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1. Effects of changes in temperature on Zika dynamics and control

   https://doi.org/10.1098/rsif.2021.0165  

   Ngonghala, Calistus N. ; Ryan, Sadie J. ; Tesla, Blanka ; Demakovsky, Leah R. ; Mordecai, Erin A. ; Murdock, Courtney C. ; Bonds, Matthew H. ( May 2021 , Journal of The Royal Society Interface)

   null (Ed.)

   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. 

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2. Scoping review of Culex mosquito life history trait heterogeneity in response to temperature

   https://doi.org/10.1186/s13071-023-05792-3  

   Moser, S. Kane ; Barnard, Martha ; Frantz, Rachel M. ; Spencer, Julie A. ; Rodarte, Katie A. ; Crooker, Isabel K. ; Bartlow, Andrew W. ; Romero-Severson, Ethan ; Manore, Carrie A. ( June 2023 , Parasites & Vectors)

   Mosquitoes in the genusCulexare primary vectors in the US for West Nile virus (WNV) and other arboviruses. Climatic drivers such as temperature have differential effects on species-specific changes in mosquito range, distribution, and abundance, posing challenges for population modeling, disease forecasting, and subsequent public health decisions. Understanding these differences in underlying biological dynamics is crucial in the face of climate change.

   We collected empirical data on thermal response for immature development rate, egg viability, oviposition, survival to adulthood, and adult lifespan forCulex pipiens, Cx. quinquefasciatus, Cx. tarsalis, andCx. restuansfrom existing literature according to the PRISMA scoping review guidelines.

   We observed linear relationships with temperature for development rate and lifespan, and nonlinear relationships for survival and egg viability, with underlying variation between species. Optimal ranges and critical minima and maxima also appeared varied. To illustrate how model output can change with experimental input data from individualCulexspecies, we applied a modified equation for temperature-dependent mosquito type reproduction number for endemic spread of WNV among mosquitoes and observed different effects.

   Current models often input theoretical parameters estimated from a single vector species; we show the need to implement the real-world heterogeneity in thermal response between species and present a useful data resource for researchers working toward that goal.

    

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3. Inferring time of infection from field data using dynamic models of antibody decay

   https://doi.org/10.1111/2041-210X.14165  

   Borremans, Benny ; Mummah, Riley O. ; Guglielmino, Angela H. ; Galloway, Renee L. ; Hens, Niel ; Prager, K. C. ; Lloyd‐Smith, James O. ( August 2023 , Methods in Ecology and Evolution)

   Studies of infectious disease ecology would benefit greatly from knowing when individuals were infected, but estimating this time of infection can be challenging, especially in wildlife. Time of infection can be estimated from various types of data, with antibody‐level data being one of the most promising sources of information. The use of antibody levels to back‐calculate infection time requires the development of a host‐pathogen system‐specific model of antibody dynamics, and a leading challenge in such quantitative serology approaches is how to model antibody dynamics in the absence of experimental infection data.

   We present a way to model antibody dynamics in a Bayesian framework that facilitates the incorporation of all available information about potential infection times and apply the model to estimate infection times of Channel Island foxes infected withLeptospira interrogans.

   Using simulated data, we show that the approach works well across a broad range of parameter settings and can lead to major improvements in infection time estimates that depend on system characteristics such as antibody decay rate and variation in peak antibody levels after exposure. When applied to field data we saw reductions up to 83% in the window of possible infection times.

   The method substantially simplifies the challenge of modelling antibody dynamics in the absence of individuals with known infection times, opens up new opportunities in wildlife disease ecology and can even be applied to cross‐sectional data once the model is trained.

    

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4. Model-Based Clustering of High-Dimensional Longitudinal Data via Regularization

   https://doi.org/10.1111/biom.13672  

   Yang, Luoying ; Wu, Tong Tong ( April 2022 , Biometrics)

   We propose a model-based clustering method for high-dimensional longitudinal data via regularization in this paper. This study was motivated by the Trial of Activity in Adolescent Girls (TAAG), which aimed to examine multilevel factors related to the change of physical activity by following up a cohort of 783 girls over 10 years from adolescence to early adulthood. Our goal is to identify the intrinsic grouping of subjects with similar patterns of physical activity trajectories and the most relevant predictors within each group. The previous analyses conducted clustering and variable selection in two steps, while our new method can perform the tasks simultaneously. Within each cluster, a linear mixed-effects model (LMM) is fitted with a doubly penalized likelihood to induce sparsity for parameter estimation and effect selection. The large-sample joint properties are established, allowing the dimensions of both fixed and random effects to increase at an exponential rate of the sample size, with a general class of penalty functions. Assuming subjects are drawn from a Gaussian mixture distribution, model effects and cluster labels are estimated via a coordinate descent algorithm nested inside the Expectation-Maximization (EM) algorithm. Bayesian Information Criterion (BIC) is used to determine the optimal number of clusters and the values of tuning parameters. Our numerical studies show that the new method has satisfactory performance and is able to accommodate complex data with multilevel and/or longitudinal effects.

    

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5. An integrated model of population genetics and community ecology

   https://doi.org/10.1111/jbi.13541  

   Overcast, Isaac ; Emerson, Brent C. ; Hickerson, Michael J. ( March 2019 , Journal of Biogeography)

   Quantifying abundance distributions is critical for understanding both how communities assemble, and how community structure varies through time and space, yet estimating abundances requires considerable investment in fieldwork. Community‐level population genetic data potentially offer a powerful way to indirectly infer richness, abundance and the history of accumulation of biodiversity within a community. Here we introduce a joint model linking neutral community assembly and comparative phylogeography to generate both community‐level richness, abundance and genetic variation under a neutral model, capturing both equilibrium and non‐equilibrium dynamics.

   Global.

   Our model combines a forward‐time individual‐based community assembly process with a rescaled backward‐time neutral coalescent model of multi‐taxa population genetics. We explore general dynamics of genetic and abundance‐based summary statistics and use approximate Bayesian computation (ABC) to estimate parameters underlying the model of island community assembly. Finally, we demonstrate two applications of the model using community‐scale mtDNAsequence data and densely sampled abundances of an arachnid community on La Réunion. First, we use genetic data alone to estimate a summary of the abundance distribution, ground‐truthing this against the observed abundances. Then, we jointly use the observed genetic data and abundances to estimate the proximity of the community to equilibrium.

   Simulation experiments of ourABCprocedure demonstrate that coupling abundance with genetic data leads to improved accuracy and precision of model parameter estimates compared with using abundance‐only data. We further demonstrate reasonable precision and accuracy in estimating a metric underlying the shape of the abundance distribution, temporal progress towards local equilibrium and several key parameters of the community assembly process. For the insular arachnid assemblage, we find the joint distribution of genetic diversity and abundance approaches equilibrium expectations, and that the Shannon entropy of the observed abundances can be estimated using genetic data alone.

   The framework that we present unifies neutral community assembly and comparative phylogeography to characterize the community‐level distribution of both abundance and genetic variation through time, providing a resource that should greatly enhance understanding of both the processes structuring ecological communities and the associated aggregate demographic histories.

    

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