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Rapid Forecasting of Cholera Risk in Mozambique: Translational Challenges and Opportunities

Published online by Cambridge University Press:  03 September 2019

Rebecca Kahn Open the ORCID record for Rebecca Kahn [Opens in a new window] ,
Ayesha S. Mahmud ,
Andrew Schroeder ,
Luis Hernando Aguilar Ramirez ,
John Crowley ,
Jennifer Chan Open the ORCID record for Jennifer Chan [Opens in a new window]  and
Caroline O. Buckee
Rebecca Kahn*
Affiliation:
Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MassachusettsUSA Department of Population Health Sciences, Harvard Graduate School of Arts and Sciences, Cambridge, MassachusettsUSA
Ayesha S. Mahmud
Affiliation:
Department of Demography, University of California, Berkeley, CaliforniaUSA
Andrew Schroeder
Affiliation:
Vice President of Research and Analysis, Direct Relief, Santa Barbara, CaliforniaUSA
Luis Hernando Aguilar Ramirez
Affiliation:
Master 2 Professional Knowledge Management, Information Management Officer at World Health Organization for the Health Cluster, Beira and Pemba, Mozambique
John Crowley
Affiliation:
Director of Information Management and Crisis Informatics at NetHope, McLean, VirginiaUSA
Jennifer Chan
Affiliation:
Department of Emergency Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois USA; NetHope, McLean, VirginiaUSA
Caroline O. Buckee
Affiliation:
Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MassachusettsUSA
*
Correspondence: Rebecca Kahn, MS Department of Epidemiology Harvard T.H. Chan School of Public Health 677 Huntington Avenue, Suite 506 Boston, Massachusetts 02115 USA E-mail: rek160@mail.harvard.edu
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Abstract

Disasters, such as cyclones, create conditions that increase the risk of infectious disease outbreaks. Epidemic forecasts can be valuable for targeting highest risk populations before an outbreak. The two main barriers to routine use of real-time forecasts include scientific and operational challenges. First, accuracy may be limited by availability of data and the uncertainty associated with the inherently stochastic processes that determine when and where outbreaks happen and spread. Second, even if data are available, the appropriate channels of communication may prevent their use for decision making.

In April 2019, only six weeks after Cyclone Idai devastated Mozambique’s central region and sparked a cholera outbreak, Cyclone Kenneth severely damaged northern areas of the country. By June 10, a total of 267 cases of cholera were confirmed, sparking a vaccination campaign. Prior to Kenneth’s landfall, a team of academic researchers, humanitarian responders, and health agencies developed a simple model to forecast areas at highest risk of a cholera outbreak. The model created risk indices for each district using combinations of four metrics: (1) flooding data; (2) previous annual cholera incidence; (3) sensitivity of previous outbreaks to the El Niño-Southern Oscillation cycle; and (4) a diffusion (gravity) model to simulate movement of infected travelers. As information on cases became available, the risk model was continuously updated. A web-based tool was produced, which identified highest risk populations prior to the cyclone and the districts at-risk following the start of the outbreak.

The model prior to Kenneth’s arrival using the metrics of previous incidence, projected flood, and El Niño sensitivity accurately predicted areas at highest risk for cholera. Despite this success, not all data were available at the scale at which the vaccination campaign took place, limiting the model’s utility, and the extent to which the forecasts were used remains unclear. Here, the science behind these forecasts and the organizational structure of this collaborative effort are discussed. The barriers to the routine use of forecasts in crisis settings are highlighted, as well as the potential for flexible teams to rapidly produce actionable insights for decision making using simple modeling tools, both before and during an outbreak.

Keywords

choleracycloneforecastingMozambiquetransdisciplinary research
Type
Special Report
Information
Prehospital and Disaster Medicine , Volume 34 , Issue 5 , October 2019 , pp. 557 - 562
Copyright
© World Association for Disaster and Emergency Medicine 2019 

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Footnotes

Note: The first and second authors contributed equally to this work.

References

World Health Organization. Tropical Cyclones Idai and Kenneth Mozambique Situation Report 06. https://www.afro.who.int/sites/default/files/2019-05/WHOSitRep6Mozambique10May2019.pdf. Accessed July 2, 2019.Google Scholar
UNICEF. UNICEF Mozambique Situation Report – 10 June 2019. https://reliefweb.int/report/mozambique/unicef-mozambique-cyclones-idai-and-kenneth-situation-report-11-01-10-june-2019. Accessed July 2, 2019.Google Scholar
Bhalla, N. Aid workers race to contain cholera outbreak in cyclone-hit Mozambique. U.S. https://www.reuters.com/article/us-mozambique-health-cholera-idUSKCN1SD21R. Published May 7, 2019. Accessed June 24, 2019.Google Scholar
Watson, JT, Gayer, M, Connolly, MA. Epidemics after natural disasters. Emerg Infect Dis. 2007;13(1):1–5.CrossRefGoogle ScholarPubMed
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. 2016;214(suppl 4):S414–S420.CrossRefGoogle ScholarPubMed
Bansal, S, Chowell, G, Simonsen, L, Vespignani, A, Viboud, C. Big data for infectious disease surveillance and modeling. J Infect Dis. 2016;214(suppl 4):S375–S379.CrossRefGoogle ScholarPubMed
Hay, SI, George, DB, Moyes, CL, Brownstein, JS. Big data opportunities for global infectious disease surveillance. PLoS Medicine. 2013;10(4):e1001413.CrossRefGoogle ScholarPubMed
MacLachlan, M. Rethinking global health research: towards integrative expertise. Global Health. 2009;5:6.CrossRefGoogle ScholarPubMed
Rimer, BK, Abrams, DB. Present and future horizons for transdisciplinary research. Am J Prev Med. 2012;42(2):200–201.CrossRefGoogle ScholarPubMed
Levine, AC. Academics are from Mars, humanitarians are from Venus: finding common ground to improve research during humanitarian emergencies. Clin Trials. 2016;13(1):79–82.CrossRefGoogle ScholarPubMed
Flood extent Solafa 2019-03-20 on Facebook Population. geonode.ingc.gov.mz. https://geonode.ingc.gov.mz/layers/geonode:hazard_aggregation_summary_13. Accessed June 24, 2019.Google Scholar
TC Kenneth Copernicus EMSR354 Flood Extent 20190502. geonode.ingc.gov.mz. https://geonode.ingc.gov.mz/layers/geonode:emsr354_observedeventmerged_20190502. Accessed June 24, 2019.Google Scholar
Lessler, J, Moore, SM, Luquero, FJ, et al. Mapping the burden of cholera in sub-Saharan Africa and implications for control: an analysis of data across geographical scales. Lancet. 2018;391(10133):1908–1915.CrossRefGoogle ScholarPubMed
Moore, SM, Azman, AS, Zaitchik, BF, et al. El Niño and the shifting geography of cholera in Africa. Proc Natl Acad Sci USA. 2017;114(17):4436–4441.CrossRefGoogle ScholarPubMed
High Resolution Population Density Maps ‐ Humanitarian Data Exchange. https://data.humdata.org/dataset/highresolutionpopulationdensitymaps. Accessed June 24, 2019.Google Scholar
Xia, Y, Bjørnstad, ON, Grenfell, BT. Measles metapopulation dynamics: a gravity model for epidemiological coupling and dynamics. Am Nat. 2004;164(2):267–281.CrossRefGoogle ScholarPubMed
Cholera in Mozambique. https://rebeccakahn.shinyapps.io/Cholera_Mozambique/. Accessed June 24, 2019.Google Scholar
Schroeder, A. If Cyclone Kenneth leads to cholera in Mozambique, who is most at risk? Direct Relief. https://www.directrelief.org/2019/04/if-cyclone-kenneth-leads-to-cholera-in-mozambique-who-is-most-at-risk/. Published April 27, 2019. Accessed June 28, 2019.Google Scholar
Schroeder, A. Predicting the spread of cholera in Mozambique as case count rises. Direct Relief. https://www.directrelief.org/2019/05/predicting-cholera-mozambique/. Published May 15, 2019. Accessed June 28, 2019.Google Scholar
GitHub. https://github.com/c2-d2/Cholera-Mozambique. Accessed June 24, 2019.Google Scholar
Funk, S, Camacho, A, Kucharski, AJ, Eggo, RM, Edmunds, WJ. Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model. Epidemics. 2018;22:56–61.CrossRefGoogle ScholarPubMed
Chretien, J-P, Riley, S, George, DB. Mathematical modeling of the West Africa Ebola epidemic. eLife. 2015;4.CrossRefGoogle Scholar
World Health Organization. Developing global norms for sharing data and results during public health emergencies. November 2015. https://www.who.int/medicines/ebola-treatment/blueprint_phe_data-share-results/en/. Accessed June 24, 2019.Google Scholar
Yozwiak, NL, Schaffner, SF, Sabeti, PC. Data sharing: make outbreak research open access. Nature. 2015;518(7540):477–479.CrossRefGoogle ScholarPubMed
Tatem, AJ. WorldPop, open data for spatial demography. Sci Data. 2017;4:170004.CrossRefGoogle ScholarPubMed
Population Density Maps – Facebook Data for Good. Facebook Data for Good. https://dataforgood.fb.com/tools/population-density-maps/. Accessed June 24, 2019.Google Scholar
Humanitarian Data Exchange. https://data.humdata.org/. Accessed June 28, 2019.Google Scholar
Lawrence, RJ, Gatzweiler, FW. Wanted: a transdisciplinary knowledge domain for urban health. J Urban Health. 2017;94(4):592–596.CrossRefGoogle ScholarPubMed
Rivers, C, Chretien, J-P, Riley, S, et al. Using “outbreak science” to strengthen the use of models during epidemics. Nat Commun. 2019;10(1):3102.CrossRefGoogle Scholar
Impact. Future Earth. https://www.futureearth.org/impact. Accessed June 24, 2019.Google Scholar