56
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Near real-time forecasting for cholera decision making in Haiti after Hurricane Matthew

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Computational models of cholera transmission can provide objective insights into the course of an ongoing epidemic and aid decision making on allocation of health care resources. However, models are typically designed, calibrated and interpreted post-hoc. Here, we report the efforts of a team from academia, field research and humanitarian organizations to model in near real-time the Haitian cholera outbreak after Hurricane Matthew in October 2016, to assess risk and to quantitatively estimate the efficacy of a then ongoing vaccination campaign. A rainfall-driven, spatially-explicit meta-community model of cholera transmission was coupled to a data assimilation scheme for computing short-term projections of the epidemic in near real-time. The model was used to forecast cholera incidence for the months after the passage of the hurricane (October-December 2016) and to predict the impact of a planned oral cholera vaccination campaign. Our first projection, from October 29 to December 31, predicted the highest incidence in the departments of Grande Anse and Sud, accounting for about 45% of the total cases in Haiti. The projection included a second peak in cholera incidence in early December largely driven by heavy rainfall forecasts, confirming the urgency for rapid intervention. A second projection (from November 12 to December 31) used updated rainfall forecasts to estimate that 835 cases would be averted by vaccinations in Grande Anse (90% Prediction Interval [PI] 476-1284) and 995 in Sud (90% PI 508-2043). The experience gained by this modeling effort shows that state-of-the-art computational modeling and data-assimilation methods can produce informative near real-time projections of cholera incidence. Collaboration among modelers and field epidemiologists is indispensable to gain fast access to field data and to translate model results into operational recommendations for emergency management during an outbreak. Future efforts should thus draw together multi-disciplinary teams to ensure model outputs are appropriately based, interpreted and communicated.

          Author summary

          Mathematical models of cholera transmission can help predict the dynamics of outbreaks in near real-time in order to inform decision making for emergency management. Following the passage of Hurricane Matthew on cholera-struck Haiti in October 2016, a large oral cholera vaccine campaign targeting approximately 760,000 individuals was planned to minimize the risk of cholera transmission after the heavy hurricane rainfall. We used a reliable spatially-explicit mathematical model and state-of-the-art data assimilation techniques to predict the number of averted cases owing to the vaccination campaign. We accounted for different forecasts of precipitation patterns, a well known risk factor for the amplification of cholera epidemics, and reported near real-time projections of cholera cases for November and December 2016 to a group of epidemiologists and field researchers of Médecins Sans Frontières. Model results were then translated into operational recommendations during the outbreak management. Our projections highlighted that the departments of Grande Anse and Sud were at risk of a second epidemic wave, thus supporting the planned vaccination campaign therein. Our projections provided estimates and prediction intervals of the actual number of averted cases due to OCV per each of the 140 Haitian communes.

          Related collections

          Most cited references37

          • Record: found
          • Abstract: not found
          • Article: not found

          The Ensemble Kalman Filter: theoretical formulation and practical implementation

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Forecasting seasonal outbreaks of influenza.

            Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003-2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Using Mobile Phone Data to Predict the Spatial Spread of Cholera

              Effective response to infectious disease epidemics requires focused control measures in areas predicted to be at high risk of new outbreaks. We aimed to test whether mobile operator data could predict the early spatial evolution of the 2010 Haiti cholera epidemic. Daily case data were analysed for 78 study areas from October 16 to December 16, 2010. Movements of 2.9 million anonymous mobile phone SIM cards were used to create a national mobility network. Two gravity models of population mobility were implemented for comparison. Both were optimized based on the complete retrospective epidemic data, available only after the end of the epidemic spread. Risk of an area experiencing an outbreak within seven days showed strong dose-response relationship with the mobile phone-based infectious pressure estimates. The mobile phone-based model performed better (AUC 0.79) than the retrospectively optimized gravity models (AUC 0.66 and 0.74, respectively). Infectious pressure at outbreak onset was significantly correlated with reported cholera cases during the first ten days of the epidemic (p < 0.05). Mobile operator data is a highly promising data source for improving preparedness and response efforts during cholera outbreaks. Findings may be particularly important for containment efforts of emerging infectious diseases, including high-mortality influenza strains.
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curation
                Role: Data curationRole: InvestigationRole: Resources
                Role: Formal analysisRole: Software
                Role: ConceptualizationRole: InvestigationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                May 2018
                16 May 2018
                : 14
                : 5
                : e1006127
                Affiliations
                [1 ] Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
                [2 ] Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
                [3 ] Epicentre, Paris, France
                [4 ] Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, United States of America
                [5 ] Epicentre, Geneva, Switzerland
                [6 ] Department of International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, United States of America
                [7 ] Department of Environmental Sciences, Informatics and Statistics, University Ca’ Foscari Venezia, Venezia Mestre, Italy
                [8 ] Department of Civil, Environmental and Architectural Engineering, University of Padua, Padova, Italy
                The Pennsylvania State University, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-6892-9826
                http://orcid.org/0000-0002-8613-5170
                http://orcid.org/0000-0001-8662-9077
                http://orcid.org/0000-0003-0885-8418
                http://orcid.org/0000-0001-5872-0666
                Article
                PCOMPBIOL-D-17-01790
                10.1371/journal.pcbi.1006127
                5973636
                29768401
                2b318dc5-633e-4be0-a2aa-34a850ce665a
                © 2018 Pasetto et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 27 October 2017
                : 9 April 2018
                Page count
                Figures: 7, Tables: 1, Pages: 22
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: 227612
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung;
                Award ID: DYCHO CR23I2 138104
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung;
                Award ID: 200021-172578
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000865, Bill and Melinda Gates Foundation;
                Award ID: OPP 90073892
                Award Recipient :
                D. Pasetto, J. Lemaitre and A. Rinaldo acknowledge funds provided by the Swiss National Science Foundation ( http://www.snf.ch), via the projects: "Dynamics and controls of large-scale cholera outbreaks" (DYCHO CR23I2 138104) and "Optimal control of intervention strategies for waterborne disease epidemics" (200021-172578). A. Rinaldo acknowledges earlier strategic funding provided by the European Research Council (ERC) Grant RINEC-227612 ( http://cordis.europa.eu/project/rcn/89293_en.html). A. Azman acknowledges funds from the Bill and Melinda Gates Foundation ( https://www.gatesfoundation.org), project number OPP 90073892. F. Finger acknowledges support through the Swiss National Science Foundation Early Postdoc Mobility Fellowship P2ELP3_175079. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Infectious Diseases
                Bacterial Diseases
                Cholera
                Medicine and Health Sciences
                Tropical Diseases
                Neglected Tropical Diseases
                Cholera
                Medicine and Health Sciences
                Infectious Diseases
                Infectious Disease Control
                Vaccines
                Cholera Vaccines
                Medicine and Health Sciences
                Infectious Diseases
                Infectious Disease Control
                Vaccines
                Earth Sciences
                Atmospheric Science
                Meteorology
                Rain
                People and places
                Geographical locations
                North America
                Caribbean
                Haiti
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Decision Making
                Biology and Life Sciences
                Psychology
                Cognitive Psychology
                Decision Making
                Social Sciences
                Psychology
                Cognitive Psychology
                Decision Making
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognition
                Decision Making
                Earth Sciences
                Geography
                Human Geography
                Human Mobility
                Social Sciences
                Human Geography
                Human Mobility
                Biology and Life Sciences
                Immunology
                Immunity
                Medicine and Health Sciences
                Immunology
                Immunity
                Custom metadata
                vor-update-to-uncorrected-proof
                2018-05-29
                All relevant data are within the paper and its Supporting Information files. We provided the URL to the sources of the used data.

                Quantitative & Systems biology
                Quantitative & Systems biology

                Comments

                Comment on this article