Covid Decision Support

We combined machine learning and epidemiological modelling to help public authorities manage critical healthcare resources, deliver targeted interventions and track disease spread within communities.

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Repository for modelling the spread of Covid19

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Interpretability of epidemiological models is a key consideration, especially when these models are used in a public health setting. Interpretability is strongly linked to the identifiability of the underlying model parameters, i.e., the ability to estimate parameter values with high confidence given observations. In this paper, we define three separate notions of identifiability that explore the different roles played by the model definition, the loss function, the fitting methodology, and the quality and quantity of data. We define an epidemiological compartmental model framework in which we highlight these non-identifiability issues and their mitigation.

Interpretability of epidemiological models is a key consideration, especially when these models are used in a public health setting. Interpretability is strongly linked to …

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Accurate forecasts of infections for localized regions are valuable for policy making and medical capacity planning. Existing compartmental and agent-based models for epidemiological forecasting employ static parameter choices and cannot be readily contextualized, while adaptive solutions focus primarily on the reproduction number. The current work proposes a novel model-agnostic Bayesian optimization approach for learning model parameters from observed data that generalizes to multiple application-specific fidelity criteria. Empirical results point to the efficacy of the proposed method with SEIR-like models on COVID-19 case forecasting tasks. A city-level forecasting system based on this method is being used for COVID-19 response in a few impacted Indian cities.

Accurate forecasts of infections for localized regions are valuable for policy making and medical capacity planning. Existing compartmental and agent-based models for epidemiological forecasting …

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During an epidemic, accurate long term forecasts are crucial for decision-makers to adopt appropriate policies and to prevent medical resources from being overwhelmed. This came to the forefront during the covid-19 pandemic, during which there were numerous efforts to predict the number of new infections. Various classes of models were employed for forecasting including compartmental models and curve-fitting approaches. Curve fitting models often have accurate short term forecasts. Their parameters, however, can be difficult to associate with actual disease dynamics. Compartmental models take these dynamics into account, allowing for more flexible and interpretable models that facilitate qualitative comparison of scenarios. This paper proposes a method of strengthening the forecasts from compartmental models by using short term predictions from a curve fitting approach as synthetic data. We discuss the method of fitting this hybrid model in a generalized manner without reliance on region specific data, making this approach easy to adapt. The model is compared to a standard approach; differences in performance are analyzed for a diverse set of covid-19 case counts.

During an epidemic, accurate long term forecasts are crucial for decision-makers to adopt appropriate policies and to prevent medical resources from being overwhelmed. This …

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As the COVID-19 outbreak continues to pose a serious worldwide threat, numerous governments choose to establish lock-downs in order to reduce disease transmission. However, imposing the strictest possible lock-down at all times has dire economic consequences, especially in areas with widespread poverty. In fact, many countries and regions have started charting paths to ease lock-down measures. Thus, planning efficient ways to tighten and relax lock-downs is a crucial and urgent problem. We develop a reinforcement learning based approach that is (1) robust to a range of parameter settings, and (2) optimizes multiple objectives related to different aspects of public health and economy, such as hospital capacity and delay of the disease. The absence of a vaccine or a cure for COVID to date implies that the infected population cannot be reduced through pharmaceutical interventions. However, non-pharmaceutical interventions (lock-downs) can slow disease spread and keep it manageable. This work focuses on how to manage the disease spread without severe economic consequences.

As the COVID-19 outbreak continues to pose a serious worldwide threat, numerous governments choose to establish lock-downs in order to reduce disease transmission. However, …

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