scholarly journals Imaging with spatio-temporal modelling to characterize the dynamics of plant-pathogen lesions

2022 ◽  
Author(s):  
Melen Leclerc ◽  
Stéphane Jumel ◽  
Frédéric M. Hamelin ◽  
Rémi Treilhaud ◽  
Nicolas Parisey ◽  
...  

Within-host spread of pathogens is an important process for the study of plant-pathogen interactions. However, the development of plant-pathogen lesions remains practically difficult to characterize and quantify beyond the common traits such as lesion area. We tackle the spatio-temporal dynamics of interactions by combining image-based phenotyping with mathematical modelling. We consider the spread of Peyronellaea pinodes on pea stipules that were monitored daily with visible imaging. We assume that pathogen propagation on host-tissues can be described by the Fisher-KPP model where lesion spread depends on both a logistic local growth and an homogeneous diffusion. Model parameters are estimated using a variational data assimilation approach on sets of registered images. This modelling framework is used to compare the spread of an aggressive isolate on two pea cultivars with contrasted levels of partial resistance. We show that the expected slower spread on the most resistant cultivar is actually due to a decrease of diffusion and, to a lesser extent, local growth. These results demonstrate that spatial models with imaging allows one to disentangle the processes involved in host-pathogen interactions. Hence, promoting model-based phenotyping of interactions would allow a better identification of quantitative traits thereafter used in genetics and ecological studies.

Author(s):  
Matthew J. Hoffman ◽  
Elizabeth M. Cherry

Modelling of cardiac electrical behaviour has led to important mechanistic insights, but important challenges, including uncertainty in model formulations and parameter values, make it difficult to obtain quantitatively accurate results. An alternative approach is combining models with observations from experiments to produce a data-informed reconstruction of system states over time. Here, we extend our earlier data-assimilation studies using an ensemble Kalman filter to reconstruct a three-dimensional time series of states with complex spatio-temporal dynamics using only surface observations of voltage. We consider the effects of several algorithmic and model parameters on the accuracy of reconstructions of known scroll-wave truth states using synthetic observations. In particular, we study the algorithm’s sensitivity to parameters governing different parts of the process and its robustness to several model-error conditions. We find that the algorithm can achieve an acceptable level of error in many cases, with the weakest performance occurring for model-error cases and more extreme parameter regimes with more complex dynamics. Analysis of the poorest-performing cases indicates an initial decrease in error followed by an increase when the ensemble spread is reduced. Our results suggest avenues for further improvement through increasing ensemble spread by incorporating additive inflation or using a parameter or multi-model ensemble. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.


2018 ◽  
Vol 24 (5) ◽  
pp. 652-665 ◽  
Author(s):  
Thomas Mang ◽  
Franz Essl ◽  
Dietmar Moser ◽  
Ingrid Kleinbauer ◽  
Stefan Dullinger

2017 ◽  
Vol 14 (136) ◽  
pp. 20170583 ◽  
Author(s):  
Kyle B. Gustafson ◽  
Joshua L. Proctor

Containing the recent West African outbreak of Ebola virus (EBOV) required the deployment of substantial global resources. Despite recent progress in analysing and modelling EBOV epidemiological data, a complete characterization of the spatio-temporal spread of Ebola cases remains a challenge. In this work, we offer a novel perspective on the EBOV epidemic in Sierra Leone that uses individual virus genome sequences to inform population-level, spatial models. Calibrated to phylogenetic linkages of virus genomes, these spatial models provide unique insight into the disease mobility of EBOV in Sierra Leone without the need for human mobility data. Consistent with other investigations, our results show that the spread of EBOV during the beginning and middle portions of the epidemic strongly depended on the size of and distance between populations. Our phylodynamic analysis also revealed a change in model preference towards a spatial model with power-law characteristics in the latter portion of the epidemic, correlated with the timing of major intervention campaigns. More generally, we believe this framework, pairing molecular diagnostics with a dynamic model selection procedure, has the potential to be a powerful forecasting tool along with offering operationally relevant guidance for surveillance and sampling strategies during an epidemic.


2009 ◽  
Vol 24 (9) ◽  
pp. 1088-1099 ◽  
Author(s):  
O. Schmitz ◽  
D. Karssenberg ◽  
W.P.A. van Deursen ◽  
C.G. Wesseling

2020 ◽  
Author(s):  
Laís Picinini Freitas ◽  
Alexandra M. Schmidt ◽  
William Cossich ◽  
Oswaldo Gonçalves Cruz ◽  
Marilia Sá Carvalho

AbstractChikungunya is an Aedes-borne disease therefore its dynamics are impacted by the vector’s ecology. We analysed the spatio-temporal distribution of the first chikungunya epidemic in Rio de Janeiro, estimating the effect of the socioeconomic and environmental factors as proxies of mosquitoes abundance. We fitted spatial models using notified cases counts by neighbourhood and week. To estimate the instantaneous and the memory effect of the temperature we used a transfer function. There were 13627 chikungunya cases in the study period. The sociodevelopment index, especially in the beginning of the epidemic, was inversely associated with the risk of cases, whereas the green area proportion effect was null for most weeks. The temperature increased the risk of chikungunya in most areas and this effect propagated for longer where the epidemic was concentrated. Factors related to the Aedes mosquitoes contribute to understanding the spatio-temporal dynamics of urban arboviral diseases.


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