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Kraemer MUG, Golding N, Bisanzio D, Bhatt S, Pigott DM, Ray SE, Brady OJ, Brownstein JS, Faria NR, Cummings DAT, Pybus OG, Smith DL, Tatem AJ, Hay SI, Reiner RC. Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings. Sci Rep 2019; 9:5151. [PMID: 30914669 PMCID: PMC6435716 DOI: 10.1038/s41598-019-41192-3] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 03/03/2019] [Indexed: 12/03/2022] Open
Abstract
Human mobility is an important driver of geographic spread of infectious pathogens. Detailed information about human movements during outbreaks are, however, difficult to obtain and may not be available during future epidemics. The Ebola virus disease (EVD) outbreak in West Africa between 2014–16 demonstrated how quickly pathogens can spread to large urban centers following one cross-species transmission event. Here we describe a flexible transmission model to test the utility of generalised human movement models in estimating EVD cases and spatial spread over the course of the outbreak. A transmission model that includes a general model of human mobility significantly improves prediction of EVD’s incidence compared to models without this component. Human movement plays an important role not only to ignite the epidemic in locations previously disease free, but over the course of the entire epidemic. We also demonstrate important differences between countries in population mixing and the improved prediction attributable to movement metrics. Given their relative rareness, locally derived mobility data are unlikely to exist in advance of future epidemics or pandemics. Our findings show that transmission patterns derived from general human movement models can improve forecasts of spatio-temporal transmission patterns in places where local mobility data is unavailable.
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Affiliation(s)
- M U G Kraemer
- Department of Zoology, University of Oxford, Oxford, UK. .,Harvard Medical School, Boston, MA, USA. .,Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA.
| | - N Golding
- Department of BioSciences, University of Melbourne, Parkville, VIC, Australia
| | - D Bisanzio
- RTI International, Washington, D.C., USA.,Epidemiology and Public Health Division, School of Medicine, University of Nottingham, Nottingham, UK
| | - S Bhatt
- Imperial College London, London, United Kingdom
| | - D M Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - S E Ray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - O J Brady
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - J S Brownstein
- Harvard Medical School, Boston, MA, USA.,Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA
| | - N R Faria
- Department of Zoology, University of Oxford, Oxford, UK
| | - D A T Cummings
- Department of Biology, University of Florida, Gainesville, FL, USA.,Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - O G Pybus
- Department of Zoology, University of Oxford, Oxford, UK
| | - D L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Sanaria Institute for Global Health and Tropical Medicine, Rockville, USA
| | - A J Tatem
- WorldPop, Department of Geography and Environmental Sciences, University of Southampton, Southampton, UK.,Flowminder Foundation, Stockholm, Sweden
| | - S I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
| | - R C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
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Hofmann SG, Barlow DH, Papp LA, Detweiler MF, Ray SE, Shear MK, Woods SW, Gorman JM. Pretreatment attrition in a comparative treatment outcome study on panic disorder. Am J Psychiatry 1998; 155:43-7. [PMID: 9433337 DOI: 10.1176/ajp.155.1.43] [Citation(s) in RCA: 59] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
OBJECTIVE Whereas the fact of attrition during the course of treatment is well documented, little is known about the factors that affect sample selection before the beginning of a study ("pretreatment attrition"). The present study reports on the degree and sources of pretreatment attrition at two sites of a multicenter study on panic disorder that compared treatment outcomes for imipramine and cognitive behavior therapy. METHOD Data were collected at two clinical research sites, one with a pharmacological treatment orientation (N = 420) and one with a psychosocial treatment orientation (N = 208). RESULTS The main source of pretreatment attrition was participant refusal. At both research sites, eligible patients most often refused participation because they were either unwilling to start treatment with imipramine (30.6% and 47.4%, respectively) or discontinue their current medication (22.6% and 35.1%, respectively). CONCLUSIONS Results from comparative treatment outcome studies are limited not only to people who meet the study criteria but also to those who are willing to begin a medication treatment and discontinue their current medication.
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Affiliation(s)
- S G Hofmann
- Phobia Clinic, Hillside Hospital, Long Island Jewish Medical Center, Glen Oaks, N.Y., USA
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Abstract
Sixty, proliferative, endocardial lesions were diagnosed in 19,304 rats, for an overall incidence of 0.3%. This population consisted of 10,127 Fischer 344, 8,737 Wistar, 200 Sprague-Dawley, and 240 Long Evans rats from chronic/oncogenicity studies reported at Lilly Research Laboratories from 1976 to 1988. Of the 60 proliferative lesions, 44 were classified as endocardial hyperplasia, 15 as endocardial schwannomas, and one as an endocardial sarcoma for prevalence rates of 0.2%, 0.08%, and 0.005%, respectively. Affected rats ranged in age from 42 to 110 weeks. There were no sex or treatment-related differences in the prevalence of the rat endocardial proliferative lesions. A review of endocardial lesions in 18 of 233 Wistar rats treated with carbamate derivatives revealed endocardial hyperplasia in 12 rats, schwannomas in five rats, and a sarcoma in one rat. One of the 12 rats with endocardial hyperplasia also had an intramural schwannoma. Of 200 Wistar rats given N-nitroso-N-methylurea, two had endocardial hyperplasia, and one had an endocardial schwannoma. Morphologic features were similar in either spontaneous or treatment-associated hyperplasia or neoplasia of the rat endocardium. Probable Schwann cell origin of the endocardial proliferative lesions was indicated by positive immunohistochemical staining for S-100 antigen in 10/12 spontaneous and 11/14 carcinogen-associated endocardial hyperplastic lesions. Further, 15/16 spontaneous and 6/7 carcinogen-associated neoplasms were immunoreactive to S-100. No tumor metastasis was recorded in either the spontaneously affected or carcinogen-treated rats.
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Affiliation(s)
- M N Novilla
- Lilly Research Laboratories, A Division of Eli Lilly and Company, Greenfield, IN
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