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Gómez G, Hufstedler H, Montenegro Morales C, Roell Y, Lozano-Parra A, Tami A, Magalhaes T, Marques ETA, Balmaseda A, Calvet G, Harris E, Brasil P, Herrera V, Villar L, Maxwell L, Jaenisch T. Pooled Cohort Profile: ReCoDID Consortium's Harmonized Acute Febrile Illness Arbovirus Meta-Cohort. JMIR Public Health Surveill 2024; 10:e54281. [PMID: 39042429 PMCID: PMC11288473 DOI: 10.2196/54281] [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: 11/06/2023] [Revised: 02/09/2024] [Accepted: 05/17/2024] [Indexed: 07/24/2024] Open
Abstract
Infectious disease (ID) cohorts are key to advancing public health surveillance, public policies, and pandemic responses. Unfortunately, ID cohorts often lack funding to store and share clinical-epidemiological (CE) data and high-dimensional laboratory (HDL) data long term, which is evident when the link between these data elements is not kept up to date. This becomes particularly apparent when smaller cohorts fail to successfully address the initial scientific objectives due to limited case numbers, which also limits the potential to pool these studies to monitor long-term cross-disease interactions within and across populations. CE data from 9 arbovirus (arthropod-borne viruses) cohorts in Latin America were retrospectively harmonized using the Maelstrom Research methodology and standardized to Clinical Data Interchange Standards Consortium (CDISC). We created a harmonized and standardized meta-cohort that contains CE and HDL data from 9 arbovirus studies from Latin America. To facilitate advancements in cross-population inference and reuse of cohort data, the Reconciliation of Cohort Data for Infectious Diseases (ReCoDID) Consortium harmonized and standardized CE and HDL from 9 arbovirus cohorts into 1 meta-cohort. Interested parties will be able to access data dictionaries that include information on variables across the data sets via Bio Studies. After consultation with each cohort, linked harmonized and curated human cohort data (CE and HDL) will be made accessible through the European Genome-phenome Archive platform to data users after their requests are evaluated by the ReCoDID Data Access Committee. This meta-cohort can facilitate various joint research projects (eg, on immunological interactions between sequential flavivirus infections and for the evaluation of potential biomarkers for severe arboviral disease).
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Affiliation(s)
- Gustavo Gómez
- Grupo de Epidemiología Clínica, Universidad Industrial de Santander, Bucaramanga, Colombia
| | - Heather Hufstedler
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Yannik Roell
- Center for Global Health, Colorado School of Public Health, Aurora, CO, United States
| | - Anyela Lozano-Parra
- Grupo de Epidemiología Clínica, Universidad Industrial de Santander, Bucaramanga, Colombia
| | - Adriana Tami
- Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
- Departamento de Estudios Clínicos, Facultad de Ciencias de la Salud, Universidad de Carabobo, Valencia, Venezuela
| | - Tereza Magalhaes
- Department of Entomology, Texas A&M University, College Station, TX, United States
- Department of Preventive and Social Medicine, School of Medicine, Universidade Federal da Bahia, Salvador, Brazil
| | - Ernesto T A Marques
- Department of Virology and Experimental Therapeutics, Aggeu Magalhães Institute, Oswaldo Cruz Foundation (Fiocruz), Recife, Brazil
- Department of Infectious Diseases and Microbiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Angel Balmaseda
- Sustainable Sciences Institute, Managua, Nicaragua
- Laboratorio Nacional de Virología, Centro Nacional de Diagnóstico y Referencia, Ministry of Health, Managua, Nicaragua
| | - Guilherme Calvet
- Evandro Chagas National Institute of Infectious Diseases, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil
| | - Eva Harris
- Division of Infectious Diseases, School of Public Health, University of California Berkeley, Berkeley, CA, United States
| | - Patricia Brasil
- Evandro Chagas National Institute of Infectious Diseases, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil
| | - Victor Herrera
- Grupo de Epidemiología Clínica, Universidad Industrial de Santander, Bucaramanga, Colombia
| | - Luis Villar
- Grupo de Epidemiología Clínica, Universidad Industrial de Santander, Bucaramanga, Colombia
- Centro de Atención y Diagnóstico de Enfermedades Infecciosas, Bucaramanga, Colombia
| | - Lauren Maxwell
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg, Germany
| | - Thomas Jaenisch
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg, Germany
- Center for Global Health, Colorado School of Public Health, Aurora, CO, United States
- Section Clinical Tropical Medicine, Department for Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany
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Williams RJ, Brintz BJ, Ribeiro Dos Santos G, Huang AT, Buddhari D, Kaewhiran S, Iamsirithaworn S, Rothman AL, Thomas S, Farmer A, Fernandez S, Cummings DAT, Anderson KB, Salje H, Leung DT. Integration of population-level data sources into an individual-level clinical prediction model for dengue virus test positivity. SCIENCE ADVANCES 2024; 10:eadj9786. [PMID: 38363842 PMCID: PMC10871531 DOI: 10.1126/sciadv.adj9786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 01/17/2024] [Indexed: 02/18/2024]
Abstract
The differentiation of dengue virus (DENV) infection, a major cause of acute febrile illness in tropical regions, from other etiologies, may help prioritize laboratory testing and limit the inappropriate use of antibiotics. While traditional clinical prediction models focus on individual patient-level parameters, we hypothesize that for infectious diseases, population-level data sources may improve predictive ability. To create a clinical prediction model that integrates patient-extrinsic data for identifying DENV among febrile patients presenting to a hospital in Thailand, we fit random forest classifiers combining clinical data with climate and population-level epidemiologic data. In cross-validation, compared to a parsimonious model with the top clinical predictors, a model with the addition of climate data, reconstructed susceptibility estimates, force of infection estimates, and a recent case clustering metric significantly improved model performance.
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Affiliation(s)
- Robert J. Williams
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | - Ben J. Brintz
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | | | - Angkana T. Huang
- Department of Genetics, University of Cambridge, Cambridge, UK
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Darunee Buddhari
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | | | | | - Alan L. Rothman
- Institute for Immunology and Informatics and Department of Cell and Molecular Biology, University of Rhode Island, Providence, RI, USA
| | - Stephen Thomas
- Department of Microbiology and Immunology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Aaron Farmer
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Stefan Fernandez
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Derek A. T. Cummings
- Department of Biology, University of Florida, Gainesville, FL, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Kathryn B. Anderson
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
- Department of Microbiology and Immunology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Daniel T. Leung
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
- Division of Microbiology and Immunology, Department of Pathology, University of Utah, Salt Lake City, UT, USA
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3
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Williams RJ, Brintz BJ, Santos GRD, Huang A, Buddhari D, Kaewhiran S, Iamsirithaworn S, Rothman AL, Thomas S, Farmer A, Fernandez S, Cummings DAT, Anderson KB, Salje H, Leung DT. Integration of population-level data sources into an individual-level clinical prediction model for dengue virus test positivity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.08.23293840. [PMID: 37609267 PMCID: PMC10441499 DOI: 10.1101/2023.08.08.23293840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
The differentiation of dengue virus (DENV) infection, a major cause of acute febrile illness in tropical regions, from other etiologies, may help prioritize laboratory testing and limit the inappropriate use of antibiotics. While traditional clinical prediction models focus on individual patient-level parameters, we hypothesize that for infectious diseases, population-level data sources may improve predictive ability. To create a clinical prediction model that integrates patient-extrinsic data for identifying DENV among febrile patients presenting to a hospital in Thailand, we fit random forest classifiers combining clinical data with climate and population-level epidemiologic data. In cross validation, compared to a parsimonious model with the top clinical predictors, a model with the addition of climate data, reconstructed susceptibility estimates, force of infection estimates, and a recent case clustering metric, significantly improved model performance.
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Affiliation(s)
- RJ Williams
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, USA
| | - Ben J. Brintz
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, USA
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, USA
| | | | - Angkana Huang
- Department of Genetics, University of Cambridge, United Kingdom
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Darunee Buddhari
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | | | | | - Alan L. Rothman
- Institute for Immunology and Informatics and Department of Cell and Molecular Biology, University of Rhode Island, Providence, USA
| | - Stephen Thomas
- Department of Microbiology and Immunology, SUNY Upstate Medical University, Syracuse, USA
| | - Aaron Farmer
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Stefan Fernandez
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Derek A T Cummings
- Department of Biology, University of Florida, Gainesville, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, USA
| | - Kathryn B Anderson
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
- Department of Microbiology and Immunology, SUNY Upstate Medical University, Syracuse, USA
| | - Henrik Salje
- Department of Genetics, University of Cambridge, United Kingdom
| | - Daniel T. Leung
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, USA
- Division of Microbiology and Immunology, Department of Pathology, University of Utah, Salt Lake City, USA
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Maxwell L, Chamorro JB, Leegstra LM, Laguna HS, Miranda Montoya MC. "How about me giving blood for the COVID vaccine and not being able to get vaccinated?" A cognitive interview study on understanding of and agreement with broad consent for future use of data and samples in Colombia and Nicaragua. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0001253. [PMID: 37195974 DOI: 10.1371/journal.pgph.0001253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 03/28/2023] [Indexed: 05/19/2023]
Abstract
Broad consent for future use, wherein researchers ask participants for permission to share participant-level data and samples collected within the study for purposes loosely related to the study objectives, is central to enabling ethical data and sample reuse. Ensuring that participants understand broad consent-related language is key to maintaining trust in the study and public health research. We conducted 52 cognitive interviews to explore cohort research participants' and their parents' understanding of the broad consent-related language in the University of California at Berkeley template informed consent (IC) form for biomedical research. Participants and their parents were recruited from long-standing infectious disease cohort studies in Nicaragua and Colombia and interviewed during the COVID-19 pandemic. We conducted semi-structured interviews to assess participants' agreement with the key concepts in the IC after clarifying them through the cognitive interview. Participants did not understand abstract concepts, including collecting and reusing genetic data. Participants wanted to learn about incidental findings, future users and uses. Trust in the research team and the belief that sharing could lead to new vaccines or treatments were critical to participant support for data and sample sharing. Participants highlighted the importance of data and sample sharing for COVID-19 response and equitable access to vaccines and treatments developed through sharing. Our findings on participants' understanding of broad consent and preferences for data and sample sharing can help inform researchers and ethics review committees working to enable ethical and equitable data and sample sharing.
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Affiliation(s)
- Lauren Maxwell
- Heidelberger Institut für Global Health, Universitätsklinikum Heidelberg, Heidelberg, Germany
| | | | - Luz Marina Leegstra
- Heidelberger Institut für Global Health, Universitätsklinikum Heidelberg, Heidelberg, Germany
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5
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Flichman DM, Pereson MJ, Baré P, Espindola SL, Carballo GM, Albrecht A, Agote F, Alter A, Bartoli S, Blanco S, Blejer J, Borda M, Bouzon N, Carrizo LH, Etcheverry L, Fernandez R, Reyes MIF, Gallego S, Hahn R, Luna SG, Marranzino G, Romanazzi JS, Rossi A, Troffe A, Lin CC, Martínez AP, García G, DI Lello FA. Epidemiology of Dengue in Argentina: Antibodies seroprevalence in blood donors and circulating serotypes. J Clin Virol 2022; 147:105078. [PMID: 34999567 DOI: 10.1016/j.jcv.2022.105078] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 12/14/2021] [Accepted: 01/03/2022] [Indexed: 11/27/2022]
Affiliation(s)
- Diego M Flichman
- Instituto de Investigaciones Biomédicas en Retrovirus y Síndrome de Inmunodeficiencia Adquirida (INBIRS)-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires, Argentina
| | - Matías J Pereson
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires, Argentina; Instituto de Investigaciones en Bacteriología y Virología Molecular (IBaViM), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Patricia Baré
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires, Argentina; Instituto de Medicina Experimental (IMEX), Academia Nacional de Medicina, Ciudad Autónoma de Buenos Aires, Argentina
| | - Sonia Lorena Espindola
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires, Argentina; Laboratorio GIGA, Instituto de Biología Subtropical (IBS), Facultad de Ciencias Exactas Químicas y Naturales, Universidad Nacional de Misiones (UNaM), Misiones, Argentina
| | | | - Andrés Albrecht
- Laboratorio Mega Rafaela, Departamento de Enfermedades Transmisibles por Transfusión, Santa Fe, Argentina
| | - Felicitas Agote
- Banco Central de Sangre "Dr. César Guerra", Tucumán (PRIS-SI.PRO.SA), Argentina
| | - Adriana Alter
- Fundación Hemocentro Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina
| | - Sonia Bartoli
- Centro regional de Hemoterapia Jujuy, San Salvador de Jujuy, Jujuy, Argentina
| | - Sebastián Blanco
- Facultad de Ciencias Médicas, Universidad Nacional de Córdoba, Córdoba, Córdoba, Argentina; Fundación Banco Central de Sangre, Córdoba, Córdoba, Argentina
| | - Jorgelina Blejer
- Fundación Hemocentro Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina
| | - Marcelo Borda
- Servicio de Hemoterapia, Instituto de Cardiología de Corrientes "Juana F. Cabral", Corrientes, Argentina
| | - Néstor Bouzon
- Banco de Sangre Bouzon, Santiago del Estero, Argentina
| | - Luis H Carrizo
- Fundación Banco Central de Sangre, Córdoba, Córdoba, Argentina
| | - Lucrecia Etcheverry
- Programa Provincial de Hemoterapia de Entre Ríos, Paraná, Entre Ríos, Argentina
| | - Roberto Fernandez
- Fundación Hemocentro Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina
| | - María Inés Figueroa Reyes
- Laboratorio de Detección de Infecciones Transmisibles por Transfusión del Centro Regional de Hemoterapia, Salta, Argentina
| | - Sandra Gallego
- Facultad de Ciencias Médicas, Universidad Nacional de Córdoba, Córdoba, Córdoba, Argentina; Fundación Banco Central de Sangre, Córdoba, Córdoba, Argentina
| | - Romina Hahn
- Banco de Sangre, Tejidos y Biológicos de la Provincia de Misiones, Misiones, Argentina
| | - Silvana Gisela Luna
- Laboratorio de Detección de Infecciones Transmisibles por Transfusión del Centro Regional de Hemoterapia, Salta, Argentina
| | - Gabriela Marranzino
- Banco Central de Sangre "Dr. César Guerra", Tucumán (PRIS-SI.PRO.SA), Argentina
| | | | - Ariel Rossi
- Servicio Hemoterapia, Hospital Delicia C. Masvernat, Concordia, Entre Ríos, Argentina
| | - Antonia Troffe
- Hospital Interzonal General de Agudos "San Felipe", San Nicolás, Argentina
| | - Chang-Chi Lin
- Institute of Preventive Medicine, National Defense Medical Center, Taipei City, Taiwan (Province of China)
| | - Alfredo P Martínez
- Sección Virología, Centro de Educación Médica e Investigaciones Clínicas Norberto Quirno "CEMIC", Ciudad Autónoma de Buenos Aires, Argentina
| | - Gabriel García
- Instituto de Investigaciones en Bacteriología y Virología Molecular (IBaViM), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Federico A DI Lello
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires, Argentina; Instituto de Investigaciones en Bacteriología y Virología Molecular (IBaViM), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Buenos Aires, Argentina.
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6
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Biggs JR, Sy AK, Sherratt K, Brady OJ, Kucharski AJ, Funk S, Reyes MAJ, Quinones MA, Jones-Warner W, Avelino FL, Sucaldito NL, Tandoc AO, la Paz ECD, Capeding MRZ, Padilla CD, Hafalla JCR, Hibberd ML. Estimating the annual dengue force of infection from the age of reporting primary infections across urban centres in endemic countries. BMC Med 2021; 19:217. [PMID: 34587957 PMCID: PMC8482604 DOI: 10.1186/s12916-021-02101-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 08/17/2021] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Stratifying dengue risk within endemic countries is crucial for allocating limited control interventions. Current methods of monitoring dengue transmission intensity rely on potentially inaccurate incidence estimates. We investigated whether incidence or alternate metrics obtained from standard, or laboratory, surveillance operations represent accurate surrogate indicators of the burden of dengue and can be used to monitor the force of infection (FOI) across urban centres. METHODS Among those who reported and resided in 13 cities across the Philippines, we collected epidemiological data from all dengue case reports between 2014 and 2017 (N 80,043) and additional laboratory data from a cross-section of sampled case reports (N 11,906) between 2014 and 2018. At the city level, we estimated the aggregated annual FOI from age-accumulated IgG among the non-dengue reporting population using catalytic modelling. We compared city-aggregated FOI estimates to aggregated incidence and the mean age of clinically and laboratory diagnosed dengue cases using Pearson's Correlation coefficient and generated predicted FOI estimates using regression modelling. RESULTS We observed spatial heterogeneity in the dengue average annual FOI across sampled cities, ranging from 0.054 [0.036-0.081] to 0.249 [0.223-0.279]. Compared to FOI estimates, the mean age of primary dengue infections had the strongest association (ρ -0.848, p value<0.001) followed by the mean age of those reporting with warning signs (ρ -0.642, p value 0.018). Using regression modelling, we estimated the predicted annual dengue FOI across urban centres from the age of those reporting with primary infections and revealed prominent spatio-temporal heterogeneity in transmission intensity. CONCLUSIONS We show the mean age of those reporting with their first dengue infection or those reporting with warning signs of dengue represent superior indicators of the dengue FOI compared to crude incidence across urban centres. Our work provides a framework for national dengue surveillance to routinely monitor transmission and target control interventions to populations most in need.
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Affiliation(s)
- Joseph R. Biggs
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Ava Kristy Sy
- Department of Virology, Research Institute for Tropical Medicine, Manila, Philippines
- Dengue Study Group, Research Institute for Tropical Medicine, Manila, Philippines
| | - Katharine Sherratt
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Oliver J. Brady
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Adam J. Kucharski
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Sebastian Funk
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Mary Anne Joy Reyes
- Department of Virology, Research Institute for Tropical Medicine, Manila, Philippines
- Dengue Study Group, Research Institute for Tropical Medicine, Manila, Philippines
| | - Mary Ann Quinones
- Department of Virology, Research Institute for Tropical Medicine, Manila, Philippines
- Dengue Study Group, Research Institute for Tropical Medicine, Manila, Philippines
| | - William Jones-Warner
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Nemia L. Sucaldito
- Department of Health, Philippine Epidemiology Bureau, Manila, Philippines
| | - Amado O. Tandoc
- Department of Virology, Research Institute for Tropical Medicine, Manila, Philippines
| | - Eva Cutiongco-de la Paz
- Institute of Human Genetics, National Institute of Health, University of the Philippines, Manila, Philippines
- Philippine Genome Centre, University of the Philippines, Manila, Philippines
| | - Maria Rosario Z. Capeding
- Dengue Study Group, Research Institute for Tropical Medicine, Manila, Philippines
- Institute of Human Genetics, National Institute of Health, University of the Philippines, Manila, Philippines
| | - Carmencita D. Padilla
- Institute of Human Genetics, National Institute of Health, University of the Philippines, Manila, Philippines
- Philippine Genome Centre, University of the Philippines, Manila, Philippines
| | - Julius Clemence R. Hafalla
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Martin L. Hibberd
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Institute of Human Genetics, National Institute of Health, University of the Philippines, Manila, Philippines
- Philippine Genome Centre, University of the Philippines, Manila, Philippines
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