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Mamenun, Koesmaryono Y, Sopaheluwakan A, Hidayati R, Dasanto BD, Aryati R. Spatiotemporal Characterization of Dengue Incidence and Its Correlation to Climate Parameters in Indonesia. INSECTS 2024; 15:366. [PMID: 38786922 PMCID: PMC11122138 DOI: 10.3390/insects15050366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 05/02/2024] [Accepted: 05/14/2024] [Indexed: 05/25/2024]
Abstract
Dengue has become a public health concern in Indonesia since it was first found in 1968. This study aims to determine dengue hotspot areas and analyze the spatiotemporal distribution of dengue and its association with dominant climate parameters nationally. Monthly data for dengue and climate observations (i.e., rainfall, relative humidity, average, maximum, and minimum temperature) at the regency/city level were utilized. Dengue hotspot areas were determined through K-means clustering, while Singular Value Decomposition (SVD) determined dominant climate parameters and their spatiotemporal distribution. Results revealed four clusters: Cluster 1 comprised cities with medium to high Incidence Rates (IR) and high Case Densities (CD) in a narrow area. Cluster 2 has a high IR and low CD, and clusters 3 and 4 featured medium and low IR and CD, respectively. SVD analysis indicated that relative humidity and rainfall were the most influential parameters on IR across all clusters. Temporal fluctuations in the first mode of IR and climate parameters were clearly delineated. The spatial distribution of heterogeneous correlation between the first mode of rainfall and relative humidity to IR exhibited higher values, which were predominantly observed in Java, Bali, Nusa Tenggara, the eastern part of Sumatra, the southern part of Kalimantan, and several locations in Sulawesi.
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Affiliation(s)
- Mamenun
- Center for Applied Climate Information and Services, Indonesian Agency for Meteorology Climatology and Geophysics, Jakarta 10720, Indonesia;
- Department of Geophysics and Meteorology, IPB University, Bogor 16680, Indonesia; (R.H.); (B.D.D.)
| | - Yonny Koesmaryono
- Department of Geophysics and Meteorology, IPB University, Bogor 16680, Indonesia; (R.H.); (B.D.D.)
| | - Ardhasena Sopaheluwakan
- Deputy for Climatology, Indonesian Agency for Meteorology Climatology and Geophysics, Jakarta 10720, Indonesia;
| | - Rini Hidayati
- Department of Geophysics and Meteorology, IPB University, Bogor 16680, Indonesia; (R.H.); (B.D.D.)
- Center for Climate Risk and Opportunity Management in South Asia Pacific, IPB University, Bogor 16143, Indonesia
| | - Bambang Dwi Dasanto
- Department of Geophysics and Meteorology, IPB University, Bogor 16680, Indonesia; (R.H.); (B.D.D.)
| | - Rita Aryati
- Directorate of Prevention and Control of Infectious Diseases, Ministry of Health, Jakarta 12950, 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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Liu Z, Zhang Q, Li L, He J, Guo J, Wang Z, Huang Y, Xi Z, Yuan F, Li Y, Li T. The effect of temperature on dengue virus transmission by Aedes mosquitoes. Front Cell Infect Microbiol 2023; 13:1242173. [PMID: 37808907 PMCID: PMC10552155 DOI: 10.3389/fcimb.2023.1242173] [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: 06/18/2023] [Accepted: 09/01/2023] [Indexed: 10/10/2023] Open
Abstract
Dengue is prevalent in tropical and subtropical regions. As an arbovirus disease, it is mainly transmitted by Aedes aegypti and Aedes albopictus. According to the previous studies, temperature is closely related to the survival of Aedes mosquitoes, the proliferation of dengue virus (DENV) and the vector competence of Aedes to transmit DENV. This review describes the correlations between temperature and dengue epidemics, and explores the potential reasons including the distribution and development of Aedes mosquitoes, the structure of DENV, and the vector competence of Aedes mosquitoes. In addition, the immune and metabolic mechanism are discussed on how temperature affects the vector competence of Aedes mosquitoes to transmit DENV.
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Affiliation(s)
- Zhuanzhuan Liu
- Department of Pathogen Biology, Center for Tropical Disease Control and Research, School of Basic Medical Sciences and Life Sciences, Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Medical University, Haikou, China
- Department of Pathogen Biology and Immunology, Jiangsu International Laboratory of Immunity and Metabolism, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, China
| | - Qingxin Zhang
- School of Imaging Medical Sciences, Xuzhou Medical University, Xuzhou, China
| | - Liya Li
- School of Imaging Medical Sciences, Xuzhou Medical University, Xuzhou, China
| | - Junjie He
- School of Imaging Medical Sciences, Xuzhou Medical University, Xuzhou, China
| | - Jinyang Guo
- School of Imaging Medical Sciences, Xuzhou Medical University, Xuzhou, China
| | - Zichen Wang
- School of Imaging Medical Sciences, Xuzhou Medical University, Xuzhou, China
| | - Yige Huang
- School of Imaging Medical Sciences, Xuzhou Medical University, Xuzhou, China
| | - Zimeng Xi
- School of Imaging Medical Sciences, Xuzhou Medical University, Xuzhou, China
| | - Fei Yuan
- Department of Pathogen Biology and Immunology, Jiangsu International Laboratory of Immunity and Metabolism, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, China
| | - Yiji Li
- Department of Pathogen Biology, Center for Tropical Disease Control and Research, School of Basic Medical Sciences and Life Sciences, Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Medical University, Haikou, China
| | - Tingting Li
- Department of Pathogen Biology, Center for Tropical Disease Control and Research, School of Basic Medical Sciences and Life Sciences, Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Medical University, Haikou, China
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Robert MA, Rodrigues HS, Herrera D, de Mata Donado Campos J, Morilla F, Del Águila Mejía J, Guardado ME, Skewes R, Colomé-Hidalgo M. Spatiotemporal and meteorological relationships in dengue transmission in the Dominican Republic, 2015-2019. Trop Med Health 2023; 51:32. [PMID: 37269000 DOI: 10.1186/s41182-023-00517-9] [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: 09/23/2022] [Accepted: 04/30/2023] [Indexed: 06/04/2023] Open
Abstract
Dengue has broadened its global distribution substantially in the past two decades, and many endemic areas are experiencing increases in incidence. The Dominican Republic recently experienced its two largest outbreaks to date with 16,836 reported cases in 2015 and 20,123 reported cases in 2019. With continued increases in dengue transmission, developing tools to better prepare healthcare systems and mosquito control agencies is of critical importance. Before such tools can be developed, however, we must first better understand potential drivers of dengue transmission. To that end, we focus in this paper on determining relationships between climate variables and dengue transmission with an emphasis on eight provinces and the capital city of the Dominican Republic in the period 2015-2019. We present summary statistics for dengue cases, temperature, precipitation, and relative humidity in this period, and we conduct an analysis of correlated lags between climate variables and dengue cases as well as correlated lags among dengue cases in each of the nine locations. We find that the southwestern province of Barahona had the largest dengue incidence in both 2015 and 2019. Among all climate variables considered, lags between relative humidity variables and dengue cases were the most frequently correlated. We found that most locations had significant correlations with cases in other locations at lags of zero weeks. These results can be used to improve predictive models of dengue transmission in the country.
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Affiliation(s)
- Michael A Robert
- Department of Mathematics and Center for Emerging, Zoonotic, and Arthropod-Borne Pathogens (CeZAP), Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
| | - Helena Sofia Rodrigues
- Escola Superior de Ciências Empresariais, Instituto Politécnico de Viana do Castelo, Valença, Portugal
- Centro de Investigação e Desenvolvimento em Matemática e Aplicações, Universidade de Aveiro, Aveiro, Portugal
| | - Demian Herrera
- Centro de Investigación en Salud Dr. Hugo Mendoza, Hospital Pediátrico Dr. Hugo Mendoza, Santo Domingo, Dominican Republic
| | - Juan de Mata Donado Campos
- Departamento de Medicina Preventiva y Salud Pública y Microbiología, Facultad de Medicina, Universidad Autónoma de Madrid, Madrid, Spain
- Instituto de Investigación Sanitaria del Hospital Universitario La Paz (IdiPAZ), Universidad Autónoma de Madrid, Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Calle Monforte de Lemos 3-5, 28029, Madrid, Spain
| | - Fernando Morilla
- Departamento de Informática y Automática, Escuela Técnica Superior de Ingeniería Informática, Universidad Nacional de Educación a Distancia, Madrid, Spain
| | - Javier Del Águila Mejía
- Departamento de Medicina Preventiva y Salud Pública y Microbiología, Facultad de Medicina, Universidad Autónoma de Madrid, Madrid, Spain
| | - María Elena Guardado
- Instituto Tecnológico de Santo Domingo (INTEC), Santo Domingo, Dominican Republic
| | - Ronald Skewes
- Dirección General de Epidemiología, Ministerio de Salud, Santo Domingo, Dominican Republic
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Optimal Validated Multi-Factorial Climate Change Risk Assessment for Adaptation Planning and Evaluation of Infectious Disease: A Case Study of Dengue Hemorrhagic Fever in Indonesia. Trop Med Infect Dis 2022; 7:tropicalmed7080172. [PMID: 36006264 PMCID: PMC9415645 DOI: 10.3390/tropicalmed7080172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/28/2022] [Accepted: 07/31/2022] [Indexed: 11/18/2022] Open
Abstract
(1) Background: This paper will present an elaboration of the risk assessment methodology by Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH (GIZ), Eurac Research and United Nations University Institute for Environment and Human Security (UNU-EHS) for the assessment of dengue. (2) Methods: We validate the risk assessment model by best-fitting it with the number of dengue cases per province using the least-square fitting method. Seven out of thirty-four provinces in Indonesia were chosen (North Sumatra, Jakarta Capital, West Java, Central Java, East Java, Bali and East Kalimantan). (3) Results: A risk assessment based on the number of dengue cases showed an increased risk in 2010, 2015 and 2016 in which the effects of El Nino and La Nina extreme climates occurred. North Sumatra, Bali, and West Java were more influenced by the vulnerability component, in line with their risk analysis that tends to be lower than the other provinces in 2010, 2015 and 2016 when El Nino and La Nina occurred. (4) Conclusion: Based on data from the last ten years, in Jakarta Capital, Central Java, East Java and East Kalimantan, dengue risks were mainly influenced by the climatic hazard component while North Sumatra, Bali and West Java were more influenced by the vulnerability component.
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