Adaptive Mitigation: Identification of the Dynamic Drivers of Effective Policy during the COVID-19 Pandemic

Thierry Bossy
2020
Student project

Abstract

Introduction: The unprecedented speed and scale of the COVID-19 pandemic necessitated the rapid implementation of untested public health measures to mitigate the consequences of viral spread. In the 8 months that have passed since the first recognized case, an enormous amount of research has been published evaluating the efficacy of the various policies implemented by different countries, however the majority of these studies focus on a specific region or time, over-representing high-income countries during periods of extreme transmission or “peak events”. The aim of this study is to provide a more general analysis that considers a global scope of the pandemic and the dynamic drivers that build effective policies to mitigate human interactions and slow the spread of disease. Methods: We collected a range of information regarding epidemic trends, weather, demographics, government response and mobility reports across the globe. We then built an hybrid Neural Network that combined an LSTM layer with a multilayer perceptron to infer the reproduction number from various non-epidemiological factors. The model was designed to predict the reproduction number (R-value) on 93 countries with available data and compare it to our ground truth estimate obtained from officially reported epidemiological data. Finally, we used an alternative model to assess the impact of public health measures on the epidemic. Findings: From the available features, we obtained the best performances using demographics combined with mobility features. The sanitary indices (beds/thousand, diabetes prevalence, ...) did not help the prediction and, more interestingly, the pressure indicator of historical weather forecast improved the prediction of the reproduction number by about 4.5%. This optimized model predicted the reproduction number with a mean absolute error of 0.254 across the 93 countries over the time of the epidemic. For many countries (Switzerland, United Kingdom, South Africa, ...) this error passed below 0.17. An alternative version of the model allowed us to estimate the impact of policies in terms of average reduction in reproduction number, and more importantly, allowed us to compare these trends between countries. For instance, we observe that the model showed that no policy had a positive impact in India as opposed to Switzerland, where most of them are associated to improved epidemic control. Conclusion: Understanding these complex interactions may allow individuals and policy makers to better adapt mitigation strategies to optimize the efficacy of the implemented policies.

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https://infoscience.epfl.ch/record/279677?ln=en

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Thierry Bossy
2020
Student project

Abstract

Official source

https://infoscience.epfl.ch/record/279677?ln=en

About this result

Related concepts (23)

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Related publications (54)

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Related publications (54)

Multilayer Perceptron Based Hierarchical Acoustic Modeling for Automatic Speech Recognition

Joel Praveen Pinto

In this thesis, we investigate a hierarchical approach for estimating the phonetic class-conditional probabilities using a multilayer perceptron (MLP) neural network. The architecture consists of two MLP classifiers in cascade. The first MLP is trained in the conventional way using standard acoustic features with a temporal context of around 90 ms. The second MLP is trained on the phonetic class-conditional probabilities (or posterior features) estimated by the first classifier, but with a relatively longer temporal context of around 150-250 ms. The hierarchical architecture is motivated towards exploiting the useful contextual information in the sequence of posterior features which includes the evolution of the probability values within a phoneme (sub-phonemic) and its transition to/from neighboring phonemes (sub-lexical). As the posterior features are sparse and simple, the second classifier is able to learn the contextual information spanning a context as long as 250 ms. Extensive experiments on the recognition of phonemes on read speech as well as conversational speech show that the hierarchical approach yields significantly higher recognition accuracies. Analysis of the second MLP classifier using Volterra series reveal that it has learned the phonetic-temporal patterns in the posterior feature space which captures the confusions in phoneme classification at the output of the first classifier as well as the phonotactics of the language as observed in the training data. Furthermore, we show that the second MLP can be simple in terms of the number of model parameters and that it can be trained on lesser training data. The usefulness of the proposed hierarchical acoustic modeling in automatic speech recognition (ASR) is demonstrated using two applications (a) task adaptation where the goal is to exploit MLPs trained on large amount of data and available off-the-shelf to new tasks and (b) large vocabulary continuous ASR on broadcast news and broadcast conversations in Mandarin. Small vocabulary isolated word recognition and task adaptation studies are performed on the Phonebook database and the large vocabulary speech recognition studies are performed on the DARPA GALE Mandarin database.

EPFL2010

In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. This identification is performed through a ranking task. Given a user query, an ad-hoc retrieval system ranks the corpus documents, so that the documents relevant to the query ideally appear above the others. In a machine learning framework, we are interested in proposing learning algorithms that can benefit from limited training data in order to identify a ranker likely to achieve high retrieval performance over unseen documents and queries. This problem presents novel challenges compared to traditional learning tasks, such as regression or classification. First, our task is a ranking problem, which means that the loss for a given query cannot be measured as a sum of an individual loss suffered for each corpus document. Second, most retrieval queries present a highly unbalanced setup, with a set of relevant documents accounting only for a very small fraction of the corpus. Third, ad-hoc retrieval corresponds to a kind of "double" generalization problem, since the learned model should not only generalize to new documents but also to new queries. Finally, our task also presents challenging efficiency constraints, since ad-hoc retrieval is typically applied to large corpora. The main objective of this thesis is to investigate the discriminative learning of ad-hoc retrieval models. For that purpose, we propose different models based on kernel machines or neural networks adapted to different retrieval contexts. The proposed approaches rely on different online learning algorithms that allow efficient learning over large corpora. The first part of the thesis focuses on text retrieval. In this case, we adopt a classical approach to the retrieval ranking problem, and order the text documents according to their estimated similarity to the text query. The assessment of semantic similarity between text items plays a key role in that setup and we propose a learning approach to identify an effective measure of text similarity. This identification is not performed relying on a set of queries with their corresponding relevant document sets, since such data are especially expensive to label and hence rare. Instead, we propose to rely on hyperlink data, since hyperlinks convey semantic proximity information that is relevant to similarity learning. This setup is hence a transfer learning setup, where we benefit from the proximity information encoded by hyperlinks to improve the performance over the ad-hoc retrieval task. We then investigate another retrieval problem, i.e. the retrieval of images from text queries. Our approach introduces a learning procedure optimizing a criterion related to the ranking performance. This criterion adapts our previous learning objective for learning textual similarity to the image retrieval problem. This yields an image ranking model that addresses the retrieval problem directly. This approach contrasts with previous research that relies on an intermediate image annotation task. Moreover, our learning procedure builds upon recent work on the online learning of kernel-based classifiers. This yields an efficient, scalable algorithm, which can benefit from recent kernels developed for image comparison. In the last part of the thesis, we show that the objective function used in the previous retrieval problems can be applied to the task of keyword spotting, i.e. the detection of given keywords in speech utterances. For that purpose, we formalize this problem as a ranking task: given a keyword, the keyword spotter should order the utterances so that the utterances containing the keyword appear above the others. Interestingly, this formulation yields an objective directly maximizing the area under the receiver operating curve, the most common keyword spotter evaluation measure. This objective is then used to train a model adapted to this intrinsically sequential problem. This model is then learned with a procedure derived from the algorithm previously introduced for the image retrieval task. To conclude, this thesis introduces machine learning approaches for ad-hoc retrieval. We propose learning models for various multi-modal retrieval setups, i.e. the retrieval of text documents from text queries, the retrieval of images from text queries and the retrieval of speech recordings from written keywords. Our approaches rely on discriminative learning and enjoy efficient training procedures, which yields effective and scalable models. In all cases, links with prior approaches were investigated and experimental comparisons were conducted.

EPFL2008

, ,

The respiratory system is one of the major components of the speech production system. Any alteration in breathing can result in changes in speech. Specific breathing characteristics, such as breathing rate and tidal volume, can indicate a person's pathological condition. More recently, neural network-based methods have started emerging for predicting the breathing signal from the speech signal. The neural networks are trained and evaluated with different objective measures, such as mean squared error (MSE) and Pearson's correlation. This paper investigates whether there is a systematic relationship between the different objective measures used for training and evaluating the neural network models and the end-goal, i.e. estimation of breathing parameters such as, breathing rate and tidal volume. Our investigations on two different data sets with two different neural network-based approaches show that there is no clear systematic relationship. In other words, obtaining a high Pearson's correlation on the evaluation set does not necessarily mean better breathing parameter estimation. Thus, indicating the need for developing other objective evaluation measures.

IEEE2021

Multilayer Perceptron Based Hierarchical Acoustic Modeling for Automatic Speech Recognition

Joel Praveen Pinto

EPFL2010

EPFL2008