1
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Cavany S, Huber JH, Wieler A, Tran QM, Alkuzweny M, Elliott M, España G, Moore SM, Perkins TA. Does ignoring transmission dynamics lead to underestimation of the impact of interventions against mosquito-borne disease? BMJ Glob Health 2023; 8:e012169. [PMID: 37652566 PMCID: PMC10476117 DOI: 10.1136/bmjgh-2023-012169] [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: 02/28/2023] [Accepted: 07/17/2023] [Indexed: 09/02/2023] Open
Abstract
New vector-control technologies to fight mosquito-borne diseases are urgently needed, the adoption of which depends on efficacy estimates from large-scale cluster-randomised trials (CRTs). The release of Wolbachia-infected mosquitoes is one promising strategy to curb dengue virus (DENV) transmission, and a recent CRT reported impressive reductions in dengue incidence following the release of these mosquitoes. Such trials can be affected by multiple sources of bias, however. We used mathematical models of DENV transmission during a CRT of Wolbachia-infected mosquitoes to explore three such biases: human movement, mosquito movement and coupled transmission dynamics between trial arms. We show that failure to account for each of these biases would lead to underestimated efficacy, and that the majority of this underestimation is due to a heretofore unrecognised bias caused by transmission coupling. Taken together, our findings suggest that Wolbachia-infected mosquitoes could be even more promising than the recent CRT suggested. By emphasising the importance of accounting for transmission coupling between arms, which requires a mathematical model, we highlight the key role that models can play in interpreting and extrapolating the results from trials of vector control interventions.
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Affiliation(s)
- Sean Cavany
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | - John H Huber
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | - Annaliese Wieler
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | - Quan Minh Tran
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | - Manar Alkuzweny
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | - Margaret Elliott
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | - Guido España
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | - Sean M Moore
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | - T Alex Perkins
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
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2
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Yin S, Ren C, Shi Y, Hua J, Yuan HY, Tian LW. A Systematic Review on Modeling Methods and Influential Factors for Mapping Dengue-Related Risk in Urban Settings. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192215265. [PMID: 36429980 PMCID: PMC9690886 DOI: 10.3390/ijerph192215265] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/11/2022] [Accepted: 11/17/2022] [Indexed: 05/12/2023]
Abstract
Dengue fever is an acute mosquito-borne disease that mostly spreads within urban or semi-urban areas in warm climate zones. The dengue-related risk map is one of the most practical tools for executing effective control policies, breaking the transmission chain, and preventing disease outbreaks. Mapping risk at a small scale, such as at an urban level, can demonstrate the spatial heterogeneities in complicated built environments. This review aims to summarize state-of-the-art modeling methods and influential factors in mapping dengue fever risk in urban settings. Data were manually extracted from five major academic search databases following a set of querying and selection criteria, and a total of 28 studies were analyzed. Twenty of the selected papers investigated the spatial pattern of dengue risk by epidemic data, whereas the remaining eight papers developed an entomological risk map as a proxy for potential dengue burden in cities or agglomerated urban regions. The key findings included: (1) Big data sources and emerging data-mining techniques are innovatively employed for detecting hot spots of dengue-related burden in the urban context; (2) Bayesian approaches and machine learning algorithms have become more popular as spatial modeling tools for predicting the distribution of dengue incidence and mosquito presence; (3) Climatic and built environmental variables are the most common factors in making predictions, though the effects of these factors vary with the mosquito species; (4) Socio-economic data may be a better representation of the huge heterogeneity of risk or vulnerability spatial distribution on an urban scale. In conclusion, for spatially assessing dengue-related risk in an urban context, data availability and the purpose for mapping determine the analytical approaches and modeling methods used. To enhance the reliabilities of predictive models, sufficient data about dengue serotyping, socio-economic status, and spatial connectivity may be more important for mapping dengue-related risk in urban settings for future studies.
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Affiliation(s)
- Shi Yin
- Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- School of Architecture, South China University of Technology, Guangzhou 510641, China
| | - Chao Ren
- Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Correspondence:
| | - Yuan Shi
- Department of Geography and Planning, University of Liverpool, Liverpool L69 3BX, UK
| | - Junyi Hua
- School of International Affairs and Public Administration, Ocean University of China, Qingdao 266100, China
| | - Hsiang-Yu Yuan
- Department of Biomedical Sciences, City University of Hong Kong, Kowloon, Hong Kong SAR, China
| | - Lin-Wei Tian
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
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3
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Lee SA, Economou T, Lowe R. A Bayesian modelling framework to quantify multiple sources of spatial variation for disease mapping. J R Soc Interface 2022; 19:20220440. [PMID: 36128702 PMCID: PMC9490350 DOI: 10.1098/rsif.2022.0440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/31/2022] [Indexed: 11/23/2022] Open
Abstract
Spatial connectivity is an important consideration when modelling infectious disease data across a geographical region. Connectivity can arise for many reasons, including shared characteristics between regions and human or vector movement. Bayesian hierarchical models include structured random effects to account for spatial connectivity. However, conventional approaches require the spatial structure to be fully defined prior to model fitting. By applying penalized smoothing splines to coordinates, we create two-dimensional smooth surfaces describing the spatial structure of the data while making minimal assumptions about the structure. The result is a non-stationary surface which is setting specific. These surfaces can be incorporated into a hierarchical modelling framework and interpreted similarly to traditional random effects. Through simulation studies, we show that the splines can be applied to any symmetric continuous connectivity measure, including measures of human movement, and that the models can be extended to explore multiple sources of spatial structure in the data. Using Bayesian inference and simulation, the relative contribution of each spatial structure can be computed and used to generate hypotheses about the drivers of disease. These models were found to perform at least as well as existing modelling frameworks, while allowing for future extensions and multiple sources of spatial connectivity.
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Affiliation(s)
- Sophie A. Lee
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Theodoros Economou
- Climate and Atmosphere Research Centre, The Cyprus Institute, Nicosia, Cyprus
| | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
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4
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Spatiotemporal Patterns of Human Mobility and Its Association with Land Use Types during COVID-19 in New York City. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2021. [DOI: 10.3390/ijgi10050344] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The novel coronavirus disease (COVID-19) pandemic has impacted every facet of society. One of the non-pharmacological measures to contain the COVID-19 infection is social distancing. Federal, state, and local governments have placed multiple executive orders for human mobility reduction to slow down the spread of COVID-19. This paper uses geotagged tweets data to reveal the spatiotemporal human mobility patterns during this COVID-19 pandemic in New York City. With New York City open data, human mobility pattern changes were detected by different categories of land use, including residential, parks, transportation facilities, and workplaces. This study further compares human mobility patterns by land use types based on an open social media platform (Twitter) and the human mobility patterns revealed by Google Community Mobility Report cell phone location, indicating that in some applications, open-access social media data can generate similar results to private data. The results of this study can be further used for human mobility analysis and the battle against COVID-19.
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5
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Lee SA, Jarvis CI, Edmunds WJ, Economou T, Lowe R. Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions. J R Soc Interface 2021; 18:20210096. [PMID: 34034534 PMCID: PMC8150046 DOI: 10.1098/rsif.2021.0096] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 04/26/2021] [Indexed: 12/14/2022] Open
Abstract
Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.
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Affiliation(s)
- Sophie A. Lee
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Christopher I. Jarvis
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - W. John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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6
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Corsi A, de Souza FF, Pagani RN, Kovaleski JL. Big data analytics as a tool for fighting pandemics: a systematic review of literature. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021; 12:9163-9180. [PMID: 33144892 PMCID: PMC7595572 DOI: 10.1007/s12652-020-02617-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 10/10/2020] [Indexed: 05/09/2023]
Abstract
Infectious and contagious diseases represent a major challenge for health systems worldwide, either in private or public sectors. More recently, with the increase in cases related to these problems, combined with the recent global pandemic of COVID-19, the need to study strategies to treat these health disturbs is even more latent. Big Data, as well as Big Data Analytics techniques, have been addressed in this context with the possibility of predicting, mapping, tracking, monitoring, and raising awareness about these epidemics and pandemics. Thus, the purpose of this study is to identify how BDA can help in cases of pandemics and epidemics. To achieve this purpose, a systematic review of literature was carried out using the methodology Methodi Ordinatio. The rigorous search resulted in a portfolio of 45 articles, retrived from scientific databases. For the collection and analysis of data, the softwares NVivo 12 and VOSviewer were used. The content analysis sought to identify how Big Data and Big Data Analytics can help fighting epidemics and pandemics. The types and sources of data used in cases of previous epidemics and pandemics were identified, as well as techniques for treating these data. The results showed that the main sources of data come from social media and Internet search engines. The most common techniques for analyzing these data involve the use of statistics, such as correlation and regression, combined with other techniques. Results shows that there is a fruitiful field of study to be explored by both areas, Big Data and Health.
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Affiliation(s)
- Alana Corsi
- Federal University of Technology-Paraná (UTFPR) Câmpus Ponta Grossa, Av. Monteiro Lobato, s/n-Km 04, Ponta Grossa, PR 84016-210 Brazil
| | - Fabiane Florencio de Souza
- Federal University of Technology-Paraná (UTFPR) Câmpus Ponta Grossa, Av. Monteiro Lobato, s/n-Km 04, Ponta Grossa, PR 84016-210 Brazil
| | - Regina Negri Pagani
- Federal University of Technology-Paraná (UTFPR) Câmpus Ponta Grossa, Av. Monteiro Lobato, s/n-Km 04, Ponta Grossa, PR 84016-210 Brazil
| | - João Luiz Kovaleski
- Federal University of Technology-Paraná (UTFPR) Câmpus Ponta Grossa, Av. Monteiro Lobato, s/n-Km 04, Ponta Grossa, PR 84016-210 Brazil
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7
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A Review of Human Mobility Research Based on Big Data and Its Implication for Smart City Development. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2020. [DOI: 10.3390/ijgi10010013] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Along with the increase of big data and the advancement of technologies, comprehensive data-driven knowledge of urban systems is becoming more attainable, yet the connection between big-data research and its application e.g., in smart city development, is not clearly articulated. Focusing on Human Mobility, one of the most frequently investigated applications of big data analytics, a framework for linking international academic research and city-level management policy was established and applied to the case of Hong Kong. Literature regarding human mobility research using big data are reviewed. These studies contribute to (1) discovering the spatial-temporal phenomenon, (2) identifying the difference in human behaviour or spatial attributes, (3) explaining the dynamic of mobility, and (4) applying to city management. Then, the application of the research to smart city development are scrutinised based on email queries to various governmental departments in Hong Kong. The identified challenges include data isolation, data unavailability, gaming between costs and quality of data, limited knowledge derived from rich data, as well as estrangement between public and private sectors. With further improvement in the practical value of data analytics and the utilization of data sourced from multiple sectors, paths to achieve smarter cities from policymaking perspectives are highlighted.
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8
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Bomfim R, Pei S, Shaman J, Yamana T, Makse HA, Andrade JS, Lima Neto AS, Furtado V. Predicting dengue outbreaks at neighbourhood level using human mobility in urban areas. J R Soc Interface 2020; 17:20200691. [PMID: 33109025 DOI: 10.1098/rsif.2020.0691] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Dengue is a vector-borne disease transmitted by the Aedes genus mosquito. It causes financial burdens on public health systems and considerable morbidity and mortality. Tropical regions in the Americas and Asia are the areas most affected by the virus. Fortaleza is a city with approximately 2.6 million inhabitants in northeastern Brazil that, during the recent decades, has been suffering from endemic dengue transmission, interspersed with larger epidemics. The objective of this paper is to study the impact of human mobility in urban areas on the spread of the dengue virus, and to test whether human mobility data can be used to improve predictions of dengue virus transmission at the neighbourhood level. We present two distinct forecasting systems for dengue transmission in Fortaleza: the first using artificial neural network methods and the second developed using a mechanistic model of disease transmission. We then present enhanced versions of the two forecasting systems that incorporate bus transportation data cataloguing movement among 119 neighbourhoods in Fortaleza. Each forecasting system was used to perform retrospective forecasts for historical dengue outbreaks from 2007 to 2015. Results show that both artificial neural networks and mechanistic models can accurately forecast dengue cases, and that the inclusion of human mobility data substantially improves the performance of both forecasting systems. While the mechanistic models perform better in capturing seasons with large-scale outbreaks, the neural networks more accurately forecast outbreak peak timing, peak intensity and annual dengue time series. These results have two practical implications: they support the creation of public policies from the use of the models created here to combat the disease and help to understand the impact of urban mobility on the epidemic in large cities.
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Affiliation(s)
- Rafael Bomfim
- Programa de Pós Graduação em Informática Aplicada Universidade de Fortaleza, Fortaleza, Brazil
| | - Sen Pei
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032, USA
| | - Teresa Yamana
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032, USA
| | - Hernán A Makse
- Levich Institute and Physics Department, City College of New York, New York, NY 10031, USA
| | - José S Andrade
- Departamento de Física, Universidade Federal do Ceará, Campus do Pici, 60451-970 Fortaleza, Ceará, Brazil
| | - Antonio S Lima Neto
- Secretaria Municipal de Saúde de Fortaleza (SMS-Fortaleza), Fortaleza, Ceará, Brazil.,Centro de Ciências da Saúde, Universidade de Fortaleza (UNIFOR), Fortaleza, Ceará, Brazil
| | - Vasco Furtado
- Programa de Pós Graduação em Informática Aplicada Universidade de Fortaleza, Fortaleza, Brazil
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9
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Walters M, Perkins TA. Hidden heterogeneity and its influence on dengue vaccination impact. Infect Dis Model 2020; 5:783-797. [PMID: 33102984 PMCID: PMC7558830 DOI: 10.1016/j.idm.2020.09.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 09/24/2020] [Indexed: 12/29/2022] Open
Abstract
The CYD-TDV vaccine was recently developed to combat dengue, a mosquito-borne viral disease that afflicts millions of people each year throughout the tropical and subtropical world. Its rollout has been complicated by recent findings that vaccinees with no prior exposure to dengue virus (DENV) experience an elevated risk of severe disease in response to their first DENV infection subsequent to vaccination. As a result of these findings, guidelines for use of CYD-TDV now require serological screening prior to vaccination to establish that an individual does not fall into this high-risk category. These complications mean that the public health impact of CYD-TDV vaccination is expected to be higher in areas with higher transmission. One important practical difficulty with tailoring vaccination policy to local transmission contexts is that DENV transmission is spatially heterogeneous, even at the scale of neighborhoods or blocks within a city. This raises the question of whether models based on data that average over spatial heterogeneity in transmission could fail to capture important aspects of CYD-TDV impact in spatially heterogeneous populations. We explored this question with a deterministic model of DENV transmission and CYD-TDV vaccination in a population comprised of two communities with differing transmission intensities. Compared to the full model, a version of the model based on the average of the two communities failed to capture benefits of targeting the intervention to the high-transmission community, which resulted in greater impact in both communities than we observed under even coverage. In addition, the model based on the average of the two communities substantially overestimated impact among vaccinated individuals in the low-transmission community. In the event that the specificity of serological screening is not high, this result suggests that models that ignore spatial heterogeneity could overlook the potential for harm to this segment of the population.
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Affiliation(s)
- Magdalene Walters
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - T Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, 46556, USA
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10
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Bisanzio D, Kraemer MUG, Brewer T, Brownstein JS, Reithinger R. Geolocated Twitter social media data to describe the geographic spread of SARS-CoV-2. J Travel Med 2020; 27:5875518. [PMID: 32701135 PMCID: PMC7454796 DOI: 10.1093/jtm/taaa120] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 06/02/2020] [Accepted: 07/16/2020] [Indexed: 11/21/2022]
Abstract
Openly available, geotagged Twitter data from 2013 to 2015 was used to estimate the 2019–2020 human mobility patterns in and outside of China to predict the spatiotemporal spread of severe acute respiratory syndrome coronavirus 2. Countries with the highest number of visiting Twitter users outside of China were the USA, Japan, UK, Germany and Turkey. A high correlation was observed when comparing country-level Twitter user visits and reported cases.
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Affiliation(s)
- Donal Bisanzio
- RTI International, Washington, DC, USA.,Epidemiology and Public Health Division, School of Medicine, University of Nottingham, Nottingham, UK
| | - Moritz U G Kraemer
- Department of Paediatrics, Harvard Medical School, Boston, USA.,Computational Epidemiology Lab, Boston Children's Hospital, Boston, USA.,Department of Zoology, University of Oxford, Oxford, UK
| | - Thomas Brewer
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, USA
| | - John S Brownstein
- Department of Paediatrics, Harvard Medical School, Boston, USA.,Computational Epidemiology Lab, Boston Children's Hospital, Boston, USA
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11
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Sedda L, Taylor BM, Eiras AE, Marques JT, Dillon RJ. Using the intrinsic growth rate of the mosquito population improves spatio-temporal dengue risk estimation. Acta Trop 2020; 208:105519. [PMID: 32389450 PMCID: PMC7315132 DOI: 10.1016/j.actatropica.2020.105519] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 04/25/2020] [Accepted: 04/25/2020] [Indexed: 12/29/2022]
Abstract
Understanding geographic population dynamics of mosquitoes is an essential requirement for estimating the risk of mosquito-borne disease transmission and geographically targeted interventions. However, the use of population dynamics measures, such as the intrinsic growth rate, as predictors in spatio-temporal point processes has not been investigated before. In this work we compared the predictive accuracy of four spatio-temporal log-Gaussian Cox models: (i) With no predictors; (ii) mosquito abundance as predictor; (iii) intrinsic growth rate as predictor; (iv) intrinsic growth rate and mosquito abundance as predictors. This analysis is based on Aedes aegypti mosquito surveillance and human dengue data obtained from the urban area of Caratinga, Brazil. We used a statistical Moran Curve approach to estimate the intrinsic growth rate and a zero inflated Poisson kriging model for estimating mosquito abundance at locations of dengue cases. The incidence of dengue cases was positively associated with mosquito intrinsic growth rate and this model outperformed, in terms of predictive accuracy, the abundance and the null models. The latter includes only the spatio-temporal random effect but no predictors. In the light of these results we suggest that the intrinsic growth rate should be investigated further as a potential tool for predicting the risk of dengue transmission and targeting health interventions for vector-borne diseases.
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Affiliation(s)
- Luigi Sedda
- Lancaster Medical School, Furness Building, Lancaster University, Lancaster, LA1 4YG, UK.
| | - Benjamín M Taylor
- Centre for Health Informatics, Computing, and Statistics (CHICAS), Lancaster Medical School, Furness Building, Lancaster University, Lancaster, LA1 4YG, UK
| | - Alvaro E Eiras
- Department of Parasitology, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, CEP 30270-901, Brazil
| | - João Trindade Marques
- Department of Biochemistry and Immunology, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, CEP 30270-901, Brazil; Institut de biologie moléculaire et cellulaire, Université de Strasbourg, CNRS UPR9022, Inserm U1257, 67084 Strasbourrg, France
| | - Rod J Dillon
- Biomedical and Life Sciences, Furness Building, Lancaster University, Lancaster, LA1 4YG, UK
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12
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Edo-Osagie O, De La Iglesia B, Lake I, Edeghere O. A scoping review of the use of Twitter for public health research. Comput Biol Med 2020; 122:103770. [PMID: 32502758 PMCID: PMC7229729 DOI: 10.1016/j.compbiomed.2020.103770] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 04/01/2020] [Accepted: 04/17/2020] [Indexed: 11/25/2022]
Abstract
Public health practitioners and researchers have used traditional medical databases to study and understand public health for a long time. Recently, social media data, particularly Twitter, has seen some use for public health purposes. Every large technological development in history has had an impact on the behaviour of society. The advent of the internet and social media is no different. Social media creates public streams of communication, and scientists are starting to understand that such data can provide some level of access into the people's opinions and situations. As such, this paper aims to review and synthesize the literature on Twitter applications for public health, highlighting current research and products in practice. A scoping review methodology was employed and four leading health, computer science and cross-disciplinary databases were searched. A total of 755 articles were retreived, 92 of which met the criteria for review. From the reviewed literature, six domains for the application of Twitter to public health were identified: (i) Surveillance; (ii) Event Detection; (iii) Pharmacovigilance; (iv) Forecasting; (v) Disease Tracking; and (vi) Geographic Identification. From our review, we were able to obtain a clear picture of the use of Twitter for public health. We gained insights into interesting observations such as how the popularity of different domains changed with time, the diseases and conditions studied and the different approaches to understanding each disease, which algorithms and techniques were popular with each domain, and more.
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Affiliation(s)
- Oduwa Edo-Osagie
- School of Computing Science, University of East Anglia, Norwich, NR4 7TJ, UK.
| | | | - Iain Lake
- School of Environmental Science, University of East Anglia, Norwich, NR4 7TJ, UK
| | - Obaghe Edeghere
- National Infection Service, Public Health England, Birmingham, B3 2PW, UK
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13
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Poom A, Järv O, Zook M, Toivonen T. COVID-19 is spatial: Ensuring that mobile Big Data is used for social good. BIG DATA & SOCIETY 2020; 7:2053951720952088. [PMID: 34191995 PMCID: PMC7453154 DOI: 10.1177/2053951720952088] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
The mobility restrictions related to COVID-19 pandemic have resulted in the biggest disruption to individual mobilities in modern times. The crisis is clearly spatial in nature, and examining the geographical aspect is important in understanding the broad implications of the pandemic. The avalanche of mobile Big Data makes it possible to study the spatial effects of the crisis with spatiotemporal detail at the national and global scales. However, the current crisis also highlights serious limitations in the readiness to take the advantage of mobile Big Data for social good, both within and beyond the interests of health sector. We propose two strategical pathways for the future use of mobile Big Data for societal impact assessment, addressing access to both raw mobile Big Data as well as aggregated data products. Both pathways require careful considerations of privacy issues, harmonized and transparent methodologies, and attention to the representativeness, reliability and continuity of data. The goal is to be better prepared to use mobile Big Data in future crises.
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Affiliation(s)
- Age Poom
- Digital Geography Lab, Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland
- Helsinki Institute of Sustainability Science, Institute of Urban and Regional Studies, University of Helsinki, Helsinki, Finland
- Mobility Lab, Department of Geography, University of Tartu, Tartu, Estonia
- Age Poom, University of Helsinki, Gustaf Hällströmin katu 2, Helsinki 00014, Finland.
| | - Olle Järv
- Digital Geography Lab, Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland
- Helsinki Institute of Sustainability Science, Institute of Urban and Regional Studies, University of Helsinki, Helsinki, Finland
| | - Matthew Zook
- Department of Geography, University of Kentucky, Lexington, KY, USA
| | - Tuuli Toivonen
- Digital Geography Lab, Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland
- Helsinki Institute of Sustainability Science, Institute of Urban and Regional Studies, University of Helsinki, Helsinki, Finland
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14
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Bisanzio D, Kraemer MUG, Bogoch II, Brewer T, Brownstein JS, Reithinger R. Use of Twitter social media activity as a proxy for human mobility to predict the spatiotemporal spread of COVID-19 at global scale. GEOSPATIAL HEALTH 2020; 15. [PMID: 32575957 DOI: 10.4081/gh.2020.882] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 04/25/2020] [Indexed: 06/11/2023]
Abstract
As of February 27, 2020, 82,294 confirmed cases of coronavirus disease (COVID-19) have been reported since December 2019, including 2,804 deaths, with cases reported throughout China, as well as in 45 international locations outside of mainland China. We predict the spatiotemporal spread of reported COVID- 19 cases at the global level during the first few weeks of the current outbreak by analyzing openly available geolocated Twitter social media data. Human mobility patterns were estimated by analyzing geolocated 2013-2015 Twitter data from users who had: i) tweeted at least twice on consecutive days from Wuhan, China, between November 1, 2013, and January 28, 2014, and November 1, 2014, and January 28, 2015; and ii) left Wuhan following their second tweet during the time period under investigation. Publicly available COVID-19 case data were used to investigate the correlation among cases reported during the current outbreak, locations visited by the study cohort of Twitter users, and airports with scheduled flights from Wuhan. Infectious Disease Vulnerability Index (IDVI) data were obtained to identify the capacity of countries receiving travellers from Wuhan to respond to COVID-19. Our study cohort comprised 161 users. Of these users, 133 (82.6%) posted tweets from 157 Chinese cities (1,344 tweets) during the 30 days after leaving Wuhan following their second tweet, with a median of 2 (IQR= 1-3) locations visited and a mean distance of 601 km (IQR= 295.2-834.7 km) traveled. Of our user cohort, 60 (37.2%) traveled abroad to 119 locations in 28 countries. Of the 82 COVID-19 cases reported outside China as of January 30, 2020, 54 cases had known geolocation coordinates and 74.1% (40 cases) were reported less than 15 km (median = 7.4 km, IQR= 2.9-285.5 km) from a location visited by at least one of our study cohort's users. Countries visited by the cohort's users and which have cases reported by January 30, 2020, had a median IDVI equal to 0.74. We show that social media data can be used to predict the spatiotemporal spread of infectious diseases such as COVID-19. Based on our analyses, we anticipate cases to be reported in Saudi Arabia and Indonesia; additionally, countries with a moderate to low IDVI (i.e. ≤0.7) such as Indonesia, Pakistan, and Turkey should be on high alert and develop COVID- 19 response plans as soon as permitting.
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Affiliation(s)
- Donal Bisanzio
- RTI International, Washington DC, USA; Epidemiology and Public Health Division, School of Medicine, University of Nottingham, Nottingham.
| | - Moritz U G Kraemer
- Department of Pediatrics, Harvard Medical School, Boston, USA; Computational Epidemiology Lab, Boston Children's Hospital, Boston, USA; Department of Zoology, University of Oxford, Oxford.
| | - Isaac I Bogoch
- Department of Medicine, University of Toronto, Toronto, Canada; Divisions of General Internal Medicine and Infectious Diseases, University Health Network, Toronto.
| | - Thomas Brewer
- Computational Epidemiology Lab, Boston Children's Hospital, Boston.
| | - John S Brownstein
- Department of Pediatrics, Harvard Medical School, Boston; Computational Epidemiology Lab, Boston Children's Hospital, Boston.
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15
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Modeling human migration across spatial scales in Colombia. PLoS One 2020; 15:e0232702. [PMID: 32379787 PMCID: PMC7205305 DOI: 10.1371/journal.pone.0232702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 04/20/2020] [Indexed: 12/03/2022] Open
Abstract
Human mobility, both short and long term, are important considerations in the study of numerous systems. Economic and technological advances have led to a more interconnected global community, further increasing the need for considerations of human mobility. While data on human mobility are better recorded in many developed countries, availability of such data remains limited in many low- and middle-income countries around the world, particularly at the fine temporal and spatial scales required by many applications. In this study, we used 5-year census-based internal migration microdata for 32 departments in Colombia (i.e., Admin-1 level) to develop a novel spatial interaction modeling approach for estimating migration, at a finer spatial scale, among the 1,122 municipalities in the country (i.e., Admin-2 level). Our modeling approach addresses a significant lack of migration data at administrative unit levels finer than those at which migration data are typically recorded. Due to the widespread availability of census-based migration microdata at the Admin-1 level, our modeling approach opens up for the possibilities of modeling migration patterns at Admin-2 and Admin-3 levels across many other countries where such data are currently lacking.
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16
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Abdur Rehman N, Salje H, Kraemer MUG, Subramanian L, Saif U, Chunara R. Quantifying the localized relationship between vector containment activities and dengue incidence in a real-world setting: A spatial and time series modelling analysis based on geo-located data from Pakistan. PLoS Negl Trop Dis 2020; 14:e0008273. [PMID: 32392225 PMCID: PMC7241855 DOI: 10.1371/journal.pntd.0008273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 05/21/2020] [Accepted: 04/07/2020] [Indexed: 11/19/2022] Open
Abstract
Increasing urbanization is having a profound effect on infectious disease risk, posing significant challenges for governments to allocate limited resources for their optimal control at a sub-city scale. With recent advances in data collection practices, empirical evidence about the efficacy of highly localized containment and intervention activities, which can lead to optimal deployment of resources, is possible. However, there are several challenges in analyzing data from such real-world observational settings. Using data on 3.9 million instances of seven dengue vector containment activities collected between 2012 and 2017, here we develop and assess two frameworks for understanding how the generation of new dengue cases changes in space and time with respect to application of different types of containment activities. Accounting for the non-random deployment of each containment activity in relation to dengue cases and other types of containment activities, as well as deployment of activities in different epidemiological contexts, results from both frameworks reinforce existing knowledge about the efficacy of containment activities aimed at the adult phase of the mosquito lifecycle. Results show a 10% (95% CI: 1-19%) and 20% reduction (95% CI: 4-34%) reduction in probability of a case occurring in 50 meters and 30 days of cases which had Indoor Residual Spraying (IRS) and fogging performed in the immediate vicinity, respectively, compared to cases of similar epidemiological context and which had no containment in their vicinity. Simultaneously, limitations due to the real-world nature of activity deployment are used to guide recommendations for future deployment of resources during outbreaks as well as data collection practices. Conclusions from this study will enable more robust and comprehensive analyses of localized containment activities in resource-scarce urban settings and lead to improved allocation of resources of government in an outbreak setting.
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Affiliation(s)
- Nabeel Abdur Rehman
- Computer Science and Engineering, Tandon School of Engineering, New York University, Brooklyn, New York, United States of America
| | | | | | | | - Umar Saif
- UNESCO Chair for ICTD, Lahore, Pakistan
| | - Rumi Chunara
- Computer Science and Engineering, Tandon School of Engineering, New York University, Brooklyn, New York, United States of America
- Department of Biostatistics, School of Global Public Health, New York University, New York, New York, United States of America
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17
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Geostatistical modeling of dengue disease in Lahore, Pakistan. SN APPLIED SCIENCES 2019. [DOI: 10.1007/s42452-019-0428-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
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18
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Kraemer MUG, Golding N, Bisanzio D, Bhatt S, Pigott DM, Ray SE, Brady OJ, Brownstein JS, Faria NR, Cummings DAT, Pybus OG, Smith DL, Tatem AJ, Hay SI, Reiner RC. Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings. Sci Rep 2019; 9:5151. [PMID: 30914669 PMCID: PMC6435716 DOI: 10.1038/s41598-019-41192-3] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 03/03/2019] [Indexed: 12/03/2022] Open
Abstract
Human mobility is an important driver of geographic spread of infectious pathogens. Detailed information about human movements during outbreaks are, however, difficult to obtain and may not be available during future epidemics. The Ebola virus disease (EVD) outbreak in West Africa between 2014-16 demonstrated how quickly pathogens can spread to large urban centers following one cross-species transmission event. Here we describe a flexible transmission model to test the utility of generalised human movement models in estimating EVD cases and spatial spread over the course of the outbreak. A transmission model that includes a general model of human mobility significantly improves prediction of EVD's incidence compared to models without this component. Human movement plays an important role not only to ignite the epidemic in locations previously disease free, but over the course of the entire epidemic. We also demonstrate important differences between countries in population mixing and the improved prediction attributable to movement metrics. Given their relative rareness, locally derived mobility data are unlikely to exist in advance of future epidemics or pandemics. Our findings show that transmission patterns derived from general human movement models can improve forecasts of spatio-temporal transmission patterns in places where local mobility data is unavailable.
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Affiliation(s)
- M U G Kraemer
- Department of Zoology, University of Oxford, Oxford, UK.
- Harvard Medical School, Boston, MA, USA.
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA.
| | - N Golding
- Department of BioSciences, University of Melbourne, Parkville, VIC, Australia
| | - D Bisanzio
- RTI International, Washington, D.C., USA
- Epidemiology and Public Health Division, School of Medicine, University of Nottingham, Nottingham, UK
| | - S Bhatt
- Imperial College London, London, United Kingdom
| | - D M Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - S E Ray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - O J Brady
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - J S Brownstein
- Harvard Medical School, Boston, MA, USA
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA
| | - N R Faria
- Department of Zoology, University of Oxford, Oxford, UK
| | - D A T Cummings
- Department of Biology, University of Florida, Gainesville, FL, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - O G Pybus
- Department of Zoology, University of Oxford, Oxford, UK
| | - D L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Sanaria Institute for Global Health and Tropical Medicine, Rockville, USA
| | - A J Tatem
- WorldPop, Department of Geography and Environmental Sciences, University of Southampton, Southampton, UK
- Flowminder Foundation, Stockholm, Sweden
| | - S I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
| | - R C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
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19
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Kraemer MUG, Cummings DAT, Funk S, Reiner RC, Faria NR, Pybus OG, Cauchemez S. Reconstruction and prediction of viral disease epidemics. Epidemiol Infect 2018; 147:e34. [PMID: 30394230 PMCID: PMC6398585 DOI: 10.1017/s0950268818002881] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 08/24/2018] [Accepted: 09/21/2018] [Indexed: 01/29/2023] Open
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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Affiliation(s)
- M. U. G. Kraemer
- Harvard Medical School, Harvard University, Boston, MA, USA
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA
- Department of Zoology, University of Oxford, Oxford, UK
| | - D. A. T. Cummings
- Department of Biology, University of Florida, Gainesville, Florida, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
| | - S. Funk
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - R. C. Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA
| | - N. R. Faria
- Department of Zoology, University of Oxford, Oxford, UK
| | - O. G. Pybus
- Department of Zoology, University of Oxford, Oxford, UK
| | - S. Cauchemez
- Mathematical Modelling of Infectious Diseases and Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France
- CNRS UMR2000: Génomique évolutive, modélisation et santé, Paris, France
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20
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Moore SM, Ten Bosch QA, Siraj AS, Soda KJ, España G, Campo A, Gómez S, Salas D, Raybaud B, Wenger E, Welkhoff P, Perkins TA. Local and regional dynamics of chikungunya virus transmission in Colombia: the role of mismatched spatial heterogeneity. BMC Med 2018; 16:152. [PMID: 30157921 PMCID: PMC6116375 DOI: 10.1186/s12916-018-1127-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 07/12/2018] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Mathematical models of transmission dynamics are routinely fitted to epidemiological time series, which must inevitably be aggregated at some spatial scale. Weekly case reports of chikungunya have been made available nationally for numerous countries in the Western Hemisphere since late 2013, and numerous models have made use of this data set for forecasting and inferential purposes. Motivated by an abundance of literature suggesting that the transmission of this mosquito-borne pathogen is localized at scales much finer than nationally, we fitted models at three different spatial scales to weekly case reports from Colombia to explore limitations of analyses of nationally aggregated time series data. METHODS We adapted the recently developed Disease Transmission Kernel (DTK)-Dengue model for modeling chikungunya virus (CHIKV) transmission, given the numerous similarities of these viruses vectored by a common mosquito vector. We fitted versions of this model specified at different spatial scales to weekly case reports aggregated at different spatial scales: (1) single-patch national model fitted to national data; (2) single-patch departmental models fitted to departmental data; and (3) multi-patch departmental models fitted to departmental data, where the multiple patches refer to municipalities within a department. We compared the consistency of simulations from fitted models with empirical data. RESULTS We found that model consistency with epidemic dynamics improved with increasing spatial granularity of the model. Specifically, the sum of single-patch departmental model fits better captured national-level temporal patterns than did a single-patch national model. Likewise, multi-patch departmental model fits better captured department-level temporal patterns than did single-patch departmental model fits. Furthermore, inferences about municipal-level incidence based on multi-patch departmental models fitted to department-level data were positively correlated with municipal-level data that were withheld from model fitting. CONCLUSIONS Our model performed better when posed at finer spatial scales, due to better matching between human populations with locally relevant risk. Confronting spatially aggregated models with spatially aggregated data imposes a serious structural constraint on model behavior by averaging over epidemiologically meaningful spatial variation in drivers of transmission, impairing the ability of models to reproduce empirical patterns.
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Affiliation(s)
- Sean M Moore
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA.
| | - Quirine A Ten Bosch
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, 75015, Paris, France
- CNRS UMR2000: Génomique évolutive, modélisation et santé (GEMS), Institut Pasteur, Paris, France
- Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, 75015, Paris, France
| | - Amir S Siraj
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - K James Soda
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - Guido España
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - Alfonso Campo
- Subdirección de Análisis de Riesgo y Respuesta Inmediata en Salud Pública, Instituto Nacional de Salud de Colombia, Bogotá, Colombia
| | - Sara Gómez
- Grupo de Enfermedades Transmisibles, Instituto Nacional de Salud de Colombia, Bogotá, Colombia
| | - Daniela Salas
- Grupo de Enfermedades Transmisibles, Instituto Nacional de Salud de Colombia, Bogotá, Colombia
| | | | | | | | - T Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA.
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