Biology subjects

Rocklov, J.

Publications and source records attributed to Rocklov, J..

3 recordsLinked to original sources

bioRxiv2024

Deciphering Memory Patterns in Eco-Epidemiological systems through the lens of BaFOMS: A Bayesian Fractional Order Model Selection Method

The evolution of eco-epidemiological systems is significantly influenced by the memory or previous history of the system. These non-Markovian dynamics are effectively modelled using fractional derivatives (FDs), incorporating memory kernels that reflect long-term or short-term memory characteristics in corresponding nonlinear fractional differential evolution equations. We introduce BaFOMS, a framework for identifying and selecting the optimal FD model for eco-epidemiological processes based on historical data. Specifically, we evaluate a class of fractional logistic growth models defined by their time correlation functions and determine the optimal model through Bayesian inference, selecting the one with the highest posterior probability. We also demonstrated BaFOMS efficiency in parameter estimation and forecasting, producing reliable results with quantified uncertainties. The method is shown to be robust across a range of eco-epidemiological datasets, offering computational efficiency and reliable inference about the evolution dynamics.

ecology↗

bioRxiv2024

A climate and population dependent diffusion model forecasts the spread of Aedes Albopictus mosquitoes in Europe

Vectors of Dengue, Chikungunya, Zika, and Yellow Fever are emerging in new areas, posing increasing public health risks. Aedes albopictus, a key vector for these diseases, is expanding its range beyond its tropical and subtropical origins, driven by suitable climate, population mobility, trade, and urbanization. Since its introduction to Europe, Ae. albopictus has rapidly spread and triggered recurrent outbreaks. Past model attempts have handled vector suitability and vector introduction as independent drivers. Here we develop a novel, highly predictive spatio-temporal vector diffusion model based on minimum temperature, median temperature, relative humidity and human population as predictors. The model predicts areas of presence or absence with an accuracy of 99% and 79% for new established vector populations. The model explains how short- and long-range spread of Ae. albopictus interacts with vector suitability. These results show that the expansion of Ae. albopictus in Europe is predictable and closely linked to climate suitability, and human population. The new model integrates in one simultaneous model framework the climate and mobility drivers, providing a better basis for anticipating future outbreaks in situations of dependent interacting co-drivers. Significance statementThe Tiger mosquito, Aedes albopictus, which spreads diseases like Dengue, Chikungunya, Zika and Yellow fever, has accelerated its presence in Europe over the past decades. This poses a public health risk as Europe face an upsurge of autochthonous arbovirus transmission events. The change is partly due to conducive environments, climate change and human mobility and trade. We develop a novel model explaining the spread of the mosquito over the years 2010-2023, observing a change in recorded mosquito presence areas from 138 regions in 2010 to 537 regions in 2023. The model incorporates patterns in both space and time, capturing with high accuracy how the mosquito spreads from region to region based on climate suitability, geographical diffusion and human population density.

molecular biology↗

bioRxiv2024

Disclosing temperature sensitivity of West Nile virus transmission: novel computational approaches to mosquito-pathogen trait responses

Temperature influences the transmission of mosquito-borne pathogens with significant implications for disease risk in the context of climate change. Mathematical models of mosquito-borne infections rely on functions that capture mosquito-pathogen interactions in response to temperature to better estimate transmission dynamics. To derive these functions, experimental studies provide valuable data on the temperature sensitivity of mosquito life-history traits and pathogen transmission. However, the scarcity of experimental data and inconsistencies in methodologies for analysing and comparing temperature responses across mosquito species and pathogens present challenges to accurately modelling mosquito-borne infections. In this study, we investigated the thermal biology of West Nile virus (WNV), a major mosquito-borne infection. We critically reviewed existing experimental studies, obtaining temperature responses for eight mosquito-pathogen traits across 15 mosquito species. Using this data, we employed Bayesian hierarchical models to estimate temperature response functions for each trait and estimate their variation between species and experiments. We incorporate the resulting functions into mathematical models to estimate the temperature sensitivity of WNV transmission, focusing on six competent mosquito species of the genus Culex. Our study finds similarities in the temperature response among Culex species, with a general optimal transmission temperature around 24{degrees}C. We demonstrate that differing mechanistic assumptions in published models can result in a temperature optimum variation exceeding 3{degrees}C, underscoring the need for model scrutiny. Additionally, we identify substantial variability between trait temperature responses across experiments on the same mosquito species, indicating significant intra-species variation in trait performance. We provide recommendations for future experimental studies, emphasizing the critical need for additional data on all mosquito-pathogen traits, except for mosquito larva-to-adult development rate and survival, which are relatively well-documented. Incorporating additional data into our multi-species approach enhances the accuracy of temperature trait response estimates. These improvements are critical for forecasting future shifts in mosquito-borne disease risk due to climate change.

ecology↗