DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting

Rodríguez, Alexander; Tabassum, Anika; Cui, Jiaming; Xie, Jiajia; Ho, Javen; Agarwal, Pulak; Adhikari, Bijaya; Prakash, B. Aditya

doi:10.1101/2020.09.28.20203109

Cited by 56 publications

(68 citation statements)

References 22 publications

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“…DELPHI differs from most other COVID‐19 forecasting models (see, e.g., Kissler et al, 2020 , Perkins & Espana, 2020 , Rodriguez et al, 2020 ) by capturing three key elements of the pandemic: Under‐detection : Many cases remain undetected due to limited testing, asymptomatic carriers, and detection errors. Ignoring them would underestimate the scale of the pandemic.…”

Section: Model Formulationmentioning

confidence: 99%

Where to locate COVID‐19 mass vaccination facilities?

Bertsimas

Digalakis

Jacquillat

et al. 2021

Naval Research Logistics

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The outbreak of COVID‐19 led to a record‐breaking race to develop a vaccine. However, the limited vaccine capacity creates another massive challenge: how to distribute vaccines to mitigate the near‐end impact of the pandemic? In the United States in particular, the new Biden administration is launching mass vaccination sites across the country, raising the obvious question of where to locate these clinics to maximize the effectiveness of the vaccination campaign. This paper tackles this question with a novel data‐driven approach to optimize COVID‐19 vaccine distribution. We first augment a state‐of‐the‐art epidemiological model, called DELPHI, to capture the effects of vaccinations and the variability in mortality rates across age groups. We then integrate this predictive model into a prescriptive model to optimize the location of vaccination sites and subsequent vaccine allocation. The model is formulated as a bilinear, nonconvex optimization model. To solve it, we propose a coordinate descent algorithm that iterates between optimizing vaccine distribution and simulating the dynamics of the pandemic. As compared to benchmarks based on demographic and epidemiological information, the proposed optimization approach increases the effectiveness of the vaccination campaign by an estimated 20%, saving an extra 4000 extra lives in the United States over a 3‐month period. The proposed solution achieves critical fairness objectives—by reducing the death toll of the pandemic in several states without hurting others—and is highly robust to uncertainties and forecast errors—by achieving similar benefits under a vast range of perturbations.

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Section: Model Formulationmentioning

confidence: 99%

Where to locate COVID‐19 mass vaccination facilities?

Bertsimas

Digalakis

Jacquillat

et al. 2021

Naval Research Logistics

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“…Deep learning-based techniques bring a unique perspective to how to detect signals from data with minimal assumptions. Fourthly, deep learning-based models provide excellent short-term forecasting, which is useful for guiding intervention and allocating resources (Rodriguez et al, 2020).…”

Section: Machine Learningmentioning

confidence: 99%

Methods, Challenges, and Practical Issues of COVID-19 Projection: A Data Science Perspective

Kim¹,

Gu²,

Yu³

et al. 2021

Journal of Data Science

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The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has placed epidemic modeling at the center of attention of public policymaking. Predicting the severity and speed of transmission of COVID-19 is crucial to resource management and developing strategies to deal with this epidemic. Based on the available data from current and previous outbreaks, many efforts have been made to develop epidemiological models, including statistical models, computer simulations, mathematical representations of the virus and its impacts, and many more. Despite their usefulness, modeling and forecasting the spread of COVID-19 remains a challenge. In this article, we give an overview of the unique features and issues of COVID-19 data and how they impact epidemic modeling and projection. In addition, we illustrate how various models could be connected to each other. Moreover, we provide new data science perspectives on the challenges of COVID-19 forecasting, from data collection, curation, and validation to the limitations of models, as well as the uncertainty of the forecast. Finally, we discuss some data science practices that are crucial to more robust and accurate epidemic forecasting.

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“…Furthermore, many modeling approaches supplement SEIR models with additional behavioral data such as mobility and governmental policies (Woody et al 2020, Chinazzi et al 2020) to adjust for compliance and non-pharmaceutical interventions. There is also significant work on approaches that do not model the disease dynamics directly, such as utilizing deep learning for predicting week-ahead mortality (Rodriguez et al 2020), or treating the past epidemiological data as a time-series forecasting problem (Mehrotra and Ivan 2020). For a more comprehensive review of the different models that have been developed, we refer the reader to Dean et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Forecasting COVID-19 and Analyzing the Effect of Government Interventions

Bouardi

Lami

et al. 2020

Preprint

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One key question in the ongoing COVID-19 pandemic is understanding the impact of government interventions, and when society can return to normal. To this end, we develop DELPHI, a novel epidemiological model that captures the effect of under-detection and government intervention. We applied DELPHI across 167 geographical areas since early April, and recorded 6% and 11% two-week out-of-sample Median Absolute Percentage Error on cases and deaths respectively. Furthermore, DELPHI successfully predicted the large-scale epidemics in many areas months before, including US, UK and Russia. Using the extracted government response, we find mass gathering restrictions and school closings on average reduced infection rates the most, at 29.9 ± 6.9% and 17.3 ± 6.7%, respectively. The most stringent policy, stay-at-home, on average reduced the infection rate by 74.4 ± 3.7% from baseline across countries that implemented it. We also illustrate how DELPHI can be extended to provide insights on reopening societies under different policies.

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DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting

Cited by 56 publications

References 22 publications

Where to locate COVID‐19 mass vaccination facilities?

Where to locate COVID‐19 mass vaccination facilities?

Methods, Challenges, and Practical Issues of COVID-19 Projection: A Data Science Perspective

Forecasting COVID-19 and Analyzing the Effect of Government Interventions

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