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


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.

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






Image courtesy Dr. Philipp H. Boersch-Supan

Measuring and modelling the lives of ocean wanderers

Speaker: Dr. Philipp H. Boersch-Supan

Postdoctoral Fellow, Department of Geography, University of Florida

Thursday, September 28, 2017

3:00-3:50 PM (Period 8)

Turlington Hall Room 3012

University of Florida

All are welcome to attend.

Albatrosses, penguins, and elephant seals exploit the resources of the world’s largest habitat – the open ocean. Understanding the foraging behaviours and life-history strategies of these fascinating animals is challenging given the vast spatial and temporal scales involved. By combining observations from remote sensing, biologging, and other technologies with mechanistic models, I aim to understand the interplay between oceanic predators and their environment.
Philipp Boersch-Supan (PhD Marine Biology, University of St Andrews, 2014) is a quantitative ecologist with a particular interest in marine ecosystems. His work is aimed at making the most of often sparse ecological data, and bridging the gap between field observations and mathematical models of living systems. He is currently a postdoctoral researcher at the University of Florida.
Bayesian fits of a mechanistic population model of different life-stages of the chytrid fungus, a pathogen that is a severe threat to amphibians worldwide. Image courtesy Dr. Philipp Boersch-Supan.
Bayesian fits of a mechanistic population model of different life-stages of the chytrid fungus, a pathogen that is a severe threat to amphibians worldwide. Image courtesy Dr. Philipp Boersch-Supan.

BOERSCH-SUPAN, RYAN – deBInfer: Bayesian inference for dynamical models of biological systems in R

Philipp H. Boersch-Supan, Sadie J. Ryan, Leah R. Johnson

Article first published online: 15 OCT 2016 Methods in Ecology and Evolution

DOI: 10.1111/2041-210X.12679


1.Understanding the mechanisms underlying biological systems, and ultimately, predicting their behaviours in a changing environment requires overcoming the gap between mathematical models and experimental or observational data. Differential equations (DEs) are commonly used to model the temporal evolution of biological systems, but statistical methods for comparing DE models to data and for parameter inference are relatively poorly developed. This is especially problematic in the context of biological systems where observations are often noisy and only a small number of time points may be available.

2.The Bayesian approach offers a coherent framework for parameter inference that can account for multiple sources of uncertainty, while making use of prior information. It offers a rigorous methodology for parameter inference, as well as modelling the link between unobservable model states and parameters, and observable quantities.

3.We present deBInfer, a package for the statistical computing environment R, implementing a Bayesian framework for parameter inference in DEs. deBInfer provides templates for the DE model, the observation model and data likelihood, and the model parameters and their prior distributions. A Markov chain Monte Carlo (MCMC) procedure processes these inputs to estimate the posterior distributions of the parameters and any derived quantities, including the model trajectories. Further functionality is provided to facilitate MCMC diagnostics, the visualisation of the posterior distributions of model parameters and trajectories, and the use of compiled DE models for improved computational performance.

4.The templating approach makes deBInfer applicable to a wide range of DE models. We demonstrate its application to ordinary and delay DE models for population ecology.

Read the full publication at Methods in Ecology and Evolution

Ryan Lab Fall 2016. Image courtesy Dr. Sadie Ryan.
Ryan Lab Fall 2016. Image courtesy Dr. Sadie Ryan.

This summer, Geography’s Ryan Lab and the Emerging Pathogens Institute were proud to host Lauren Fregosi as a summer research intern working on Dr. Sadie Ryan‘s National Science Foundation’s Ecology and Evolution of Infectious Diseases (NSF EEID) grant. The internship, which was offered through the NSF Research Experience for Undergraduates (REU) program, was focused on modelling approaches on the effects of climate, land use, and socioeconomic conditions on vector-borne disease transmission.

Ms. Fregosi is a native of Long Island’s south shore, and is a rising senior at Syracuse University (class of 2017), pursuing a bachelor’s degree in Biotechnology, with a minor in Applied Statistics, and conducting research at SU’s Falk School of Public Health. She was excited to work with the Ryan Lab because of her passionate interest in vectorborne disease control. This work built on her existing experience working on a project analyzing biting rates of different mosquito species and urbanization in Ecuador, at the Falk School of Public Health. Fregosi has enjoyed learning multiple strategies for organizing, analyzing, and describing datasets, in R, developing models in both R and GARP, and becoming well versed with GIS, and ArcGIS model builder.

When not polishing her GIS skills, Ms. Fregosi donates her free time volunteering at Syracuse’s Upstate Golisano Children’s Hospital and Habitat for Humanity. She also organizes phlanthropic activities, community projects, and fundraisers for her sorority.

The Department of Geography thanks Lauren for all of her hard work this summer, and wishes her luck in her continued studies!

SOUTHWORTHDemonstrating correspondence between decision-support models and dynamics of real-world environmental systems

Ray Huffaker, Rafael Muñoz-Carpena, Miguel A. Campo-Bescós, Jane Southworth

Article first published online: 24 May 2016 Environmental Modelling & Software

DOI: 10.1016/j.envsoft.2016.04.024

ABSTRACT: There are increasing calls to audit decision-support models used for environmental policy to ensure that they correspond with the reality facing policy makers. Modelers can establish correspondence by providing empirical evidence of real-world behavior that their models skillfully simulate. Since real-world behavior—especially in environmental systems—is often complex, credibly modeling underlying dynamics is essential. We present a pre-modeling diagnostic framework based on Nonlinear Time Series (NLTS) methods for reconstructing real-world environmental dynamics from observed data. The framework is illustrated with a case study of saltwater intrusion into coastal wetlands in Everglades National Park, Florida, USA. We propose that environmental modelers test for systematic dynamic behavior in observed data before resorting to conventional stochastic exploratory approaches unable to detect this valuable information. Reconstructed data dynamics can be used, along with other expert information, as a rigorous benchmark to guide specification and testing of environmental decision-support models corresponding with real-world behavior.

Read the full publication at Environmental Modelling & Software

Image courtesy Dr. Rachata Muneepeerakul
Image courtesy Dr. Rachata Muneepeerakul

On Analyzing Occupation Interdependence in Urban Economies and Modeling Coupled Natural-Human Systems

Speaker: Dr. Rachata Muneepeerakul

Associate Professor, Department of Agricultural & Biological Engineering

Thursday, February 4, 2016

3:00-3:50 PM (Period 8)

Turlington Hall Room 3012

University of Florida

All are welcome to attend.

KEELLINGS – Evaluation of downscaled CMIP5 model skill in simulating daily maximum temperature over the southeastern United States

David Keellings

Article first published online:25 JAN 2016 International Journal of Climatology

DOI: 10.1002/joc.4612

ABSTRACT: Downscaled CMIP5 (Coupled Model Intercomparison Project Phase 5) climate projections of maximum daily temperature from the Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections archive are examined regionally over the southeastern United States. Three measures of model skill (means-based, distribution-based, extreme-based) are utilized to assess the ability of 15 downscaled models to simulate daily maximum temperature observations. A new test is proposed to determine statistical significance of the probability density function-based skill measures. Skill scores are found to be generally high for all three measures throughout the study region, but lower scores are present in coastal and mountainous areas. Application of the significance test shows that while the skill scores may be high, they are not significantly higher than could be expected at random in some areas. The distribution-based skill scores are not significant in much of Florida and the Appalachians. The extreme-based skill scores are not significant in more than 90% of the region for all models investigated. The findings suggest that although the downscaled models have simulated observed means well and are a good match to the entire distribution of observations, they are not simulating the occurrence of extreme (above 90th percentile) maximum daily temperatures.

Read the full publication at International Journal of Climatology.