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Baker RE, Yang W, Vecchi GA, Metcalf CJE, Grenfell BT. Susceptible supply limits the role of climate in the early SARS-CoV-2 pandemic. Science 2020; 369:315-319. [PMID: 32423996 PMCID: PMC7243362 DOI: 10.1126/science.abc2535] [Citation(s) in RCA: 189] [Impact Index Per Article: 47.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 05/14/2020] [Indexed: 01/14/2023]
Abstract
Preliminary evidence suggests that climate may modulate the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Yet it remains unclear whether seasonal and geographic variations in climate can substantially alter the pandemic trajectory, given that high susceptibility is a core driver. Here, we use a climate-dependent epidemic model to simulate the SARS-CoV-2 pandemic by probing different scenarios based on known coronavirus biology. We find that although variations in weather may be important for endemic infections, during the pandemic stage of an emerging pathogen, the climate drives only modest changes to pandemic size. A preliminary analysis of nonpharmaceutical control measures indicates that they may moderate the pandemic-climate interaction through susceptible depletion. Our findings suggest that without effective control measures, strong outbreaks are likely in more humid climates and summer weather will not substantially limit pandemic growth.
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Affiliation(s)
- Rachel E Baker
- Princeton Environmental Institute, Princeton University, Princeton, NJ, USA. .,Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Wenchang Yang
- Department of Geosciences, Princeton University, Princeton, NJ, USA
| | - Gabriel A Vecchi
- Princeton Environmental Institute, Princeton University, Princeton, NJ, USA.,Department of Geosciences, Princeton University, Princeton, NJ, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA.,Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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52
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Kissler SM, Viboud C, Grenfell BT, Gog JR. Symbolic transfer entropy reveals the age structure of pandemic influenza transmission from high-volume influenza-like illness data. J R Soc Interface 2020; 17:20190628. [PMID: 32183640 PMCID: PMC7115222 DOI: 10.1098/rsif.2019.0628] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Existing methods to infer the relative roles of age groups in epidemic transmission can normally only accommodate a few age classes, and/or require data that are highly specific for the disease being studied. Here, symbolic transfer entropy (STE), a measure developed to identify asymmetric transfer of information between stochastic processes, is presented as a way to reveal asymmetric transmission patterns between age groups in an epidemic. STE provides a ranking of which age groups may dominate transmission, rather than a reconstruction of the explicit between-age-group transmission matrix. Using simulations, we establish that STE can identify which age groups dominate transmission even when there are differences in reporting rates between age groups and even if the data are noisy. Then, the pairwise STE is calculated between time series of influenza-like illness for 12 age groups in 884 US cities during the autumn of 2009. Elevated STE from 5 to 19 year-olds indicates that school-aged children were likely the most important transmitters of infection during the autumn wave of the 2009 pandemic in the USA. The results may be partially confounded by higher rates of physician-seeking behaviour in children compared to adults, but it is unlikely that differences in reporting rates can explain the observed differences in STE.
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Affiliation(s)
- Stephen M Kissler
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, UK.,Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MA, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, University of Princeton, Princeton, NJ, USA
| | - Julia R Gog
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, UK
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53
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Mahsin MD, Deardon R, Brown P. Geographically dependent individual-level models for infectious diseases transmission. Biostatistics 2020; 23:1-17. [PMID: 32118253 DOI: 10.1093/biostatistics/kxaa009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 11/22/2019] [Accepted: 01/29/2020] [Indexed: 11/14/2022] Open
Abstract
Infectious disease models can be of great use for understanding the underlying mechanisms that influence the spread of diseases and predicting future disease progression. Modeling has been increasingly used to evaluate the potential impact of different control measures and to guide public health policy decisions. In recent years, there has been rapid progress in developing spatio-temporal modeling of infectious diseases and an example of such recent developments is the discrete-time individual-level models (ILMs). These models are well developed and provide a common framework for modeling many disease systems; however, they assume the probability of disease transmission between two individuals depends only on their spatial separation and not on their spatial locations. In cases where spatial location itself is important for understanding the spread of emerging infectious diseases and identifying their causes, it would be beneficial to incorporate the effect of spatial location in the model. In this study, we thus generalize the ILMs to a new class of geographically dependent ILMs, to allow for the evaluation of the effect of spatially varying risk factors (e.g., education, social deprivation, environmental), as well as unobserved spatial structure, upon the transmission of infectious disease. Specifically, we consider a conditional autoregressive (CAR) model to capture the effects of unobserved spatially structured latent covariates or measurement error. This results in flexible infectious disease models that can be used for formulating etiological hypotheses and identifying geographical regions of unusually high risk to formulate preventive action. The reliability of these models is investigated on a combination of simulated epidemic data and Alberta seasonal influenza outbreak data ($2009$). This new class of models is fitted to data within a Bayesian statistical framework using Markov chain Monte Carlo methods.
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Affiliation(s)
- M D Mahsin
- Department of Mathematics and Statistics and Faculty of Veterinary Medicine, University of Calgary, 2500 University Dr NW, Calgary AB T2N 1N4, Canada
| | - Rob Deardon
- Department of Mathematics and Statistics and Faculty of Veterinary Medicine, University of Calgary, 2500 University Dr NW, Calgary AB T2N 1N4, Canada
| | - Patrick Brown
- Department of Statistical Sciences, University of Toronto, Canada
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Biggerstaff M, Dahlgren FS, Fitzner J, George D, Hammond A, Hall I, Haw D, Imai N, Johansson MA, Kramer S, McCaw JM, Moss R, Pebody R, Read JM, Reed C, Reich NG, Riley S, Vandemaele K, Viboud C, Wu JT. Coordinating the real-time use of global influenza activity data for better public health planning. Influenza Other Respir Viruses 2020; 14:105-110. [PMID: 32096594 PMCID: PMC7040973 DOI: 10.1111/irv.12705] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 11/07/2019] [Accepted: 11/08/2019] [Indexed: 11/29/2022] Open
Abstract
Health planners from global to local levels must anticipate year-to-year and week-to-week variation in seasonal influenza activity when planning for and responding to epidemics to mitigate their impact. To help with this, countries routinely collect incidence of mild and severe respiratory illness and virologic data on circulating subtypes and use these data for situational awareness, burden of disease estimates and severity assessments. Advanced analytics and modelling are increasingly used to aid planning and response activities by describing key features of influenza activity for a given location and generating forecasts that can be translated to useful actions such as enhanced risk communications, and informing clinical supply chains. Here, we describe the formation of the Influenza Incidence Analytics Group (IIAG), a coordinated global effort to apply advanced analytics and modelling to public influenza data, both epidemiological and virologic, in real-time and thus provide additional insights to countries who provide routine surveillance data to WHO. Our objectives are to systematically increase the value of data to health planners by applying advanced analytics and forecasting and for results to be immediately reproducible and deployable using an open repository of data and code. We expect the resources we develop and the associated community to provide an attractive option for the open analysis of key epidemiological data during seasonal epidemics and the early stages of an influenza pandemic.
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Affiliation(s)
| | | | - Julia Fitzner
- Global Influenza ProgrammeWorld Health OrganizationGenevaSwitzerland
| | | | - Aspen Hammond
- Global Influenza ProgrammeWorld Health OrganizationGenevaSwitzerland
| | - Ian Hall
- Department of Mathematics and School of Health SciencesUniversity of ManchesterManchesterUK
| | - David Haw
- MRC Centre for Global Infectious Disease AnalysisSchool of Public HealthImperial College LondonLondonUK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease AnalysisSchool of Public HealthImperial College LondonLondonUK
| | - Michael A. Johansson
- Division of Vector‐Borne DiseasesCenters for Disease Control and PreventionSan JuanPRUSA
| | - Sarah Kramer
- Department of Environmental Health SciencesMailman School of Public HealthColumbia UniversityNew YorkNYUSA
| | - James M. McCaw
- Modelling and Simulation UnitCentre for Epidemiology and BiostatisticsMelbourne School of Population and Global HealthThe University of MelbourneMelbourneVic.Australia
- School of Mathematics and StatisticsThe University of MelbourneMelbourneAustralia
| | - Robert Moss
- Modelling and Simulation UnitCentre for Epidemiology and BiostatisticsMelbourne School of Population and Global HealthThe University of MelbourneMelbourneVic.Australia
| | - Richard Pebody
- Immunisation and Countermeasures DivisionNational Infection ServicePublic Health EnglandLondonUK
| | - Jonathan M. Read
- Centre for Health Informatics, Computing, and Statistics, Lancaster Medical SchoolFaculty of Health and MedicineLancaster UniversityLancashireUK
| | - Carrie Reed
- Influenza DivisionCenters for Disease Control and PreventionAtlantaGAUSA
| | - Nicholas G. Reich
- Department of Biostatistics and EpidemiologyUniversity of MassachusettsAmherstMAUSA
| | - Steven Riley
- MRC Centre for Global Infectious Disease AnalysisSchool of Public HealthImperial College LondonLondonUK
| | | | - Cecile Viboud
- Division of International Epidemiology and Population StudiesFogarty International CenterNational Institutes of HealthBethesdaMAUSA
| | - Joseph T. Wu
- WHO Collaborating Center for Infectious Disease Epidemiology and ControlSchool of Public HealthThe University of Hong KongHong Kong SARChina
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Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. LANCET DIGITAL HEALTH 2020; 2:e85-e93. [PMID: 33334565 DOI: 10.1016/s2589-7500(19)30222-5] [Citation(s) in RCA: 138] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 12/02/2019] [Accepted: 12/06/2019] [Indexed: 01/01/2023]
Abstract
BACKGROUND Acute infections can cause an individual to have an elevated resting heart rate (RHR) and change their routine daily activities due to the physiological response to the inflammatory insult. Consequently, we aimed to evaluate if population trends of seasonal respiratory infections, such as influenza, could be identified through wearable sensors that collect RHR and sleep data. METHODS We obtained de-identified sensor data from 200 000 individuals who used a Fitbit wearable device from March 1, 2016, to March 1, 2018, in the USA. We included users who wore a Fitbit for at least 60 days and used the same wearable throughout the entire period, and focused on the top five states with the most Fitbit users in the dataset: California, Texas, New York, Illinois, and Pennsylvania. Inclusion criteria included having a self-reported birth year between 1930 and 2004, height greater than 1 m, and weight greater than 20 kg. We excluded daily measurements with missing RHR, missing wear time, and wear time less than 1000 min per day. We compared sensor data with weekly estimates of influenza-like illness (ILI) rates at the state level, as reported by the US Centers for Disease Control and Prevention (CDC), by identifying weeks in which Fitbit users displayed elevated RHRs and increased sleep levels. For each state, we modelled ILI case counts with a negative binomial model that included 3-week lagged CDC ILI rate data (null model) and the proportion of weekly Fitbit users with elevated RHR and increased sleep duration above a specified threshold (full model). We also evaluated weekly change in ILI rate by linear regression using change in proportion of elevated Fitbit data. Pearson correlation was used to compare predicted versus CDC reported ILI rates. FINDINGS We identified 47 249 users in the top five states who wore a Fitbit consistently during the study period, including more than 13·3 million total RHR and sleep measures. We found the Fitbit data significantly improved ILI predictions in all five states, with an average increase in Pearson correlation of 0·12 (SD 0·07) over baseline models, corresponding to an improvement of 6·3-32·9%. Correlations of the final models with the CDC ILI rates ranged from 0·84 to 0·97. Week-to-week changes in the proportion of Fitbit users with abnormal data were associated with week-to-week changes in ILI rates in most cases. INTERPRETATION Activity and physiological trackers are increasingly used in the USA and globally to monitor individual health. By accessing these data, it could be possible to improve real-time and geographically refined influenza surveillance. This information could be vital to enact timely outbreak response measures to prevent further transmission of influenza cases during outbreaks. FUNDING Partly supported by the US National Institutes of Health National Center for Advancing Translational Sciences.
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Abd El Ghany M, Fouz N, Hill-Cawthorne GA. Human Movement and Transmission of Antimicrobial-Resistant Bacteria. THE HANDBOOK OF ENVIRONMENTAL CHEMISTRY 2020:311-344. [DOI: 10.1007/698_2020_560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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57
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Jung J, Im JH, Ko YJ, Huh K, Yoon CG, Rhee C, Kim YE, Go DS, Kim A, Jung Y, Radnaabaatar M, Yoon SJ. Complementing conventional infectious disease surveillance with national health insurance claims data in the Republic of Korea. Sci Rep 2019; 9:8750. [PMID: 31217476 PMCID: PMC6584579 DOI: 10.1038/s41598-019-45409-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 06/06/2019] [Indexed: 12/19/2022] Open
Abstract
Surveillance remains an important tool for timely outbreak detection and response. Many countries, including Korea, have established national infectious disease surveillance systems with clinical notification. We aimed to evaluate the National Health Insurance Claims-based Surveillance (NHICS) compared to conventional passive report-based National Infectious Diseases Surveillance (NIDS). Reported to claimed cases ratios (R/C ratio) were evaluated from monthly notifiable disease cases captured by NIDS and NHICS. The relationships between 26 infectious diseases and each surveillance system were analysed using Pearson's correlation analysis and linear regression. There was an overall increase in R/C ratio from 2010-2017 (0.37 to 0.78). In 22 infectious diseases, there was a correlation between NIDS and NHICS. Moreover, claim-based surveillance showed less fluctuating disease incidence rates than report-based surveillance for specific infectious diseases, such as varicella, mumps, and scarlet fever. However, for infectious diseases with episodic outbreaks or low incidence, it was difficult to assess NHICS usefulness. Claim-based surveillance is less affected by limitations of conventional report-based surveillance systems, such as reporting rate. Given delays in claim systems, a claim-based surveillance is expected to be complementary to conventional systems for the detection of various infectious diseases with the advancement of bio-information technology.
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Affiliation(s)
- Jaehun Jung
- Department of Preventive Medicine, Gachon University College of Medicine, Incheon, Korea
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jae Hyoung Im
- Department of Infectious Diseases, Inha University School of Medicine, Incheon, Republic of Korea
| | - Young-Jin Ko
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Kyungmin Huh
- Department of Infectious Diseases, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Chang-Gyo Yoon
- Preventive Medicine Program, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Chulwoo Rhee
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Young-Eun Kim
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Dun-Sol Go
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Arim Kim
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Yunsun Jung
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Munkhzul Radnaabaatar
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Seok-Jun Yoon
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, Republic of Korea.
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Germann TC, Gao H, Gambhir M, Plummer A, Biggerstaff M, Reed C, Uzicanin A. School dismissal as a pandemic influenza response: When, where and for how long? Epidemics 2019; 28:100348. [PMID: 31235334 PMCID: PMC6956848 DOI: 10.1016/j.epidem.2019.100348] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 05/06/2019] [Accepted: 06/03/2019] [Indexed: 01/02/2023] Open
Abstract
We used individual-based computer simulation models at community,
regional and national levels to evaluate the likely impact of coordinated
pre-emptive school dismissal policies during an influenza pandemic. Such
policies involve three key decisions: when, over what geographical scale, and
how long to keep schools closed. Our evaluation includes uncertainty and
sensitivity analyses, as well as model output uncertainties arising from
variability in serial intervals and presumed modifications of social contacts
during school dismissal periods. During the period before vaccines become widely
available, school dismissals are particularly effective in delaying the epidemic
peak, typically by 4–6 days for each additional week of dismissal.
Assuming the surveillance is able to correctly and promptly diagnose at least
5–10% of symptomatic individuals within the jurisdiction, dismissals at
the city or county level yield the greatest reduction in disease incidence for a
given dismissal duration for all but the most severe pandemic scenarios
considered here. Broader (multi-county) dismissals should be considered for the
most severe and fast-spreading (1918-like) pandemics, in which multi-month
closures may be necessary to delay the epidemic peak sufficiently to allow for
vaccines to be implemented.
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Affiliation(s)
- Timothy C Germann
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545 USA
| | - Hongjiang Gao
- Community Interventions for Infection Control Unit, Centers for Disease Control and Prevention, Atlanta, GA 30329 USA.
| | - Manoj Gambhir
- National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333 USA; School of Public Health and Preventive Medicine, Monash University, Victoria 3800 Australia
| | - Andrew Plummer
- Community Interventions for Infection Control Unit, Centers for Disease Control and Prevention, Atlanta, GA 30329 USA
| | - Matthew Biggerstaff
- National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333 USA
| | - Carrie Reed
- National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333 USA
| | - Amra Uzicanin
- Community Interventions for Infection Control Unit, Centers for Disease Control and Prevention, Atlanta, GA 30329 USA
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Moss R, Naghizade E, Tomko M, Geard N. What can urban mobility data reveal about the spatial distribution of infection in a single city? BMC Public Health 2019; 19:656. [PMID: 31142311 PMCID: PMC6542035 DOI: 10.1186/s12889-019-6968-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 05/14/2019] [Indexed: 01/06/2023] Open
Abstract
Background Infectious diseases spread through inherently spatial processes. Road and air traffic data have been used to model these processes at national and global scales. At metropolitan scales, however, mobility patterns are fundamentally different and less directly observable. Estimating the spatial distribution of infection has public health utility, but few studies have investigated this at an urban scale. In this study we address the question of whether the use of urban-scale mobility data can improve the prediction of spatial patterns of influenza infection. We compare the use of different sources of urban-scale mobility data, and investigate the impact of other factors relevant to modelling mobility, including mixing within and between regions, and the influence of hub and spoke commuting patterns. Methods We used journey-to-work (JTW) data from the Australian 2011 Census, and GPS journey data from the Sygic GPS Navigation & Maps mobile app, to characterise population mixing patterns in a spatially-explicit SEIR (susceptible, exposed, infectious, recovered) meta-population model. Results Using the JTW data to train the model leads to an increase in the proportion of infections that arise in central Melbourne, which is indicative of the city’s spoke-and-hub road and public transport networks, and of the commuting patterns reflected in these data. Using the GPS data increased the infections in central Melbourne to a lesser extent than the JTW data, and produced a greater heterogeneity in the middle and outer regions. Despite the limitations of both mobility data sets, the model reproduced some of the characteristics observed in the spatial distribution of reported influenza cases. Conclusions Urban mobility data sets can be used to support models that capture spatial heterogeneity in the transmission of infectious diseases at a metropolitan scale. These data should be adjusted to account for relevant urban features, such as highly-connected hubs where the resident population is likely to experience a much lower force of infection that the transient population. In contrast to national and international scales, the relationship between mobility and infection at an urban level is much less apparent, and requires a richer characterisation of population mobility and contact. Electronic supplementary material The online version of this article (10.1186/s12889-019-6968-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Robert Moss
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
| | - Elham Naghizade
- Department of Infrastructure Engineering, The University of Melbourne, Melbourne, Australia
| | - Martin Tomko
- Department of Infrastructure Engineering, The University of Melbourne, Melbourne, Australia
| | - Nicholas Geard
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.,School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia.,The Doherty Institute for Infection and Immunity, Melbourne, Australia
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Polyakov P, Souty C, Böelle PY, Breban R. Classification of Spatiotemporal Data for Epidemic Alert Systems: Monitoring Influenza-Like Illness in France. Am J Epidemiol 2019; 188:724-733. [PMID: 30576414 DOI: 10.1093/aje/kwy254] [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: 09/26/2017] [Revised: 11/10/2018] [Accepted: 11/12/2018] [Indexed: 11/13/2022] Open
Abstract
Surveillance data used by epidemic alert systems are typically fully aggregated in space at the national level. However, epidemics may be spatially heterogeneous, undergoing distinct dynamics in distinct regions of the surveillance area. We unveiled this in retrospective analyses by classifying incidence time series. We used Pearson correlation to quantify the similarity between local time series and then classified them using modularity maximization. The surveillance area was thus divided into regions with different incidence patterns. We analyzed 31 years (1985-2016) of data on influenza-like illness from the French Sentinelles system and found spatial heterogeneity in 19 of 31 influenza seasons. However, distinct epidemic regions could be identified only 4-5 weeks after a nationwide alert. The impact of spatial heterogeneity on influenza epidemiology was complex. First, when a nationwide alert was triggered, 32%-41% of the administrative regions of France were experiencing an epidemic, while the others were not. Second, the nationwide alert was timely for the whole surveillance area, but subsequently regions experienced distinct epidemic dynamics. Third, the epidemic dynamics were homogeneous in space. Spatial heterogeneity analyses can provide information on the timing of the peak and end of the epidemic, in various regions, for use in tailoring disease monitoring and control.
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Affiliation(s)
- Pavel Polyakov
- Unité d’Epidémiologie des Maladies Emergentes, Institut Pasteur, Paris, France
| | - Cécile Souty
- Institut Pierre Louis d’Epidémiologie et de Santé Publique, Sorbonne Université, Paris, France
- Unité 1136, Institut National de la Santé et de la Recherche Médicale, Paris, France
| | - Pierre-Yves Böelle
- Institut Pierre Louis d’Epidémiologie et de Santé Publique, Sorbonne Université, Paris, France
- Unité 1136, Institut National de la Santé et de la Recherche Médicale, Paris, France
- Hôpital Saint-Antoine, Assistance Publique–Hôpitaux de Paris, Paris, France
| | - Romulus Breban
- Unité d’Epidémiologie des Maladies Emergentes, Institut Pasteur, Paris, France
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Social network analysis for better understanding of influenza. J Biomed Inform 2019; 93:103161. [PMID: 30940598 DOI: 10.1016/j.jbi.2019.103161] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 03/17/2019] [Accepted: 03/29/2019] [Indexed: 11/22/2022]
Abstract
INTRODUCTION The objective of this study is to improve the understanding of spatial spreading of complicated cases of influenza that required hospitalizations, by creating heatmaps and social networks. They will allow to identify critical hubs and routes of spreading of Influenza, in specific geographic locations, in order to contain infections and prevent complications, that require hospitalizations. MATERIAL AND METHODS Data were downloaded from the Healthcare Cost and Utilization Project (HCUP) - SID, New York State database. Patients hospitalized with flu complications, between 2003 and 2012 were included in the research (30,380 cases). A novel approach was designed, by constructing heatmaps for specific geographic regions in New York state and power law networks, in order to analyze distribution of hospitalized flu cases. RESULTS Heatmaps revealed that distributions of patients follow urban areas and big roads, indicating that flu spreads along routes, that people use to travel. A scale-free network, created from correlations among zip codes, discovered that, the highest populated zip codes didn't have the largest number of patients with flu complications. Among the top five most affected zip codes, four were in Bronx. Demographics of top affected zip codes were presented in results. Normalized numbers of cases per population revealed that, none of zip codes from Bronx were in the top 20. All zip codes with the highest node degrees were in New York City area. DISCUSSION Heatmaps identified geographic distribution of hospitalized flu patients and network analysis identified hubs of the infection. Our results will enable better estimation of resources for prevention and treatment of hospitalized patients with complications of Influenza. CONCLUSION Analyses of geographic distribution of hospitalized patients with Influenza and demographic characteristics of populations, help us to make better planning and management of resources for Influenza patients, that require hospitalization. Obtained results could potentially help to save many lives and improve the health of the population.
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Tamerius J, Uejio C, Koss J. Seasonal characteristics of influenza vary regionally across US. PLoS One 2019; 14:e0212511. [PMID: 30840644 PMCID: PMC6402651 DOI: 10.1371/journal.pone.0212511] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 02/04/2019] [Indexed: 12/14/2022] Open
Abstract
Given substantial regional differences in absolute humidity across the US and our understanding of the relationship between absolute humidity and influenza, we may expect important differences in regional seasonal influenza activity. Here, we assessed cross-seasonal influenza activity by comparing counts of positive influenza A and B rapid test results during the influenza season versus summer baseline periods for the 2016/2017 and 2017/2018 influenza years. Our analysis indicates significant regional patterns in cross-seasonal influenza activity, with relatively fewer influenza cases during the influenza season compared to summertime baseline periods in humid areas of the US, particularly in Florida and Hawaii. The cross-seasonal ratios vary from year-to-year and influenza type, but the geographic patterning of the ratios is relatively consistent. Mixed-effects regression models indicated absolute humidity during the influenza season was the strongest predictor of cross-seasonal influenza activity, suggesting a relationship between absolute humidity and cross-seasonal influenza activity. There was also evidence that absolute humidity during the summer plays a role, as well. This analysis suggests that spatial variation in seasonal absolute humidity levels may generate important regional differences in seasonal influenza activity and dynamics in the US.
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Affiliation(s)
- James Tamerius
- University of Iowa, Iowa City, Iowa, United States of America
- * E-mail:
| | - Christopher Uejio
- Florida State University, Tallahassee, Florida, United States of America
| | - Jeffrey Koss
- University of Iowa, Iowa City, Iowa, United States of America
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Backer J, Wallinga J, Meijer A, Donker G, van der Hoek W, van Boven M. The impact of influenza vaccination on infection, hospitalisation and mortality in the Netherlands between 2003 and 2015. Epidemics 2019; 26:77-85. [DOI: 10.1016/j.epidem.2018.10.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 08/23/2018] [Accepted: 10/03/2018] [Indexed: 12/22/2022] Open
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Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building. Epidemics 2019; 26:116-127. [PMID: 30446431 PMCID: PMC7105018 DOI: 10.1016/j.epidem.2018.10.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 08/06/2018] [Accepted: 10/17/2018] [Indexed: 12/24/2022] Open
Abstract
Due to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequencing technologies and the 'big data' revolution present new tools for studying the epidemiology of outbreaks in real time. For the past two decades, the Division of International Epidemiology and Population Studies (DIEPS) of the NIH Fogarty International Center has spearheaded two synergistic programs to better understand and devise control strategies for global infectious disease threats. The Multinational Influenza Seasonal Mortality Study (MISMS) has strengthened global capacity to study the epidemiology and evolutionary dynamics of influenza viruses in 80 countries by organizing international research activities and training workshops. The Research and Policy in Infectious Disease Dynamics (RAPIDD) program and its precursor activities has established a network of global experts in infectious disease modeling operating at the research-policy interface, with collaborators in 78 countries. These activities have provided evidence-based recommendations for disease control, including during large-scale outbreaks of pandemic influenza, Ebola and Zika virus. Together, these programs have coordinated international collaborative networks to advance the study of emerging disease threats and the field of computational epidemic modeling. A global community of researchers and policy-makers have used the tools and trainings developed by these programs to interpret infectious disease patterns in their countries, understand modeling concepts, and inform control policies. Here we reflect on the scientific achievements and lessons learnt from these programs (h-index = 106 for RAPIDD and 79 for MISMS), including the identification of outstanding researchers and fellows; funding flexibility for timely research workshops and working groups (particularly relative to more traditional investigator-based grant programs); emphasis on group activities such as large-scale modeling reviews, model comparisons, forecasting challenges and special journal issues; strong quality control with a light touch on outputs; and prominence of training, data-sharing, and joint publications.
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65
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Cai J, Zhang B, Xu B, Chan KKY, Chowell G, Tian H, Xu B. A maximum curvature method for estimating epidemic onset of seasonal influenza in Japan. BMC Infect Dis 2019; 19:181. [PMID: 30786869 PMCID: PMC6383251 DOI: 10.1186/s12879-019-3777-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 02/04/2019] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Detecting the onset of influenza epidemic is important for epidemiological surveillance and for investigating the factors driving spatiotemporal transmission patterns. Most approaches define the epidemic onset based on thresholds, which use subjective criteria and are specific to individual surveillance systems. METHODS We applied the empirical threshold method (ETM), together with two non-thresholding methods, including the maximum curvature method (MCM) that we proposed and the segmented regression method (SRM), to determine onsets of influenza epidemics in each prefecture of Japan, using sentinel surveillance data of influenza-like illness (ILI) from 2012/2013 through 2017/2018. Performance of the MCM and SRM was evaluated, in terms of epidemic onset, end, and duration, with those derived from the ETM using the nationwide epidemic onset indicator of 1.0 ILI case per sentinel per week. RESULTS The MCM and SRM yielded complete estimates for each of Japan's 47 prefectures. In contrast, ETM estimates for Kagoshima during 2012/2013 and for Okinawa during all six influenza seasons, except 2013/2014, were invalid. The MCM showed better agreement in all estimates with the ETM than the SRM (R2 = 0.82, p < 0.001 vs. R2 = 0.34, p < 0.001 for epidemic onset; R2 = 0.18, p < 0.001 vs. R2 = 0.05, p < 0.001 for epidemic end; R2 = 0.28, p < 0.001 vs. R2 < 0.01, p = 0.35 for epidemic duration). Prefecture-specific thresholds for epidemic onset and end were established using the MCM. CONCLUSIONS The Japanese national epidemic onset threshold is not applicable to all prefectures, particularly Okinawa. The MCM could be used to establish prefecture-specific epidemic thresholds that faithfully characterize influenza activity, serving as useful complements to the influenza surveillance system in Japan.
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Affiliation(s)
- Jun Cai
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084 China
- Joint Center for Global Change Studies, Beijing, 100875 China
| | - Bing Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, 518107 China
| | - Bo Xu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084 China
- Joint Center for Global Change Studies, Beijing, 100875 China
| | - Karen Kie Yan Chan
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084 China
- Joint Center for Global Change Studies, Beijing, 100875 China
| | - Gerardo Chowell
- School of Public Health, Georgia State University, Atlanta, GA 30302 USA
| | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875 China
| | - Bing Xu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084 China
- Joint Center for Global Change Studies, Beijing, 100875 China
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66
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Data-driven efficient network and surveillance-based immunization. Knowl Inf Syst 2019. [DOI: 10.1007/s10115-018-01326-x] [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]
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67
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Cai J, Xu B, Chan KKY, Zhang X, Zhang B, Chen Z, Xu B. Roles of Different Transport Modes in the Spatial Spread of the 2009 Influenza A(H1N1) Pandemic in Mainland China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E222. [PMID: 30646629 PMCID: PMC6352022 DOI: 10.3390/ijerph16020222] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 01/04/2019] [Accepted: 01/09/2019] [Indexed: 11/16/2022]
Abstract
There is increasing concern about another influenza pandemic in China. However, the understanding of the roles of transport modes in the 2009 influenza A(H1N1) pandemic spread across mainland China is limited. Herein, we collected 127,797 laboratory-confirmed cases of influenza A(H1N1)pdm09 in mainland China from May 2009 to April 2010. Arrival days and peak days were calculated for all 340 prefectures to characterize the dissemination patterns of the pandemic. We first evaluated the effects of airports and railway stations on arrival days and peak days, and then we applied quantile regressions to quantify the relationships between arrival days and air, rail, and road travel. Our results showed that early arrival of the virus was not associated with an early incidence peak. Airports and railway stations in prefectures significantly advanced arrival days but had no significant impact on peak days. The pandemic spread across mainland China from the southeast to the northwest in two phases that were split at approximately 1 August 2009. Both air and road travel played a significant role in accelerating the spread during phases I and II, but rail travel was only significant during phase II. In conclusion, in addition to air and road travel, rail travel also played a significant role in accelerating influenza A(H1N1)pdm09 spread between prefectures. Establishing a multiscale mobility network that considers the competitive advantage of rail travel for mid to long distances is essential for understanding the influenza pandemic transmission in China.
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Affiliation(s)
- Jun Cai
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
- Joint Center for Global Change Studies, Beijing 100875, China.
| | - Bo Xu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
- Joint Center for Global Change Studies, Beijing 100875, China.
| | - Karen Kie Yan Chan
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
- Joint Center for Global Change Studies, Beijing 100875, China.
| | - Xueying Zhang
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Bing Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China.
| | - Ziyue Chen
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China.
| | - Bing Xu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
- Joint Center for Global Change Studies, Beijing 100875, China.
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68
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Lu FS, Hattab MW, Clemente CL, Biggerstaff M, Santillana M. Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches. Nat Commun 2019; 10:147. [PMID: 30635558 PMCID: PMC6329822 DOI: 10.1038/s41467-018-08082-0] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 12/12/2018] [Indexed: 12/01/2022] Open
Abstract
In the presence of health threats, precision public health approaches aim to provide targeted, timely, and population-specific interventions. Accurate surveillance methodologies that can estimate infectious disease activity ahead of official healthcare-based reports, at relevant spatial resolutions, are important for achieving this goal. Here we introduce a methodological framework which dynamically combines two distinct influenza tracking techniques, using an ensemble machine learning approach, to achieve improved state-level influenza activity estimates in the United States. The two predictive techniques behind the ensemble utilize (1) a self-correcting statistical method combining influenza-related Google search frequencies, information from electronic health records, and historical flu trends within each state, and (2) a network-based approach leveraging spatio-temporal synchronicities observed in historical influenza activity across states. The ensemble considerably outperforms each component method in addition to previously proposed state-specific methods for influenza tracking, with higher correlations and lower prediction errors.
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Affiliation(s)
- Fred S Lu
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, 02115, USA.
| | - Mohammad W Hattab
- Wyss Institute for Biologically Inspired Engineering, Harvard Medical School, Boston, MA, 02115, USA
| | | | - Matthew Biggerstaff
- Influenza Division, National Center for Immunization and Respiratory Disease, Centers for Disease Control and Prevention, Atlanta, GA, 30333, USA
| | - Mauricio Santillana
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, 02115, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA.
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69
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Dalziel BD, Kissler S, Gog JR, Viboud C, Bjørnstad ON, Metcalf CJE, Grenfell BT. Urbanization and humidity shape the intensity of influenza epidemics in U.S. cities. Science 2019; 362:75-79. [PMID: 30287659 PMCID: PMC6510303 DOI: 10.1126/science.aat6030] [Citation(s) in RCA: 175] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 08/10/2018] [Indexed: 01/14/2023]
Abstract
Influenza epidemics vary in intensity from year to year, driven by climatic conditions and by viral antigenic evolution. However, important spatial variation remains unexplained. Here we show predictable differences in influenza incidence among cities, driven by population size and structure. Weekly incidence data from 603 cities in the United States reveal that epidemics in smaller cities are focused on shorter periods of the influenza season, whereas in larger cities, incidence is more diffuse. Base transmission potential estimated from city-level incidence data is positively correlated with population size and with spatiotemporal organization in population density, indicating a milder response to climate forcing in metropolises. This suggests that urban centers incubate critical chains of transmission outside of peak climatic conditions, altering the spatiotemporal geometry of herd immunity.
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Affiliation(s)
- Benjamin D Dalziel
- Department of Integrative Biology, Oregon State University, Corvallis, OR, USA. .,Department of Mathematics, Oregon State University, Corvallis, OR, USA
| | - Stephen Kissler
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Julia R Gog
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Cecile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Ottar N Bjørnstad
- Department of Entomology, Pennsylvania State University, State College, PA, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Bryan T Grenfell
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.,Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA
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70
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Reyes O, Lee EC, Sah P, Viboud C, Chandra S, Bansal S. Spatiotemporal Patterns and Diffusion of the 1918 Influenza Pandemic in British India. Am J Epidemiol 2018; 187:2550-2560. [PMID: 30252017 PMCID: PMC6269240 DOI: 10.1093/aje/kwy209] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 09/05/2018] [Accepted: 09/07/2018] [Indexed: 12/21/2022] Open
Abstract
The factors that drive spatial heterogeneity and diffusion of pandemic influenza remain debated. We characterized the spatiotemporal mortality patterns of the 1918 influenza pandemic in British India and studied the role of demographic factors, environmental variables, and mobility processes on the observed patterns of spread. Fever-related and all-cause excess mortality data across 206 districts in India from January 1916 to December 1920 were analyzed while controlling for variation in seasonality particular to India. Aspects of the 1918 autumn wave in India matched signature features of influenza pandemics, with high disease burden among young adults, (moderate) spatial heterogeneity in burden, and highly synchronized outbreaks across the country deviating from annual seasonality. Importantly, we found population density and rainfall explained the spatial variation in excess mortality, and long-distance travel via railroad was predictive of the observed spatial diffusion of disease. A spatiotemporal analysis of mortality patterns during the 1918 influenza pandemic in India was integrated in this study with data on underlying factors and processes to reveal transmission mechanisms in a large, intensely connected setting with significant climatic variability. The characterization of such heterogeneity during historical pandemics is crucial to prepare for future pandemics.
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Affiliation(s)
- Olivia Reyes
- Department of Biology, Georgetown University, Washington, DC
| | - Elizabeth C Lee
- Department of Biology, Georgetown University, Washington, DC
| | - Pratha Sah
- Department of Biology, Georgetown University, Washington, DC
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland
| | - Siddharth Chandra
- Asian Studies Center, James Madison College, and the Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan
| | - Shweta Bansal
- Department of Biology, Georgetown University, Washington, DC
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland
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71
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Kissler SM, Gog JR, Viboud C, Charu V, Bjørnstad ON, Simonsen L, Grenfell BT. Geographic transmission hubs of the 2009 influenza pandemic in the United States. Epidemics 2018; 26:86-94. [PMID: 30327253 DOI: 10.1016/j.epidem.2018.10.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 10/05/2018] [Accepted: 10/08/2018] [Indexed: 10/28/2022] Open
Abstract
A key issue in infectious disease epidemiology is to identify and predict geographic sites of epidemic establishment that contribute to onward spread, especially in the context of invasion waves of emerging pathogens. Conventional wisdom suggests that these sites are likely to be in densely-populated, well-connected areas. For pandemic influenza, however, epidemiological data have not been available at a fine enough geographic resolution to test this assumption. Here, we make use of fine-scale influenza-like illness incidence data derived from electronic medical claims records gathered from 834 3-digit ZIP (postal) codes across the US to identify the key geographic establishment sites, or "hubs", of the autumn wave of the 2009 A/H1N1pdm influenza pandemic in the United States. A mechanistic spatial transmission model is fit to epidemic onset times inferred from the data. Hubs are identified by tracing the most probable transmission routes back to a likely first establishment site. Four hubs are identified: two in the southeastern US, one in the central valley of California, and one in the midwestern US. According to the model, 75% of the 834 observed ZIP-level outbreaks in the US were seeded by these four hubs or their epidemiological descendants. Counter-intuitively, the pandemic hubs do not coincide with large and well-connected cities, indicating that factors beyond population density and travel volume are necessary to explain the establishment sites of the major autumn wave of the pandemic. Geographic regions are identified where infection can be statistically traced back to a hub, providing a testable prediction of the outbreak's phylogeography. Our method therefore provides an important way forward to reconcile spatial diffusion patterns inferred from epidemiological surveillance data and pathogen sequence data.
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Affiliation(s)
- Stephen M Kissler
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, United Kingdom.
| | - Julia R Gog
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, United Kingdom
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Vivek Charu
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA; Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Ottar N Bjørnstad
- Department of Entomology, Pennsylvania State University, University Park, PA, USA
| | - Lone Simonsen
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA; Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, University of Princeton, Princeton, NJ, USA; Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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72
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Morita H, Kramer S, Heaney A, Gil H, Shaman J. Influenza forecast optimization when using different surveillance data types and geographic scale. Influenza Other Respir Viruses 2018; 12:755-764. [PMID: 30028083 PMCID: PMC6185890 DOI: 10.1111/irv.12594] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 07/11/2018] [Indexed: 11/15/2022] Open
Abstract
Background Advance warning of influenza incidence levels from skillful forecasts could help public health officials and healthcare providers implement more timely preparedness and intervention measures to combat outbreaks. Compared to influenza predictions generated at regional and national levels, those generated at finer scales could offer greater value in determining locally appropriate measures; however, to date, the various influenza surveillance data that are collected by state and county departments of health have not been well utilized in influenza prediction. Objectives To assess whether an influenza forecast model system can be optimized to generate accurate forecasts using novel surveillance data streams. Methods Here, we generate retrospective influenza forecasts with a dynamic, compartmental model‐inference system using surveillance data for influenza‐like illness (ILI), laboratory‐confirmed cases, and pneumonia and influenza mortality at state and county levels. We evaluate how specification of 3 system inputs—scaling, observational error variance (OEV), and filter divergence (lambda)—affects forecast accuracy. Results In retrospective forecasts, and across data types, there were no clear optimal combinations for the 3 system inputs; however, scaling was most critical to forecast accuracy, whereas OEV and lambda were not. Conclusions Forecasts using new data streams should be tested to determine an appropriate scaling value using historical data and analyzed for forecast accuracy.
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Affiliation(s)
- Haruka Morita
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York City, New York
| | - Sarah Kramer
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York City, New York
| | - Alexandra Heaney
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York City, New York
| | - Harold Gil
- Marion County Public Health Department, Indianapolis, Indiana
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York City, New York
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73
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Birrell PJ, Pebody RG, Charlett A, Zhang XS, De Angelis D. Real-time modelling of a pandemic influenza outbreak. Health Technol Assess 2018; 21:1-118. [PMID: 29058665 DOI: 10.3310/hta21580] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Real-time modelling is an essential component of the public health response to an outbreak of pandemic influenza in the UK. A model for epidemic reconstruction based on realistic epidemic surveillance data has been developed, but this model needs enhancing to provide spatially disaggregated epidemic estimates while ensuring that real-time implementation is feasible. OBJECTIVES To advance state-of-the-art real-time pandemic modelling by (1) developing an existing epidemic model to capture spatial variation in transmission, (2) devising efficient computational algorithms for the provision of timely statistical analysis and (3) incorporating the above into freely available software. METHODS Markov chain Monte Carlo (MCMC) sampling was used to derive Bayesian statistical inference using 2009 pandemic data from two candidate modelling approaches: (1) a parallel-region (PR) approach, splitting the pandemic into non-interacting epidemics occurring in spatially disjoint regions; and (2) a meta-region (MR) approach, treating the country as a single meta-population with long-range contact rates informed by census data on commuting. Model discrimination is performed through posterior mean deviance statistics alongside more practical considerations. In a real-time context, the use of sequential Monte Carlo (SMC) algorithms to carry out real-time analyses is investigated as an alternative to MCMC using simulated data designed to sternly test both algorithms. SMC-derived analyses are compared with 'gold-standard' MCMC-derived inferences in terms of estimation quality and computational burden. RESULTS The PR approach provides a better and more timely fit to the epidemic data. Estimates of pandemic quantities of interest are consistent across approaches and, in the PR approach, across regions (e.g. R0 is consistently estimated to be 1.76-1.80, dropping by 43-50% during an over-summer school holiday). A SMC approach was developed, which required some tailoring to tackle a sudden 'shock' in the data resulting from a pandemic intervention. This semi-automated SMC algorithm outperforms MCMC, in terms of both precision of estimates and their timely provision. Software implementing all findings has been developed and installed within Public Health England (PHE), with key staff trained in its use. LIMITATIONS The PR model lacks the predictive power to forecast the spread of infection in the early stages of a pandemic, whereas the MR model may be limited by its dependence on commuting data to describe transmission routes. As demand for resources increases in a severe pandemic, data from general practices and on hospitalisations may become unreliable or biased. The SMC algorithm developed is semi-automated; therefore, some statistical literacy is required to achieve optimal performance. CONCLUSIONS Following the objectives, this study found that timely, spatially disaggregate, real-time pandemic inference is feasible, and a system that assumes data as per pandemic preparedness plans has been developed for rapid implementation. FUTURE WORK RECOMMENDATIONS Modelling studies investigating the impact of pandemic interventions (e.g. vaccination and school closure); the utility of alternative data sources (e.g. internet searches) to augment traditional surveillance; and the correct handling of test sensitivity and specificity in serological data, propagating this uncertainty into the real-time modelling. TRIAL REGISTRATION Current Controlled Trials ISRCTN40334843. FUNDING This project was funded by the National Institute for Health Research (NIHR) Health Technology programme and will be published in full in Health Technology Assessment; Vol. 21, No. 58. See the NIHR Journals Library website for further project information. Daniela De Angelis was supported by the UK Medical Research Council (Unit Programme Number U105260566) and by PHE. She received funding under the NIHR grant for 10% of her time. The rest of her salary was provided by the MRC and PHE jointly.
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Affiliation(s)
- Paul J Birrell
- Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK
| | | | - André Charlett
- National Infections Service, Public Health England, London, UK
| | - Xu-Sheng Zhang
- National Infections Service, Public Health England, London, UK
| | - Daniela De Angelis
- Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK.,National Infections Service, Public Health England, London, UK
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74
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Morris SE, Freiesleben de Blasio B, Viboud C, Wesolowski A, Bjørnstad ON, Grenfell BT. Analysis of multi-level spatial data reveals strong synchrony in seasonal influenza epidemics across Norway, Sweden, and Denmark. PLoS One 2018; 13:e0197519. [PMID: 29771952 PMCID: PMC5957349 DOI: 10.1371/journal.pone.0197519] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Accepted: 05/03/2018] [Indexed: 12/02/2022] Open
Abstract
Population structure, spatial diffusion, and climatic conditions mediate the spatiotemporal spread of seasonal influenza in temperate regions. However, much of our knowledge of these dynamics stems from a few well-studied countries, such as the United States (US), and the extent to which this applies in different demographic and climatic environments is not fully understood. Using novel data from Norway, Sweden, and Denmark, we applied wavelet analysis and non-parametric spatial statistics to explore the spatiotemporal dynamics of influenza transmission at regional and international scales. We found the timing and amplitude of epidemics were highly synchronized both within and between countries, despite the geographical isolation of many areas in our study. Within Norway, this synchrony was most strongly modulated by population size, confirming previous findings that hierarchical spread between larger populations underlies seasonal influenza dynamics at regional levels. However, we found no such association when comparing across countries, suggesting that other factors become important at the international scale. Finally, to frame our results within a wider global context, we compared our findings from Norway to those from the US. After correcting for differences in geographic scale, we unexpectedly found higher levels of synchrony in Norway, despite its smaller population size. We hypothesize that this greater synchrony may be driven by more favorable and spatially uniform climatic conditions, although there are other likely factors we were unable to consider (such as reduced variation in school term times and differences in population movements). Overall, our results highlight the importance of comparing influenza spread at different spatial scales and across diverse geographic regions in order to better understand the complex mechanisms underlying disease dynamics.
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Affiliation(s)
- Sinead E. Morris
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, United States of America
| | - Birgitte Freiesleben de Blasio
- Department of Biostatistics, Oslo Centre for Biostatistics and Epidemiology, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Department of Infectious Disease Epidemiology and Modelling, Norwegian Institute of Public Health, Oslo, Norway
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Ottar N. Bjørnstad
- Department of Biology, Pennsylvania State University, University Park, PA, United States of America
- Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, United States of America
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, United States of America
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
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75
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Uscher-Pines L, Schwartz HL, Ahmed F, Zheteyeva Y, Meza E, Baker G, Uzicanin A. School practices to promote social distancing in K-12 schools: review of influenza pandemic policies and practices. BMC Public Health 2018; 18:406. [PMID: 29587707 PMCID: PMC5870081 DOI: 10.1186/s12889-018-5302-3] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 03/12/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND During an evolving influenza pandemic, community mitigation strategies, such as social distancing, can slow down virus transmission in schools and surrounding communities. To date, research on school practices to promote social distancing in primary and secondary schools has focused on prolonged school closure, with little attention paid to the identification and feasibility of other more sustainable interventions. To develop a list and typology of school practices that have been proposed and/or implemented in an influenza pandemic and to uncover any barriers identified, lessons learned from their use, and documented impacts. METHODS We conducted a review of the peer-reviewed and grey literature on social distancing interventions in schools other than school closure. We also collected state government guidance documents directed to local education agencies or schools to assess state policies regarding social distancing. We collected standardized information from each document using an abstraction form and generated descriptive statistics on common plan elements. RESULTS The document review revealed limited literature on school practices to promote social distancing, as well as limited incorporation of school practices to promote social distancing into state government guidance documents. Among the 38 states that had guidance documents that met inclusion criteria, fewer than half (42%) mentioned a single school practice to promote social distancing, and none provided any substantive detail about the policies or practices needed to enact them. The most frequently identified school practices were cancelling or postponing after-school activities, canceling classes or activities with a high rate of mixing/contact that occur within the school day, and reducing mixing during transport. CONCLUSION Little information is available to schools to develop policies and procedures on social distancing. Additional research and guidance are needed to assess the feasibility and effectiveness of school practices to promote social distancing.
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Affiliation(s)
| | | | - Faruque Ahmed
- Division of Global Migration and Quarantine, National Center for Emerging and Zoonotic Infectious Diseases, CDC, Atlanta, GA, USA
| | - Yenlik Zheteyeva
- Division of Global Migration and Quarantine, National Center for Emerging and Zoonotic Infectious Diseases, CDC, Atlanta, GA, USA
| | | | | | - Amra Uzicanin
- Division of Global Migration and Quarantine, National Center for Emerging and Zoonotic Infectious Diseases, CDC, Atlanta, GA, USA
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76
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Klepac P, Kissler S, Gog J. Contagion! The BBC Four Pandemic - The model behind the documentary. Epidemics 2018; 24:49-59. [PMID: 29576516 DOI: 10.1016/j.epidem.2018.03.003] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 03/14/2018] [Accepted: 03/14/2018] [Indexed: 11/19/2022] Open
Abstract
To mark the centenary of the 1918 influenza pandemic, the broadcasting network BBC have put together a 75-min documentary called 'Contagion! The BBC Four Pandemic'. Central to the documentary is a nationwide citizen science experiment, during which volunteers in the United Kingdom could download and use a custom mobile phone app called BBC Pandemic, and contribute their movement and contact data for a day. As the 'maths team', we were asked to use the data from the app to build and run a model of how a pandemic would spread in the UK. The headline results are presented in the TV programme. Here, we document in detail how the model works, and how we shaped it according the incredibly rich data coming from the BBC Pandemic app. We have barely scratched the depth of the volunteer data available from the app. The work presented in this article had the sole purpose of generating a single detailed simulation of a pandemic influenza-like outbreak in the UK. When the BBC Pandemic app has completed its collection period, the vast dataset will be made available to the scientific community (expected early 2019). It will take much more time and input from a broad range of researchers to fully exploit all that this dataset has to offer. But here at least we were able to harness some of the power of the BBC Pandemic data to contribute something which we hope will capture the interest and engagement of a broad audience.
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Affiliation(s)
- Petra Klepac
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK; Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London, UK.
| | - Stephen Kissler
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Julia Gog
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
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77
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Roussel M, Pontier D, Cohen JM, Lina B, Fouchet D. Linking influenza epidemic onsets to covariates at different scales using a dynamical model. PeerJ 2018; 6:e4440. [PMID: 29568702 PMCID: PMC5845579 DOI: 10.7717/peerj.4440] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2016] [Accepted: 02/12/2018] [Indexed: 11/20/2022] Open
Abstract
Background Evaluating the factors favoring the onset of influenza epidemics is a critical public health issue for surveillance, prevention and control. While past outbreaks provide important insights for understanding epidemic onsets, their statistical analysis is challenging since the impact of a factor can be viewed at different scales. Indeed, the same factor can explain why epidemics are more likely to begin (i) during particular weeks of the year (global scale); (ii) earlier in particular regions (spatial scale) or years (annual scale) than others and (iii) earlier in some years than others within a region (spatiotemporal scale). Methods Here, we present a statistical approach based on dynamical modeling of infectious diseases to study epidemic onsets. We propose a method to disentangle the role of covariates at different scales and use a permutation procedure to assess their significance. Epidemic data gathered from 18 French regions over six epidemic years were provided by the Regional Influenza Surveillance Group (GROG) sentinel network. Results Our results failed to highlight a significant impact of mobility flows on epidemic onset dates. Absolute humidity had a significant impact, but only at the spatial scale. No link between demographic covariates and influenza epidemic onset dates could be established. Discussion Dynamical modeling presents an interesting basis to analyze spatiotemporal variations in the outcome of epidemic onsets and how they are related to various types of covariates. The use of these models is quite complex however, due to their mathematical complexity. Furthermore, because they attempt to integrate migration processes of the virus, such models have to be much more explicit than pure statistical approaches. We discuss the relation of this approach to survival analysis, which present significant differences but may constitute an interesting alternative for non-methodologists.
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Affiliation(s)
- Marion Roussel
- Laboratoire de Biométrie et Biologie Evolutive URM5558-CNRS, Université de Lyon, Université Claude Bernard Lyon 1, Villeurbanne, France.,Université Claude Bernard Lyon 1, LabEx ECOFECT Ecoevolutionary Dynamics of Infectious Diseases, Lyon, France
| | - Dominique Pontier
- Laboratoire de Biométrie et Biologie Evolutive URM5558-CNRS, Université de Lyon, Université Claude Bernard Lyon 1, Villeurbanne, France.,Université Claude Bernard Lyon 1, LabEx ECOFECT Ecoevolutionary Dynamics of Infectious Diseases, Lyon, France
| | - Jean-Marie Cohen
- OPEN ROME (Organize and Promote Epidemiological Network), Paris, France
| | - Bruno Lina
- Laboratory of Virology, Centre National de Référence des Virus Influenzae, Hospices Civils de Lyon, Lyon, France.,Virpath, EA4610, Faculty of Medicine Lyon Est, University Claude Bernard Lyon 1, Lyon, France
| | - David Fouchet
- Laboratoire de Biométrie et Biologie Evolutive URM5558-CNRS, Université de Lyon, Université Claude Bernard Lyon 1, Villeurbanne, France.,Université Claude Bernard Lyon 1, LabEx ECOFECT Ecoevolutionary Dynamics of Infectious Diseases, Lyon, France
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78
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Lee EC, Arab A, Goldlust SM, Viboud C, Grenfell BT, Bansal S. Deploying digital health data to optimize influenza surveillance at national and local scales. PLoS Comput Biol 2018. [PMID: 29513661 PMCID: PMC5858836 DOI: 10.1371/journal.pcbi.1006020] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The surveillance of influenza activity is critical to early detection of epidemics and pandemics and the design of disease control strategies. Case reporting through a voluntary network of sentinel physicians is a commonly used method of passive surveillance for monitoring rates of influenza-like illness (ILI) worldwide. Despite its ubiquity, little attention has been given to the processes underlying the observation, collection, and spatial aggregation of sentinel surveillance data, and its subsequent effects on epidemiological understanding. We harnessed the high specificity of diagnosis codes in medical claims from a database that represented 2.5 billion visits from upwards of 120,000 United States healthcare providers each year. Among influenza seasons from 2002-2009 and the 2009 pandemic, we simulated limitations of sentinel surveillance systems such as low coverage and coarse spatial resolution, and performed Bayesian inference to probe the robustness of ecological inference and spatial prediction of disease burden. Our models suggest that a number of socio-environmental factors, in addition to local population interactions, state-specific health policies, as well as sampling effort may be responsible for the spatial patterns in U.S. sentinel ILI surveillance. In addition, we find that biases related to spatial aggregation were accentuated among areas with more heterogeneous disease risk, and sentinel systems designed with fixed reporting locations across seasons provided robust inference and prediction. With the growing availability of health-associated big data worldwide, our results suggest mechanisms for optimizing digital data streams to complement traditional surveillance in developed settings and enhance surveillance opportunities in developing countries. Influenza contributes substantially to global morbidity and mortality each year, and epidemiological surveillance for influenza is typically conducted by sentinel physicians and health care providers recruited to report cases of influenza-like illness. While population coverage and representativeness, and geographic distribution are considered during sentinel provider recruitment, systems cannot always achieve these standards due to the administrative burdens of data collection. We present spatial estimates of influenza disease burden across United States counties by leveraging the volume and fine spatial resolution of medical claims data, and existing socio-environmental hypotheses about the determinants of influenza disease disease burden. Using medical claims as a testbed, this study adds to literature on the optimization of surveillance system design by considering conditions of limited reporting and spatial aggregation. We highlight the importance of considering sampling biases and reporting locations when interpreting surveillance data, and suggest that local mobility and regional policies may be critical to understanding the spatial distribution of reported influenza-like illness.
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Affiliation(s)
- Elizabeth C. Lee
- Department of Biology, Georgetown University, Washington, DC, United States of America
- * E-mail: (ECL); (SB)
| | - Ali Arab
- Department of Mathematics & Statistics, Georgetown University, Washington, DC, United States of America
| | - Sandra M. Goldlust
- Department of Biology, Georgetown University, Washington, DC, United States of America
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Bryan T. Grenfell
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
- Department of Ecology & Evolutionary Biology and Woodrow Wilson School, Princeton University, Princeton, New Jersey, United States of America
| | - Shweta Bansal
- Department of Biology, Georgetown University, Washington, DC, United States of America
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail: (ECL); (SB)
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79
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Chattopadhyay I, Kiciman E, Elliott JW, Shaman JL, Rzhetsky A. Conjunction of factors triggering waves of seasonal influenza. eLife 2018; 7:e30756. [PMID: 29485041 PMCID: PMC5864297 DOI: 10.7554/elife.30756] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Accepted: 02/13/2018] [Indexed: 02/04/2023] Open
Abstract
Using several longitudinal datasets describing putative factors affecting influenza incidence and clinical data on the disease and health status of over 150 million human subjects observed over a decade, we investigated the source and the mechanistic triggers of influenza epidemics. We conclude that the initiation of a pan-continental influenza wave emerges from the simultaneous realization of a complex set of conditions. The strongest predictor groups are as follows, ranked by importance: (1) the host population's socio- and ethno-demographic properties; (2) weather variables pertaining to specific humidity, temperature, and solar radiation; (3) the virus' antigenic drift over time; (4) the host population'€™s land-based travel habits, and; (5) recent spatio-temporal dynamics, as reflected in the influenza wave auto-correlation. The models we infer are demonstrably predictive (area under the Receiver Operating Characteristic curve 80%) when tested with out-of-sample data, opening the door to the potential formulation of new population-level intervention and mitigation policies.
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Affiliation(s)
- Ishanu Chattopadhyay
- Institute of Genomics and Systems BiologyUniversity of ChicagoChicagoUnited States
- Department of MedicineUniversity of ChicagoChicagoUnited States
| | - Emre Kiciman
- Information and Data Science GroupMicrosoft ResearchRedmondUnited States
| | | | - Jeffrey L Shaman
- Department of Environmental Health Sciences, Mailman School of Public HealthColumbia UniversityNew YorkUnited States
| | - Andrey Rzhetsky
- Institute of Genomics and Systems BiologyUniversity of ChicagoChicagoUnited States
- Department of MedicineUniversity of ChicagoChicagoUnited States
- Computation InstituteUniversity of ChicagoChicagoUnited States
- Departments of Human GeneticsUniversity of ChicagoChicagoUnited States
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80
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Abstract
Recurrent outbreaks of seasonal and pandemic influenza create a need for forecasts of the geographic spread of this pathogen. Although it is well established that the spatial progression of infection is largely attributable to human mobility, difficulty obtaining real-time information on human movement has limited its incorporation into existing infectious disease forecasting techniques. In this study, we develop and validate an ensemble forecast system for predicting the spatiotemporal spread of influenza that uses readily accessible human mobility data and a metapopulation model. In retrospective state-level forecasts for 35 US states, the system accurately predicts local influenza outbreak onset,-i.e., spatial spread, defined as the week that local incidence increases above a baseline threshold-up to 6 wk in advance of this event. In addition, the metapopulation prediction system forecasts influenza outbreak onset, peak timing, and peak intensity more accurately than isolated location-specific forecasts. The proposed framework could be applied to emergent respiratory viruses and, with appropriate modifications, other infectious diseases.
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81
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Khalenkov A, He Y, Reed JL, Kreil TR, McVey J, Norton M, Scott J, Scott DE. Characterization of source plasma from self-identified vaccinated or convalescent donors during the 2009 H1N1 pandemic. Transfusion 2018; 58:1108-1116. [DOI: 10.1111/trf.14530] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 12/27/2017] [Accepted: 12/27/2017] [Indexed: 11/29/2022]
Affiliation(s)
- Alexey Khalenkov
- Center for Biologics Evaluation and Research; Food and Drug Administration; Silver Spring Maryland
| | - Yong He
- Center for Biologics Evaluation and Research; Food and Drug Administration; Silver Spring Maryland
| | - Jennifer L. Reed
- Center for Biologics Evaluation and Research; Food and Drug Administration; Silver Spring Maryland
| | | | | | - Malgorzata Norton
- Center for Biologics Evaluation and Research; Food and Drug Administration; Silver Spring Maryland
| | - John Scott
- Center for Biologics Evaluation and Research; Food and Drug Administration; Silver Spring Maryland
| | - Dorothy E. Scott
- Center for Biologics Evaluation and Research; Food and Drug Administration; Silver Spring Maryland
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82
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Molecular detection of influenza A(H1N1)pdm09 viruses with M genes from human pandemic strains among Nigerian pigs, 2013-2015: implications and associated risk factors. Epidemiol Infect 2017; 145:3345-3360. [PMID: 29166978 DOI: 10.1017/s0950268817002503] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
In the post-pandemic period, influenza A(H1N1)pdm09 virus has been detected in swine populations in different parts of the world. This study was conducted to determine the presence and spatial patterns of this human pandemic virus among Nigerian pigs and identify associated risk factors. Using a two-stage stratified random sampling method, nasal swab specimens were obtained from pigs in Ibadan, Nigeria during the 2013-2014 and 2014-2015 influenza seasons, and the virus was detected by reverse transcriptase-polymerase chain reaction (RT-PCR). Purified RT-PCR products were sequenced in both directions, and sequences were aligned using MUSCLE. Phylogenetic analysis was conducted in MEGA6. Purely spatial scan statistics and a spatial lag regression model were used to identify spatial clusters and associated risk factors. The virus was detected in both seasons, with an overall prevalence of 8·7%. Phylogenetic analyses revealed that the M genes were similar to those of pandemic strains which circulated in humans prior to and during the study. Cluster analysis revealed a significant primary spatial cluster (RR = 4·71, LLR = 5·66, P = 0·0046), while 'hours spent with pigs (R 2 = 0·90, P = 0·0018)' and 'hours spent with pigs from different farms (R 2 = 0·91, P = 0·0001)' were identified as significant risk factors (P < 0·05). These findings reveal that there is considerable risk of transmission of the pandemic virus, either directly from pig handlers or through fomites, to swine herds in Ibadan, Nigeria. Active circulation of the virus among Nigerian pigs could enhance its reassortment with endemic swine influenza viruses. Campaigns for adoption of biosecurity measures in West African piggeries and abattoirs should be introduced and sustained in order to prevent the emergence of a new influenza epicentre in the sub-region.
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83
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Marziano V, Pugliese A, Merler S, Ajelli M. Detecting a Surprisingly Low Transmission Distance in the Early Phase of the 2009 Influenza Pandemic. Sci Rep 2017; 7:12324. [PMID: 28951551 PMCID: PMC5615056 DOI: 10.1038/s41598-017-12415-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 09/07/2017] [Indexed: 11/09/2022] Open
Abstract
The spread of the 2009 H1N1 influenza pandemic in England was characterized by two major waves of infections: the first one was highly spatially localized (mainly in the London area), while the second one spread homogeneously through the entire country. The reasons behind this complex spatiotemporal dynamics have yet to be clarified. In this study, we perform a Bayesian analysis of five models entailing different hypotheses on the possible determinants of the observed pattern. We find a consensus among all models in showing a surprisingly low transmission distance (defined as the geographic distance between the place of residence of the infectors and her/his infectees) during the first wave: about 1.5 km (2.2 km if infections linked to household and school transmission are excluded). The best-fitting model entails a change in human activity regarding contacts not related to household and school. By using this model we estimate that the transmission distance sharply increased to 5.3 km (10 km when excluding infections linked to household and school transmission) during the second wave. Our study reveals a possible explanation for the observed pattern and highlights the need of better understanding human mobility and activity patterns under the pressure posed by a pandemic threat.
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Affiliation(s)
- Valentina Marziano
- Bruno Kessler Foundation, Trento, Italy.,Department of Mathematics, University of Trento, Trento, Italy
| | - Andrea Pugliese
- Department of Mathematics, University of Trento, Trento, Italy
| | | | - Marco Ajelli
- Bruno Kessler Foundation, Trento, Italy. .,Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
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84
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Wesolowski A, Buckee CO, Engø-Monsen K, Metcalf CJE. Connecting Mobility to Infectious Diseases: The Promise and Limits of Mobile Phone Data. J Infect Dis 2017; 214:S414-S420. [PMID: 28830104 DOI: 10.1093/infdis/jiw273] [Citation(s) in RCA: 112] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Human travel can shape infectious disease dynamics by introducing pathogens into susceptible populations or by changing the frequency of contacts between infected and susceptible individuals. Quantifying infectious disease-relevant travel patterns on fine spatial and temporal scales has historically been limited by data availability. The recent emergence of mobile phone calling data and associated locational information means that we can now trace fine scale movement across large numbers of individuals. However, these data necessarily reflect a biased sample of individuals across communities and are generally aggregated for both ethical and pragmatic reasons that may further obscure the nuance of individual and spatial heterogeneities. Additionally, as a general rule, the mobile phone data are not linked to demographic or social identifiers, or to information about the disease status of individual subscribers (although these may be made available in smaller-scale specific cases). Combining data on human movement from mobile phone data-derived population fluxes with data on disease incidence requires approaches that can tackle varying spatial and temporal resolutions of each data source and generate inference about dynamics on scales relevant to both pathogen biology and human ecology. Here, we review the opportunities and challenges of these novel data streams, illustrating our examples with analyses of 2 different pathogens in Kenya, and conclude by outlining core directions for future research.
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Affiliation(s)
- Amy Wesolowski
- Department of Epidemiology.,Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Caroline O Buckee
- Department of Epidemiology.,Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | | | - C J E Metcalf
- Department of Ecology and Evolutionary Biology.,Office of Population Research, Woodrow Wilson School, Princeton University, New Jersey
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85
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Pearce DC, McCaw JM, McVernon J, Mathews JD. Influenza as a trigger for cardiovascular disease: An investigation of serotype, subtype and geographic location. ENVIRONMENTAL RESEARCH 2017; 156:688-696. [PMID: 28477579 DOI: 10.1016/j.envres.2017.04.024] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 04/20/2017] [Accepted: 04/20/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND Seasonal peaks of influenza and cardiovascular disease tend to coincide. Many excess deaths may be triggered by influenza, and the severity of this effect may vary with the virulence of the circulating influenza strain and host susceptibility. We aimed to explore the association between hospital admissions for influenza and/or pneumonia (IP) and acute myocardial infarction (AMI) or ischaemic heart disease (IHD) in Queensland, Australia, taking into account temporal and spatial variation of influenza virus type and subtype in 2007, 2008 and 2009. METHODS This ecological study at Statistical Subdivision level (SSD, n=38) used linked patient-level data. For each study year, Standardized Morbidity Ratios (SMRs) were calculated for hospital admissions with diagnoses of IP, AMI and IHD. We investigated the associations between IP and AMI or IHD using spatial autoregressive modelling, adjusting for socio-demographic factors. RESULTS Spatial autocorrelation was detected in SMRs, possibly reflecting underlying social and behavioural risk factors, but consistent with infectious disease spread. SMRs for IP were consistently predictive of SMRs for AMI and IHD when adjusted for socioeconomic status, population density and per cent Indigenous population (coefficient: 0.707, 95% confidence interval (CI): 0.318 - 1.096; 0.553, 0.222 - 0.884; 0.598, 0.307 - 0.888 and 1.017, 0.711 - 1.323; 0.650, 0.342 - 0.958; 1.031, 0.827 - 1.236) in 2007, 2008 and 2009, respectively. CONCLUSIONS This ecological study provides further evidence that severe respiratory infections may trigger the onset of cardiovascular events, implicating the influenza virus as a contributing factor.
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Affiliation(s)
- D C Pearce
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Level 3, 207 Bouverie Street, The University of Melbourne, Victoria 3010, Australia; Faculty of Science & Technology, Federation University Australia, University Drive, Mt Helen, Victoria 3350, Australia.
| | - J M McCaw
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Level 3, 207 Bouverie Street, The University of Melbourne, Victoria 3010, Australia; Modelling and Simulation, Infection and Immunity Theme, Murdoch Childrens Research Institute, 9th floor, The Royal Children's Hospital, 50 Flemington Road, Parkville, Victoria 3052, Australia; School of Mathematics and Statistics, Level 3, Old Geology South, The University of Melbourne, Victoria 3010, Australia.
| | - J McVernon
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Level 3, 207 Bouverie Street, The University of Melbourne, Victoria 3010, Australia; Modelling and Simulation, Infection and Immunity Theme, Murdoch Childrens Research Institute, 9th floor, The Royal Children's Hospital, 50 Flemington Road, Parkville, Victoria 3052, Australia.
| | - J D Mathews
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Level 3, 207 Bouverie Street, The University of Melbourne, Victoria 3010, Australia.
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86
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Pasquini-Descomps H, Brender N, Maradan D. Value for Money in H1N1 Influenza: A Systematic Review of the Cost-Effectiveness of Pandemic Interventions. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2017; 20:819-827. [PMID: 28577700 DOI: 10.1016/j.jval.2016.05.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Revised: 05/03/2016] [Accepted: 05/08/2016] [Indexed: 06/07/2023]
Abstract
BACKGROUND The 2009 A/H1N1 influenza pandemic generated additional data and triggered new studies that opened debate over the optimal strategy for handling a pandemic. The lessons-learned documents from the World Health Organization show the need for a cost estimation of the pandemic response during the risk-assessment phase. Several years after the crisis, what conclusions can we draw from this field of research? OBJECTIVE The main objective of this article was to provide an analysis of the studies that present cost-effectiveness or cost-benefit analyses for A/H1N1 pandemic interventions since 2009 and to identify which measures seem most cost-effective. METHODS We reviewed 18 academic articles that provide cost-effectiveness or cost-benefit analyses for A/H1N1 pandemic interventions since 2009. Our review converts the studies' results into a cost-utility measure (cost per disability-adjusted life-year or quality-adjusted life-year) and presents the contexts of severity and fatality. RESULTS The existing studies suggest that hospital quarantine, vaccination, and usage of the antiviral stockpile are highly cost-effective, even for mild pandemics. However, school closures, antiviral treatments, and social distancing may not qualify as efficient measures, for a virus like 2009's H1N1 and a willingness-to-pay threshold of $45,000 per disability-adjusted life-year. Such interventions may become cost-effective for severe crises. CONCLUSIONS This study helps to shed light on the cost-utility of various interventions, and may support decision making, among other criteria, for future pandemics. Nonetheless, one should consider these results carefully, considering these may not apply to a specific crisis or country, and a dedicated cost-effectiveness assessment should be conducted at the time.
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Affiliation(s)
- Hélène Pasquini-Descomps
- Haute École de Gestion Genève (Geneva School of Business Administration), HES-SO University of Applied Sciences Western Switzerland, Carouge, Switzerland; University of Geneva, Switzerland.
| | - Nathalie Brender
- Haute École de Gestion Genève (Geneva School of Business Administration), HES-SO University of Applied Sciences Western Switzerland, Carouge, Switzerland
| | - David Maradan
- Haute École de Gestion Genève (Geneva School of Business Administration), HES-SO University of Applied Sciences Western Switzerland, Carouge, Switzerland; University of Geneva, Switzerland
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87
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Charu V, Zeger S, Gog J, Bjørnstad ON, Kissler S, Simonsen L, Grenfell BT, Viboud C. Human mobility and the spatial transmission of influenza in the United States. PLoS Comput Biol 2017; 13:e1005382. [PMID: 28187123 PMCID: PMC5349690 DOI: 10.1371/journal.pcbi.1005382] [Citation(s) in RCA: 115] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Revised: 03/14/2017] [Accepted: 01/26/2017] [Indexed: 11/18/2022] Open
Abstract
Seasonal influenza epidemics offer unique opportunities to study the invasion and re-invasion waves of a pathogen in a partially immune population. Detailed patterns of spread remain elusive, however, due to lack of granular disease data. Here we model high-volume city-level medical claims data and human mobility proxies to explore the drivers of influenza spread in the US during 2002–2010. Although the speed and pathways of spread varied across seasons, seven of eight epidemics likely originated in the Southern US. Each epidemic was associated with 1–5 early long-range transmission events, half of which sparked onward transmission. Gravity model estimates indicate a sharp decay in influenza transmission with the distance between infectious and susceptible cities, consistent with spread dominated by work commutes rather than air traffic. Two early-onset seasons associated with antigenic novelty had particularly localized modes of spread, suggesting that novel strains may spread in a more localized fashion than previously anticipated. The underlying mechanisms dictating the spatial spread of seasonal influenza remain poorly understood, in part because of the lack of spatially resolved disease data to quantify patterns of spread. In this paper, we address this issue by analyzing fine-grain insurance claims data on influenza-like-illnesses over eight seasons in ~300 locations throughout the United States. Using statistical methods, we found that seven of eight epidemics likely originated in the Southern US, that influenza spatial transmission is dominated by local traffic between cities, and that seasons marked by novel influenza virus circulation had a particularly radial, localized spatial structure. These findings are in stark contrast to prevailing theories of influenza spatial transmission that suggest that transmission is favored in low humidity environments and that spread is a dominated by air traffic between populous hubs.
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Affiliation(s)
- Vivek Charu
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- * E-mail:
| | - Scott Zeger
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Julia Gog
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Ottar N. Bjørnstad
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
- Department of Entomology, Pennsylvania State University, State College, Pennsylvania, United States of America
| | - Stephen Kissler
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Lone Simonsen
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
- Department of Public Health, Copenhagen University, Copenhagen, Denmark
| | - Bryan T. Grenfell
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, United States of America
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
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88
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Almogy G, Stone L, Bernevig BA, Wolf DG, Dorozko M, Moses AE, Nir-Paz R. Analysis of Influenza and RSV dynamics in the community using a 'Local Transmission Zone' approach. Sci Rep 2017; 7:42012. [PMID: 28181554 PMCID: PMC5299452 DOI: 10.1038/srep42012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Accepted: 01/03/2017] [Indexed: 12/31/2022] Open
Abstract
Understanding the dynamics of pathogen spread within urban areas is critical for the effective prevention and containment of communicable diseases. At these relatively small geographic scales, short-distance interactions and tightly knit sub-networks dominate the dynamics of pathogen transmission; yet, the effective boundaries of these micro-scale groups are generally not known and often ignored. Using clinical test results from hospital admitted patients we analyze the spatio-temporal distribution of Influenza Like Illness (ILI) in the city of Jerusalem over a period of three winter seasons. We demonstrate that this urban area is not a single, perfectly mixed ecology, but is in fact comprised of a set of more basic, relatively independent pathogen transmission units, which we term here Local Transmission Zones, LTZs. By identifying these LTZs, and using the dynamic pathogen-content information contained within them, we are able to differentiate between disease-causes at the individual patient level often with near-perfect predictive accuracy.
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Affiliation(s)
- Gal Almogy
- Flurensics Inc., Tel Aviv, 64101 Israel.,School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, Australia
| | - Lewi Stone
- School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, Australia.,Department of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - B Andrei Bernevig
- Department of Physics, Princeton University, Princeton, NJ 08544, USA
| | - Dana G Wolf
- Department of Clinical Microbiology and Infectious Diseases, Hadassah-Hebrew University Medical Center, Jerusalem 91120, Isreal
| | - Marina Dorozko
- Department of Clinical Microbiology and Infectious Diseases, Hadassah-Hebrew University Medical Center, Jerusalem 91120, Isreal
| | - Allon E Moses
- Department of Clinical Microbiology and Infectious Diseases, Hadassah-Hebrew University Medical Center, Jerusalem 91120, Isreal
| | - Ran Nir-Paz
- Department of Clinical Microbiology and Infectious Diseases, Hadassah-Hebrew University Medical Center, Jerusalem 91120, Isreal
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89
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Abstract
The frequency and global impact of infectious disease outbreaks, particularly those caused by emerging viruses, demonstrate the need for a better understanding of how spatial ecology and pathogen evolution jointly shape epidemic dynamics. Advances in computational techniques and the increasing availability of genetic and geospatial data are helping to address this problem, particularly when both information sources are combined. Here, we review research at the intersection of evolutionary biology, human geography and epidemiology that is working towards an integrated view of spatial incidence, host mobility and viral genetic diversity. We first discuss how empirical studies have combined viral spatial and genetic data, focusing particularly on the contribution of evolutionary analyses to epidemiology and disease control. Second, we explore the interplay between virus evolution and global dispersal in more depth for two pathogens: human influenza A virus and chikungunya virus. We discuss the opportunities for future research arising from new analyses of human transportation and trade networks, as well as the associated challenges in accessing and sharing relevant spatial and genetic data.
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Affiliation(s)
- Oliver G Pybus
- Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK
| | - Andrew J Tatem
- Department of Geography and Environment, University of Southampton, Highfield, Southampton SO17 1BJ, UK Fogarty International Center, National Institutes of Health, Bethesda, MA, USA Flowminder Foundation, Stockholm, Sweden
| | - Philippe Lemey
- Department of Microbiology and Immunology, Rega Institute, KU Leuven-University of Leuven, Leuven, Belgium
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90
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Perra N. The spreading of infectious diseases in modern socio-technical systems Comment on "Pattern transitions in spatial epidemics: Mechanisms and emergent properties" by Gui-Quan Sun et al. Phys Life Rev 2016; 19:87-89. [PMID: 27665175 PMCID: PMC7129380 DOI: 10.1016/j.plrev.2016.09.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Accepted: 09/15/2016] [Indexed: 11/16/2022]
Affiliation(s)
- Nicola Perra
- Centre for Business Network Analysis, Greenwich University, London, SE10 9LS, UK.
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91
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Lee EC, Asher JM, Goldlust S, Kraemer JD, Lawson AB, Bansal S. Mind the Scales: Harnessing Spatial Big Data for Infectious Disease Surveillance and Inference. J Infect Dis 2016; 214:S409-S413. [PMID: 28830109 PMCID: PMC5144899 DOI: 10.1093/infdis/jiw344] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Spatial big data have the velocity, volume, and variety of big data sources and contain additional geographic information. Digital data sources, such as medical claims, mobile phone call data records, and geographically tagged tweets, have entered infectious diseases epidemiology as novel sources of data to complement traditional infectious disease surveillance. In this work, we provide examples of how spatial big data have been used thus far in epidemiological analyses and describe opportunities for these sources to improve disease-mitigation strategies and public health coordination. In addition, we consider the technical, practical, and ethical challenges with the use of spatial big data in infectious disease surveillance and inference. Finally, we discuss the implications of the rising use of spatial big data in epidemiology to health risk communication, and public health policy recommendations and coordination across scales.
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Affiliation(s)
| | | | | | - John D Kraemer
- Department of Health Systems Administration, Georgetown University
| | - Andrew B Lawson
- Department of Public Health Sciences, Medical University of South Carolina, Charleston
| | - Shweta Bansal
- Department of Biology
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland
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92
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Simonsen L, Gog JR, Olson D, Viboud C. Infectious Disease Surveillance in the Big Data Era: Towards Faster and Locally Relevant Systems. J Infect Dis 2016; 214:S380-S385. [PMID: 28830112 PMCID: PMC5144901 DOI: 10.1093/infdis/jiw376] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
While big data have proven immensely useful in fields such as marketing and earth sciences, public health is still relying on more traditional surveillance systems and awaiting the fruits of a big data revolution. A new generation of big data surveillance systems is needed to achieve rapid, flexible, and local tracking of infectious diseases, especially for emerging pathogens. In this opinion piece, we reflect on the long and distinguished history of disease surveillance and discuss recent developments related to use of big data. We start with a brief review of traditional systems relying on clinical and laboratory reports. We then examine how large-volume medical claims data can, with great spatiotemporal resolution, help elucidate local disease patterns. Finally, we review efforts to develop surveillance systems based on digital and social data streams, including the recent rise and fall of Google Flu Trends. We conclude by advocating for increased use of hybrid systems combining information from traditional surveillance and big data sources, which seems the most promising option moving forward. Throughout the article, we use influenza as an exemplar of an emerging and reemerging infection which has traditionally been considered a model system for surveillance and modeling.
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Affiliation(s)
- Lone Simonsen
- Division of International Epidemiology and Population Studies, Fogarty International Center, US National Institutes of Health, Bethesda, Maryland
- Department of Public Health, University of Copenhagen, Denmark
| | - Julia R Gog
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom
| | - Don Olson
- Division of International Epidemiology and Population Studies, Fogarty International Center, US National Institutes of Health, Bethesda, Maryland
| | - Cécile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, US National Institutes of Health, Bethesda, Maryland
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93
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Yang W, Olson DR, Shaman J. Forecasting Influenza Outbreaks in Boroughs and Neighborhoods of New York City. PLoS Comput Biol 2016; 12:e1005201. [PMID: 27855155 PMCID: PMC5113861 DOI: 10.1371/journal.pcbi.1005201] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Accepted: 10/05/2016] [Indexed: 11/26/2022] Open
Abstract
The ideal spatial scale, or granularity, at which infectious disease incidence should be monitored and forecast has been little explored. By identifying the optimal granularity for a given disease and host population, and matching surveillance and prediction efforts to this scale, response to emergent and recurrent outbreaks can be improved. Here we explore how granularity and representation of spatial structure affect influenza forecast accuracy within New York City. We develop network models at the borough and neighborhood levels, and use them in conjunction with surveillance data and a data assimilation method to forecast influenza activity. These forecasts are compared to an alternate system that predicts influenza for each borough or neighborhood in isolation. At the borough scale, influenza epidemics are highly synchronous despite substantial differences in intensity, and inclusion of network connectivity among boroughs generally improves forecast accuracy. At the neighborhood scale, we observe much greater spatial heterogeneity among influenza outbreaks including substantial differences in local outbreak timing and structure; however, inclusion of the network model structure generally degrades forecast accuracy. One notable exception is that local outbreak onset, particularly when signal is modest, is better predicted with the network model. These findings suggest that observation and forecast at sub-municipal scales within New York City provides richer, more discriminant information on influenza incidence, particularly at the neighborhood scale where greater heterogeneity exists, and that the spatial spread of influenza among localities can be forecast. Influenza, or the flu, causes significant morbidity and mortality during both seasonal and pandemic outbreaks. Recently developed influenza forecast systems have the potential to aid public health planning for and mitigation of the burden of this disease. However, current forecasts are often generated at spatial scales (e.g. national level) that are coarser than the scales at which public health measures and interventions are implemented (e.g. community level). Here we build and test influenza forecast systems at county and community levels, which either include spatial connectivity among locations or are run in isolation. We test these four flu forecast systems (i.e. 2 models × 2 spatial scales) using data collected from 2008 to 2013, including the 2009 pandemic, for the five boroughs (corresponding to county level) and 42 neighborhoods (corresponding to community level) in New York City. We compare the performance of the four forecast systems in predicting the onset, duration, and intensity of flu outbreaks and found that the performance varied by spatial scale (borough vs. neighborhood), season (non-pandemic vs. pandemic) and metric (onset, duration, and intensity). In general, the inclusion of spatial network connectivity in the forecast model improves forecast accuracy at the borough scale but degrades accuracy at the neighborhood scale.
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Affiliation(s)
- Wan Yang
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
- * E-mail:
| | - Donald R. Olson
- New York City Department of Health and Mental Hygiene, New York, New York, United States of America
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
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94
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Buckee CO, Tatem AJ, Metcalf CJE. Seasonal Population Movements and the Surveillance and Control of Infectious Diseases. Trends Parasitol 2016; 33:10-20. [PMID: 27865741 DOI: 10.1016/j.pt.2016.10.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Revised: 10/08/2016] [Accepted: 10/19/2016] [Indexed: 10/20/2022]
Abstract
National policies designed to control infectious diseases should allocate resources for interventions based on regional estimates of disease burden from surveillance systems. For many infectious diseases, however, there is pronounced seasonal variation in incidence. Policy-makers must routinely manage a public health response to these seasonal fluctuations with limited understanding of their underlying causes. Two complementary and poorly described drivers of seasonal disease incidence are the mobility and aggregation of human populations, which spark outbreaks and sustain transmission, respectively, and may both exhibit distinct seasonal variations. Here we highlight the key challenges that seasonal migration creates when monitoring and controlling infectious diseases. We discuss the potential of new data sources in accounting for seasonal population movements in dynamic risk mapping strategies.
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Affiliation(s)
- Caroline O Buckee
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA; Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, USA.
| | - Andrew J Tatem
- Flowminder Foundation, Stockholm, Sweden; WorldPop, Department of Geography and Environment, University of Southampton, Southampton, UK
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, USA; Office of Population Research, Woodrow Wilson School, Princeton University, Princeton, USA
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95
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How social structures, space, and behaviors shape the spread of infectious diseases using chikungunya as a case study. Proc Natl Acad Sci U S A 2016; 113:13420-13425. [PMID: 27821727 DOI: 10.1073/pnas.1611391113] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Whether an individual becomes infected in an infectious disease outbreak depends on many interconnected risk factors, which may relate to characteristics of the individual (e.g., age, sex), his or her close relatives (e.g., household members), or the wider community. Studies monitoring individuals in households or schools have helped elucidate the determinants of transmission in small social structures due to advances in statistical modeling; but such an approach has so far largely failed to consider individuals in the wider context they live in. Here, we used an outbreak of chikungunya in a rural community in Bangladesh as a case study to obtain a more comprehensive characterization of risk factors in disease spread. We developed Bayesian data augmentation approaches to account for uncertainty in the source of infection, recall uncertainty, and unobserved infection dates. We found that the probability of chikungunya transmission was 12% [95% credible interval (CI): 8-17%] between household members but dropped to 0.3% for those living 50 m away (95% CI: 0.2-0.5%). Overall, the mean transmission distance was 95 m (95% CI: 77-113 m). Females were 1.5 times more likely to become infected than males (95% CI: 1.2-1.8), which was virtually identical to the relative risk of being at home estimated from an independent human movement study in the country. Reported daily use of antimosquito coils had no detectable impact on transmission. This study shows how the complex interplay between the characteristics of an individual and his or her close and wider environment contributes to the shaping of infectious disease epidemics.
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96
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97
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Birrell PJ, Zhang XS, Pebody RG, Gay NJ, De Angelis D. Reconstructing a spatially heterogeneous epidemic: Characterising the geographic spread of 2009 A/H1N1pdm infection in England. Sci Rep 2016; 6:29004. [PMID: 27404957 PMCID: PMC4941410 DOI: 10.1038/srep29004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Accepted: 06/09/2016] [Indexed: 11/08/2022] Open
Abstract
Understanding how the geographic distribution of and movements within a population influence the spatial spread of infections is crucial for the design of interventions to curb transmission. Existing knowledge is typically based on results from simulation studies whereas analyses of real data remain sparse. The main difficulty in quantifying the spatial pattern of disease spread is the paucity of available data together with the challenge of incorporating optimally the limited information into models of disease transmission. To address this challenge the role of routine migration on the spatial pattern of infection during the epidemic of 2009 pandemic influenza in England is investigated here through two modelling approaches: parallel-region models, where epidemics in different regions are assumed to occur in isolation with shared characteristics; and meta-region models where inter-region transmission is expressed as a function of the commuter flux between regions. Results highlight that the significantly less computationally demanding parallel-region approach is sufficiently flexible to capture the underlying dynamics. This suggests that inter-region movement is either inaccurately characterized by the available commuting data or insignificant once its initial impact on transmission has subsided.
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MESH Headings
- Adolescent
- Adult
- Age Distribution
- Aged
- Antibodies, Viral/biosynthesis
- Antibodies, Viral/blood
- Child
- Child, Preschool
- Commerce
- Computer Simulation
- England/epidemiology
- Geography, Medical
- Holidays
- Humans
- Infant
- Influenza A Virus, H1N1 Subtype/immunology
- Influenza A Virus, H1N1 Subtype/isolation & purification
- Influenza, Human/epidemiology
- Influenza, Human/transmission
- Influenza, Human/virology
- London/epidemiology
- Middle Aged
- Models, Theoretical
- Pandemics
- Schools
- Seasons
- Seroconversion
- Transportation
- Young Adult
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Affiliation(s)
- Paul J. Birrell
- Medical Research Council Biostatistics Unit, Cambridge Insitute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK
| | - Xu-Sheng Zhang
- Centre for Infectious Disease Surveillance and Control, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK
| | - Richard G. Pebody
- Centre for Infectious Disease Surveillance and Control, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK
| | | | - Daniela De Angelis
- Medical Research Council Biostatistics Unit, Cambridge Insitute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK
- Centre for Infectious Disease Surveillance and Control, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK
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98
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Özmen Ö, Pullum LL, Ramanathan A, Nutaro JJ. Augmenting Epidemiological Models with Point-Of-Care Diagnostics Data. PLoS One 2016; 11:e0153769. [PMID: 27096162 PMCID: PMC4838229 DOI: 10.1371/journal.pone.0153769] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 04/04/2016] [Indexed: 11/24/2022] Open
Abstract
Although adoption of newer Point-of-Care (POC) diagnostics is increasing, there is a significant challenge using POC diagnostics data to improve epidemiological models. In this work, we propose a method to process zip-code level POC datasets and apply these processed data to calibrate an epidemiological model. We specifically develop a calibration algorithm using simulated annealing and calibrate a parsimonious equation-based model of modified Susceptible-Infected-Recovered (SIR) dynamics. The results show that parsimonious models are remarkably effective in predicting the dynamics observed in the number of infected patients and our calibration algorithm is sufficiently capable of predicting peak loads observed in POC diagnostics data while staying within reasonable and empirical parameter ranges reported in the literature. Additionally, we explore the future use of the calibrated values by testing the correlation between peak load and population density from Census data. Our results show that linearity assumptions for the relationships among various factors can be misleading, therefore further data sources and analysis are needed to identify relationships between additional parameters and existing calibrated ones. Calibration approaches such as ours can determine the values of newly added parameters along with existing ones and enable policy-makers to make better multi-scale decisions.
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Affiliation(s)
- Özgür Özmen
- Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America
| | - Laura L. Pullum
- Computational Science and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America
- * E-mail:
| | - Arvind Ramanathan
- Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America
| | - James J. Nutaro
- Computational Science and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America
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99
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Huang X, Clements ACA, Williams G, Mengersen K, Tong S, Hu W. Bayesian estimation of the dynamics of pandemic (H1N1) 2009 influenza transmission in Queensland: A space-time SIR-based model. ENVIRONMENTAL RESEARCH 2016; 146:308-14. [PMID: 26799511 DOI: 10.1016/j.envres.2016.01.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Revised: 12/10/2015] [Accepted: 01/11/2016] [Indexed: 05/19/2023]
Abstract
BACKGROUND A pandemic strain of influenza A spread rapidly around the world in 2009, now referred to as pandemic (H1N1) 2009. This study aimed to examine the spatiotemporal variation in the transmission rate of pandemic (H1N1) 2009 associated with changes in local socio-environmental conditions from May 7-December 31, 2009, at a postal area level in Queensland, Australia. METHOD We used the data on laboratory-confirmed H1N1 cases to examine the spatiotemporal dynamics of transmission using a flexible Bayesian, space-time, Susceptible-Infected-Recovered (SIR) modelling approach. The model incorporated parameters describing spatiotemporal variation in H1N1 infection and local socio-environmental factors. RESULTS The weekly transmission rate of pandemic (H1N1) 2009 was negatively associated with the weekly area-mean maximum temperature at a lag of 1 week (LMXT) (posterior mean: -0.341; 95% credible interval (CI): -0.370--0.311) and the socio-economic index for area (SEIFA) (posterior mean: -0.003; 95% CI: -0.004--0.001), and was positively associated with the product of LMXT and the weekly area-mean vapour pressure at a lag of 1 week (LVAP) (posterior mean: 0.008; 95% CI: 0.007-0.009). There was substantial spatiotemporal variation in transmission rate of pandemic (H1N1) 2009 across Queensland over the epidemic period. High random effects of estimated transmission rates were apparent in remote areas and some postal areas with higher proportion of indigenous populations and smaller overall populations. CONCLUSIONS Local SEIFA and local atmospheric conditions were associated with the transmission rate of pandemic (H1N1) 2009. The more populated regions displayed consistent and synchronized epidemics with low average transmission rates. The less populated regions had high average transmission rates with more variations during the H1N1 epidemic period.
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Affiliation(s)
- Xiaodong Huang
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Archie C A Clements
- Research School of Population Health, The Australian National University, Canberra, ACT, Australia
| | - Gail Williams
- School of Public Health, the University of Queensland, Brisbane, Queensland, Australia
| | - Kerrie Mengersen
- Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Shilu Tong
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Wenbiao Hu
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
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100
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Guclu H, Read J, Vukotich CJ, Galloway DD, Gao H, Rainey JJ, Uzicanin A, Zimmer SM, Cummings DAT. Social Contact Networks and Mixing among Students in K-12 Schools in Pittsburgh, PA. PLoS One 2016; 11:e0151139. [PMID: 26978780 PMCID: PMC4792376 DOI: 10.1371/journal.pone.0151139] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2015] [Accepted: 02/23/2016] [Indexed: 11/18/2022] Open
Abstract
Students attending schools play an important role in the transmission of influenza. In this study, we present a social network analysis of contacts among 1,828 students in eight different schools in urban and suburban areas in and near Pittsburgh, Pennsylvania, United States of America, including elementary, elementary-middle, middle, and high schools. We collected social contact information of students who wore wireless sensor devices that regularly recorded other devices if they are within a distance of 3 meters. We analyzed these networks to identify patterns of proximal student interactions in different classes and grades, to describe community structure within the schools, and to assess the impact of the physical environment of schools on proximal contacts. In the elementary and middle schools, we observed a high number of intra-grade and intra-classroom contacts and a relatively low number of inter-grade contacts. However, in high schools, contact networks were well connected and mixed across grades. High modularity of lower grades suggests that assumptions of homogeneous mixing in epidemic models may be inappropriate; whereas lower modularity in high schools suggests that homogenous mixing assumptions may be more acceptable in these settings. The results suggest that interventions targeting subsets of classrooms may work better in elementary schools than high schools. Our work presents quantitative measures of age-specific, school-based contacts that can be used as the basis for constructing models of the transmission of infections in schools.
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Affiliation(s)
- Hasan Guclu
- Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Public Health Dynamics Laboratory, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Statistics, Faculty of Science, Istanbul Medeniyet University, Istanbul, Turkey
- * E-mail:
| | - Jonathan Read
- Department of Epidemiology and Population Health, The Farr Institute @HeRC, Institute of Infection and Global Health, University of Liverpool, Liverpool, L69 3GL, United Kingdom
- Lancaster Medical School, Lancaster University, Lancaster, LA1 4YG, United Kingdom
| | - Charles J. Vukotich
- School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - David D. Galloway
- Public Health Dynamics Laboratory, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Hongjiang Gao
- Division of Global Migration and Quarantine, US Centers of Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Jeanette J. Rainey
- Division of Global Migration and Quarantine, US Centers of Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Amra Uzicanin
- Division of Global Migration and Quarantine, US Centers of Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Shanta M. Zimmer
- School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Derek A. T. Cummings
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
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