RYANTemperature drives Zika virus transmission: evidence from empirical and mathematical models

Blanka Tesla, Leah R. Demakovsky, Erin A. Mordecai, Sadie J. Ryan, Matthew H. Bonds, Calistus N. Ngonghala, Melinda A. Brindley, Courtney C. Murdock

Article first published online: 15 AUG 2018 Proceedings of the Royal Society B

DOI: 10.1098/rspb.2018.0795

ABSTRACT: Temperature is a strong driver of vector-borne disease transmission. Yet, for emerging arboviruses we lack fundamental knowledge on the relationship between transmission and temperature. Current models rely on the untested assumption that Zika virus responds similarly to dengue virus, potentially limiting our ability to accurately predict the spread of Zika. We conducted experiments to estimate the thermal performance of Zika virus (ZIKV) in field-derived Aedes aegypti across eight constant temperatures. We observed strong, unimodal effects of temperature on vector competence, extrinsic incubation period and mosquito survival. We used thermal responses of these traits to update an existing temperature-dependent model to infer temperature effects on ZIKV transmission. ZIKV transmission was optimized at 29°C, and had a thermal range of 22.7°C–34.7°C. Thus, as temperatures move towards the predicted thermal optimum (29°C) owing to climate change, urbanization or seasonality, Zika could expand north and into longer seasons. By contrast, areas that are near the thermal optimum were predicted to experience a decrease in overall environmental suitability. We also demonstrate that the predicted thermal minimum for Zika transmission is 5°C warmer than that of dengue, and current global estimates on the environmental suitability for Zika are greatly over-predicting its possible range.

Read the full publication at Proceedings of the Royal Society B

 

 

 

 

 

LIPPI, RYANNonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study

Rachel Lowe, Antonio Gasparrini, Cédric J. Van Meerbeeck, Catherine A. Lippi, Roché Mahon, Adrian R. Trotman, Leslie Rollock, Avery Q. J. Hinds, Sadie J. Ryan, Anna M. Stewart-Ibarra

Article first published online: 17 JUL 2018 PLOS Medicine

DOI: 10.1371/journal.pmed.1002613

ABSTRACT:

Background
Over the last 5 years (2013–2017), the Caribbean region has faced an unprecedented crisis of co-occurring epidemics of febrile illness due to arboviruses transmitted by the Aedes sp. mosquito (dengue, chikungunya, and Zika). Since 2013, the Caribbean island of Barbados has experienced 3 dengue outbreaks, 1 chikungunya outbreak, and 1 Zika fever outbreak. Prior studies have demonstrated that climate variability influences arbovirus transmission and vector population dynamics in the region, indicating the potential to develop public health interventions using climate information. The aim of this study is to quantify the nonlinear and delayed effects of climate indicators, such as drought and extreme rainfall, on dengue risk in Barbados from 1999 to 2016.

Methods and findings
Distributed lag nonlinear models (DLNMs) coupled with a hierarchal mixed-model framework were used to understand the exposure–lag–response association between dengue relative risk and key climate indicators, including the standardised precipitation index (SPI) and minimum temperature (Tmin). The model parameters were estimated in a Bayesian framework to produce probabilistic predictions of exceeding an island-specific outbreak threshold. The ability of the model to successfully detect outbreaks was assessed and compared to a baseline model, representative of standard dengue surveillance practice. Drought conditions were found to positively influence dengue relative risk at long lead times of up to 5 months, while excess rainfall increased the risk at shorter lead times between 1 and 2 months. The SPI averaged over a 6-month period (SPI-6), designed to monitor drought and extreme rainfall, better explained variations in dengue risk than monthly precipitation data measured in millimetres. Tmin was found to be a better predictor than mean and maximum temperature. Furthermore, including bidimensional exposure–lag–response functions of these indicators—rather than linear effects for individual lags—more appropriately described the climate–disease associations than traditional modelling approaches. In prediction mode, the model was successfully able to distinguish outbreaks from nonoutbreaks for most years, with an overall proportion of correct predictions (hits and correct rejections) of 86% (81%:91%) compared with 64% (58%:71%) for the baseline model. The ability of the model to predict dengue outbreaks in recent years was complicated by the lack of data on the emergence of new arboviruses, including chikungunya and Zika.

Conclusion
We present a modelling approach to infer the risk of dengue outbreaks given the cumulative effect of climate variations in the months leading up to an outbreak. By combining the dengue prediction model with climate indicators, which are routinely monitored and forecasted by the Regional Climate Centre (RCC) at the Caribbean Institute for Meteorology and Hydrology (CIMH), probabilistic dengue outlooks could be included in the Caribbean Health-Climatic Bulletin, issued on a quarterly basis to provide climate-smart decision-making guidance for Caribbean health practitioners. This flexible modelling approach could be extended to model the risk of dengue and other arboviruses in the Caribbean region.

Read the full publication at PLOS Medicine

 

 

 

 

 

 

Aedes control – image courtesy Mr. Dany Krom

GAINESVILLE, FL – New research co-authored by UF Geography’s Dr. Sadie Ryan and Ms. Cat Lippi sheds light on the climate suitability for Aedes aegypti and Aedes albopictus mosquitos and transmission rates of Zika, chikungunya, and dengue fever.

The study, published in PLOS Neglected Tropical Diseases compares new data driven models of Zika, chikungunya, and dengue fever transmission to real world measurements of human infections caused by bites from Aedes aegypti and Ae. Albopictus mosquitoes. These models confirm that temperature is the single most important factor for predicting the rate and geographic spread of epidemics of these mosquito-borne diseases. Temperature influences transmissibility in many ways – affecting the lifespan of an individual mosquito, and determining biting frequency and the reproductive rate of the virus within the mosquito.

The collaborative research team includes experts in epidemiology, public health, ecology, mathematical modeling, and geography, and was funded by a grant from the National Science Foundation’s Ecology and Evolution of Infectious Disease program (NSF-DEB 1518681).