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Seasonality of agricultural exposure as an important predictor of seasonal yellow fever spillover in Brazil.

ABSTRACT: Yellow fever virus (YFV) is a zoonotic arbovirus affecting both humans and non-human primates (NHP's) in Africa and South America. Previous descriptions of YF's seasonality have relied purely on climatic explanations, despite the high proportion of cases occurring in people involved in agriculture. We use a series of random forest classification models to predict the monthly occurrence of YF in humans and NHP's across Brazil, by fitting four classes of covariates related to the seasonality of climate and agriculture (planting and harvesting), crop output and host demography. We find that models captured seasonal YF reporting in humans and NHPs when they considered seasonality of agriculture rather than climate, particularly for monthly aggregated reports. These findings illustrate the seasonality of exposure, through agriculture, as a component of zoonotic spillover. Additionally, by highlighting crop types and anthropogenic seasonality, these results could directly identify areas at highest risk of zoonotic spillover.

SUBMITTER: Hamlet A

PROVIDER: S-EPMC8206143 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Seasonality of agricultural exposure as an important predictor of seasonal yellow fever spillover in Brazil.

Hamlet Arran A Ramos Daniel Garkauskas DG Gaythorpe Katy A M KAM Romano Alessandro Pecego Martins APM Garske Tini T Ferguson Neil M NM

Nature communications 20210615 1

Yellow fever virus (YFV) is a zoonotic arbovirus affecting both humans and non-human primates (NHP's) in Africa and South America. Previous descriptions of YF's seasonality have relied purely on climatic explanations, despite the high proportion of cases occurring in people involved in agriculture. We use a series of random forest classification models to predict the monthly occurrence of YF in humans and NHP's across Brazil, by fitting four classes of covariates related to the seasonality of cl ...[more]

PMID: 34131128

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Project description:BackgroundYellow 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).ResultsOf 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.ConclusionsThe 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.

Project description:The recent reemergence of yellow fever virus (YFV) in Brazil has raised serious concerns due to the rapid dissemination of the virus in the southeastern region. To better understand YFV genetic diversity and dynamics during the recent outbreak in southeastern Brazil, we generated 18 complete and nearly complete genomes from the peak of the epidemic curve from nonhuman primates (NHPs) and human infected cases across the Espírito Santo and Rio de Janeiro states. Genomic sequencing of 18 YFV genomes revealed the estimated timing, source, and likely routes of yellow fever virus transmission and dispersion during one of the largest outbreaks ever registered in Brazil. We showed that during the recent epidemic, YFV was reintroduced from Minas Gerais to the Espírito Santo and Rio de Janeiro states multiple times between 2016 and 2019. The analysis of data from portable sequencing could identify the corridor of spread of YFV. These findings reinforce the idea that continued genomic surveillance strategies can provide information on virus genetic diversity and transmission dynamics that might assist in understanding arbovirus epidemics.IMPORTANCE Arbovirus infections in Brazil, including yellow fever, dengue, zika, and chikungunya, result in considerable morbidity and mortality and are pressing public health concerns. However, our understanding of these outbreaks is hampered by the limited availability of genomic data. In this study, we investigated the genetic diversity and spatial distribution of YFV during the current outbreak by analyzing genomic data from areas in southeastern Brazil not covered by other previous studies. To gain insights into the routes of YFV introduction and dispersion, we tracked the virus by sequencing YFV genomes sampled from nonhuman primates and infected patients from the southeastern region. Our study provides an understanding of how YFV initiates transmission in new Brazilian regions and illustrates that genomics in the field can augment traditional approaches to infectious disease surveillance and control.

Dataset Information

Seasonality of agricultural exposure as an important predictor of seasonal yellow fever spillover in Brazil.

Publications

Seasonality of agricultural exposure as an important predictor of seasonal yellow fever spillover in Brazil.

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