scholarly journals Global risk model for vector-borne transmission of Zika virus reveals the role of El Niño 2015

2016 ◽  
Vol 114 (1) ◽  
pp. 119-124 ◽  
Author(s):  
Cyril Caminade ◽  
Joanne Turner ◽  
Soeren Metelmann ◽  
Jenny C. Hesson ◽  
Marcus S. C. Blagrove ◽  
...  
Keyword(s):  
South America ◽  
El Niño ◽  
Zika Virus ◽  
El Nino ◽  
Southern China ◽  
Significant Risk ◽  
The United States ◽  
Transmission Risk ◽  
Boreal Summer ◽  
Mosquito Vector

Zika, a mosquito-borne viral disease that emerged in South America in 2015, was declared a Public Health Emergency of International Concern by the WHO in February of 2016. We developed a climate-driven R0 mathematical model for the transmission risk of Zika virus (ZIKV) that explicitly includes two key mosquito vector species: Aedes aegypti and Aedes albopictus. The model was parameterized and calibrated using the most up to date information from the available literature. It was then driven by observed gridded temperature and rainfall datasets for the period 1950–2015. We find that the transmission risk in South America in 2015 was the highest since 1950. This maximum is related to favoring temperature conditions that caused the simulated biting rates to be largest and mosquito mortality rates and extrinsic incubation periods to be smallest in 2015. This event followed the suspected introduction of ZIKV in Brazil in 2013. The ZIKV outbreak in Latin America has very likely been fueled by the 2015–2016 El Niño climate phenomenon affecting the region. The highest transmission risk globally is in South America and tropical countries where Ae. aegypti is abundant. Transmission risk is strongly seasonal in temperate regions where Ae. albopictus is present, with significant risk of ZIKV transmission in the southeastern states of the United States, in southern China, and to a lesser extent, over southern Europe during the boreal summer season.

scholarly journals The Seasonality of the Great Plains Low-Level Jet and ENSO Relationship

2015 ◽  
Vol 28 (11) ◽  
pp. 4525-4544 ◽  
Author(s):  
Lakshmi Krishnamurthy ◽  
Gabriel A. Vecchi ◽  
Rym Msadek ◽  
Andrew Wittenberg ◽  
Thomas L. Delworth ◽  
...  
Keyword(s):  
Great Plains ◽  
El Niño ◽  
Seasonal Changes ◽  
Climate Model ◽  
El Nino ◽  
Southern Oscillation ◽  
The United States ◽  
Boreal Summer ◽  
Low Level ◽  
Low Level Jet

Abstract This study investigates the seasonality of the relationship between the Great Plains low-level jet (GPLLJ) and the Pacific Ocean from spring to summer, using observational analysis and coupled model experiments. The observed GPLLJ and El Niño–Southern Oscillation (ENSO) relation undergoes seasonal changes with a stronger GPLLJ associated with La Niña in boreal spring and El Niño in boreal summer. The ability of the GFDL Forecast-Oriented Low Ocean Resolution (FLOR) global coupled climate model, which has the high-resolution atmospheric and land components, to simulate the observed seasonality in the GPLLJ–ENSO relationship is assessed. The importance of simulating the magnitude and phase locking of ENSO accurately in order to better simulate its seasonal teleconnections with the Intra-Americas Sea (IAS) is demonstrated. This study explores the mechanisms for seasonal changes in the GPLLJ–ENSO relation in model and observations. It is hypothesized that ENSO affects the GPLLJ variability through the Caribbean low-level jet (CLLJ) during the summer and spring seasons. These results suggest that climate models with improved ENSO variability would advance our ability to simulate and predict seasonal variations of the GPLLJ and their associated impacts on the United States.

scholarly journals Future increase in extreme El Nino events under greenhouse warming increases Zika virus incidence in South America

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
V. Brahmananda Rao ◽  
K. Maneesha ◽  
Panangipalli Sravya ◽  
Sergio H. Franchito ◽  
Hariprasad Dasari ◽  
...  
Keyword(s):  
South America ◽  
El Niño ◽  
Zika Virus ◽  
El Nino ◽  
Greenhouse Warming ◽  
Virus Incidence ◽  
El Niño Events ◽  
Extreme El Niño ◽  
El Nino Events

scholarly journals Changes in Spread and Predictability Associated with ENSO in an Ensemble Coupled GCM

2006 ◽  
Vol 19 (17) ◽  
pp. 4378-4396 ◽  
Author(s):  
Renguang Wu ◽  
Ben P. Kirtman
Keyword(s):  
Surface Pressure ◽  
El Niño ◽  
El Nino ◽  
Tropical Pacific ◽  
La Niña ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Central Pacific ◽  
La Nina ◽  
Signal Change

Abstract The present study documents the influence of El Niño and La Niña events on the spread and predictability of rainfall, surface pressure, and 500-hPa geopotential height, and contrasts the relative contribution of signal and noise changes to the predictability change based on a long-term integration of an interactive ensemble coupled general circulation model. It is found that the pattern of the El Niño–Southern Oscillation (ENSO)-induced noise change for rainfall follows closely that of the corresponding signal change in most of the tropical regions. The noise for tropical Pacific surface pressure is larger (smaller) in regions of lower (higher) mean pressure. The ENSO-induced noise change for 500-hPa height displays smaller spatial scales compared to and has no systematic relationship with the signal change. The predictability for tropical rainfall and surface pressure displays obvious contrasts between the summer and winter over the Bay of Bengal, the western North Pacific, and the tropical southwestern Indian Ocean. The predictability for tropical 500-hPa height is higher in boreal summer than in boreal winter. In the equatorial central Pacific, the predictability for rainfall is much higher in La Niña years than in El Niño years. This occurs because of a larger percent reduction in the amplitude of noise compared to the percent decrease in the magnitude of signal from El Niño to La Niña years. A consistent change is seen in the predictability for surface pressure near the date line. In the western North and South Pacific, the predictability for boreal winter rainfall is higher in El Niño years than in La Niña years. This is mainly due to a stronger signal in El Niño years compared to La Niña years. The predictability for 500-hPa height increases over most of the Tropics in El Niño years. Over western tropical Pacific–Australia and East Asia, the predictability for boreal winter surface pressure and 500-hPa height is higher in El Niño years than in La Niña years. The predictability change for 500-hPa height is primarily due to the signal change.

scholarly journals Precipitation Prediction Skill for the West Coast United States: From Short to Extended Range

2018 ◽  
Vol 32 (1) ◽  
pp. 161-182 ◽  
Author(s):  
Baoxiang Pan ◽  
Kuolin Hsu ◽  
Amir AghaKouchak ◽  
Soroosh Sorooshian ◽  
Wayne Higgins
Keyword(s):  
United States ◽  
West Coast ◽  
El Niño ◽  
El Nino ◽  
The United States ◽  
Prediction Skill ◽  
Precipitation Prediction ◽  
Skill Scores ◽  
Extended Range ◽  
To Receive

Abstract Precipitation variability significantly influences the heavily populated West Coast of the United States, raising the need for reliable predictions. We investigate the region’s short- to extended-range precipitation prediction skill using the hindcast database of the Subseasonal-to-Seasonal Prediction Project (S2S). The prediction skill–lead time relationship is evaluated, using both deterministic and probabilistic skill scores. Results show that the S2S models display advantageous deterministic skill at week 1. For week 2, prediction is useful for the best-performing model, with a Pearson correlation coefficient larger than 0.6. Beyond week 2, predictions generally provide little useful deterministic skill. Sources of extended-range predictability are investigated, focusing on El Niño–Southern Oscillation (ENSO) and the Madden–Julian oscillation (MJO). We found that periods of heavy precipitation associated with ENSO are more predictable at the extended range period. During El Niño years, Southern California tends to receive more precipitation in late winter, and most models show better extended-range prediction skill. On the contrary, during La Niña years Oregon tends to receive more precipitation in winter, with most models showing better extended-range skill. We believe the excessive precipitation and improved extended-range prediction skill are caused by the meridional shift of baroclinic systems as modulated by ENSO. Through examining precipitation anomalies conditioned on the MJO, we verified that active MJO events systematically modulate the area’s precipitation distribution. Our results show that most models do not represent the MJO or its associated teleconnections, especially at phases 3–4. However, some models exhibit enhanced extended-range prediction skills under active MJO conditions.

scholarly journals Vector distribution and transmission risk of the Zika virus in South and Central America

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7920
Author(s):  
Sarah Cunze ◽  
Judith Kochmann ◽  
Lisa K. Koch ◽  
Elisa Genthner ◽  
Sven Klimpel
Keyword(s):  
South America ◽  
Central America ◽  
Zika Virus ◽  
Habitat Suitability ◽  
Virus Transmission ◽  
Transmission Risk ◽  
Eastern Coast ◽  
Suitable Habitat ◽  
Virus Infections ◽  
Vector Species

Background Zika is of great medical relevance due to its rapid geographical spread in 2015 and 2016 in South America and its serious implications, for example, certain birth defects. Recent epidemics urgently require a better understanding of geographic patterns of the Zika virus transmission risk. This study aims to map the Zika virus transmission risk in South and Central America. We applied the maximum entropy approach, which is common for species distribution modelling, but is now also widely in use for estimating the geographical distribution of infectious diseases. Methods As predictor variables we used a set of variables considered to be potential drivers of both direct and indirect effects on the emergence of Zika. Specifically, we considered (a) the modelled habitat suitability for the two main vector species Aedes aegypti and Ae. albopictus as a proxy of vector species distributions; (b) temperature, as it has a great influence on virus transmission; (c) commonly called evidence consensus maps (ECM) of human Zika virus infections on a regional scale as a proxy for virus distribution; (d) ECM of human dengue virus infections and, (e) as possibly relevant socio-economic factors, population density and the gross domestic product. Results The highest values for the Zika transmission risk were modelled for the eastern coast of Brazil as well as in Central America, moderate values for the Amazon basin and low values for southern parts of South America. The following countries were modelled to be particularly affected: Brazil, Colombia, Cuba, Dominican Republic, El Salvador, Guatemala, Haiti, Honduras, Jamaica, Mexico, Puerto Rico and Venezuela. While modelled vector habitat suitability as predictor variable showed the highest contribution to the transmission risk model, temperature of the warmest quarter contributed only comparatively little. Areas with optimal temperature conditions for virus transmission overlapped only little with areas of suitable habitat conditions for the two main vector species. Instead, areas with the highest transmission risk were characterised as areas with temperatures below the optimum of the virus, but high habitat suitability modelled for the two main vector species. Conclusion Modelling approaches can help estimating the spatial and temporal dynamics of a disease. We focused on the key drivers relevant in the Zika transmission cycle (vector, pathogen, and hosts) and integrated each single component into the model. Despite the uncertainties generally associated with modelling, the approach applied in this study can be used as a tool and assist decision making and managing the spread of Zika.

Sign in / Sign up

Export Citation Format

Share Document