirs of dengue. The latter model further indicate that clustering of dengue cases in a radius of approximately 150 m in high risk areas indicating areas need intensive attention in future vector surveillances.
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Affiliation(s)
- Gayan P Withanage
- Molecular Medicine Unit, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - Malika Gunawardana
- Postgraduate Institute of Science, University of Peradeniya, Peradeniya, Sri Lanka
| | - Sameera D Viswakula
- Department of Statistics, Faculty of Science, University of Colombo, Colombo, 07, Sri Lanka
| | - Krishantha Samaraweera
- Epidemiology Unit, Office of the Regional Director of Health Services, Gampaha, Sri Lanka
| | - Nilmini S Gunawardena
- Molecular Medicine Unit, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - Menaka D Hapugoda
- Molecular Medicine Unit, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka.
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Salim NAM, Wah YB, Reeves C, Smith M, Yaacob WFW, Mudin RN, Dapari R, Sapri NNFF, Haque U. Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques. Sci Rep 2021; 11:939. [PMID: 33441678 PMCID: PMC7806812 DOI: 10.1038/s41598-020-79193-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 11/17/2020] [Indexed: 01/26/2023] Open
Abstract
Dengue fever is a mosquito-borne disease that affects nearly 3.9 billion people globally. Dengue remains endemic in Malaysia since its outbreak in the 1980's, with its highest concentration of cases in the state of Selangor. Predictors of dengue fever outbreaks could provide timely information for health officials to implement preventative actions. In this study, five districts in Selangor, Malaysia, that demonstrated the highest incidence of dengue fever from 2013 to 2017 were evaluated for the best machine learning model to predict Dengue outbreaks. Climate variables such as temperature, wind speed, humidity and rainfall were used in each model. Based on results, the SVM (linear kernel) exhibited the best prediction performance (Accuracy = 70%, Sensitivity = 14%, Specificity = 95%, Precision = 56%). However, the sensitivity for SVM (linear) for the testing sample increased up to 63.54% compared to 14.4% for imbalanced data (original data). The week-of-the-year was the most important predictor in the SVM model. This study exemplifies that machine learning has respectable potential for the prediction of dengue outbreaks. Future research should consider boosting, or using, nature inspired algorithms to develop a dengue prediction model.
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Affiliation(s)
- Nurul Azam Mohd Salim
- Advanced Analytics Engineering Centre, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia
| | - Yap Bee Wah
- Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Kelantan, Kampus Kota Bharu, Lembah Sirrh, 15050, Kota Bharu, Kelantan, Malaysia
| | - Caitlynn Reeves
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA
| | - Madison Smith
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA
| | - Wan Fairos Wan Yaacob
- Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Kelantan, Kampus Kota Bharu, Lembah Sirrh, 15050, Kota Bharu, Kelantan, Malaysia
| | - Rose Nani Mudin
- Vector Borne Disease Sector, Disease Control Division, Ministry of Health Malaysia, Level 4, Block E10, Complex E, Federal Government Administration Complex, 62590, Putrajaya, Malaysia
| | - Rahmat Dapari
- Vector Borne Disease Sector, Disease Control Division, Ministry of Health Malaysia, Level 4, Block E10, Complex E, Federal Government Administration Complex, 62590, Putrajaya, Malaysia
| | - Nik Nur Fatin Fatihah Sapri
- Advanced Analytics Engineering Centre, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia
| | - Ubydul Haque
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA.
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Chakraborty A, Chandru V. A Robust and Non-parametric Model for Prediction of Dengue Incidence. J Indian Inst Sci 2020. [DOI: 10.1007/s41745-020-00202-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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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 PMCID: PMC7485513 DOI: 10.5365/wpsar.2018.9.2.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [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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Bajwala VR, John D, Rajasekar TD, Murhekar MV. Severity and costs associated with hospitalization for dengue in public and private hospitals of Surat city, Gujarat, India, 2017-2018. Trans R Soc Trop Med Hyg 2020; 113:661-669. [PMID: 31294808 DOI: 10.1093/trstmh/trz057] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Revised: 04/16/2019] [Accepted: 05/30/2019] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Dengue is major public health problem in India, especially in urban areas. We conducted a study to estimate the severity and costs of treatment among hospitalized dengue patients in Surat city, Gujarat, India. METHODS We reviewed the medical records of dengue patients hospitalized in five tertiary care facilities (private [n=2], semi-government [n=2] and government [n=1]) between April 2017 and March 2018. We used the World Health Organization 2009 classification to classify the severity of dengue. A resource utilization approach was used to estimate the cost of illness in US dollars (US$) (inflation adjusted to 2018) from a quasi-societal perspective (excluding non-medical cost) for dengue hospitalization. RESULTS Of the 732 hospitalized dengue patients, 44.7% had no warning symptoms, 39.5% had warning signs and 15.8% had severe dengue. The mean cost of hospitalization was US$86.9±170.7. The cost of hospitalization was 28.8 times higher in private hospitals compared with government hospitals. Consultant charges in private hospitals, laboratory investigations in semi-government hospitals and registration with admission charges in government hospitals accounted for 27.3%, 39.4% and 53% of the direct cost in these facilities, respectively. CONCLUSIONS A better triage system for hospitalization, subsidizing costs in the public sector and cost capping in the private sector can help to reduce the cost of hospitalization due to dengue so as to ensure affordability for larger portion of the society for universal health coverage.
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Affiliation(s)
- Viral R Bajwala
- Department of Health and Hospital, Surat Municipal Corporation, Gujarat, India
| | - Denny John
- Campbell Collaboration, New Delhi, India.,ICMR-National Institute of Medical Statistics, New Delhi, India
| | - T Daniel Rajasekar
- National Institute of Epidemiology, Indian Council of Medical Research, Chennai, India
| | - Manoj V Murhekar
- National Institute of Epidemiology, Indian Council of Medical Research, Chennai, India
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Zhang Y, Riera J, Ostrow K, Siddiqui S, de Silva H, Sarkar S, Fernando L, Gardner L. Modeling the relative role of human mobility, land-use and climate factors on dengue outbreak emergence in Sri Lanka. BMC Infect Dis 2020; 20:649. [PMID: 32883213 PMCID: PMC7469426 DOI: 10.1186/s12879-020-05369-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 08/25/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND More than 80,000 dengue cases including 215 deaths were reported nationally in less than 7 months between 2016 and 2017, a fourfold increase in the number of reported cases compared to the average number over 2010-2016. The region of Negombo, located in the Western province, experienced the greatest number of dengue cases in the country and is the focus area of our study, where we aim to capture the spatial-temporal dynamics of dengue transmission. METHODS We present a statistical modeling framework to evaluate the spatial-temporal dynamics of the 2016-2017 dengue outbreak in the Negombo region of Sri Lanka as a function of human mobility, land-use, and climate patterns. The analysis was conducted at a 1 km × 1 km spatial resolution and a weekly temporal resolution. RESULTS Our results indicate human mobility to be a stronger indicator for local outbreak clusters than land-use or climate variables. The minimum daily temperature was identified as the most influential climate variable on dengue cases in the region; while among the set of land-use patterns considered, urban areas were found to be most prone to dengue outbreak, followed by areas with stagnant water and then coastal areas. The results are shown to be robust across spatial resolutions. CONCLUSIONS Our study highlights the potential value of using travel data to target vector control within a region. In addition to illustrating the relative relationship between various potential risk factors for dengue outbreaks, the results of our study can be used to inform where and when new cases of dengue are likely to occur within a region, and thus help more effectively and innovatively, plan for disease surveillance and vector control.
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Affiliation(s)
- Ying Zhang
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218 USA
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD 21218 USA
| | - Jefferson Riera
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD 21205 USA
| | - Kayla Ostrow
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218 USA
| | - Sauleh Siddiqui
- Department of Environmental Science, American University, Washington, DC 20016 USA
| | - Harendra de Silva
- Department of Pediatrics, University of Colombo, Colombo, 00900 Sri Lanka
| | - Sahotra Sarkar
- Department of Philosophy, Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712 USA
| | - Lakkumar Fernando
- Centre for Clinical Management of Dengue and Dengue Haemorrhagic Fever, Negombo, 11500 Sri Lanka
| | - Lauren Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218 USA
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Arsalan M, Mubin O, Alnajjar F, Alsinglawi B, Zaki N. Global and Temporal COVID-19 Risk Evaluation. Front Public Health 2020; 8:440. [PMID: 32850611 PMCID: PMC7430161 DOI: 10.3389/fpubh.2020.00440] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 07/17/2020] [Indexed: 11/28/2022] Open
Abstract
The COVID-19 pandemic has caused unprecedented crisis across the world, with many countries struggling with the pandemic. In order to understand how each country is impacted by the virus and assess the risk on a global scale we present a regression based analysis using two pre-existing indexes, namely the Inform and Infectious Disease Vulnerability Index, in conjunction with the number of elderly living in the population. Further we introduce a temporal layer in our modeling by incorporating the stringency level employed by each country over a period of 6 time intervals. Our results show that the indexes and level of stringency are not ideally suited for explaining variation in COVID-19 risk, however the ratio of elderly in the population is a stand out indicator in terms of its predictive power for mortality risk. In conclusion, we discuss how such modeling approaches can assist public health policy.
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Affiliation(s)
- Mudassar Arsalan
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, NSW, Australia
| | - Omar Mubin
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, NSW, Australia
| | - Fady Alnajjar
- College of IT, UAE University, Al Ain, United Arab Emirates
| | - Belal Alsinglawi
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, NSW, Australia
| | - Nazar Zaki
- College of IT, UAE University, Al Ain, United Arab Emirates
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Gyawali N, Murphy AK, Hugo LE, Devine GJ. A micro-PRNT for the detection of Ross River virus antibodies in mosquito blood meals: A useful tool for inferring transmission pathways. PLoS One 2020; 15:e0229314. [PMID: 32706777 PMCID: PMC7380888 DOI: 10.1371/journal.pone.0229314] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 07/06/2020] [Indexed: 01/12/2023] Open
Abstract
Introduction Many arboviruses of public health significance are maintained in zoonotic cycles with complex transmission pathways. The presence of serum antibody against arboviruses in vertebrates provides evidence of their historical exposure but reveals nothing about the vector-reservoir relationship. Moreover, collecting blood or tissue samples from vertebrate hosts is ethically and logistically challenging. We developed a novel approach for screening the immune status of vertebrates against Ross River virus that allows us to implicate the vectors that form the transmission pathways for this commonly notified Australian arboviral disease. Methods A micro-plaque reduction neutralisation test (micro-PRNT) was developed and validated on koala (Phascolarctos cinereus) sera against a standard PRNT. The ability of the micro-PRNT to detect RRV antibodies in mosquito blood meals was then tested using two mosquito models. Laboratory-reared Aedes aegypti were fed, via a membrane, on sheep blood supplemented with RRV seropositive and seronegative human sera. Aedes notoscriptus were fed on RRV seropositive and seronegative human volunteers. Blood-fed mosquitoes were harvested at various time points after feeding and their blood meals analysed for the presence of RRV neutralising antibodies using the micro-PRNT. Results There was significant agreement of the plaque neutralisation resulting from the micro-PRNT and standard PRNT techniques (R2 = 0.65; P<0.0001) when applied to RRV antibody detection in koala sera. Sensitivity and specificity of the micro-PRNT assay were 88.2% and 96%, respectively, in comparison with the standard PRNT. Blood meals from mosquitoes fed on sheep blood supplemented with RRV antibodies, and on blood from RRV seropositive humans neutralised the virus by ≥50% until 48 hr post feeding. The vertebrate origin of the blood meal was also ascertained for the same samples, in parallel, using established molecular techniques. Conclusions The small volumes of blood present in mosquito abdomens can be used to identify RRV antibodies and therefore host exposure to arbovirus infection. In tandem with the accurate identification of the mosquito, and diagnostics for the host origin of the blood meal, this technique has tremendous potential for exploring RRV transmission pathways. It can be adapted for similar studies on other mosquito borne zoonoses.
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Affiliation(s)
- Narayan Gyawali
- Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- * E-mail:
| | - Amanda K. Murphy
- Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Leon E. Hugo
- Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Gregor J. Devine
- Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
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Jeffrey Gutiérrez EH, Walker KR, Ernst KC, Riehle MA, Davidowitz G. Size as a Proxy for Survival in Aedes aegypti (Diptera: Culicidae) Mosquitoes. JOURNAL OF MEDICAL ENTOMOLOGY 2020; 57:1228-1238. [PMID: 32266939 PMCID: PMC7768678 DOI: 10.1093/jme/tjaa055] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Indexed: 06/11/2023]
Abstract
The Aedes aegypti mosquito is the primary vector of dengue, yellow fever, chikungunya, and Zika viruses. Infection with the dengue virus alone occurs in an estimated 400 million people each year. Likelihood of infection with a virus transmitted by Ae. aegypti is most commonly attributed to abundance of the mosquito. However, the Arizona-Sonora desert region has abundant Ae. aegypti in most urban areas, yet local transmission of these arboviruses has not been reported in many of these cities. Previous work examined the role of differential Ae. aegypti longevity as a potential explanation for these discrepancies in transmission. To determine factors that were associated with Ae. aegypti longevity in the region, we collected eggs from ovitraps in Tucson, AZ and reared them under multiple experimental conditions in the laboratory to examine the relative impact of temperature and crowding during development, body size, fecundity, and relative humidity during the adult stage. Of the variables studied, we found that the combination of temperature during development, relative humidity, and body size produced the best model to explain variation in age at death. El mosquito Aedes aegypti es el vector primario de los virus de dengue, fiebre amarilla, chikungunya y Zika. Solamente las infecciones con los virus de dengue ocurren en aproximadamente 400 millones de personas cada año. La probabilidad de infección con un virus transmitido por Ae. aegypti es frecuentemente atribuido a la abundancia del mosquito. No obstante, la región del desierto de Arizona-Sonora tiene una abundancia de Ae. aegypti en la mayoría de las áreas urbanas, pero la transmisión local de estos arbovirus no ha sido reportada en muchas de estas ciudades. Trabajos previos han examinado el rol de las diferencias de longevidad en Ae. aegypti como explicación potencial por estas discrepancias en la transmisión. Para determinar que factores fueron asociados con longevidad en Ae. aegypti en la región, colectamos huevos de ovitrampas en Tucson, Arizona y los criamos debajo de múltiples condiciones experimentales en el laboratorio para examinar el impacto relativo de temperatura y competencia para nutrición durante desarrollo, tamaño del cuerpo, capacidad reproductiva, y humedad relativa durante adultez. De las variables estudiados, encontramos que la combinación de temperatura durante desarrollo, humedad relativa, y tamaño del cuerpo produjo el mejor modelo para explicar variación en edad al tiempo de la muerte.
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Affiliation(s)
| | | | - Kacey C Ernst
- Department of Epidemiology and Biostatistics, College of Public Health, University of Arizona, Tucson, AZ
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Pham NTT, Nguyen CT, Vu HH. Assessing and modelling vulnerability to dengue in the Mekong Delta of Vietnam by geospatial and time-series approaches. ENVIRONMENTAL RESEARCH 2020; 186:109545. [PMID: 32361079 DOI: 10.1016/j.envres.2020.109545] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 04/14/2020] [Accepted: 04/15/2020] [Indexed: 05/16/2023]
Abstract
Dengue fever has continuously been a disease burden in Vietnam during the last 20 years, particularly in the Mekong Delta region (MDR), which is one of the most vulnerable to climate change. Variations in temperature and precipitation are likely to alter the incidence and distribution of vector-borne diseases such as dengue. This study focuses on assessing dengue risk via the vulnerability concept, which is composed of exposure and susceptibility using a combined approach of mapping and modelling for the MDR of Vietnam during the period between 2001 and 2016. Multisource remote sensing data from Global Satellite Mapping of Precipitation (GSMaP) and Moderate Resolution Imaging Spectrophotometer (MODIS) was used for presenting climate and environment variables in mapping and modelling vulnerability. Monthly and yearly maps of vulnerability to dengue in the MDR, produced for 15-year period, aided analysis of the temporal and spatial patterns of vulnerability to dengue in the study region and were used for constructing time-series modelling of vulnerability for the following year. The results showed that there is a clear seasonal variation in the vulnerability due to variability of the climate factor and its strong dispersion across the study region, with higher vulnerability in the scattered areas of urban and mixed horticulture land and lower vulnerability in areas covered by forest and bare soil lands. The Pearson's correlation was applied to evaluate the association between dengue rates and vulnerability values aggregated at the provincial level. Reasonable linear association, with correlation coefficients of 0.41-0.63, was found in two-thirds of the provinces. The predicted vulnerabilities to dengue during 2016 were comparable with the estimated values and trends for most provinces of the MDR. Our demonstrated approach with integrated geospatial data seems to be a promising tool in supporting the public health sector in assessing potential space and time of a subsequent increase in vulnerability to dengue, particularly in the context of climate change.
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Affiliation(s)
- Nga T T Pham
- Vietnam National Space Center, Vietnam Academy of Science and Technology, Viet Nam.
| | - Cong T Nguyen
- Vietnam National Space Center, Vietnam Academy of Science and Technology, Viet Nam
| | - Hoa H Vu
- Faculty of Chemical and Environmental Engineering, Thuyloi University, Viet Nam
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Diptyanusa A, Lazuardi L, Jatmiko RH. Implementation of geographical information systems for the study of diseases caused by vector-borne arboviruses in Southeast Asia: A review based on the publication record. GEOSPATIAL HEALTH 2020; 15. [PMID: 32575973 DOI: 10.4081/gh.2020.862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 04/14/2020] [Indexed: 06/11/2023]
Abstract
The spread of mosquito-borne diseases in Southeast Asia has dramatically increased in the latest decades. These infections include dengue, chikungunya and Japanese Encephalitis (JE), high-burden viruses sharing overlapping disease manifestation and vector distribution. The use of Geographical Information Systems (GIS) to monitor the dynamics of disease and vector distribution can assist in disease epidemic prediction and public health interventions, particularly in Southeast Asia where sustained high temperatures drive the epidemic spread of these mosquito-borne viruses. Due to lack of accurate data, the spatial and temporal dynamics of these mosquito-borne viral disease transmission countries are poorly understood, which has limited disease control effort. By following studies carried out on these three viruses across the region in a specific time period revealing general patterns of research activities and characteristics, this review finds the need to improve decision-support by disease mapping and management. The results presented, based on a publication search with respect to diseases due to arboviruses, specifically dengue, chikungunya and Japanese encephalitis, should improve opportunities for future studies on the implementation of GIS in the control of mosquito-borne viral diseases in Southeast Asia.
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Affiliation(s)
- Ajib Diptyanusa
- Department of Parasitology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Jalan Farmako, Sekip Utara.
| | - Lutfan Lazuardi
- Department of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Jalan Farmako, Sekip Utara.
| | - Retnadi Heru Jatmiko
- Centre for Remote Sensing and Geographical Information System (PUSPICS), Universitas Gadjah Mada, Sekip Utara, Yogyakarta.
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Henry S, Mendonça FDA. Past, Present, and Future Vulnerability to Dengue in Jamaica: A Spatial Analysis of Monthly Variations. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E3156. [PMID: 32369951 PMCID: PMC7246587 DOI: 10.3390/ijerph17093156] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Revised: 04/11/2020] [Accepted: 04/13/2020] [Indexed: 11/18/2022]
Abstract
Over the years, Jamaica has experienced sporadic cases of dengue fever. Even though the island is vulnerable to dengue, there is paucity in the spatio-temporal analysis of the disease using Geographic Information Systems (GIS) and remote sensing tools. Further, access to time series dengue data at the community level is a major challenge on the island. This study therefore applies the Water-Associated Disease Index (WADI) framework to analyze vulnerability to dengue in Jamaica based on past, current and future climate change conditions using three scenarios: (1) WorldClim rainfall and temperature dataset from 1970 to 2000; (2) Climate Hazard Group InfraRed Precipitation with Station data (CHIRPS) rainfall and land surface temperature (LST) as proxy for air temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2002 to 2016, and (3) maximum temperature and rainfall under the Representative Concentration Pathway (RCP) 8.5 climate change scenario for 2030 downscaled at 25 km based on the Regional Climate Model, RegCM4.3.5. Although vulnerability to dengue varies spatially and temporally, a higher vulnerability was depicted in urban areas in comparison to rural areas. The results also demonstrate the possibility for expansion in the geographical range of dengue in higher altitudes under climate change conditions based on scenario 3. This study provides an insight into the use of data with different temporal and spatial resolution in the analysis of dengue vulnerability.
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Affiliation(s)
- Sheika Henry
- Department of Geography, Federal University of Parana, Curitiba 81531-980, Brazil;
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Liu K, Hou X, Ren Z, Lowe R, Wang Y, Li R, Liu X, Sun J, Lu L, Song X, Wu H, Wang J, Yao W, Zhang C, Sang S, Gao Y, Li J, Li J, Xu L, Liu Q. Climate factors and the East Asian summer monsoon may drive large outbreaks of dengue in China. ENVIRONMENTAL RESEARCH 2020; 183:109190. [PMID: 32311903 DOI: 10.1016/j.envres.2020.109190] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 01/17/2020] [Accepted: 01/25/2020] [Indexed: 05/19/2023]
Abstract
OBJECTIVE To investigate the relationship between climate variables, East Asian summer monsoon (EASM) and large outbreaks of dengue in China. METHODS We constructed ecological niche models (ENMs) to analyse the influence of climate factors on dengue occurrence and predict dengue outbreak areas in China. Furthermore, we formulated a generalised additive model (GAM) to quantify the impact of the EASM on dengue occurrence in mainland China from 1980 to 2016. RESULTS Mean Temperature of Coldest Quarter had a 62.6% contribution to dengue outbreaks. Southern China including Guangdong, Guangxi, Fujian and Yunnan provinces are more vulnerable to dengue emergence and resurgence. In addition, we found population density had a 68.7% contribution to dengue widely distribution in China using ENMs. Statistical analysis indicated a dome-shaped association between EASM and dengue outbreak using GAM, with the greatest impact in the South-East of China. Besides, there was a positive nonlinear association between monthly average temperature and dengue occurrence. CONCLUSION We demonstrated the influence of climate factors and East Asian summer monsoon on dengue outbreaks, providing a framework for future studies on the association between climate change and vector-borne diseases.
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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, 102206, China; Shandong Academy of Clinical Medicine, Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, China
| | - Xiang Hou
- Shaanxi Key Laboratory for Animal Conservation, Shaanxi Institute of Zoology, Xi'an, 710032, China
| | - Zhoupeng Ren
- State Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Diseases and Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, United Kingdom; Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
| | - Yiguan Wang
- School of Biological Sciences, University of Queensland, QLD, 4072, Australia
| | - Ruiyun Li
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, W2 1PG, United Kingdom
| | - 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, 102206, China
| | - Jimin Sun
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - 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, 102206, China
| | - Xiupin Song
- 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, 102206, 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, 102206, China
| | - Jun Wang
- 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, 102206, China
| | - Wenwu Yao
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Chutian Zhang
- College of Natural Resources and Environment, Northwest A&F University, Yangling, 712100, China
| | - Shaowei Sang
- Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Jinan, 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, 102206, China
| | - Jing Li
- Division of Environmental Health, School of Public Health and Management, Weifang Medical University, Weifang, 261053, Shandong Province, PR China
| | - Jianping Li
- Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES), Key Laboratory of Physical Oceanography, Institute for Advanced Ocean Studies, Ocean University of China, Qingdao 266100, China; Laboratory for Ocean Dynamics and Climate, Pilot Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, 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, 102206, China.
| | - 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, 102206, China.
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A Mapping Review on Urban Landscape Factors of Dengue Retrieved from Earth Observation Data, GIS Techniques, and Survey Questionnaires. REMOTE SENSING 2020. [DOI: 10.3390/rs12060932] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
To date, there is no effective treatment to cure dengue fever, a mosquito-borne disease which has a major impact on human populations in tropical and sub-tropical regions. Although the characteristics of dengue infection are well known, factors associated with landscape are highly scale dependent in time and space, and therefore difficult to monitor. We propose here a mapping review based on 78 articles that study the relationships between landscape factors and urban dengue cases considering household, neighborhood and administrative levels. Landscape factors were retrieved from survey questionnaires, Geographic Information Systems (GIS), and remote sensing (RS) techniques. We structured these into groups composed of land cover, land use, and housing type and characteristics, as well as subgroups referring to construction material, urban typology, and infrastructure level. We mapped the co-occurrence networks associated with these factors, and analyzed their relevance according to a three-valued interpretation (positive, negative, non significant). From a methodological perspective, coupling RS and GIS techniques with field surveys including entomological observations should be systematically considered, as none digital land use or land cover variables appears to be an univocal determinant of dengue occurrences. Remote sensing urban mapping is however of interest to provide a geographical frame to distribute human population and movement in relation to their activities in the city, and as spatialized input variables for epidemiological and entomological models.
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Liu D, Guo S, Zou M, Chen C, Deng F, Xie Z, Hu S, Wu L. A dengue fever predicting model based on Baidu search index data and climate data in South China. PLoS One 2019; 14:e0226841. [PMID: 31887118 PMCID: PMC6936853 DOI: 10.1371/journal.pone.0226841] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 12/04/2019] [Indexed: 12/12/2022] Open
Abstract
With the acceleration of global urbanization and climate change, dengue fever is spreading worldwide. Different levels of dengue fever have also occurred in China, especially in southern China, causing enormous economic losses. Unfortunately, there is no effective treatment for dengue, and the most popular dengue vaccine does not exhibit good curative effects. Therefore, we developed a Generalized Additive Mixed Model (GAMM) that gathered climate factors (mean temperature, relative humidity and precipitation) and Baidu search data during 2011-2015 in Guangzhou city to improve the accuracy of dengue fever prediction. Firstly, the time series dengue fever data were decomposed into seasonal, trend and remainder components by the seasonal-trend decomposition procedure based on loess (STL). Secondly, the time lag of variables was determined in cross-correlation analysis and the order of autocorrelation was estimated using autocorrelation (ACF) and partial autocorrelation functions (PACF). Finally, the GAMM was built and evaluated by comparing it with Generalized Additive Mode (GAM). Experimental results indicated that the GAMM (R2: 0.95 and RMSE: 34.1) has a superior prediction capability than GAM (R2: 0.86 and RMSE: 121.9). The study could help the government agencies and hospitals respond early to dengue fever outbreak.
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Affiliation(s)
- Dan Liu
- School of Medicine, Wuhan University of Science and Technology, Wuhan, China
| | - Songjing Guo
- School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
| | - Mingjun Zou
- School of Medicine, Wuhan University of Science and Technology, Wuhan, China
| | - Cong Chen
- School of Medicine, Wuhan University of Science and Technology, Wuhan, China
| | - Fei Deng
- State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, China
| | - Zhong Xie
- School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
- National Engineering Research Center for GIS, Wuhan, China
| | - Sheng Hu
- School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
| | - Liang Wu
- School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
- National Engineering Research Center for GIS, Wuhan, China
- * E-mail:
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Bett B, Grace D, Lee HS, Lindahl J, Nguyen-Viet H, Phuc PD, Quyen NH, Tu TA, Phu TD, Tan DQ, Nam VS. Spatiotemporal analysis of historical records (2001-2012) on dengue fever in Vietnam and development of a statistical model for forecasting risk. PLoS One 2019; 14:e0224353. [PMID: 31774823 PMCID: PMC6881000 DOI: 10.1371/journal.pone.0224353] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 10/12/2019] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Dengue fever is the most widespread infectious disease of humans transmitted by Aedes mosquitoes. It is the leading cause of hospitalization and death in children in the Southeast Asia and western Pacific regions. We analyzed surveillance records from health centers in Vietnam collected between 2001-2012 to determine seasonal trends, develop risk maps and an incidence forecasting model. METHODS The data were analyzed using a hierarchical spatial Bayesian model that approximates its posterior parameter distributions using the integrated Laplace approximation algorithm (INLA). Meteorological, altitude and land cover (LC) data were used as predictors. The data were grouped by province (n = 63) and month (n = 144) and divided into training (2001-2009) and validation (2010-2012) sets. Thirteen meteorological variables, 7 land cover data and altitude were considered as predictors. Only significant predictors were kept in the final multivariable model. Eleven dummy variables representing month were also fitted to account for seasonal effects. Spatial and temporal effects were accounted for using Besag-York-Mollie (BYM) and autoregressive (1) models. Their levels of significance were analyzed using deviance information criterion (DIC). The model was validated based on the Theil's coefficient which compared predicted and observed incidence estimated using the validation data. Dengue incidence predictions for 2010-2012 were also used to generate risk maps. RESULTS The mean monthly dengue incidence during the period was 6.94 cases (SD 14.49) per 100,000 people. Analyses on the temporal trends of the disease showed regular seasonal epidemics that were interrupted every 3 years (specifically in July 2004, July 2007 and September 2010) by major fluctuations in incidence. Monthly mean minimum temperature, rainfall, area under urban settlement/build-up areas and altitude were significant in the final model. Minimum temperature and rainfall had non-linear effects and lagging them by two months provided a better fitting model compared to using unlagged variables. Forecasts for the validation period closely mirrored the observed data and accurately captured the troughs and peaks of dengue incidence trajectories. A favorable Theil's coefficient of inequality of 0.22 was generated. CONCLUSIONS The study identified temperature, rainfall, altitude and area under urban settlement as being significant predictors of dengue incidence. The statistical model fitted the data well based on Theil's coefficient of inequality, and risk maps generated from its predictions identified most of the high-risk provinces throughout the country.
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Affiliation(s)
- Bernard Bett
- International Livestock Research Institute, Nairobi, Kenya
- * E-mail:
| | - Delia Grace
- International Livestock Research Institute, Nairobi, Kenya
| | - Hu Suk Lee
- International Livestock Research Institute, Regional Office for East and Southeast Asia, Hanoi, Vietnam
| | - Johanna Lindahl
- International Livestock Research Institute, Nairobi, Kenya
- Uppsala University, Uppsala, Sweden
- Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Hung Nguyen-Viet
- International Livestock Research Institute, Regional Office for East and Southeast Asia, Hanoi, Vietnam
- Centre for Public Health and Ecosystem Research (CENPHER), Hanoi University of Public Health, Hanoi, Vietnam
| | - Pham-Duc Phuc
- Centre for Public Health and Ecosystem Research (CENPHER), Hanoi University of Public Health, Hanoi, Vietnam
| | - Nguyen Huu Quyen
- Vietnam Institute of Meteorology, Hydrology and Climate Change (IMHEN), Hanoi, Vietnam
| | - Tran Anh Tu
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
| | - Tran Dac Phu
- General Department of Preventive Medicine, Ministry of Health, Hanoi, Vietnam
| | - Dang Quang Tan
- General Department of Preventive Medicine, Ministry of Health, Hanoi, Vietnam
| | - Vu Sinh Nam
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
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Lee EH, Miller RH, Masuoka P, Schiffman E, Wanduragala DM, DeFraites R, Dunlop SJ, Stauffer WM, Hickey PW. Predicting Risk of Imported Disease with Demographics: Geospatial Analysis of Imported Malaria in Minnesota, 2010-2014. Am J Trop Med Hyg 2019; 99:978-986. [PMID: 30062987 PMCID: PMC6159573 DOI: 10.4269/ajtmh.18-0357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Although immigrants who visit friends and relatives (VFRs) account for most of the travel-acquired malaria cases in the United States, there is limited evidence on community-level risk factors and best practices for prevention appropriate for various VFR groups. Using 2010–2014 malaria case reports, sociodemographic census data, and health services data, we explored and mapped community-level characteristics to understand who is at risk and where imported malaria infections occur in Minnesota. We examined associations with malaria incidence using Poisson and negative binomial regression. Overall, mean incidence was 0.4 cases per 1,000 sub-Saharan African (SSA)–born in communities reporting malaria, with cases concentrated in two areas of Minneapolis–St. Paul. We found moderate and positive associations between imported malaria and counts of SSA- and Asian-born populations, respectively. Our findings may inform future studies to understand the knowledge, attitudes, and practices of VFR travelers and facilitate and focus intervention strategies to reduce imported malaria in the United States.
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Affiliation(s)
- Elizabeth H Lee
- The Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Robin H Miller
- The Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Penny Masuoka
- The Henry M Jackson Foundation, Bethesda, Maryland.,The Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | | | | | - Robert DeFraites
- The Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Stephen J Dunlop
- University of Minnesota, Minneapolis, Minnesota.,Hennepin County Medical Center, Minneapolis, Minnesota
| | | | - Patrick W Hickey
- The Uniformed Services University of the Health Sciences, Bethesda, Maryland
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45
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Lee JS, Farlow A. The threat of climate change to non-dengue-endemic countries: increasing risk of dengue transmission potential using climate and non-climate datasets. BMC Public Health 2019; 19:934. [PMID: 31296193 PMCID: PMC6625070 DOI: 10.1186/s12889-019-7282-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 07/04/2019] [Indexed: 11/10/2022] Open
Abstract
Background Dengue is a major public health problem in the tropics and sub-tropics, but the disease is less known to non-dengue-endemic countries including in Northeast Asia. However, an unexpected dengue outbreak occurred in 2014 in Japan. Given that autochthonous (domestic) dengue cases had not been reported for the past 70 years in Japan, this outbreak was highly unusual and suggests that several environmental factors might have changed in a way that favors vector mosquitoes in the Northeast Asian region. Methods A Climate Risk Factor (CRF) index, as validated in previous work, was constructed using climate and non-climate factors. This CRF index was compared to the number of reported dengue cases in Tokyo, Japan where the outbreak was observed in 2014. In order to identify high-risk areas, the CRF index was further estimated at the 5 km by 5 km resolution and mapped for Japan and South Korea. Results The high-risk areas determined by the CRF index corresponded well to the provinces where a high number of autochthonous cases were reported during the outbreak in Japan. At the provincial-level, high-risk areas for dengue fever were the Eastern part of Tokyo and Kanakawa, the South-Eastern part of Saitama, and the North-Western part of Chiba. While a relatively small number of high-risk areas were identified in South Korea compared with Japan, the high-risk areas in South Korea include popular tourist destinations where international visitors have been increasing. Conclusion The recent dengue outbreak in Japan may signal that the two adjacent non-dengue-endemic countries are also exposed to the risk of temporal and sporadic behavior of dengue fever. It is critical to understand potential high-risk areas for future outbreaks and to set up appropriate prevention activities at the governmental-level.
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Affiliation(s)
- Jung-Seok Lee
- University of Oxford, Nuffield Department of Population Health, Old Road Campus, Oxford, OX3 7LF, UK.
| | - Andrew Farlow
- University of Oxford, Oxford Martin School, 34 Broad Street, Oxford, OX1 3BD, UK
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46
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Haddawy P, Wettayakorn P, Nonthaleerak B, Su Yin M, Wiratsudakul A, Schöning J, Laosiritaworn Y, Balla K, Euaungkanakul S, Quengdaeng P, Choknitipakin K, Traivijitkhun S, Erawan B, Kraisang T. Large scale detailed mapping of dengue vector breeding sites using street view images. PLoS Negl Trop Dis 2019; 13:e0007555. [PMID: 31356617 PMCID: PMC6687207 DOI: 10.1371/journal.pntd.0007555] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 08/08/2019] [Accepted: 06/17/2019] [Indexed: 01/21/2023] Open
Abstract
Targeted environmental and ecosystem management remain crucial in control of dengue. However, providing detailed environmental information on a large scale to effectively target dengue control efforts remains a challenge. An important piece of such information is the extent of the presence of potential dengue vector breeding sites, which consist primarily of open containers such as ceramic jars, buckets, old tires, and flowerpots. In this paper we present the design and implementation of a pipeline to detect outdoor open containers which constitute potential dengue vector breeding sites from geotagged images and to create highly detailed container density maps at unprecedented scale. We implement the approach using Google Street View images which have the advantage of broad coverage and of often being two to three years old which allows correlation analyses of container counts against historical data from manual surveys. Containers comprising eight of the most common breeding sites are detected in the images using convolutional neural network transfer learning. Over a test set of images the object recognition algorithm has an accuracy of 0.91 in terms of F-score. Container density counts are generated and displayed on a decision support dashboard. Analyses of the approach are carried out over three provinces in Thailand. The container counts obtained agree well with container counts from available manual surveys. Multi-variate linear regression relating densities of the eight container types to larval survey data shows good prediction of larval index values with an R-squared of 0.674. To delineate conditions under which the container density counts are indicative of larval counts, a number of factors affecting correlation with larval survey data are analyzed. We conclude that creation of container density maps from geotagged images is a promising approach to providing detailed risk maps at large scale.
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Affiliation(s)
- Peter Haddawy
- Faculty of ICT, Mahidol University, Salaya, Thailand
- Bremen Spatial Cognition Center, University of Bremen, Bremen, Germany
| | | | | | - Myat Su Yin
- Faculty of ICT, Mahidol University, Salaya, Thailand
| | | | | | | | - Klestia Balla
- Computer Science Department, School of Science and Technology, University of Camerino, Camerino, Italy
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47
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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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48
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Bartlow AW, Manore C, Xu C, Kaufeld KA, Del Valle S, Ziemann A, Fairchild G, Fair JM. Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment. Vet Sci 2019; 6:E40. [PMID: 31064099 PMCID: PMC6632117 DOI: 10.3390/vetsci6020040] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Revised: 04/12/2019] [Accepted: 04/29/2019] [Indexed: 12/20/2022] Open
Abstract
Infectious diseases are changing due to the environment and altered interactions among hosts, reservoirs, vectors, and pathogens. This is particularly true for zoonotic diseases that infect humans, agricultural animals, and wildlife. Within the subset of zoonoses, vector-borne pathogens are changing more rapidly with climate change, and have a complex epidemiology, which may allow them to take advantage of a changing environment. Most mosquito-borne infectious diseases are transmitted by mosquitoes in three genera: Aedes, Anopheles, and Culex, and the expansion of these genera is well documented. There is an urgent need to study vector-borne diseases in response to climate change and to produce a generalizable approach capable of generating risk maps and forecasting outbreaks. Here, we provide a strategy for coupling climate and epidemiological models for zoonotic infectious diseases. We discuss the complexity and challenges of data and model fusion, baseline requirements for data, and animal and human population movement. Disease forecasting needs significant investment to build the infrastructure necessary to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions. These investments can contribute to building a modeling community around the globe to support public health officials so as to reduce disease burden through forecasts with quantified uncertainty.
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Affiliation(s)
- Andrew W Bartlow
- Los Alamos National Laboratory, Biosecurity and Public Health, Los Alamos, NM 87545, USA.
| | - Carrie Manore
- Los Alamos National Laboratory, Information Systems and Modeling, Los Alamos, NM 87545, USA.
| | - Chonggang Xu
- Los Alamos National Laboratory, Earth Systems Observations, Los Alamos, NM 87545, USA.
| | - Kimberly A Kaufeld
- Los Alamos National Laboratory, Statistical Sciences, Los Alamos, NM 87545, USA.
| | - Sara Del Valle
- Los Alamos National Laboratory, Information Systems and Modeling, Los Alamos, NM 87545, USA.
| | - Amanda Ziemann
- Los Alamos National Laboratory, Space Data Science and Systems, Los Alamos, NM 87545, USA.
| | - Geoffrey Fairchild
- Los Alamos National Laboratory, Information Systems and Modeling, Los Alamos, NM 87545, USA.
| | - Jeanne M Fair
- Los Alamos National Laboratory, Biosecurity and Public Health, Los Alamos, NM 87545, USA.
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Wilder-Smith A, Chawla T, Ooi EE. Dengue: An Expanding Neglected Tropical Disease. NEGLECTED TROPICAL DISEASES - EAST ASIA 2019. [DOI: 10.1007/978-3-030-12008-5_4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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50
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Abstract
Dengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.
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