skip to main content


Title: Effects of changes in temperature on Zika dynamics and control
When a rare pathogen emerges to cause a pandemic, it is critical to understand its dynamics and the impact of mitigation measures. We use experimental data to parametrize a temperature-dependent model of Zika virus (ZIKV) transmission dynamics and analyse the effects of temperature variability and control-related parameters on the basic reproduction number ( R 0 ) and the final epidemic size of ZIKV. Sensitivity analyses show that these two metrics are largely driven by different parameters, with the exception of temperature, which is the dominant driver of epidemic dynamics in the models. Our R 0 estimate has a single optimum temperature (≈30°C), comparable to other published results (≈29°C). However, the final epidemic size is maximized across a wider temperature range, from 24 to 36°C. The models indicate that ZIKV is highly sensitive to seasonal temperature variation. For example, although the model predicts that ZIKV transmission cannot occur at a constant temperature below 23°C (≈ average annual temperature of Rio de Janeiro, Brazil), the model predicts substantial epidemics for areas with a mean temperature of 20°C if there is seasonal variation of 10°C (≈ average annual temperature of Tampa, Florida). This suggests that the geographical range of ZIKV is wider than indicated from static R 0 models, underscoring the importance of climate dynamics and variation in the context of broader climate change on emerging infectious diseases.  more » « less
Award ID(s):
2011147
NSF-PAR ID:
10289326
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Journal of The Royal Society Interface
Volume:
18
Issue:
178
ISSN:
1742-5662
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Because of limited data, much remains uncertain about parameters related to transmission dynamics of Zika virus (ZIKV). Estimating a large number of parameters from the limited information in data may not provide useful knowledge about the ZIKV. Here, we developed a method that utilizes a mathematical model of ZIKV dynamics and the complex-step derivative approximation technique to identify parameters that can be estimated from the available data. Applying our method to epidemic data from the ZIKV outbreaks in French Polynesia and Yap Island, we identified the parameters that can be estimated from these island data. Our results suggest that the parameters that can be estimated from a given data set, as well as the estimated values of those parameters, vary from Island to Island. Our method allowed us to estimate some ZIKV-related parameters with reasonable confidence intervals. We also computed the basic reproduction number to be from 2.03 to 3.20 across islands. Furthermore, using our model, we evaluated potential prevention strategies and found that peak prevalence can be reduced to nearly 10% by reducing mosquito-to-human contact by at least 60% or increasing mosquito death by at least a factor of three of the base case. With these preventions, the final outbreak-size is predicted to be negligible, thereby successfully controlling ZIKV epidemics.

     
    more » « less
  2. Influenza epidemics cause considerable morbidity and mortality every year worldwide. Climate-driven epidemiological models are mainstream tools to understand seasonal transmission dynamics and predict future trends of influenza activity, especially in temperate regions. Testing the structural identifiability of these models is a fundamental prerequisite for the model to be applied in practice, by assessing whether the unknown model parameters can be uniquely determined from epidemic data. In this study, we applied a scaling method to analyse the structural identifiability of four types of commonly used humidity-driven epidemiological models. Specifically, we investigated whether the key epidemiological parameters (i.e., infectious period, the average duration of immunity, the average latency period, and the maximum and minimum daily basic reproductive number) can be uniquely determined simultaneously when prevalence data is observable. We found that each model is identifiable when the prevalence of infection is observable. The structural identifiability of these models will lay the foundation for testing practical identifiability in the future using synthetic prevalence data when considering observation noise. In practice, epidemiological models should be examined with caution before using them to estimate model parameters from epidemic data. 
    more » « less
  3. Abstract

    Controlling persistent infectious disease in wildlife populations is an ongoing challenge for wildlife managers and conservationists worldwide, and chronic diseases in particular remain a pernicious problem.

    Here, we develop a dynamic pathogen transmission model capturing key features ofMycoplasma ovipneumoniaeinfection, a major cause of population declines in North American bighorn sheepOvis canadensis. We explore the effects of model assumptions and parameter values on disease dynamics, including density‐ versus frequency‐dependent transmission, the inclusion of a carrier class versus a longer infectious period, host survival rates, disease‐induced mortality and recovery rates and the epidemic growth rate. Along the way, we estimate the basic reproductive ratio,R0, forM. ovipneumoniaein bighorn sheep to fall between approximately 1.36 and 1.74.

    We apply the model to compare efficacies across a suite of management actions following an epidemic, including test‐and‐remove, depopulation‐and‐reintroduction, range expansion, herd augmentation and density reduction.

    Our results suggest that test‐and‐remove, depopulation‐and‐reintroduction and range expansion could help persistently infected bighorn sheep herds recovery following an epidemic. By contrast, augmentation could lead to worse outcomes than those expected in the absence of management. Other management actions that improve host survival or reduce disease‐induced mortality are also likely to improve population size and persistence of chronically infected herds.

    Synthesis and applications. Dynamic transmission models like the one employed here offer a structured, logical approach for exploring hypotheses, planning field experiments and designing adaptive management. We find that management strategies that removed infected animals or isolated them within a structured metapopulation were most successful at facilitating herd recovery from a low‐prevalence, chronic pathogen. Ideally, models like ours should operate iteratively with field experiments to triangulate on better approaches for managing wildlife diseases.

     
    more » « less
  4. Abstract Aim

    The climate variability hypothesis proposes that species subjected to wide variation in climatic conditions will evolve wider niches, resulting in larger distributions. We test this hypothesis in tropical plants across a broad elevational gradient; specifically, we use a species‐level approach to evaluate whether elevational range sizes are explained by the levels of thermal variability experienced by species.

    Location

    Central Andes.

    Time Period

    Present day.

    Taxon

    Woody plants.

    Methods

    Combining data from 479 forest plots, we determined the elevational distributions of nearly 2300 species along an elevational gradient (~209–3800 m). For each species, we calculated the maximum annual variation in temperature experienced across its elevational distribution. We used phylogenetic generalized least square models to evaluate the effect of thermal variability on range size. Our models included additional covariates that might affect range size: body size, local abundance, mean temperature and total precipitation. We also considered interactions between thermal variability and mean temperature or precipitation. To account for geometric constraints, we repeated our analyses with a standardized measure of range size, calculated by comparing observed range sizes with values obtained from a null model.

    Results

    Our results supported the main prediction of the climate variability hypothesis. Thermal variability had a strong positive effect on the range size, with species exposed to higher thermal variability having broader elevational distributions. Body size and local abundance also had positive, yet weak effects, on elevational range size. Furthermore, there was a strong positive interaction between thermal variability and mean annual temperature.

    Main Conclusions

    Thermal variability had an overriding importance in driving elevational range sizes of woody plants in the Central Andes. Moreover, the relationship between thermal variability and range size might be even stronger in warmer regions, underlining the potential vulnerability of tropical montane floras to the effects of global warming.

     
    more » « less
  5. Abstract

    Selection pressures along climate gradients give rise to predictable variation in plant functional traits of individual species suggestive of local adaptation. Species whose ranges include winter rainfall, Mediterranean climates, or other strongly seasonal climates, may be exposed to divergent selection pressures at different ends of seasonality gradients.

    Here, we evaluate how rainfall seasonality in conjunction with other key climatic variables impacts patterns of trait variation inPelargonium scabrum, a woody shrub from the Greater Cape Floristic Region of South Africa. This biodiversity hotspot encompasses a Mediterranean climate (wet winters and hot, dry summers) and displays steep gradients in temperature and water availability.

    We used Bayesian regression models to evaluate leaf trait–trait and trait–climate relationships among 26 populations. Models included rainfall seasonality and its interaction with other climate variables (mean annual temperature, mean annual precipitation and potential evapotranspiration) as predictors to test for the impact of climate variation on three leaf traits: size, dissection and leaf mass per area (LMA). We evaluated model explanatory power by calculating BayesianR2values, and predictive power via leave‐one‐out cross‐validation.

    Trait–trait associations were modulated by rainfall seasonality, including a reversal in the relationship between leaf size and dissection depending on the proportion of rain received in winter. Trait–climate models were improved by including rainfall seasonality as a predictor for both explanatory and predictive power. For leaf dissection and LMA, we detected significant interactions between rainfall seasonality and other environmental variables, leading to reversals in the relationships between these traits and the three environmental variables depending on the proportion of winter rainfall.

    Differences in the timing of rainfall, coupled with strong differences in the covariation of climate variables, impose divergent selection pressures onP. scabrumpopulations resulting in divergence of trait values, trait integration and responses to climate gradients. These patterns are consistent with local adaptation ofP. scabrumpopulations mediated by the interactions between temperature and the amount and timing of rainfall. Species arrayed along broad climate gradients represent an excellent opportunity for investigating patterns of trait variation and abundances and distributions of species in relation to future changes in climate.

    A freePlain Language Summarycan be found within the Supporting Information of this article.

     
    more » « less