Spatio-temporal spillover risk of yellow fever in Brazil

• Published in: Parasites & vectors Vol. 11; no. 1; p. 488
• Main Authors: Kaul, RajReni B, Evans, Michelle V, Murdock, Courtney C, Drake, John M
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 29-08-2018; BioMed Central; BMC
• Subjects: Accuracy; Aedes - virology; Animal models; Animals; Aquatic insects; Arboviruses; Brazil; Brazil - epidemiology; Coastal zone; Demographics; Disease Outbreaks - prevention & control; Disease Reservoirs - veterinary; Disease Reservoirs - virology; Disease transmission; Distribution; Dynamics; Ecological and Environmental Phenomena; Ecology; Epidemiology; Fever; Health services; Humans; Infectious diseases; Logistic Models; Malaria; Medical geography; Medical research; Model accuracy; Mosquito Vectors - virology; Mosquitoes; Municipalities; Predictions; Primates; Primates - virology; Public health; Regions; Risk; Risk allocation; Risk factors; Risk mapping; River basins; Rivers; Seasonal variations; Seasonality; Seasons; Spatial epidemiology; Spatio-Temporal Analysis; Species richness; Tropical diseases; Vector-borne diseases; Vectors; Viruses; West Nile virus; Yellow fever; Yellow Fever - epidemiology; Yellow Fever - virology; Yellow fever virus - isolation & purification; Brazil; United States > US; Amazon River; Brazil; Spatial epidemiology; Arboviruses; Vectors; Risk mapping; Yellow fever
• ISSN: 1756-3305; 1756-3305
• DOI: 10.1186/s13071-018-3063-6

Yellow fever virus is a mosquito-borne flavivirus that persists in an enzoonotic cycle in non-human primates (NHPs) in Brazil, causing disease in humans through spillover events. Yellow fever (YF) re-emerged in the early 2000s, spreading from the Amazon River basin towards the previously considered low-risk, southeastern region of the country. Previous methods mapping YF spillover risk do not incorporate the temporal dynamics and ecological context of the disease, and are therefore unable to predict seasonality in spatial risk across Brazil. We present the results of a bagged logistic regression predicting the propensity for YF spillover per municipality (administrative sub-district) in Brazil from environmental and demographic covariates aggregated by month. Ecological context was incorporated by creating National and Regional models of spillover dynamics, where the Regional model consisted of two separate models determined by the regions' NHP reservoir species richness (high vs low).
Of the 5560 municipalities, 82 reported YF cases from 2001 to 2013. Model accuracy was high for the National and low reservoir richness (LRR) models (AUC = 0.80), while the high reservoir richness (HRR) model accuracy was lower (AUC = 0.63). The National model predicted consistently high spillover risk in the Amazon, while the Regional model predicted strong seasonality in spillover risk. Within the Regional model, seasonality of spillover risk in the HRR region was asynchronous to the LRR region. However, the observed seasonality of spillover risk in the LRR Regional model mirrored the national model predictions.
The predicted risk of YF spillover varies with space and time. Seasonal trends differ between regions indicating, at times, spillover risk can be higher in the urban coastal regions than the Amazon River basin which is counterintuitive based on current YF risk maps. Understanding the spatio-temporal patterns of YF spillover risk could better inform allocation of public health services.