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      Transmission dynamics of cholera in Yemen, 2017: a real time forecasting

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          Abstract

          Background

          A large epidemic of cholera, caused by Vibrio cholerae, serotype Ogawa, has been ongoing in Yemen, 2017. To improve the situation awareness, the present study aimed to forecast the cholera epidemic, explicitly addressing the reporting delay and ascertainment bias.

          Methods

          Using weekly incidence of suspected cases, updated as a revised epidemic curve every week, the reporting delay was explicitly incorporated into the estimation model. Using the weekly case fatality risk as calculated by the World Health Organization, ascertainment bias was adjusted, enabling us to parameterize the family of logistic curves (i.e., logistic and generalized logistic models) for describing the unbiased incidence in 2017.

          Results

          The cumulative incidence at the end of the epidemic, was estimated at 790,778 (95% CI: 700,495, 914,442) cases and 767,029 (95% CI: 690,877, 871,671) cases, respectively, by using logistic and generalized logistic models. It was also estimated that we have just passed through the epidemic peak by week 26, 2017. From week 27 onwards, the weekly incidence was predicted to decrease.

          Conclusions

          Cholera epidemic in Yemen, 2017 was predicted to soon start to decrease. If the weekly incidence is reported in the up-to-the-minute manner and updated in later weeks, not a single data point but the entire epidemic curve must be precisely updated.

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          Most cited references21

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          Cholera epidemic in Haiti, 2010: using a transmission model to explain spatial spread of disease and identify optimal control interventions.

          Haiti is in the midst of a cholera epidemic. Surveillance data for formulating models of the epidemic are limited, but such models can aid understanding of epidemic processes and help define control strategies. To predict, by using a mathematical model, the sequence and timing of regional cholera epidemics in Haiti and explore the potential effects of disease-control strategies. Compartmental mathematical model allowing person-to-person and waterborne transmission of cholera. Within- and between-region epidemic spread was modeled, with the latter dependent on population sizes and distance between regional centroids (a "gravity" model). Haiti, 2010 to 2011. Haitian hospitalization data, 2009 census data, literature-derived parameter values, and model calibration. Dates of epidemic onset and hospitalizations. The plausible range for cholera's basic reproductive number (R(0), defined as the number of secondary cases per primary case in a susceptible population without intervention) was 2.06 to 2.78. The order and timing of regional cholera outbreaks predicted by the gravity model were closely correlated with empirical observations. Analysis of changes in disease dynamics over time suggests that public health interventions have substantially affected this epidemic. A limited vaccine supply provided late in the epidemic was projected to have a modest effect. Assumptions were simplified, which was necessary for modeling. Projections are based on the initial dynamics of the epidemic, which may change. Despite limited surveillance data from the cholera epidemic in Haiti, a model simulating between-region disease transmission according to population and distance closely reproduces reported disease patterns. This model is a tool that planners, policymakers, and medical personnel seeking to manage the epidemic could use immediately.
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            Transmission dynamics and control of cholera in Haiti: an epidemic model.

            Official projections of the cholera epidemic in Haiti have not incorporated existing disease trends or patterns of transmission, and proposed interventions have been debated without comparative estimates of their effect. We used a mathematical model of the epidemic to provide projections of future morbidity and mortality, and to produce comparative estimates of the effects of proposed interventions. We designed mathematical models of cholera transmission based on existing models and fitted them to incidence data reported in Haiti for each province from Oct 31, 2010, to Jan 24, 2011. We then simulated future epidemic trajectories from March 1 to Nov 30, 2011, to estimate the effect of clean water, vaccination, and enhanced antibiotic distribution programmes. We project 779,000 cases of cholera in Haiti (95% CI 599,000-914,000) and 11,100 deaths (7300-17,400) between March 1 and Nov 30, 2011. We expect that a 1% per week reduction in consumption of contaminated water would avert 105,000 cases (88,000-116,000) and 1500 deaths (1100-2300). We predict that the vaccination of 10% of the population, from March 1, will avert 63,000 cases (48,000-78,000) and 900 deaths (600-1500). The proposed extension of the use of antibiotics to all patients with severe dehydration and half of patients with moderate dehydration is expected to avert 9000 cases (8000-10,000) and 1300 deaths (900-2000). A decline in cholera prevalence in early 2011 is part of the natural course of the epidemic, and should not be interpreted as indicative of successful intervention. Substantially more cases of cholera are expected than official estimates used for resource allocation. Combined, clean water provision, vaccination, and expanded access to antibiotics might avert thousands of deaths. National Institutes of Health. Copyright © 2011 Elsevier Ltd. All rights reserved.
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              Examining rainfall and cholera dynamics in Haiti using statistical and dynamic modeling approaches.

              Haiti has been in the midst of a cholera epidemic since October 2010. Rainfall is thought to be associated with cholera here, but this relationship has only begun to be quantitatively examined. In this paper, we quantitatively examine the link between rainfall and cholera in Haiti for several different settings (including urban, rural, and displaced person camps) and spatial scales, using a combination of statistical and dynamic models. Statistical analysis of the lagged relationship between rainfall and cholera incidence was conducted using case crossover analysis and distributed lag nonlinear models. Dynamic models consisted of compartmental differential equation models including direct (fast) and indirect (delayed) disease transmission, where indirect transmission was forced by empirical rainfall data. Data sources include cholera case and hospitalization time series from the Haitian Ministry of Public Health, the United Nations Water, Sanitation and Health Cluster, International Organization for Migration, and Hôpital Albert Schweitzer. Rainfall data was obtained from rain gauges from the U.S. Geological Survey and Haiti Regeneration Initiative, and remote sensing rainfall data from the National Aeronautics and Space Administration Tropical Rainfall Measuring Mission. A strong relationship between rainfall and cholera was found for all spatial scales and locations examined. Increased rainfall was significantly correlated with increased cholera incidence 4-7 days later. Forcing the dynamic models with rainfall data resulted in good fits to the cholera case data, and rainfall-based predictions from the dynamic models closely matched observed cholera cases. These models provide a tool for planning and managing the epidemic as it continues. Copyright © 2013 Elsevier B.V. All rights reserved.
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                Author and article information

                Contributors
                nishiurah@med.hokudai.ac.jp
                stsuzuki@med.hokudai.ac.jp
                baoyinyuan@outlook.com
                tyamaguchi@med.hokudai.ac.jp
                yusuke.asai@med.hokudai.ac.jp
                Journal
                Theor Biol Med Model
                Theor Biol Med Model
                Theoretical Biology & Medical Modelling
                BioMed Central (London )
                1742-4682
                26 July 2017
                26 July 2017
                2017
                : 14
                : 14
                Affiliations
                [1 ]ISNI 0000 0001 2173 7691, GRID grid.39158.36, Graduate School of Medicine, , Hokkaido University, ; Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo, 060-8638 Japan
                [2 ]ISNI 0000 0004 1754 9200, GRID grid.419082.6, , CREST, Japan Science and Technology Agency, ; 4-1-8, Honcho, Kawaguchi-shi, Saitama, 332-0012 Japan
                Author information
                http://orcid.org/0000-0003-0941-8537
                Article
                61
                10.1186/s12976-017-0061-x
                5527441
                28747188
                8aae8973-ccc0-40c5-b3ff-c5c0604decd4
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 7 July 2017
                : 20 July 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100009619, Japan Agency for Medical Research and Development;
                Funded by: Japanese Society for the Promotion of Science
                Award ID: 16KT0130, 16K15356 and 17H04701
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003382, Core Research for Evolutional Science and Technology;
                Award ID: JPMJCR1413
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002241, Japan Science and Technology Agency;
                Award ID: RISTEX program for Science of Science, Technology and Innovation Policy
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004543, China Scholarship Council;
                Categories
                Research
                Custom metadata
                © The Author(s) 2017

                Quantitative & Systems biology
                vibrio cholerae,outbreak,epidemiology,prediction,backcalculation
                Quantitative & Systems biology
                vibrio cholerae, outbreak, epidemiology, prediction, backcalculation

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