Publications related to Real-time projections of cholera outbreaks through data assimilation and rainfall forecasting

Related publications (26)

Challenges of COVID-19 Case Forecasting in the US, 2020-2021

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and ...

Public Library Science2024

Good enough: Intermediate complexity atmospheric models improve the representation of processes affecting seasonal snow

Dylan Stewart Reynolds

Snow plays a crucial role in processes regulating ecosystems, the climate, and human development. Mountain snowpack in particular has great relevance for downstream communities. Knowledge about the distribution and properties of the snowpack thus help in p ...

EPFL2024

CONTROL OF HYPERBOLIC AND PARABOLIC EQUATIONS ON NETWORKS AND SINGULAR LIMITS

Nicola De Nitti

vanishing viscosity, networks. This work has received funding from the Alexander von Humboldt-Professorship program, the Transregio 154 Project "Mathematical Modelling, Simulation and Optimization Using the Example of Gas Networks" of the DFG, the grant PI ...

Amer Inst Mathematical Sciences-Aims2024

Network time series forecasting in photovoltaics power production

Jelena Simeunovic

Accurate forecasting of photovoltaic (PV) power production is crucial for the integration of more renewable energy sources into the power grid. PV power production is highly intermittent, due to the stochastic cloud behaviour and cloud dynamics. Previous w ...

EPFL2024

A 7-year record of vertical profiles of radar measurements and precipitation estimates at Dumont d'Urville, Adélie Land, East Antarctica

Alexis Berne, Jacopo Grazioli

Studying precipitation falling over Antarctica is crucial as snowfall represents the main water input term for the polar cap. However, precipitation observations still remain scarce-and, more particularly, in the atmospheric column-due to numerous experime ...

2024

On distributional autoregression and iterated transportation

Victor Panaretos, Laya Ghodrati

We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...

Wiley2024

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Ntorina Thanou, Nicola Parolini, Benjamin Bejar Haro, Daniel Rodrigo Lopez, Ekaterina Krymova, Andrea Kraus, David Kraus

Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive p ...

eLIFE SCIENCES PUBL LTD2023

Assessing the simulation of snowfall at Dumont d'Urville, Antarctica, during the YOPP-SH special observing campaign

Alexis Berne, Etienne Gabriel Henri Vignon

The performance of a set of atmospheric models and meteorological reanalyses in the representation of precipitation days in Antarctica is assessed using ground-based observations such as a precipitation gauge and a Micro Rain Radar during the Year Of Polar ...

WILEY2023

Deep Generative Models for Autonomous Driving: from Motion Forecasting to Realistic Image Synthesis

Saeed Saadatnejad

Forecasting is a capability inherent in humans when navigating. Humans routinely plan their paths, considering the potential future movements of those around them. Similarly, to achieve comparable sophistication and safety, autonomous systems must embrace ...

EPFL2023

Optimized numerical solutions of SIRDVW multiage model controlling SARS-CoV-2 vaccine roll out: An application to the Italian scenario

Alfio Quarteroni, Luca Dede', Nicola Parolini

In the context of SARS-CoV-2 pandemic, mathematical modelling has played a funda-mental role for making forecasts, simulating scenarios and evaluating the impact of pre-ventive political, social and pharmaceutical measures. Optimal control theory represent ...

KEAI PUBLISHING LTD2023

Evaluation of 30 urban land surface models in the Urban-PLUMBER project: Phase 1 results

Gabriele Manoli

Accurately predicting weather and climate in cities is critical for safeguarding human health and strengthening urban resilience. Multimodel evaluations can lead to model improvements; however, there have been no major intercomparisons of urban-focussed la ...

Wiley2023

Data-driven modeling of beam loss in the LHC

Tatiana Pieloni, Michael Schenk, Ekaterina Krymova, Loïc Thomas Davies Coyle

In the Large Hadron Collider, the beam losses are continuously measured for machine protection. By design, most of the particle losses occur in the collimation system, where the particles with high oscilla-tion amplitudes or large momentum error are scrape ...

FRONTIERS MEDIA SA2023

Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes

Jesus Sanchez Rodriguez

Machine learning is often cited as a new paradigm in control theory, but is also often viewed as empirical and less intuitive for students than classical model-based methods. This is particularly the case for reinforcement learning, an approach that does n ...

PUBLIC LIBRARY SCIENCE2023

Climatological trends of mean and extreme daily precipitation in Arizona (USA)

Aldo Brandi

Precipitation in Arizona, USA, represents a fundamental resource for the needs of agriculture, people, and the environment. However, due to the risk associated with flash flooding, extreme precipitation also constitutes a natural hazard. Available research ...

Academic Press Ltd- Elsevier Science Ltd2023

National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021

Ekaterina Krymova

Background During the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases an ...

Springernature2022

A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1

Firat Ozdemir, Damien Bouffard, Camille Roland Marie Minaudo

We present a Bayesian inference for a three-dimensional hydrodynamic model of Lake Geneva with stochastic weather forcing and high-frequency observational datasets. This is achieved by coupling a Bayesian inference package, SPUX, with a hydrodynamics packa ...

COPERNICUS GESELLSCHAFT MBH2022

A Comprehensive Timing Model for Accurate Frequency Tuning in Dataflow Circuits

Paolo Ienne, Andrea Guerrieri, Lana Josipovic

The ability of dataflow circuits to implement dynamic scheduling promises to overcome the conservatism of static scheduling techniques that high-level synthesis tools typically rely on. Yet, the same distributed control mechanism that allows dataflow circu ...

IEEE COMPUTER SOC2022

Calibration of ground surface albedo models

Christophe Ballif, Arttu Matias Tuomiranta

The emergence of bifacial photovoltaics as a serious option for large-scale power generation makes the accuracy of the in-situ estimation of ground surface albedo increasingly critical. Recent studies have evaluated the performance of different models but ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Epidemicity of cholera spread and the fate of infection control measures

Andrea Rinaldo, Lorenzo Mari, Damiano Pasetto, Joseph Chadi Benoit Lemaitre, Cristiano Trevisin

The fate of ongoing infectious disease outbreaks is predicted through reproduction numbers, defining the long-term establishment of the infection, and epidemicity indices, tackling the reactivity of the infectious pool to new contagions. Prognostic metrics ...

2022

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

Andrea Zanoni

We study the problem of learning unknown parameters of stochastic dynamical models from data. Often, these models are high dimensional and contain several scales and complex structures. One is then interested in obtaining a reduced, coarse-grained descript ...

EPFL2022