A novel approach to modeling epidemic vulnerability, applied to Aedes aegypti-vectored diseases in Perú

BMC Infectious Diseases

Cory W. Morin¹  Lauren A. Frisbie²  Peter M. Rabinowitz²  César V. Munayco³  Julianne Meisner⁴  David M. Pigott⁵  Patricia J. García⁶  César P. Cárcamo⁶ 

[1] Center for Health and the Global Environment, Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA;Center for One Health Research, Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA;Centro Nacional de Epidemiología, Prevención y Control de Enfermedades, Peruvian Ministry of Health, Lima, Peru;Department of Epidemiology, University of Washington, Seattle, WA, USA;Center for One Health Research, Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA;Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA;School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru;

关键词: Outbreak;    Epidemic;    Spatial epidemiology;    Vectorborne diseases;    Perú;    Dengue;   

DOI  :  10.1186/s12879-021-06530-9

来源: Springer

【 摘 要 】

BackgroundA proactive approach to preventing and responding to emerging infectious diseases is critical to global health security. We present a three-stage approach to modeling the spatial distribution of outbreak vulnerability to Aedes aegypti-vectored diseases in Perú.MethodsExtending a framework developed for modeling hemorrhagic fever vulnerability in Africa, we modeled outbreak vulnerability in three stages: index case potential (stage 1), outbreak receptivity (stage 2), and epidemic potential (stage 3), stratifying scores on season and El Niño events. Subsequently, we evaluated the validity of these scores using dengue surveillance data and spatial models.ResultsWe found high validity for stage 1 and 2 scores, but not stage 3 scores. Vulnerability was highest in Selva Baja and Costa, and in summer and during El Niño events, with index case potential (stage 1) being high in both regions but outbreak receptivity (stage 2) being generally high in Selva Baja only.ConclusionsStage 1 and 2 scores are well-suited to predicting outbreaks of Ae. aegypti-vectored diseases in this setting, however stage 3 scores appear better suited to diseases with direct human-to-human transmission. To prevent outbreaks, measures to detect index cases should be targeted to both Selva Baja and Costa, while Selva Baja should be prioritized for healthcare system strengthening. Successful extension of this framework from hemorrhagic fevers in Africa to an arbovirus in Latin America indicates its broad utility for outbreak and pandemic preparedness and response activities.

【 授权许可】

CC BY