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APPLICATION OF EPIDEMIOLOGICAL MODELLING OF Sparicotyle chrysophrii TRANSMISSION IN Sparus aurata FARMS Elisa Stella1, *, Roberto Pastres1 , Damiano Pasetto1 , Matko Kolega2 , Danijel Mejdandžić2 , Slavica Čolak2 , Lorenzo Mari3 , Enrico Bertuzzo1 1 Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice Scientific Campus, Via Torino, 155, 30172, Mestre- Venice, Italy 2 Cromaris d.d., Zadar, Croatia 3 Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano Via Ponzio 34/5, 20133, Milan, Italy E-mail: elisa.stella@unive.it Introduction Mediterranean aquaculture production is known to have impacts on the environmental and marine habitat, impairing water and bottom sediment quality. Furthermore, rapid development of cage fish farming has been associated with propagation of infectious diseases, pathogens and parasites. Sparicotyle chrysophrii is a common parasite of cultured Gilthead sea bream (Sparus aurata), an important species for Mediterranean aquaculture production. The parasite attaches on fish gills and can cause lethal epizootics in sea cages. Infections depend on environmental factors, in particular on water temperature (Antonelli et al., 2010). Epidemiological modeling represents a valid tool to help fish farmers understand parasite transmission and evaluate possible control measures. To this purpose, in the following we analyze a novel epidemiological model of S. chrysophrii transmission. Methods We developed a novel stratified compartmental model where each compartment Xja represents the abundance of fish infected with j juvenile parasites and a adults. The model further accounts for environmental abundance of eggs and miracidia, where the latter determines the force of infection, i.e. the rate at which parasite larvae attach to fish. The dynamics further accounts for egg production and hatching, parasite development, baseline mortality for parasites and fish, and parasite-induced fish mortality. Critical parameters controlling the progression of the disease are assumed to be temperature dependent. We applied the model to data collected in six cages of a sea bream farm managed by Cromaris (Bisage, Croatia). A controlled experiment was run between February and November 2021 in which 30 fish were collected each month and, for each fish, all eight arc gills were examined to count the number of attached parasites. We estimated model parameters in a Bayesyan framework, sampling the posterior distribution with a Markov Chain Monte Carlo algorithm. Specifically, the model estimates the temporal dynamics of the probability that a sampled fish hosts a certain number of adult parasites. The likelihood of a monthly sample is calculated assuming a multinomial distribution, where the event probabilities are estimated through the deterministic model simulation given a certain parameter set. We further included the total number of fish alive at any time in the determination of the likelihood. Results Results show that the model is able to reproduce the distribution of the number of parasites hosted by the sampled fish (Figure 1), as well as the temporal progression of the prevalence of infection (i.e., the number of fish hosting at least one parasite). Data shows that prevalence has a peak in August, after the period with the warmest water temperature, and then decreases. Discussion and conclusions We applied a novel epidemiological model to a dataset of S. chrysophrii infections collected in a sea bream farm. The model succeeds in simulating the effects of environmental factors (e.g., water temperature) on the infection dynamics. Posterior distribution of parameters shed insights on crucial process rates that control disease transmission. Future developments could involve coupling the epidemiological model with a fish growth model to simulate the effect of parasite burden on fish metabolism. The knowledge gained through this combined experimental and modeling exercise could be used in the future for designing and implementing strategies to control the spreading of the infection and, accordingly, to improve both fish health and aquaculture production. (Continued on next page) 1250 Acknowledgements The research leading to these results has received funding from the European Union’s HORIZON 2020 Framework Programme under Grant Agreement no. 862658. References Antonelli, L., Quilichini, Y., & Marchand, B (2010). Sparicotyle chrysophrii (Van Beneden and Hesse, 1863) (Monogenea: Polyopisthocotylea) parasite of cultured gilthead sea bream Sparus aurata (Linnaeus 1758) (Pisces: Teleostei) from Corsica: Ecological and morphological study. Parasitology Research, 107:389–398.

[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAU; International audience; Physically-based models represent detailed surface/subsurface transfer, but the required spatial information does not allow their operational use. In situ data on pesticides in a catchment are usually rare and not continuous in time and space. Satellite images, on the other hand, well describe data in space, but only water related, and at limited time frequency. This study aims to exploit these 3 types of information (model,in situ data, images) with data assimilation methods adapted to image data, in order to improve pesticide and hydrological parameters and better understand physical processes. This poster discusses the proposed methodology as well as the available study site data and modeling components.

The present thesis work concerns the study of Monte Carlo (MC)-based data assimilation methods applied to the numerical simulation of complex hydrological models with stochastic parameters. The ensemble Kalman filter (EnKF) and the sequential importance resampling (SIR) are implemented in the CATHY model, a solver that couples the subsurface water flow in porous media with the surface water dynamics. A detailed comparison of the results given by the two filters in a synthetic test case highlights the main benefits and drawbacks associated to these techniques. A modification of the SIR update is suggested to improve the performance of the filter in case of small ensemble sizes and small variances of the measurement errors. With this modification, both filters are able to assimilate pressure head and streamflow measurements and correct model errors, such as biased initial and boundary conditions. SIR technique seems to be better suited for the simulations at hand as they do not make use of the Gaussian approximation inherent the EnKF method. Further research is needed, however, to assess the robustness of the particle filters methods in particular to ensure accuracy of the results even when relatively small ensemble sizes are employed. In the second part of the thesis the focus is shifted to reducing the computational burden associated with the construction of the MC realizations (which constitutes the core of the EnKF and SIR). With this goal, we analyze the computational saving associated to the use of reduced order models (RM) for the generation of the ensemble of solutions. The proper orthogonal decomposition (POD) is applied to the linear equations of the groundwater flow in saturated porous media with a randomly distributed recharge and random heterogeneous hydraulic conductivity. Several test cases are used to assess the errors on the ensemble statistics caused by the RM approximation. Particular attention is given to the efficient computation of the principal components that are needed to project the model equations in the reduced space. The greedy algorithm selects the snapshots in the set of the MC realizations in such a way that the final principal components are parameter independent. An innovative residual-based estimation of the error associated to the RM solution is used to assess the precision of the RM and to stop the iterations of the greedy algorithm. By way of numerical applications in synthetic and real scenarios, we demonstrate that this modified greedy algorithm determines the minimum number of principal components to use in the reduction and, thus, leads to important computational savings.

Presentation given at the Aquaculture Europe 2021 conference in the Precision Farming, Modeling and Artificial Intelligence session

In a previous report, we presented model projections based on data available up to epi-week 43 (ending Oct-29). These projections suggested that, due to anticipated heavy rainfalls at the end of November, a second peak of cholera might be observed in early December in the departments of Grand’Anse and Sud, with potential propagation to other departments. These projections supported the strategy of the MSPP 2 OCV 3 campaign to vaccinate 800,000 people in these two departments, which were also the areas most affected by damage from Hurricane Mathew in early October. Eventually, the vaccine campaign took place from 11 to 18 November but heavy rainfall was not recorded in late November and no second wave of cholera was observed. In this context, the benefits of the vaccine campaign are difficult to assess since the limited spread is likely the result of non-favourable environmental conditions and the reduction of the susceptible population thanks to the immunization campaign. Here we show that, had heavy rainfall occurred in late November and early December 2016 (as NOAA was forecasting in late October), the vaccine campaign was effectively designed to considerably reduce the risk of a second epidemic wave in Grand’Anse and Sud, with more limited effects in Ouest department.

Presentation given at the Aquaculture Europe 2021 conference in the Precision Farming, Modeling and Artificial Intelligence session

A seepage face is a nonlinear dynamic boundary that strongly affects pressure head distributions, water table fluctuations, and flow patterns. Its handling in hydrological models, especially under complex conditions such as heterogeneity and coupled surface/subsurface flow, has not been extensively studied. In this paper, we compare the treatment of the seepage face as a static (Dirichlet) versus dynamic boundary condition, we assess its resolution under conditions of layered heterogeneity, we examine its interaction with a catchment outlet boundary, and we investigate the effects of surface/subsurface exchanges on seepage faces forming at the land surface. The analyses are carried out with an integrated catchment hydrological model. Numerical simulations are performed for a synthetic rectangular sloping aquifer and for an experimental hillslope from the Landscape Evolution Observatory. The results show that the static boundary condition is not always an adequate stand-in for a dynamic seepage face boundary condition, especially under conditions of high rainfall, steep slope, or heterogeneity; that hillslopes with layered heterogeneity give rise to multiple seepage faces that can be highly dynamic; that seepage face and outlet boundaries can coexist in an integrated hydrological model and both play an important role; and that seepage faces at the land surface are not always controlled by subsurface flow. The paper also presents a generalized algorithm for resolving seepage face outflow that handles heterogeneity in a simple way, is applicable to unstructured grids, and is shown experimentally to be equivalent to the treatment of atmospheric boundary conditions in subsurface flow models.