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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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Palmeiro-Silva YK, Lescano AG, Flores EC, Astorga E Y, Rojas L, Chavez MG, Mora-Rivera W, Hartinger SM. Identifying gaps on health impacts, exposures, and vulnerabilities to climate change on human health and wellbeing in South America: a scoping review. LANCET REGIONAL HEALTH. AMERICAS 2023; 26:100580. [PMID: 37876675 PMCID: PMC10593580 DOI: 10.1016/j.lana.2023.100580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 05/29/2023] [Accepted: 08/04/2023] [Indexed: 10/26/2023]
Abstract
There is an important gap in regional information on climate change and health, limiting the development of science-based climate policies in South American countries. This study aims to identify the main gaps in the existing scientific literature on the impacts, exposure, and vulnerabilities of climate change on population health. A scoping review was performed guided by four sub-questions focused on the impacts of climate change on physical and mental health, exposure and vulnerability factors of population to climate hazards. The main findings showed that physical impacts mainly included infectious diseases, while mental health impacts included trauma, depression, and anxiety. Evidence on population exposure to climate hazards is limited, and social determinants of health and individual factors were identified as vulnerability factors. Overall, evidence on the intersection between climate change and health is limited in South America and has been generated in silos, with limited transdisciplinary research. More formal and systematic information should be generated to inform public policy. Funding None.
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Affiliation(s)
- Yasna K. Palmeiro-Silva
- Institute for Global Health, University College London, London, United Kingdom
- Centro de Políticas Públicas, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Andres G. Lescano
- Clima, Latin American Center of Excellence for Climate Change and Health, Universidad Peruana Cayetano Heredia, Lima, Peru
- Emerge, Emerging Diseases and Climate Change Research Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Elaine C. Flores
- Centre on Climate Change and Planetary Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
- The Stanford Center for Innovation in Global Health, Stanford University, Stanford, CA, USA
| | - Yamileth Astorga E
- Escuela de Tecnologías en Salud, Universidad de Costa Rica, San Pedro, San José, Costa Rica
| | - Luciana Rojas
- Clima, Latin American Center of Excellence for Climate Change and Health, Universidad Peruana Cayetano Heredia, Lima, Peru
- Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Mario G. Chavez
- Emerge, Emerging Diseases and Climate Change Research Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru
- Sociedad Científica de San Fernando, Universidad Nacional Mayor de San Marcos, Lima, Peru
| | - Wendel Mora-Rivera
- InterAmerican Center for Global Health (CISG), Puntarenas, Costa Rica
- Escuela de Enfermería, Universidad Latina de Costa Rica, San José, Costa Rica
| | - Stella M. Hartinger
- Clima, Latin American Center of Excellence for Climate Change and Health, Universidad Peruana Cayetano Heredia, Lima, Peru
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Servadio JL, Convertino M, Fiecas M, Muñoz‐Zanzi C. Weekly Forecasting of Yellow Fever Occurrence and Incidence via Eco-Meteorological Dynamics. GEOHEALTH 2023; 7:e2023GH000870. [PMID: 37885914 PMCID: PMC10599710 DOI: 10.1029/2023gh000870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/31/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Yellow Fever (YF), a mosquito-borne disease, requires ongoing surveillance and prevention due to its persistence and ability to cause major epidemics, including one that began in Brazil in 2016. Forecasting based on factors influencing YF risk can improve efficiency in prevention. This study aimed to produce weekly forecasts of YF occurrence and incidence in Brazil using weekly meteorological and ecohydrological conditions. Occurrence was forecast as the probability of observing any cases, and incidence was forecast to represent morbidity if YF occurs. We fit gamma hurdle models, selecting predictors from several meteorological and ecohydrological factors, based on forecast accuracy defined by receiver operator characteristic curves and mean absolute error. We fit separate models for data before and after the start of the 2016 outbreak, forecasting occurrence and incidence for all municipalities of Brazil weekly. Different predictor sets were found to produce most accurate forecasts in each time period, and forecast accuracy was high for both time periods. Temperature, precipitation, and previous YF burden were most influential predictors among models. Minimum, maximum, mean, and range of weekly temperature, precipitation, and humidity contributed to forecasts, with optimal lag times of 2, 6, and 7 weeks depending on time period. Results from this study show the use of environmental predictors in providing regular forecasts of YF burden and producing nationwide forecasts. Weekly forecasts, which can be produced using the forecast model developed in this study, are beneficial for informing immediate preparedness measures.
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Affiliation(s)
- Joseph L. Servadio
- Department of BiologyCenter for Infectious Disease DynamicsPennsylvania State UniversityUniversity ParkPAUSA
- Division of Environmental Health SciencesSchool of Public HealthUniversity of MinnesotaMinneapolisMNUSA
| | | | - Mark Fiecas
- Division of BiostatisticsSchool of Public HealthUniversity of MinnesotaMinneapolisMNUSA
| | - Claudia Muñoz‐Zanzi
- Division of Environmental Health SciencesSchool of Public HealthUniversity of MinnesotaMinneapolisMNUSA
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Silva NIO, Albery GF, Arruda MS, Oliveira GFG, Costa TA, de Mello ÉM, Moreira GD, Reis EV, da Silva SA, Silva MC, de Almeida MG, Becker DJ, Carlson CJ, Vasilakis N, Hanley KA, Drumond BP. Ecological drivers of sustained enzootic yellow fever virus transmission in Brazil, 2017-2021. PLoS Negl Trop Dis 2023; 17:e0011407. [PMID: 37276217 PMCID: PMC10270639 DOI: 10.1371/journal.pntd.0011407] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 06/15/2023] [Accepted: 05/22/2023] [Indexed: 06/07/2023] Open
Abstract
Beginning December 2016, sylvatic yellow fever (YF) outbreaks spread into southeastern Brazil, and Minas Gerais state experienced two sylvatic YF waves (2017 and 2018). Following these massive YF waves, we screened 187 free-living non-human primate (NHPs) carcasses collected throughout the state between January 2019 and June 2021 for YF virus (YFV) using RTqPCR. One sample belonging to a Callithrix, collected in June 2020, was positive for YFV. The viral strain belonged to the same lineage associated with 2017-2018 outbreaks, showing the continued enzootic circulation of YFV in the state. Next, using data from 781 NHPs carcasses collected in 2017-18, we used generalized additive mixed models (GAMMs) to identify the spatiotemporal and host-level drivers of YFV infection and intensity (an estimation of genomic viral load in the liver of infected NHP). Our GAMMs explained 65% and 68% of variation in virus infection and intensity, respectively, and uncovered strong temporal and spatial patterns for YFV infection and intensity. NHP infection was higher in the eastern part of Minas Gerais state, where 2017-2018 outbreaks affecting humans and NHPs were concentrated. The odds of YFV infection were significantly lower in NHPs from urban areas than from urban-rural or rural areas, while infection intensity was significantly lower in NHPs from urban areas or the urban-rural interface relative to rural areas. Both YFV infection and intensity were higher during the warm/rainy season compared to the cold/dry season. The higher YFV intensity in NHPs in warm/rainy periods could be a result of higher exposure to vectors and/or higher virus titers in vectors during this time resulting in the delivery of a higher virus dose and higher viral replication levels within NHPs. Further studies are needed to better test this hypothesis and further compare the dynamics of YFV enzootic cycles between different seasons.
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Affiliation(s)
| | - Gregory F. Albery
- Department of Biology, Georgetown University, Washington, DC, United States of America
| | - Matheus Soares Arruda
- Department of Microbiology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Thaís Alkifeles Costa
- Department of Microbiology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Érica Munhoz de Mello
- Department of Microbiology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Laboratório de Zoonoses—Centro de Controle de Zoonoses, Prefeitura de Belo Horizonte, Belo Horizonte, Minas Gerais, Brazil
| | - Gabriel Dias Moreira
- Department of Microbiology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Erik Vinícius Reis
- Department of Microbiology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Simone Agostinho da Silva
- Laboratório de Zoonoses—Centro de Controle de Zoonoses, Prefeitura de Belo Horizonte, Belo Horizonte, Minas Gerais, Brazil
| | - Marlise Costa Silva
- Laboratório de Zoonoses—Centro de Controle de Zoonoses, Prefeitura de Belo Horizonte, Belo Horizonte, Minas Gerais, Brazil
| | - Munique Guimarães de Almeida
- Laboratório de Zoonoses—Centro de Controle de Zoonoses, Prefeitura de Belo Horizonte, Belo Horizonte, Minas Gerais, Brazil
| | - Daniel J. Becker
- Department of Biology, University of Oklahoma, Norman, Oklahoma, United States of America
| | - Colin J. Carlson
- Department of Biology, Georgetown University, Washington, DC, United States of America
- Center for Global Health Science and Security, Georgetown University, Washington, D.C., United States of America
| | - Nikos Vasilakis
- Department of Pathology, University of Texas Medical Branch, Galveston, Texas, United States of America
- Center for Vector-Borne and Zoonotic Diseases, The University of Texas Medical Branch, Galveston, Texas, United States of America
- Institute for Human Infection and Immunity, University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Kathryn A. Hanley
- Department of Biology, New Mexico State University, Las Cruces, New Mexico, United States of America
| | - Betânia Paiva Drumond
- Department of Microbiology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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Servadio JL, Muñoz-Zanzi C, Convertino M. Environmental determinants predicting population vulnerability to high yellow fever incidence. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220086. [PMID: 35316947 PMCID: PMC8889195 DOI: 10.1098/rsos.220086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 02/07/2022] [Indexed: 06/14/2023]
Abstract
Yellow fever (YF) is an endemic mosquito-borne disease in Brazil, though many locations have not observed cases in recent decades. Some locations with low disease burden may resemble locations with higher disease burden through environmental and ecohydrological characteristics, which are known to impact YF burden, motivating increased or continued prevention measures such as vaccination, mosquito control or surveillance. This study aimed to use environmental characteristics to estimate vulnerability to observing high YF burden among all Brazilian municipalities. Vulnerability was defined in three categories based on yearly incidence between 2000 and 2017: minimal, low and high vulnerability. A cumulative logit model was fit to these categories using environmental and ecohydrological predictors, selecting those that provided the most accurate model fit. Per cent of days with precipitation, mean temperature, biome, population density, elevation, vegetation and nearby disease occurrence were included in best-fitting models. Model results were applied to estimate vulnerability nationwide. Municipalities with highest probability of observing high vulnerability was found in the North and Central-West (2000-2016) as well as the Southeast (2017) regions. Results of this study serve to identify specific locations to prioritize new or ongoing surveillance and prevention of YF based on underlying ecohydrological conditions.
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Affiliation(s)
- Joseph L. Servadio
- Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, MN, USA
- Center for Infectious Disease Dynamics and Department of Biology, Pennsylvania State University, University Park, PA, USA
| | - Claudia Muñoz-Zanzi
- Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Matteo Convertino
- Future Ecosystems Lab, Tsinghua SIGS, Tsinghua University, Shenzhen, People's Republic of China
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Yellow Fever: Origin, Epidemiology, Preventive Strategies and Future Prospects. Vaccines (Basel) 2022; 10:vaccines10030372. [PMID: 35335004 PMCID: PMC8955180 DOI: 10.3390/vaccines10030372] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/15/2022] [Accepted: 02/22/2022] [Indexed: 12/10/2022] Open
Abstract
Yellow fever (YF) virus still represents a major threat in low resource countries in both South America and Africa despite the presence of an effective vaccine. YF outbreaks are not only due to insufficient vaccine coverage for insufficient vaccine supply, but also to the increase in people without history of vaccination living in endemic areas. Globalization, continuous population growth, urbanization associated with inadequate public health infrastructure, and climate changes constitute important promoting factors for the spread of this virus to tropical and subtropical areas in mosquito-infested regions capable of spreading the disease. In the present review, we focus on the origin of the virus and its transmission, representing two debated topics throughout the nineteenth century, going deeply into the history of YF vaccines until the development of the vaccine still used nowadays. Besides surveillance, we highlight the urgent need of routine immunization and vaccination campaigns associated to diverse and innovative mosquito control technologies in endemic areas for YF virus in order to minimize the risk of new YF outbreaks and the global burden of YF in the future.
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Gaythorpe KA, Hamlet A, Jean K, Garkauskas Ramos D, Cibrelus L, Garske T, Ferguson N. The global burden of yellow fever. eLife 2021; 10:64670. [PMID: 33722340 PMCID: PMC7963473 DOI: 10.7554/elife.64670] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 02/23/2021] [Indexed: 12/22/2022] Open
Abstract
Yellow fever (YF) is a viral, vector-borne, haemorrhagic fever endemic in tropical regions of Africa and South America. The vaccine for YF is considered safe and effective, but intervention strategies need to be optimised; one of the tools for this is mathematical modelling. We refine and expand an existing modelling framework for Africa to account for transmission in South America. We fit to YF occurrence and serology data. We then estimate the subnational forces of infection for the entire endemic region. Finally, using demographic and vaccination data, we examine the impact of vaccination activities. We estimate that there were 109,000 (95% credible interval [CrI] [67,000–173,000]) severe infections and 51,000 (95% CrI [31,000–82,000]) deaths due to YF in Africa and South America in 2018. We find that mass vaccination activities in Africa reduced deaths by 47% (95% CrI [10%–77%]). This methodology allows us to evaluate the effectiveness of vaccination and illustrates the need for continued vigilance and surveillance of YF.
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Affiliation(s)
- Katy Am Gaythorpe
- WHO Collaborating Centre for Infectious Disease Modelling, MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, United Kingdom
| | - Arran Hamlet
- WHO Collaborating Centre for Infectious Disease Modelling, MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, United Kingdom
| | - Kévin Jean
- Maître de conférences, Laboratoire MESuRS - Cnam Paris, Paris, France
| | | | | | - Tini Garske
- WHO Collaborating Centre for Infectious Disease Modelling, MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, United Kingdom
| | - Neil Ferguson
- WHO Collaborating Centre for Infectious Disease Modelling, MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, United Kingdom
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