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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.

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