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Barcellos C, Matos V, Lana RM, Lowe R. Climate change, thermal anomalies, and the recent progression of dengue in Brazil. Sci Rep 2024; 14:5948. [PMID: 38467690 PMCID: PMC10928122 DOI: 10.1038/s41598-024-56044-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: 12/15/2023] [Accepted: 03/01/2024] [Indexed: 03/13/2024] Open
Abstract
Dengue is rapidly expanding its transmission area across Brazil and much of South America. In this study, data-mining techniques were used to identify climatic and demographic indicators that could explain the recent (2014-2020) and simultaneous trends of expansion and exacerbation of the incidence in some regions of Brazil. The previous circulation of the virus (dengue incidence rates between 2007 and 2013), urbanization, and the occurrence of temperature anomalies for a prolonged period were the main factors that led to increased incidence of dengue in the central region of Brazil. Regions with high altitudes, which previously acted as a barrier for dengue transmission, became areas of high incidence rates. The algorithm that was developed during this study can be utilized to assess future climate scenarios and plan preventive actions.
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Affiliation(s)
- Christovam Barcellos
- Climate and Health Observatory, Institute of Health Information and Communication, Oswaldo Cruz Foundation (ICICT/Fiocruz), Avenida Brasil 4365, Manguinhos, Rio de Janeiro, RJ, 21040-900, Brazil.
| | - Vanderlei Matos
- Climate and Health Observatory, Institute of Health Information and Communication, Oswaldo Cruz Foundation (ICICT/Fiocruz), Avenida Brasil 4365, Manguinhos, Rio de Janeiro, RJ, 21040-900, Brazil
| | | | - Rachel Lowe
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
- Centre on Climate Change and Planetary Health and Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
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2
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Gaspari M. A Low-Cost Early Warning Method for Infectious Diseases with Asymptomatic Carriers. Healthcare (Basel) 2024; 12:469. [PMID: 38391844 PMCID: PMC10888077 DOI: 10.3390/healthcare12040469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 02/06/2024] [Accepted: 02/09/2024] [Indexed: 02/24/2024] Open
Abstract
At the beginning of 2023, the Italian former prime minister, the former health minister and 17 others including the current president of the Lombardy region were placed under investigation on suspicion of aggravated culpable epidemic in connection with the government's response at the start of the COVID-19 pandemic. The charges revolve around the failure by authorities to take adequate measures to prevent the spread of the virus in the Bergamo area, which experienced a significant excess of deaths during the initial outbreak. The aim of this paper is to analyse the pandemic data of Italy and the Lombardy region in the first 10 days of the pandemic, spanning from the 24th of February 2020 to the 4th of March 2020. The objective is to determine whether the use of early warning indicators could have facilitated the identification of a critical increase in infections. This identification, in turn, would have enabled the timely formulation of strategies for pandemic containment, thereby reducing the number of deaths. In conclusion, to translate our findings into practical guidelines, we propose a low-cost early warning method for infectious respiratory diseases with asymptomatic carriers.
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Affiliation(s)
- Mauro Gaspari
- Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy
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3
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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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Rocklöv J, Semenza JC, Dasgupta S, Robinson EJ, Abd El Wahed A, Alcayna T, Arnés-Sanz C, Bailey M, Bärnighausen T, Bartumeus F, Borrell C, Bouwer LM, Bretonnière PA, Bunker A, Chavardes C, van Daalen KR, Encarnação J, González-Reviriego N, Guo J, Johnson K, Koopmans MP, Máñez Costa M, Michaelakis A, Montalvo T, Omazic A, Palmer JR, Preet R, Romanello M, Shafiul Alam M, Sikkema RS, Terrado M, Treskova M, Urquiza D, Lowe R. Decision-support tools to build climate resilience against emerging infectious diseases in Europe and beyond. THE LANCET REGIONAL HEALTH. EUROPE 2023; 32:100701. [PMID: 37583927 PMCID: PMC10424206 DOI: 10.1016/j.lanepe.2023.100701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/11/2023] [Accepted: 07/12/2023] [Indexed: 08/17/2023]
Abstract
Climate change is one of several drivers of recurrent outbreaks and geographical range expansion of infectious diseases in Europe. We propose a framework for the co-production of policy-relevant indicators and decision-support tools that track past, present, and future climate-induced disease risks across hazard, exposure, and vulnerability domains at the animal, human, and environmental interface. This entails the co-development of early warning and response systems and tools to assess the costs and benefits of climate change adaptation and mitigation measures across sectors, to increase health system resilience at regional and local levels and reveal novel policy entry points and opportunities. Our approach involves multi-level engagement, innovative methodologies, and novel data streams. We take advantage of intelligence generated locally and empirically to quantify effects in areas experiencing rapid urban transformation and heterogeneous climate-induced disease threats. Our goal is to reduce the knowledge-to-action gap by developing an integrated One Health-Climate Risk framework.
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Affiliation(s)
- Joacim Rocklöv
- Heidelberg Institute of Global Health (HIGH) & Interdisciplinary Centre for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Jan C. Semenza
- Heidelberg Institute of Global Health (HIGH) & Interdisciplinary Centre for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Shouro Dasgupta
- Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Venice, Italy
- Graham Research Institute on Climate Change and the Environment, London School of Economics and Political Science (LSE), London, United Kingdom
| | - Elizabeth J.Z. Robinson
- Graham Research Institute on Climate Change and the Environment, London School of Economics and Political Science (LSE), London, United Kingdom
| | - Ahmed Abd El Wahed
- Faculty of Veterinary Medicine, Institute of Animal Hygiene and Veterinary Public Health, Leipzig University, Leipzig, Germany
| | - Tilly Alcayna
- Red Cross Red Crescent Centre on Climate Change and Disaster Preparedness, The Hague, the Netherlands
- Centre on Climate Change & Planetary Health, London School of Hygiene & Tropical Medicine (LSHTM), London, United Kingdom
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine (LSHTM), London, United Kingdom
- Health in Humanitarian Crises Centre, London School of Hygiene & Tropical Medicine (LSHTM), London, United Kingdom
| | - Cristina Arnés-Sanz
- Heidelberg Institute of Global Health (HIGH) & Interdisciplinary Centre for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
| | - Meghan Bailey
- Red Cross Red Crescent Centre on Climate Change and Disaster Preparedness, The Hague, the Netherlands
| | - Till Bärnighausen
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Frederic Bartumeus
- Theoretical and Computational Ecology Group, Centre d’Estudis Avançats de Blanes (CEAB-CSIC), Blanes, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Centre de Recerca Ecològica i Aplicacions Forestals (CREAF), Barcelona, Spain
| | - Carme Borrell
- Pest Surveillance and Control, Agència de Salut Pública de Barcelona (ASPB), Barcelona, Spain
- Biomedical Research Center Network for Epidemiology and Public Health (CIBERESP), Barcelona, Spain
| | - Laurens M. Bouwer
- Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany
| | | | - Aditi Bunker
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Center for Climate, Health and the Global Environment, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Kim R. van Daalen
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
| | | | | | - Junwen Guo
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Katie Johnson
- Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Venice, Italy
| | - Marion P.G. Koopmans
- Department of Viroscience, Erasmus Medical Center, University Medical Center, Rotterdam, the Netherlands
| | - María Máñez Costa
- Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany
| | - Antonios Michaelakis
- Laboratory of Insects & Parasites of Medical Importance, Benaki Phytopathological Institute (BPI), Attica, Greece
| | - Tomás Montalvo
- Agència de Salut Pública de Barcelona (ASPB), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Institut d'Investigació Biomèdica Sant Pau (IIB SANT PAU), Barcelona, Spain
| | - Anna Omazic
- Department of Chemistry, Environment, and Feed Hygiene, National Veterinary Institute (SVA), Uppsala, Sweden
| | - John R.B. Palmer
- Department of Political and Social Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Raman Preet
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Marina Romanello
- Institute for Global Health, University College London (UCL), London, United Kingdom
| | - Mohammad Shafiul Alam
- Infectious Disease Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | - Reina S. Sikkema
- Department of Viroscience, Erasmus Medical Center, University Medical Center, Rotterdam, the Netherlands
| | - Marta Terrado
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Marina Treskova
- Heidelberg Institute of Global Health (HIGH) & Interdisciplinary Centre for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
| | - Diana Urquiza
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Rachel Lowe
- Centre on Climate Change & Planetary Health, London School of Hygiene & Tropical Medicine (LSHTM), London, United Kingdom
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine (LSHTM), London, United Kingdom
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
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Yang H, Nguyen TN, Chuang TW. An Integrative Explainable Artificial Intelligence Approach to Analyze Fine-Scale Land-Cover and Land-Use Factors Associated with Spatial Distributions of Place of Residence of Reported Dengue Cases. Trop Med Infect Dis 2023; 8:tropicalmed8040238. [PMID: 37104363 PMCID: PMC10142856 DOI: 10.3390/tropicalmed8040238] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 04/06/2023] [Accepted: 04/18/2023] [Indexed: 04/28/2023] Open
Abstract
Dengue fever is a prevalent mosquito-borne disease that burdens communities in subtropical and tropical regions. Dengue transmission is ecologically complex; several environmental conditions are critical for the spatial and temporal distribution of dengue. Interannual variability and spatial distribution of dengue transmission are well-studied; however, the effects of land cover and use are yet to be investigated. Therefore, we applied an explainable artificial intelligence (AI) approach to integrate the EXtreme Gradient Boosting and Shapley Additive Explanation (SHAP) methods to evaluate spatial patterns of the residences of reported dengue cases based on various fine-scale land-cover land-use types, Shannon's diversity index, and household density in Kaohsiung City, Taiwan, between 2014 and 2015. We found that the proportions of general roads and residential areas play essential roles in dengue case residences with nonlinear patterns. Agriculture-related features were negatively associated with dengue incidence. Additionally, Shannon's diversity index showed a U-shaped relationship with dengue infection, and SHAP dependence plots showed different relationships between various land-use types and dengue incidence. Finally, landscape-based prediction maps were generated from the best-fit model and highlighted high-risk zones within the metropolitan region. The explainable AI approach delineated precise associations between spatial patterns of the residences of dengue cases and diverse land-use characteristics. This information is beneficial for resource allocation and control strategy modification.
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Affiliation(s)
- Hsiu Yang
- Department of Molecular Parasitology and Tropical Diseases, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Thi-Nhung Nguyen
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Ting-Wu Chuang
- Department of Molecular Parasitology and Tropical Diseases, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
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Aryaprema VS, Steck MR, Peper ST, Xue RD, Qualls WA. A systematic review of published literature on mosquito control action thresholds across the world. PLoS Negl Trop Dis 2023; 17:e0011173. [PMID: 36867651 PMCID: PMC10016652 DOI: 10.1371/journal.pntd.0011173] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 03/15/2023] [Accepted: 02/14/2023] [Indexed: 03/04/2023] Open
Abstract
BACKGROUND Despite the use of numerous methods of control measures, mosquito populations and mosquito-borne diseases are still increasing globally. Evidence-based action thresholds to initiate or intensify control activities have been identified as essential in reducing mosquito populations to required levels at the correct/optimal time. This systematic review was conducted to identify different mosquito control action thresholds existing across the world and associated surveillance and implementation characteristics. METHODOLOGY/PRINCIPAL FINDINGS Searches for literature published from 2010 up to 2021 were performed using two search engines, Google Scholar and PubMed Central, according to PRISMA guidelines. A set of inclusion/exclusion criteria were identified and of the 1,485 initial selections, only 87 were included in the final review. Thirty inclusions reported originally generated thresholds. Thirteen inclusions were with statistical models that seemed intended to be continuously utilized to test the exceedance of thresholds in a specific region. There was another set of 44 inclusions that solely mentioned previously generated thresholds. The inclusions with "epidemiological thresholds" outnumbered those with "entomological thresholds". Most of the inclusions came from Asia and those thresholds were targeted toward Aedes and dengue control. Overall, mosquito counts (adult and larval) and climatic variables (temperature and rainfall) were the most used parameters in thresholds. The associated surveillance and implementation characteristics of the identified thresholds are discussed here. CONCLUSIONS/SIGNIFICANCE The review identified 87 publications with different mosquito control thresholds developed across the world and published during the last decade. Associated surveillance and implementation characteristics will help organize surveillance systems targeting the development and implementation of action thresholds, as well as direct awareness towards already existing thresholds for those with programs lacking available resources for comprehensive surveillance systems. The findings of the review highlight data gaps and areas of focus to fill in the action threshold compartment of the IVM toolbox.
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Affiliation(s)
- Vindhya S. Aryaprema
- Anastasia Mosquito Control District, St. Augustine, Florida, United States of America
| | - Madeline R. Steck
- Anastasia Mosquito Control District, St. Augustine, Florida, United States of America
| | - Steven T. Peper
- Anastasia Mosquito Control District, St. Augustine, Florida, United States of America
| | - Rui-de Xue
- Anastasia Mosquito Control District, St. Augustine, Florida, United States of America
| | - Whitney A. Qualls
- Anastasia Mosquito Control District, St. Augustine, Florida, United States of America
- * E-mail:
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Meckawy R, Stuckler D, Mehta A, Al-Ahdal T, Doebbeling BN. Effectiveness of early warning systems in the detection of infectious diseases outbreaks: a systematic review. BMC Public Health 2022; 22:2216. [PMCID: PMC9707072 DOI: 10.1186/s12889-022-14625-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/14/2022] [Indexed: 11/30/2022] Open
Abstract
Abstract
Background
Global pandemics have occurred with increasing frequency over the past decade reflecting the sub-optimum operationalization of surveillance systems handling human health data. Despite the wide array of current surveillance methods, their effectiveness varies with multiple factors. Here, we perform a systematic review of the effectiveness of alternative infectious diseases Early Warning Systems (EWSs) with a focus on the surveillance data collection methods, and taking into consideration feasibility in different settings.
Methods
We searched PubMed and Scopus databases on 21 October 2022. Articles were included if they covered the implementation of an early warning system and evaluated infectious diseases outbreaks that had potential to become pandemics. Of 1669 studies screened, 68 were included in the final sample. We performed quality assessment using an adapted CASP Checklist.
Results
Of the 68 articles included, 42 articles found EWSs successfully functioned independently as surveillance systems for pandemic-wide infectious diseases outbreaks, and 16 studies reported EWSs to have contributing surveillance features through complementary roles. Chief complaints from emergency departments’ data is an effective EWS but it requires standardized formats across hospitals. Centralized Public Health records-based EWSs facilitate information sharing; however, they rely on clinicians’ reporting of cases. Facilitated reporting by remote health settings and rapid alarm transmission are key advantages of Web-based EWSs. Pharmaceutical sales and laboratory results did not prove solo effectiveness. The EWS design combining surveillance data from both health records and staff was very successful. Also, daily surveillance data notification was the most successful and accepted enhancement strategy especially during mass gathering events. Eventually, in Low Middle Income Countries, working to improve and enhance existing systems was more critical than implementing new Syndromic Surveillance approaches.
Conclusions
Our study was able to evaluate the effectiveness of Early Warning Systems in different contexts and resource settings based on the EWSs’ method of data collection. There is consistent evidence that EWSs compiling pre-diagnosis data are more proactive to detect outbreaks. However, the fact that Syndromic Surveillance Systems (SSS) are more proactive than diagnostic disease surveillance should not be taken as an effective clue for outbreaks detection.
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Vargas-Parada L. Why climate change matters for pandemic preparedness. Nature 2022; 610:S45. [DOI: 10.1038/d41586-022-03359-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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9
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Chen J, Ding RL, Liu KK, Xiao H, Hu G, Xiao X, Yue Q, Lu JH, Han Y, Bu J, Dong GH, Lin Y. Collaboration between meteorology and public health: Predicting the dengue epidemic in Guangzhou, China, by meteorological parameters. Front Cell Infect Microbiol 2022; 12:881745. [PMID: 36017372 PMCID: PMC9397942 DOI: 10.3389/fcimb.2022.881745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 06/08/2022] [Indexed: 12/02/2022] Open
Abstract
Background Dengue has become an increasing public health threat around the world, and climate conditions have been identified as important factors affecting the transmission of dengue, so this study was aimed to establish a prediction model of dengue epidemic by meteorological methods. Methods The dengue case information and meteorological data were collected from Guangdong Provincial Center for Disease Prevention and Control and Guangdong Meteorological Bureau, respectively. We used spatio-temporal analysis to characterize dengue epidemics. Spearman correlation analysis was used to analyze the correlation between lagged meteorological factors and dengue fever cases and determine the maximum lagged correlation coefficient of different meteorological factors. Then, Generalized Additive Models were used to analyze the non-linear influence of lagged meteorological factors on local dengue cases and to predict the number of local dengue cases under different weather conditions. Results We described the temporal and spatial distribution characteristics of dengue fever cases and found that sporadic single or a small number of imported cases had a very slight influence on the dengue epidemic around. We further created a forecast model based on the comprehensive consideration of influence of lagged 42-day meteorological factors on local dengue cases, and the results showed that the forecast model has a forecast effect of 98.8%, which was verified by the actual incidence of dengue from 2005 to 2016 in Guangzhou. Conclusion A forecast model for dengue epidemic was established with good forecast effects and may have a potential application in global dengue endemic areas after modification according to local meteorological conditions. High attention should be paid on sites with concentrated patients for the control of a dengue epidemic.
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Affiliation(s)
- Jing Chen
- School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
- Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou, China
| | - Rui-Lian Ding
- Hospital for Skin Diseases (Institute of Dermatology), Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
| | - Kang-Kang Liu
- Department of Research Center for Medicine, the Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Hui Xiao
- Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou, China
| | - Gang Hu
- School of Agriculture, Sun Yat-sen University, Guangzhou, China
| | - Xiang Xiao
- Department of Geography, Hong Kong Baptist University, Hong Kong, China
| | - Qian Yue
- Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou, China
| | - Jia-Hai Lu
- NMPA Key Laboratory for Quality Monitoring and Evaluation of Vaccines and Biological Products, Sun Yat-sen University, Guangzhou, China
| | - Yan Han
- Hospital for Skin Diseases (Institute of Dermatology), Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
| | - Jin Bu
- Hospital for Skin Diseases (Institute of Dermatology), Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
- *Correspondence: Jin Bu, ; Guang-Hui Dong, ; Yu Lin,
| | - Guang-Hui Dong
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Jin Bu, ; Guang-Hui Dong, ; Yu Lin,
| | - Yu Lin
- Guangzhou South China Biomedical Research Institute co., Ltd, Guangzhou, China
- Shenzhen Withsum Technology Limited, Shenzhen, China
- *Correspondence: Jin Bu, ; Guang-Hui Dong, ; Yu Lin,
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Ray EL, Brooks LC, Bien J, Biggerstaff M, Bosse NI, Bracher J, Cramer EY, Funk S, Gerding A, Johansson MA, Rumack A, Wang Y, Zorn M, Tibshirani RJ, Reich NG. Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States. INTERNATIONAL JOURNAL OF FORECASTING 2022:S0169-2070(22)00096-6. [PMID: 35791416 PMCID: PMC9247236 DOI: 10.1016/j.ijforecast.2022.06.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensemble that combines forecasts from these teams. These experiments have informed the ensemble methods used by the Hub. To be most useful to policy makers, ensemble forecasts must have stable performance in the presence of two key characteristics of the component forecasts: (1) occasional misalignment with the reported data, and (2) instability in the relative performance of component forecasters over time. Our results indicate that in the presence of these challenges, an untrained and robust approach to ensembling using an equally weighted median of all component forecasts is a good choice to support public health decision makers. In settings where some contributing forecasters have a stable record of good performance, trained ensembles that give those forecasters higher weight can also be helpful.
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Affiliation(s)
- Evan L Ray
- School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America
| | - Logan C Brooks
- Machine Learning Department, Carnegie Mellon University, United States of America
| | - Jacob Bien
- Department of Data Sciences and Operations, University of Southern California, United States of America
| | - Matthew Biggerstaff
- COVID-19 Response, U.S. Centers for Disease Control and Prevention, United States of America
| | - Nikos I Bosse
- London School of Hygiene & Tropical Medicine, United Kingdom
| | - Johannes Bracher
- Chair of Statistical Methods and Econometrics, Karlsruhe Institute of Technology, Germany
- Computational Statistics Group, Heidelberg Institute for Theoretical Studies, Germany
| | - Estee Y Cramer
- School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America
| | - Sebastian Funk
- London School of Hygiene & Tropical Medicine, United Kingdom
| | - Aaron Gerding
- School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America
| | - Michael A Johansson
- COVID-19 Response, U.S. Centers for Disease Control and Prevention, United States of America
| | - Aaron Rumack
- Machine Learning Department, Carnegie Mellon University, United States of America
| | - Yijin Wang
- School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America
| | - Martha Zorn
- School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America
| | - Ryan J Tibshirani
- Machine Learning Department, Carnegie Mellon University, United States of America
| | - Nicholas G Reich
- School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America
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11
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Zhao Q, Yu P, Mahendran R, Huang W, Gao Y, Yang Z, Ye T, Wen B, Wu Y, Li S, Guo Y. Global climate change and human health: Pathways and possible solutions. ECO-ENVIRONMENT & HEALTH (ONLINE) 2022; 1:53-62. [PMID: 38075529 PMCID: PMC10702927 DOI: 10.1016/j.eehl.2022.04.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 04/13/2022] [Accepted: 04/28/2022] [Indexed: 12/13/2023]
Abstract
Global warming has been changing the planet's climate pattern, leading to increasing frequency, intensity and duration of extreme weather events and natural disasters. These climate-changing events affect various health outcomes adversely through complicated pathways. This paper reviews the main signs of climate change so far, e.g., suboptimal ambient temperature, sea-level rise and other conditions, and depicts the interactive pathways between different climate-changing events such as suboptimal temperature, wildfires, and floods with a broad range of health outcomes. Meanwhile, the modifying effect of socioeconomic, demographic and environmental factors on the pathways is summarised, such that the youth, elderly, females, poor and those living in coastal regions are particularly susceptible to climate change. Although Earth as a whole is expected to suffer from climate change, this review article discusses some potential benefits for certain regions, e.g., a more liveable environment and sufficient food supply. Finally, we summarise certain mitigation and adaptation strategies against climate change and how these strategies may benefit human health in other ways. This review article provides a comprehensive and concise introduction of the pathways between climate change and human health and possible solutions, which may map directions for future research.
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Affiliation(s)
- Qi Zhao
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Australia
| | - Pei Yu
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Australia
| | - Rahini Mahendran
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Australia
| | - Wenzhong Huang
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Australia
| | - Yuan Gao
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Australia
| | - Zhengyu Yang
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Australia
| | - Tingting Ye
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Australia
| | - Bo Wen
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Australia
| | - Yao Wu
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Australia
| | - Shanshan Li
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Australia
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Australia
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12
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Faridah L, Fauziah N, Agustian D, Mindra Jaya IGN, Eka Putra R, Ekawardhani S, Hidayath N, Damar Djati I, Carvajal TM, Mayasari W, Ruluwedrata Rinawan F, Watanabe K. Temporal Correlation Between Urban Microclimate, Vector Mosquito Abundance, and Dengue Cases. JOURNAL OF MEDICAL ENTOMOLOGY 2022; 59:1008-1018. [PMID: 35305089 PMCID: PMC9113159 DOI: 10.1093/jme/tjac005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Indexed: 05/04/2023]
Abstract
Dengue Hemorrhagic Fever (DHF) is a major mosquito-borne viral disease. Studies have reported a strong correlation between weather, the abundance of Aedes aegypti, the vector of DHF virus, and dengue incidence. However, this conclusion has been based on the general climate pattern of wide regions. In general, however, the human population, level of infrastructure, and land-use change in rural and urban areas often produce localized climate patterns that may influence the interaction between climate, vector abundance, and dengue incidence. Thoroughly understanding this correlation will allow the development of a customized and precise local early warning system. To achieve this purpose, we conducted a cohort study, during January-December 2017, in 16 districts in Bandung, West Java, Indonesia. In the selected areas, local weather stations and modified light mosquito traps were set up to obtain data regarding daily weather and the abundance of adult female Ae. aegypti. A generalized linear model was applied to analyze the effect of local weather and female adult Ae. aegypti on the number of dengue cases. The result showed a significant non-linear correlation among mosquito abundance, maximum temperature, and dengue cases. Using our model, the data showed that the addition of a single adult Ae. aegypti mosquito increased the risk of dengue infection by 1.8%, while increasing the maximum temperature by one degree decreased the risk by 17%. This finding suggests specific actionable insights needed to supplement existing mosquito eradication programs.
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Affiliation(s)
- Lia Faridah
- Parasitology Division, Department of Biomedical Sciences, Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
- Graduate School of Science and Engineering, Ehime University, Bunkyo-cho 3, Matsuyama, Ehime, 790-8577, Japan
- Corresponding author, e-mail: ;
| | - Nisa Fauziah
- Parasitology Division, Department of Biomedical Sciences, Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
| | - Dwi Agustian
- Department of Public Health Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
| | - I Gede Nyoman Mindra Jaya
- Department of Statistics Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
| | - Ramadhani Eka Putra
- School of Life Sciences and Technology, Insitut Teknologi Bandung, Jl. Ganeca 10, Bandung, 40132, West Java, Indonesia
- Biology Department, Insitut Teknologi Sumatera, Jl. Terusan Ryacudu, Desa Way Hui, Bandar Lampung, 35365, Lampung, Indonesia
| | - Savira Ekawardhani
- Parasitology Division, Department of Biomedical Sciences, Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
| | - Nurrachman Hidayath
- Dengue Study Group, Faculty of Medicine, Universitas Padjadjaran, Jl. Prof. Eyckman 38, Bandung, 40131, West Java, Indonesia
| | - Imam Damar Djati
- Faculty of Visual Art and Design, Industrial Design Section, Bandung Institute of Technology, Jl. Ganeca 10, Bandung, 40132, West Java, Indonesia
| | - Thaddeus M Carvajal
- Biological Control Research Unit, Center for Natural Science and Environmental Research-De La Salle University, Taft Ave Manila, Philippines
- Center for Marine Environmental Studies (CMES), Ehime University, Bunkyo-cho 3, Matsuyama, Ehime, Japan
| | - Wulan Mayasari
- Anatomy Division, Department of Biomedical Science, Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang 45363, West Java, Indonesia
| | - Fedri Ruluwedrata Rinawan
- Department of Public Health Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
| | - Kozo Watanabe
- Center for Marine Environmental Studies (CMES), Ehime University, Bunkyo-cho 3, Matsuyama, Ehime, Japan
- Corresponding author, e-mail: ;
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13
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Geographical drivers and climate-linked dynamics of Lassa fever in Nigeria. Nat Commun 2021; 12:5759. [PMID: 34599162 PMCID: PMC8486829 DOI: 10.1038/s41467-021-25910-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 09/08/2021] [Indexed: 02/08/2023] Open
Abstract
Lassa fever is a longstanding public health concern in West Africa. Recent molecular studies have confirmed the fundamental role of the rodent host (Mastomys natalensis) in driving human infections, but control and prevention efforts remain hampered by a limited baseline understanding of the disease's true incidence, geographical distribution and underlying drivers. Here, we show that Lassa fever occurrence and incidence is influenced by climate, poverty, agriculture and urbanisation factors. However, heterogeneous reporting processes and diagnostic laboratory access also appear to be important drivers of the patchy distribution of observed disease incidence. Using spatiotemporal predictive models we show that including climatic variability added retrospective predictive value over a baseline model (11% decrease in out-of-sample predictive error). However, predictions for 2020 show that a climate-driven model performs similarly overall to the baseline model. Overall, with ongoing improvements in surveillance there may be potential for forecasting Lassa fever incidence to inform health planning.
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14
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Pollett S, Johansson MA, Reich NG, Brett-Major D, Del Valle SY, Venkatramanan S, Lowe R, Porco T, Berry IM, Deshpande A, Kraemer MUG, Blazes DL, Pan-ngum W, Vespigiani A, Mate SE, Silal SP, Kandula S, Sippy R, Quandelacy TM, Morgan JJ, Ball J, Morton LC, Althouse BM, Pavlin J, van Panhuis W, Riley S, Biggerstaff M, Viboud C, Brady O, Rivers C. Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines. PLoS Med 2021; 18:e1003793. [PMID: 34665805 PMCID: PMC8525759 DOI: 10.1371/journal.pmed.1003793] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research. METHODS AND FINDINGS We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies. CONCLUSIONS These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.
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Affiliation(s)
- Simon Pollett
- Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America
| | - Michael A. Johansson
- Division of Vector-Borne Diseases, Centers for Disease Control & Prevention, San Juan, Puerto Rico, United States of America
| | - Nicholas G. Reich
- University of Massachusetts–Amherst, School of Public Health and Health Sciences, Amherst, Massachusetts, United States of America
| | - David Brett-Major
- University of Nebraska Medical Center, Omaha, Nebraska, United States of America
| | - Sara Y. Del Valle
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Srinivasan Venkatramanan
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, Virginia, United States of America
| | - 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, Barcelona, Spain
| | - Travis Porco
- University of California at San Francisco, San Francisco, California, United States of America
| | - Irina Maljkovic Berry
- Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America
| | - Alina Deshpande
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | | | - David L. Blazes
- Bill and Melinda Gates Foundation, Seattle, Washington, United States of America
| | - Wirichada Pan-ngum
- Mahidol-Oxford Tropical Medicine Research Unit and Department of Tropical Hygiene, Mahidol University, Thailand
| | - Alessandro Vespigiani
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Suzanne E. Mate
- Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America
| | - Sheetal P. Silal
- Modelling and Simulation Hub, Africa, Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Sasikiran Kandula
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York City, New York, United States of America
| | - Rachel Sippy
- Institute for Global Health and Translational Science, State University of New York Upstate Medical University, Syracuse, New York, United States of America
| | - Talia M. Quandelacy
- Division of Vector-Borne Diseases, Centers for Disease Control & Prevention, San Juan, Puerto Rico, United States of America
| | - Jeffrey J. Morgan
- Catholic University of America, Washington, DC, United States of America
| | - Jacob Ball
- U.S. Army Public Health Center, Edgewood, Maryland, United States of America
| | - Lindsay C. Morton
- Armed Forces Health Surveillance Division, Global Emerging Infections Surveillance, Silver Spring, Maryland, United States of America
- George Washington University, Milken Institute School of Public Health, Washington, DC, United States of America
| | - Benjamin M. Althouse
- University of Washington, Seattle, Washington, United States of America
- Institute for Disease Modeling, Bellevue, Washington, United States of America
- New Mexico State University, Las Cruces, New Mexico, United States of America
| | - Julie Pavlin
- National Academies of Sciences, Engineering, and Medicine, Washington, DC, United States of America
| | - Wilbert van Panhuis
- University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, United States of America
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College, London, United Kingdom
| | - Matthew Biggerstaff
- Influenza Division, Centers for Disease Control & Prevention, Atlanta, Georgia, United States of America
| | - Cecile Viboud
- Fogarty International Center, National Institutes for Health, Bethesda, Maryland, United States of America
| | - Oliver Brady
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Caitlin Rivers
- Johns Hopkins Center for Health Security, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
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15
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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] [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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16
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Developing Public Health Emergency Response Leaders in Incident Management: A Scoping Review of Educational Interventions. Disaster Med Public Health Prep 2021; 16:2149-2178. [PMID: 34462032 DOI: 10.1017/dmp.2021.164] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
During emergency responses, public health leaders frequently serve in incident management roles that differ from their routine job functions. Leaders' familiarity with incident management principles and functions can influence response outcomes. Therefore, training and exercises in incident management are often required for public health leaders. To describe existing methods of incident management training and exercises in the literature, we queried 6 English language databases and found 786 relevant articles. Five themes emerged: (1) experiential learning as an established approach to foster engaging and interactive learning environments and optimize training design; (2) technology-aided decision support tools are increasingly common for crisis decision-making; (3) integration of leadership training in the education continuum is needed for developing public health response leaders; (4) equal emphasis on competency and character is needed for developing capable and adaptable leaders; and (5) consistent evaluation methodologies and metrics are needed to assess the effectiveness of educational interventions.These findings offer important strategic and practical considerations for improving the design and delivery of educational interventions to develop public health emergency response leaders. This review and ongoing real-world events could facilitate further exploration of current practices, emerging trends, and challenges for continuous improvements in developing public health emergency response leaders.
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17
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Colón-González FJ, Soares Bastos L, Hofmann B, Hopkin A, Harpham Q, Crocker T, Amato R, Ferrario I, Moschini F, James S, Malde S, Ainscoe E, Sinh Nam V, Quang Tan D, Duc Khoa N, Harrison M, Tsarouchi G, Lumbroso D, Brady OJ, Lowe R. Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles. PLoS Med 2021; 18:e1003542. [PMID: 33661904 PMCID: PMC7971894 DOI: 10.1371/journal.pmed.1003542] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 03/18/2021] [Accepted: 01/22/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND With enough advanced notice, dengue outbreaks can be mitigated. As a climate-sensitive disease, environmental conditions and past patterns of dengue can be used to make predictions about future outbreak risk. These predictions improve public health planning and decision-making to ultimately reduce the burden of disease. Past approaches to dengue forecasting have used seasonal climate forecasts, but the predictive ability of a system using different lead times in a year-round prediction system has been seldom explored. Moreover, the transition from theoretical to operational systems integrated with disease control activities is rare. METHODS AND FINDINGS We introduce an operational seasonal dengue forecasting system for Vietnam where Earth observations, seasonal climate forecasts, and lagged dengue cases are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to 6 months ahead. Bayesian spatiotemporal models were fit to 19 years (2002-2020) of dengue data at the province level across Vietnam. A superensemble of these models then makes probabilistic predictions of dengue incidence at various future time points aligned with key Vietnamese decision and planning deadlines. We demonstrate that the superensemble generates more accurate predictions of dengue incidence than the individual models it incorporates across a suite of time horizons and transmission settings. Using historical data, the superensemble made slightly more accurate predictions (continuous rank probability score [CRPS] = 66.8, 95% CI 60.6-148.0) than a baseline model which forecasts the same incidence rate every month (CRPS = 79.4, 95% CI 78.5-80.5) at lead times of 1 to 3 months, albeit with larger uncertainty. The outbreak detection capability of the superensemble was considerably larger (69%) than that of the baseline model (54.5%). Predictions were most accurate in southern Vietnam, an area that experiences semi-regular seasonal dengue transmission. The system also demonstrated added value across multiple areas compared to previous practice of not using a forecast. We use the system to make a prospective prediction for dengue incidence in Vietnam for the period May to October 2020. Prospective predictions made with the superensemble were slightly more accurate (CRPS = 110, 95% CI 102-575) than those made with the baseline model (CRPS = 125, 95% CI 120-168) but had larger uncertainty. Finally, we propose a framework for the evaluation of probabilistic predictions. Despite the demonstrated value of our forecasting system, the approach is limited by the consistency of the dengue case data, as well as the lack of publicly available, continuous, and long-term data sets on mosquito control efforts and serotype-specific case data. CONCLUSIONS This study shows that by combining detailed Earth observation data, seasonal climate forecasts, and state-of-the-art models, dengue outbreaks can be predicted across a broad range of settings, with enough lead time to meaningfully inform dengue control. While our system omits some important variables not currently available at a subnational scale, the majority of past outbreaks could be predicted up to 3 months ahead. Over the next 2 years, the system will be prospectively evaluated and, if successful, potentially extended to other areas and other climate-sensitive disease systems.
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Affiliation(s)
- Felipe J. Colón-González
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Tyndall Centre for Climate Change Research, University of East Anglia, Norwich, United Kingdom
- * E-mail:
| | - Leonardo Soares Bastos
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Scientific Computing Programme, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro
| | | | - Alison Hopkin
- HR Wallingford, Wallingford, Oxfordshire, United Kingdom
| | | | | | | | | | | | - Samuel James
- HR Wallingford, Wallingford, Oxfordshire, United Kingdom
| | - Sajni Malde
- HR Wallingford, Wallingford, Oxfordshire, United Kingdom
| | | | - Vu Sinh Nam
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
| | - Dang Quang Tan
- General Department of Preventive Medicine, Hanoi, Vietnam
| | | | | | - Gina Tsarouchi
- HR Wallingford, Wallingford, Oxfordshire, United Kingdom
| | | | - Oliver J. Brady
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Rachel Lowe
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
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18
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Gibb R, Franklinos LHV, Redding DW, Jones KE. Ecosystem perspectives are needed to manage zoonotic risks in a changing climate. BMJ 2020; 371:m3389. [PMID: 33187958 PMCID: PMC7662085 DOI: 10.1136/bmj.m3389] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Rory Gibb
- Centre for Biodiversity and Environment Research, Division of Biosciences, University College London, London, UK
| | - Lydia H V Franklinos
- Centre for Biodiversity and Environment Research, Division of Biosciences, University College London, London, UK
- Institute for Global Health, University College London, London, UK
| | - David W Redding
- Centre for Biodiversity and Environment Research, Division of Biosciences, University College London, London, UK
- Institute of Zoology, Zoological Society of London, London, UK
| | - Kate E Jones
- Centre for Biodiversity and Environment Research, Division of Biosciences, University College London, London, UK
- Institute of Zoology, Zoological Society of London, London, UK
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19
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Zaitchik BF, Sweijd N, Shumake-Guillemot J, Morse A, Gordon C, Marty A, Trtanj J, Luterbacher J, Botai J, Behera S, Lu Y, Olwoch J, Takahashi K, Stowell JD, Rodó X. A framework for research linking weather, climate and COVID-19. Nat Commun 2020; 11:5730. [PMID: 33184283 PMCID: PMC7661498 DOI: 10.1038/s41467-020-19546-7] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 10/21/2020] [Indexed: 01/06/2023] Open
Abstract
Early studies of weather, seasonality, and environmental influences on COVID-19 have yielded inconsistent and confusing results. To provide policy-makers and the public with meaningful and actionable environmentally-informed COVID-19 risk estimates, the research community must meet robust methodological and communication standards.
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Affiliation(s)
- Benjamin F Zaitchik
- Department of Earth and Planetary Sciences, Johns Hopkins University, 3400N. Charles St., Baltimore, MD, 21218, USA.
| | - Neville Sweijd
- Alliance for Collaboration on Climate and Earth Systems Science (ACCESS) c/o Council for Scientific and Industrial Research (CSIR), 15 Lower Hope Road, Cape Town, 7700, South Africa
| | - Joy Shumake-Guillemot
- WHO/WMO Climate and Health Joint Office, World Meteorological Organization, 7bis Avenue de la Paix, C.P. 2300, CH-1211, Geneva, Switzerland
| | - Andy Morse
- School of Environmental Sciences, University of Liverpool, Liverpool, L69 7BX, UK
| | - Chris Gordon
- CDKN CEL-Ghana and Institute for Environment and Sanitation Studies, College of Basic and Applied Sciences, University of Ghana, Legon, Accra, Ghana
| | - Aileen Marty
- Herbert Wertheim College of Medicine, 11200 SW 8th St, AHC2 675, Miami, FL, 33199, USA
| | - Juli Trtanj
- Climate Program Office, National Oceanic and Atmospheric Administration, 1315 East-West Highway Suite 100, Silver Spring, MD, 20910, USA
| | - Juerg Luterbacher
- Science and Innovation Department, World Meteorological Organization, 7bis Avenue de la Paix, C.P. 2300, CH-1211, Geneva, Switzerland
| | - Joel Botai
- South African Weather Service, 01 Ecopark Drive, Ecoglades Block B, Centurion, Pretoria, 0157, South Africa
| | | | - Yonglong Lu
- Key Laboratory of the Ministry of Education for Coastal Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, 361102, Fujian, China
| | - Jane Olwoch
- SASSCAL Regional Secretariat, 28 Robert Mugabe Avenue, Windhoek, Namibia
| | - Ken Takahashi
- Servicio Nacional de Meteorología e Hidrología del Perú-SENAMHI, Jr. Cahuide 785, Jesús María, Lima, 15072, Peru
| | - Jennifer D Stowell
- Boston University, 715 Albany Street, The Talbot Building, T4W, Boston, MA, 02118, USA
| | - Xavier Rodó
- ICREA and Climate and Health Program, ISGlobal, Doctor Aiguader 88, Barcelona, 08003, Barcelona, Spain
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20
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Ashmore P, Lindahl JF, Colón-González FJ, Sinh Nam V, Quang Tan D, Medley GF. Spatiotemporal and Socioeconomic Risk Factors for Dengue at the Province Level in Vietnam, 2013-2015: Clustering Analysis and Regression Model. Trop Med Infect Dis 2020; 5:tropicalmed5020081. [PMID: 32438628 PMCID: PMC7345007 DOI: 10.3390/tropicalmed5020081] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 05/05/2020] [Accepted: 05/14/2020] [Indexed: 01/22/2023] Open
Abstract
Dengue is a serious infectious disease threat in Vietnam, but its spatiotemporal and socioeconomic risk factors are not currently well understood at the province level across the country and on a multiannual scale. We explore spatial trends, clusters and outliers in dengue case counts at the province level from 2011–2015 and use this to extract spatiotemporal variables for regression analysis of the association between dengue case counts and selected spatiotemporal and socioeconomic variables from 2013–2015. Dengue in Vietnam follows anticipated spatial trends, with a potential two-year cycle of high-high clusters in some southern provinces. Small but significant associations are observed between dengue case counts and mobility, population density, a province’s dengue rates the previous year, and average dengue rates two years previous in first and second order contiguous neighbours. Significant associations were not found between dengue case counts and housing pressure, access to electricity, clinician density, province-adjusted poverty rate, percentage of children below one vaccinated, or percentage of population in urban settings. These findings challenge assumptions about socioeconomic and spatiotemporal risk factors for dengue, and support national prevention targeting in Vietnam at the province level. They may also be of wider relevance for the study of other arboviruses, including Japanese encephalitis, Zika, and Chikungunya.
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Affiliation(s)
- Polly Ashmore
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1H 9SH, UK
| | - Johanna F Lindahl
- Department of Medical Biochemistry and Microbiology, Uppsala University, SE-751 23 Uppsala, Sweden
- International Livestock Research Institute, Hanoi 10 000, Vietnam
- Department of Clinical Sciences, Swedish University of Agricultural Sciences, SE-750 07 Uppsala, Sweden
| | - Felipe J Colón-González
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1H 9SH, UK
| | - Vu Sinh Nam
- National Institute of Hygiene and Epidemiology, Hanoi 10 000, Vietnam
| | - Dang Quang Tan
- General Department of Preventive Medicine, Ministry of Health of Vietnam, Hanoi 10 000, Vietnam
| | - Graham F Medley
- Department of Global Health and Development, London School of Hygiene & Tropical Medicine, London WC1H 9SH, UK
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21
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Abstract
A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.
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22
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Yamana TK, Shaman J. A framework for evaluating the effects of observational type and quality on vector-borne disease forecast. Epidemics 2019; 30:100359. [PMID: 31439454 PMCID: PMC7315892 DOI: 10.1016/j.epidem.2019.100359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 05/31/2019] [Accepted: 08/02/2019] [Indexed: 11/03/2022] Open
Abstract
Recent research has advanced infectious disease forecasting from an aspiration to an operational reality. The accuracy of such operational forecasting depends on the quantity and quality of observations available for system optimization. In particular, for forecasting systems that use combined mechanistic model-inference approaches, a broad suite of epidemiological observations could be utilized, if these data were available in near real time. In cases where such data are limited, an in silica, synthetic framework for evaluating the potential benefits of observations on forecasting accuracy can allow researchers and public health officials to more optimally allocate resources for disease surveillance and monitoring. Here, we demonstrate the application of such a framework, using a model-inference system designed to predict dengue, and identify the type and quality of observations that would improve forecasting accuracy.
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Affiliation(s)
- Teresa K Yamana
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, United States.
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, United States
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23
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Capinha C. Predicting the timing of ecological phenomena using dates of species occurrence records: a methodological approach and test case with mushrooms. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2019; 63:1015-1024. [PMID: 31001681 DOI: 10.1007/s00484-019-01714-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 03/15/2019] [Accepted: 03/27/2019] [Indexed: 06/09/2023]
Abstract
Spatiotemporal predictions of ecological phenomena are highly useful and significant in scientific and socio-economic applications. However, the inadequate availability of ecological time-series data often impedes the development of statistical predictions. On the other hand, considerable amounts of temporally discrete biological records (commonly known as 'species occurrence records') are being stored in public databases, and often include the location and date of the observation. In this paper, we describe an approach to develop spatiotemporal predictions based on the dates and locations found in species occurrence records. The approach is based on 'time-series classification', a field of machine learning, and consists of applying a machine-learning algorithm to classify between time series representing the environmental variation that precedes the occurrence records and time series representing the full range of environmental variation that is available in the location of the records. We exemplify the application of the approach for predicting the timing of emergence of fruiting bodies of two mushroom species (Boletus edulis and Macrolepiota procera) in Europe, from 2009 to 2015. Predictions made from this approach were superior to those provided by a 'null' model representing the average seasonality of the species. Given the increased availability and information contained in species occurrence records, particularly those supplemented with photographs, the range of environmental events that could be possible to predict using this approach is vast.
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Affiliation(s)
- César Capinha
- CIBIO/InBio, Centro de Investigação em Biodiversidade e Recursos Genéticos, Laboratório Associado, Campus Agrário de Vairão, Universidade do Porto, Vairão, 4485-661, Porto, Portugal.
- CIBIO/InBio, Centro de Investigação em Biodiversidade e Recursos Genéticos, Laboratório Associado, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017, Lisbon, Portugal.
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24
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Putra PS, Susanto H, Nuraini N. Turing Patterns of Non-linear S-I Model on Random and Real-Structure Networks with Diarrhea Data. Sci Rep 2019; 9:8892. [PMID: 31221999 PMCID: PMC6586790 DOI: 10.1038/s41598-019-45069-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 05/28/2019] [Indexed: 11/26/2022] Open
Abstract
Most developed models for solving problems in epidemiology use deterministic approach. To cover the lack of spatial sense in the method, one uses statistical modeling, reaction-diffusion in continuous medium, or multi-patch model to depict epidemic activities in several connected locations. Here, we show that an epidemic model that is set as an organized system on networks can yield Turing patterns and other interesting behaviors that are sensitive to the initial conditions. The formed patterns can be used to determine the epidemic arrival time, its first peak occurrence and the peak duration. These epidemic quantities are beneficial to identify contribution of a disease source node to the others. Using a real structure network, the system also exhibits a comparable disease spread pattern of Diarrhea in Jakarta.
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Affiliation(s)
- Prama Setia Putra
- Industrial and Financial Mathematics Research Group, Department of Mathematics, Institut Teknologi Bandung, Ganesha 10, Bandung, 40132, Indonesia.
| | - Hadi Susanto
- Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, United Kingdom
| | - Nuning Nuraini
- Industrial and Financial Mathematics Research Group, Department of Mathematics, Institut Teknologi Bandung, Ganesha 10, Bandung, 40132, Indonesia
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25
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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:vetsci6020040. [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] [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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26
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Zaki R, Roffeei SN, Hii YL, Yahya A, Appannan M, Said MA, Wan NC, Aghamohammadi N, Hairi NN, Bulgiba A, Quam M, Rocklov J. Public perception and attitude towards dengue prevention activity and response to dengue early warning in Malaysia. PLoS One 2019; 14:e0212497. [PMID: 30818394 PMCID: PMC6394956 DOI: 10.1371/journal.pone.0212497] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Accepted: 02/04/2019] [Indexed: 11/18/2022] Open
Abstract
An early warning system for dengue is meant to predict outbreaks and prevent dengue cases by aiding timely decision making and deployment of interventions. However, only a system which is accepted and utilised by the public would be sustainable in the long run. This study aimed to explore the perception and attitude of the Malaysian public towards a dengue early warning system. The sample consisted of 847 individuals who were 18 years and above and living/working in the Petaling District, an area adjacent to Kuala Lumpur, Malaysia. A questionnaire consisting of personal information and three sub-measures of; i) perception, ii) attitude towards dengue early warning and iii) response towards early warning; was distributed to participants. We found that most of the respondents know about dengue fever (97.1%) and its association with climate factors (90.6%). Most of them wanted to help reduce the number of dengue cases in their area (91.5%). A small percentage of the respondents admitted that they were not willing to be involved in public activities, and 64% of them admitted that they did not check dengue situations or hotspots around their area regularly. Despite the high awareness on the relationship between climate and dengue, about 45% of respondents do not know or are not sure how this can be used to predict dengue. Respondents would like to know more about how climate data can be used to predict a dengue outbreak (92.7%). Providing more information on how climate can influence dengue cases would increase public acceptability and improve response towards climate-based warning system. The most preferred way of communicating early warning was through the television (66.4%). This study shows that the public in Petaling District considers it necessary to have a dengue warning system to be necessary, but more education is required.
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Affiliation(s)
- Rafdzah Zaki
- Centre for Epidemiology and Evidence-Based Practice, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Malaysia
| | - Siti Norsyuhada Roffeei
- Centre for Epidemiology and Evidence-Based Practice, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Malaysia
| | - Yien Ling Hii
- Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umea University, Umea, Sweden
| | - Abqariyah Yahya
- Centre for Epidemiology and Evidence-Based Practice, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Malaysia
| | - Mahesh Appannan
- Centre for Epidemiology and Evidence-Based Practice, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Malaysia
| | - Mas Ayu Said
- Centre for Epidemiology and Evidence-Based Practice, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Malaysia
| | - Ng Chiu Wan
- Centre for Epidemiology and Evidence-Based Practice, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Malaysia
| | - Nasrin Aghamohammadi
- Centre for Occupational and Environmental Health, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Malaysia
| | - Noran Naqiah Hairi
- Centre for Epidemiology and Evidence-Based Practice, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Malaysia
| | - Awang Bulgiba
- Centre for Epidemiology and Evidence-Based Practice, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Malaysia
| | - Mikkel Quam
- Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umea University, Umea, Sweden
| | - Joacim Rocklov
- Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umea University, Umea, Sweden
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27
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Abstract
A growing number of infectious pathogens are spreading among geographic regions. Some pathogens that were previously not considered to pose a general threat to human health have emerged at regional and global scales, such as Zika and Ebola Virus Disease. Other pathogens, such as yellow fever virus, were previously thought to be under control but have recently re-emerged, causing new challenges to public health organisations. A wide array of new modelling techniques, aided by increased computing capabilities, novel diagnostic tools, and the increased speed and availability of genomic sequencing allow researchers to identify new pathogens more rapidly, assess the likelihood of geographic spread, and quantify the speed of human-to-human transmission. Despite some initial successes in predicting the spread of acute viral infections, the practicalities and sustainability of such approaches will need to be evaluated in the context of public health responses.
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28
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Tompkins AM, Thomson MC. Uncertainty in malaria simulations in the highlands of Kenya: Relative contributions of model parameter setting, driving climate and initial condition errors. PLoS One 2018; 13:e0200638. [PMID: 30256799 PMCID: PMC6157844 DOI: 10.1371/journal.pone.0200638] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Accepted: 06/29/2018] [Indexed: 11/23/2022] Open
Abstract
In this study, experiments are conducted to gauge the relative importance of model, initial condition, and driving climate uncertainty for simulations of malaria transmission at a highland plantation in Kericho, Kenya. A genetic algorithm calibrates each of these three factors within their assessed prior uncertainty in turn to see which allows the best fit to a timeseries of confirmed cases. It is shown that for high altitude locations close to the threshold for transmission, the spatial representativeness uncertainty for climate, in particular temperature, dominates the uncertainty due to model parameter settings. Initial condition uncertainty plays little role after the first two years, and is thus important in the early warning system context, but negligible for decadal and climate change investigations. Thus, while reducing uncertainty in the model parameters would improve the quality of the simulations, the uncertainty in the temperature driving data is critical. It is emphasized that this result is a function of the mean climate of the location itself, and it is shown that model uncertainty would be relatively more important at warmer, lower altitude locations.
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Affiliation(s)
- Adrian M. Tompkins
- Earth System Physics, The Abdus Salam International Centre for Theoretical Physics (ICTP), Strada Costiera 11, Trieste, Italy
- * E-mail:
| | - Madeleine C. Thomson
- International Research Institute for Climate and Society, Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, United States of America
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29
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Lowe R, Gasparrini A, Van Meerbeeck CJ, Lippi CA, Mahon R, Trotman AR, Rollock L, Hinds AQJ, Ryan SJ, Stewart-Ibarra AM. Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study. PLoS Med 2018; 15:e1002613. [PMID: 30016319 PMCID: PMC6049902 DOI: 10.1371/journal.pmed.1002613] [Citation(s) in RCA: 103] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2018] [Accepted: 06/15/2018] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Over the last 5 years (2013-2017), the Caribbean region has faced an unprecedented crisis of co-occurring epidemics of febrile illness due to arboviruses transmitted by the Aedes sp. mosquito (dengue, chikungunya, and Zika). Since 2013, the Caribbean island of Barbados has experienced 3 dengue outbreaks, 1 chikungunya outbreak, and 1 Zika fever outbreak. Prior studies have demonstrated that climate variability influences arbovirus transmission and vector population dynamics in the region, indicating the potential to develop public health interventions using climate information. The aim of this study is to quantify the nonlinear and delayed effects of climate indicators, such as drought and extreme rainfall, on dengue risk in Barbados from 1999 to 2016. METHODS AND FINDINGS Distributed lag nonlinear models (DLNMs) coupled with a hierarchal mixed-model framework were used to understand the exposure-lag-response association between dengue relative risk and key climate indicators, including the standardised precipitation index (SPI) and minimum temperature (Tmin). The model parameters were estimated in a Bayesian framework to produce probabilistic predictions of exceeding an island-specific outbreak threshold. The ability of the model to successfully detect outbreaks was assessed and compared to a baseline model, representative of standard dengue surveillance practice. Drought conditions were found to positively influence dengue relative risk at long lead times of up to 5 months, while excess rainfall increased the risk at shorter lead times between 1 and 2 months. The SPI averaged over a 6-month period (SPI-6), designed to monitor drought and extreme rainfall, better explained variations in dengue risk than monthly precipitation data measured in millimetres. Tmin was found to be a better predictor than mean and maximum temperature. Furthermore, including bidimensional exposure-lag-response functions of these indicators-rather than linear effects for individual lags-more appropriately described the climate-disease associations than traditional modelling approaches. In prediction mode, the model was successfully able to distinguish outbreaks from nonoutbreaks for most years, with an overall proportion of correct predictions (hits and correct rejections) of 86% (81%:91%) compared with 64% (58%:71%) for the baseline model. The ability of the model to predict dengue outbreaks in recent years was complicated by the lack of data on the emergence of new arboviruses, including chikungunya and Zika. CONCLUSION We present a modelling approach to infer the risk of dengue outbreaks given the cumulative effect of climate variations in the months leading up to an outbreak. By combining the dengue prediction model with climate indicators, which are routinely monitored and forecasted by the Regional Climate Centre (RCC) at the Caribbean Institute for Meteorology and Hydrology (CIMH), probabilistic dengue outlooks could be included in the Caribbean Health-Climatic Bulletin, issued on a quarterly basis to provide climate-smart decision-making guidance for Caribbean health practitioners. This flexible modelling approach could be extended to model the risk of dengue and other arboviruses in the Caribbean region.
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Affiliation(s)
- Rachel Lowe
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Barcelona Institute for Global Health (ISGLOBAL), Barcelona, Spain
| | - Antonio Gasparrini
- Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | | | - Catherine A. Lippi
- Quantitative Disease Ecology and Conservation Lab Group, Department of Geography and Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
| | - Roché Mahon
- Caribbean Institute for Meteorology and Hydrology, St. James, Barbados
| | - Adrian R. Trotman
- Caribbean Institute for Meteorology and Hydrology, St. James, Barbados
| | | | | | - Sadie J. Ryan
- Quantitative Disease Ecology and Conservation Lab Group, Department of Geography and Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
- School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Anna M. Stewart-Ibarra
- Institute for Global Health and Translational Science, SUNY Upstate Medical University, Syracuse, New York, United States of America
- Department of Medicine and Department of Public Health and Preventative Medicine, SUNY Upstate Medical University, Syracuse, New York, United States of America
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30
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Baquero OS, Santana LMR, Chiaravalloti-Neto F. Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models. PLoS One 2018; 13:e0195065. [PMID: 29608586 PMCID: PMC5880372 DOI: 10.1371/journal.pone.0195065] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Accepted: 03/15/2018] [Indexed: 11/19/2022] Open
Abstract
Globally, the number of dengue cases has been on the increase since 1990 and this trend has also been found in Brazil and its most populated city-São Paulo. Surveillance systems based on predictions allow for timely decision making processes, and in turn, timely and efficient interventions to reduce the burden of the disease. We conducted a comparative study of dengue predictions in São Paulo city to test the performance of trained seasonal autoregressive integrated moving average models, generalized additive models and artificial neural networks. We also used a naïve model as a benchmark. A generalized additive model with lags of the number of cases and meteorological variables had the best performance, predicted epidemics of unprecedented magnitude and its performance was 3.16 times higher than the benchmark and 1.47 higher that the next best performing model. The predictive models captured the seasonal patterns but differed in their capacity to anticipate large epidemics and all outperformed the benchmark. In addition to be able to predict epidemics of unprecedented magnitude, the best model had computational advantages, since its training and tuning was straightforward and required seconds or at most few minutes. These are desired characteristics to provide timely results for decision makers. However, it should be noted that predictions are made just one month ahead and this is a limitation that future studies could try to reduce.
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Affiliation(s)
- Oswaldo Santos Baquero
- Department of Preventive Veterinary Medicine and Animal Health, School of Veterinary Medicine, University of São Paulo, São Paulo, Brazil
- Department of Epidemiology, School of Public Health, University of São Paulo, São Paulo, Brazil
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31
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Lauer SA, Sakrejda K, Ray EL, Keegan LT, Bi Q, Suangtho P, Hinjoy S, Iamsirithaworn S, Suthachana S, Laosiritaworn Y, Cummings DAT, Lessler J, Reich NG. Prospective forecasts of annual dengue hemorrhagic fever incidence in Thailand, 2010-2014. Proc Natl Acad Sci U S A 2018; 115:E2175-E2182. [PMID: 29463757 PMCID: PMC5877997 DOI: 10.1073/pnas.1714457115] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Dengue hemorrhagic fever (DHF), a severe manifestation of dengue viral infection that can cause severe bleeding, organ impairment, and even death, affects between 15,000 and 105,000 people each year in Thailand. While all Thai provinces experience at least one DHF case most years, the distribution of cases shifts regionally from year to year. Accurately forecasting where DHF outbreaks occur before the dengue season could help public health officials prioritize public health activities. We develop statistical models that use biologically plausible covariates, observed by April each year, to forecast the cumulative DHF incidence for the remainder of the year. We perform cross-validation during the training phase (2000-2009) to select the covariates for these models. A parsimonious model based on preseason incidence outperforms the 10-y median for 65% of province-level annual forecasts, reduces the mean absolute error by 19%, and successfully forecasts outbreaks (area under the receiver operating characteristic curve = 0.84) over the testing period (2010-2014). We find that functions of past incidence contribute most strongly to model performance, whereas the importance of environmental covariates varies regionally. This work illustrates that accurate forecasts of dengue risk are possible in a policy-relevant timeframe.
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Affiliation(s)
- Stephen A Lauer
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA 01003;
| | - Krzysztof Sakrejda
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA 01003
| | - Evan L Ray
- Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, MA 01075
| | - Lindsay T Keegan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
| | - Qifang Bi
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
| | - Paphanij Suangtho
- Bureau of Epidemiology, Ministry of Public Health, Nonthaburi 11000, Thailand
| | - Soawapak Hinjoy
- Bureau of Epidemiology, Ministry of Public Health, Nonthaburi 11000, Thailand
| | - Sopon Iamsirithaworn
- Department of Disease Control, Bureau of Epidemiology, Ministry of Public Health, Nonthaburi 11000, Thailand
| | - Suthanun Suthachana
- Bureau of Epidemiology, Ministry of Public Health, Nonthaburi 11000, Thailand
| | | | - Derek A T Cummings
- Department of Biology and the Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
| | - Nicholas G Reich
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA 01003
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Lowe R, Barcellos C, Brasil P, Cruz OG, Honório NA, Kuper H, Carvalho MS. The Zika Virus Epidemic in Brazil: From Discovery to Future Implications. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:E96. [PMID: 29315224 PMCID: PMC5800195 DOI: 10.3390/ijerph15010096] [Citation(s) in RCA: 203] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Revised: 12/27/2017] [Accepted: 01/02/2018] [Indexed: 12/13/2022]
Abstract
The first confirmed case of Zika virus infection in the Americas was reported in Northeast Brazil in May 2015, although phylogenetic studies indicate virus introduction as early as 2013. Zika rapidly spread across Brazil and to more than 50 other countries and territories on the American continent. The Aedesaegypti mosquito is thought to be the principal vector responsible for the widespread transmission of the virus. However, sexual transmission has also been reported. The explosively emerging epidemic has had diverse impacts on population health, coinciding with cases of Guillain-Barré Syndrome and an unexpected epidemic of newborns with microcephaly and other neurological impairments. This led to Brazil declaring a national public health emergency in November 2015, followed by a similar decision by the World Health Organization three months later. While dengue virus serotypes took several decades to spread across Brazil, the Zika virus epidemic diffused within months, extending beyond the area of permanent dengue transmission, which is bound by a climatic barrier in the south and low population density areas in the north. This rapid spread was probably due to a combination of factors, including a massive susceptible population, climatic conditions conducive for the mosquito vector, alternative non-vector transmission, and a highly mobile population. The epidemic has since subsided, but many unanswered questions remain. In this article, we provide an overview of the discovery of Zika virus in Brazil, including its emergence and spread, epidemiological surveillance, vector and non-vector transmission routes, clinical complications, and socio-economic impacts. We discuss gaps in the knowledge and the challenges ahead to anticipate, prevent, and control emerging and re-emerging epidemics of arboviruses in Brazil and worldwide.
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Affiliation(s)
- Rachel Lowe
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK.
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK.
- Barcelona Institute for Global Health (ISGLOBAL), Doctor Aiguader, 88, 08003 Barcelona, Spain.
| | - Christovam Barcellos
- Institute of Health Communication and Information, Oswaldo Cruz Foundation (Fiocruz), Avenida Brasil 4365, Rio de Janeiro 21045-900, Brazil.
| | - Patrícia Brasil
- Instituto Nacional de Infectologia Evandro Chagas, Oswaldo Cruz Foundation (Fiocruz), Avenida Brasil 4365, Rio de Janeiro 21045-900, Brazil.
| | - Oswaldo G Cruz
- Scientific Computation Program, Oswaldo Cruz Foundation (Fiocruz), Avenida Brasil 4365, Rio de Janeiro 21045-900, Brazil.
| | - Nildimar Alves Honório
- Laboratório de Mosquitos Transmissores de Hematozoários, Instituto Oswaldo Cruz (Fiocruz), Avenida Brasil 4365, Rio de Janeiro 21045-900, Brazil.
- Núcleo Operacional Sentinela de Mosquitos Vetores-Nosmove/Fiocruz, Avenida Brasil 4365, Rio de Janeiro 21045-900, Brazil.
| | - Hannah Kuper
- International Centre for Evidence in Disability, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK.
| | - Marilia Sá Carvalho
- Scientific Computation Program, Oswaldo Cruz Foundation (Fiocruz), Avenida Brasil 4365, Rio de Janeiro 21045-900, Brazil.
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Marques-Toledo CDA, Degener CM, Vinhal L, Coelho G, Meira W, Codeço CT, Teixeira MM. Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level. PLoS Negl Trop Dis 2017; 11:e0005729. [PMID: 28719659 PMCID: PMC5533462 DOI: 10.1371/journal.pntd.0005729] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 07/28/2017] [Accepted: 06/20/2017] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Infectious diseases are a leading threat to public health. Accurate and timely monitoring of disease risk and progress can reduce their impact. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Dengue is the fastest growing mosquito-borne viral disease, with an estimated annual incidence of 390 million infections, of which 96 million manifest clinically. Dengue burden is likely to increase in the future owing to trends toward increased urbanization, scarce water supplies and, possibly, environmental change. The epidemiological dynamic of Dengue is complex and difficult to predict, partly due to costly and slow surveillance systems. METHODOLOGY / PRINCIPAL FINDINGS In this study, we aimed to quantitatively assess the usefulness of data acquired by Twitter for the early detection and monitoring of Dengue epidemics, both at country and city level at a weekly basis. Here, we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast, in comparison with other available web-based data, Google Trends and Wikipedia access logs. Also, we studied the factors that might influence the goodness-of-fit of the model. We built a simple model based on tweets that was able to 'nowcast', i.e. estimate disease numbers in the same week, but also 'forecast' disease in future weeks. At the country level, tweets are strongly associated with Dengue cases, and can estimate present and future Dengue cases until 8 weeks in advance. At city level, tweets are also useful for estimating Dengue activity. Our model can be applied successfully to small and less developed cities, suggesting a robust construction, even though it may be influenced by the incidence of the disease, the activity of Twitter locally, and social factors, including human development index and internet access. CONCLUSIONS Tweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low-cost. Tweets are able to successfully nowcast, i.e. estimate Dengue in the present week, but also forecast, i.e. predict Dengue at until 8 weeks in the future, both at country and city level with high estimation capacity.
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Affiliation(s)
- Cecilia de Almeida Marques-Toledo
- Departamento de Bioquimica e Imunologia do Instituto de Ciencias Biologicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Consultoria Tecnica, Ecovec LTDA, Belo Horizonte, Minas Gerais, Brazil
| | - Carolin Marlen Degener
- Programa de Computacao Cientifica, Fundacao Oswaldo Cruz, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Livia Vinhal
- Secretaria de Vigilancia em Saude, Ministerio da Saude, Brasilia, Brazil
| | - Giovanini Coelho
- Secretaria de Vigilancia em Saude, Ministerio da Saude, Brasilia, Brazil
| | - Wagner Meira
- Departamento de Ciencia da Computacao do Instituto de Ciencias Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Claudia Torres Codeço
- Programa de Computacao Cientifica, Fundacao Oswaldo Cruz, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Mauro Martins Teixeira
- Departamento de Bioquimica e Imunologia do Instituto de Ciencias Biologicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
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Lowe R, Stewart-Ibarra AM, Petrova D, García-Díez M, Borbor-Cordova MJ, Mejía R, Regato M, Rodó X. Climate services for health: predicting the evolution of the 2016 dengue season in Machala, Ecuador. Lancet Planet Health 2017; 1:e142-e151. [PMID: 29851600 DOI: 10.1016/s2542-5196(17)30064-5] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 05/30/2017] [Accepted: 06/09/2017] [Indexed: 06/08/2023]
Abstract
BACKGROUND El Niño and its effect on local meteorological conditions potentially influences interannual variability in dengue transmission in southern coastal Ecuador. El Oro province is a key dengue surveillance site, due to the high burden of dengue, seasonal transmission, co-circulation of all four dengue serotypes, and the recent introduction of chikungunya and Zika. In this study, we used climate forecasts to predict the evolution of the 2016 dengue season in the city of Machala, following one of the strongest El Niño events on record. METHODS We incorporated precipitation, minimum temperature, and Niño3·4 index forecasts in a Bayesian hierarchical mixed model to predict dengue incidence. The model was initiated on Jan 1, 2016, producing monthly dengue forecasts until November, 2016. We accounted for misreporting of dengue due to the introduction of chikungunya in 2015, by using active surveillance data to correct reported dengue case data from passive surveillance records. We then evaluated the forecast retrospectively with available epidemiological information. FINDINGS The predictions correctly forecast an early peak in dengue incidence in March, 2016, with a 90% chance of exceeding the mean dengue incidence for the previous 5 years. Accounting for the proportion of chikungunya cases that had been incorrectly recorded as dengue in 2015 improved the prediction of the magnitude of dengue incidence in 2016. INTERPRETATION This dengue prediction framework, which uses seasonal climate and El Niño forecasts, allows a prediction to be made at the start of the year for the entire dengue season. Combining active surveillance data with routine dengue reports improved not only model fit and performance, but also the accuracy of benchmark estimates based on historical seasonal averages. This study advances the state-of-the-art of climate services for the health sector, by showing the potential value of incorporating climate information in the public health decision-making process in Ecuador. FUNDING European Union FP7, Royal Society, and National Science Foundation.
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Affiliation(s)
- Rachel Lowe
- Centre for the Mathematical Modelling of Infectious Diseases and Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; CLIMA-Climate and Health Programme, Barcelona Institute for Global Health (ISGLOBAL), Barcelona, Spain.
| | - Anna M Stewart-Ibarra
- Center for Global Health and Translational Science and Department of Medicine, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Desislava Petrova
- CLIMA-Climate and Health Programme, Barcelona Institute for Global Health (ISGLOBAL), Barcelona, Spain
| | | | - Mercy J Borbor-Cordova
- School of Maritime Engineering, Biological Sciences, Oceanic and Natural Resources, Escuela Superior Politecnica del Litoral (ESPOL), Guayaquil, Ecuador
| | - Raúl Mejía
- National Institute of Meteorology and Hydrology (INAMHI), Guayaquil, Ecuador
| | - Mary Regato
- National Institute of Public Health Research (INSPI) of the Ministry of Health, Guayaquil, Ecuador
| | - Xavier Rodó
- CLIMA-Climate and Health Programme, Barcelona Institute for Global Health (ISGLOBAL), Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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Effects of local and regional climatic fluctuations on dengue outbreaks in southern Taiwan. PLoS One 2017; 12:e0178698. [PMID: 28575035 PMCID: PMC5456348 DOI: 10.1371/journal.pone.0178698] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Accepted: 05/17/2017] [Indexed: 11/19/2022] Open
Abstract
Background Southern Taiwan has been a hotspot for dengue fever transmission since 1998. During 2014 and 2015, Taiwan experienced unprecedented dengue outbreaks and the causes are poorly understood. This study aims to investigate the influence of regional and local climate conditions on the incidence of dengue fever in Taiwan, as well as to develop a climate-based model for future forecasting. Methodology/Principle findings Historical time-series data on dengue outbreaks in southern Taiwan from 1998 to 2015 were investigated. Local climate variables were analyzed using a distributed lag non-linear model (DLNM), and the model of best fit was used to predict dengue incidence between 2013 and 2015. The cross-wavelet coherence approach was used to evaluate the regional El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) effects on dengue incidence and local climate variables. The DLNM results highlighted the important non-linear and lag effects of minimum temperature and precipitation. Minimum temperature above 23°C or below 17°C can increase dengue incidence rate with lag effects of 10 to 15 weeks. Moderate to high precipitation can increase dengue incidence rates with a lag of 10 or 20 weeks. The model of best fit successfully predicted dengue transmission between 2013 and 2015. The prediction accuracy ranged from 0.7 to 0.9, depending on the number of weeks ahead of the prediction. ENSO and IOD were associated with nonstationary inter-annual patterns of dengue transmission. IOD had a greater impact on the seasonality of local climate conditions. Conclusions/Significance Our findings suggest that dengue transmission can be affected by regional and local climatic fluctuations in southern Taiwan. The climate-based model developed in this study can provide important information for dengue early warning systems in Taiwan. Local climate conditions might be influenced by ENSO and IOD, to result in unusual dengue outbreaks.
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Muñoz ÁG, Thomson MC, Goddard L, Aldighieri S. Analyzing climate variations at multiple timescales can guide Zika virus response measures. Gigascience 2016; 5:1-6. [PMID: 27716414 PMCID: PMC5053076 DOI: 10.1186/s13742-016-0146-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2016] [Accepted: 09/11/2016] [Indexed: 11/30/2022] Open
Abstract
Background The emergence of Zika virus (ZIKV) in Latin America and the Caribbean in 2014–2016 occurred during a period of severe drought and unusually high temperatures, conditions that have been associated with the 2015–2016 El Niño event, and/or climate change; however, no quantitative assessment has been made to date. Analysis of related flaviviruses transmitted by the same vectors suggests that ZIKV dynamics are sensitive to climate seasonality and longer-term variability and trends. A better understanding of the climate conditions conducive to the 2014–2016 epidemic may permit the development of climate-informed short and long-term strategies for ZIKV prevention and control. Results Using a novel timescale-decomposition methodology, we demonstrate that the extreme climate anomalies observed in most parts of South America during the current epidemic are not caused exclusively by El Niño or climate change, but by a combination of climate signals acting at multiple timescales. In Brazil, the dry conditions present in 2013–2015 are primarily explained by year-to-year variability superimposed on decadal variability, but with little contribution of long-term trends. In contrast, the warm temperatures of 2014–2015 resulted from the compound effect of climate change, decadal and year-to-year climate variability. Conclusions ZIKV response strategies made in Brazil during the drought concurrent with the 2015-2016 El Niño event, may require revision in light of the likely return of rainfall associated with the borderline La Niña event expected in 2016–2017. Temperatures are likely to remain warm given the importance of long term and decadal scale climate signals.
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Affiliation(s)
- Ángel G Muñoz
- Atmospheric and Oceanic Sciences/Geophysical Fluid Dynamics Laboratory, Princeton University, Forrestal Campus. Forrestal Road 201, Princeton, NJ, USA.,International Research Institute for Climate and Society, Earth Institute, Columbia University, New York, NY, USA.,Latin American Observatory for Climate Events, Centro de Modelado Científico, Universidad del Zulia, Maracaibo, Venezuela
| | - Madeleine C Thomson
- International Research Institute for Climate and Society, Earth Institute, Columbia University, New York, NY, USA.,Mailman School of Public Health, Department of Environmental Health Sciences, Columbia University, New York, NY, USA.,World Health Organization Collaborating Centre on Early Warning Systems for Malaria and other Climate Sensitive Diseases, New York, NY, USA
| | - Lisa Goddard
- International Research Institute for Climate and Society, Earth Institute, Columbia University, New York, NY, USA
| | - Sylvain Aldighieri
- International Health Regulations/Epidemic Alert and Response, and Water Borne Diseases, Communicable Diseases and Health Analysis Department, Pan American Health Organization, Washington DC, USA
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Massad E, Tan SH, Khan K, Wilder-Smith A. Estimated Zika virus importations to Europe by travellers from Brazil. Glob Health Action 2016; 9:31669. [PMID: 27193266 PMCID: PMC4871896 DOI: 10.3402/gha.v9.31669] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2016] [Revised: 04/22/2016] [Accepted: 04/22/2016] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Given the interconnectivity of Brazil with the rest of the world, Zika virus (ZIKV) infections have the potential to spread rapidly around the world via viremic travellers. The extent of spread depends on the travel volume and the endemicity in the exporting country. In the absence of reliable surveillance data, we did mathematical modelling to estimate the number of importations of ZIKV from Brazil into Europe. DESIGN We applied a previously developed mathematical model on importations of dengue to estimate the number of ZIKV importations into Europe, based on the travel volume, the probability of being infected at the time of travel, the population size of Brazil, and the estimated incidence of ZIKV infections. RESULTS Our model estimated between 508 and 1,778 imported infections into Europe in 2016, of which we would expect between 116 and 355 symptomatic Zika infections; with the highest number of importations being into France, Portugal and Italy. CONCLUSIONS Our model identified high-risk countries in Europe. Such data can assist policymakers and public health professionals in estimating the extent of importations in order to prepare for the scale up of laboratory diagnostic assays and estimate the occurrence of Guillain-Barré Syndrome, potential sexual transmission, and infants with congenital ZIKV syndrome.
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Affiliation(s)
- Eduardo Massad
- Department of Medicine, University of Sao Paolo, Sao Paolo, Brazil
- London School of Hygiene and Tropical Medicine, London, UK
| | - Ser-Han Tan
- School of Computer Engineering, Nanyang Technological University, Singapore
| | - Kamran Khan
- Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Canada
| | - Annelies Wilder-Smith
- Institute of Public Health, University of Heidelberg, Germany
- Department Public Health and Clinical Medicine, Epidemiology and Global Health, Umeå University, SE-901 85 Umeå, Sweden
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore;
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