This paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Strikingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the basic reproduction number. The model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously inform policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19.

This paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Strikingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the basic reproduction number. The model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously inform policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19.

A group of graduate students was assigned to design and conduct training as their group project. Nevertheless, the whole world, including Malaysia was hit by the Covid-19 pandemic. Everyone in the country was under the first phase of the Movement Control Order (MCO). Hence, face-to-face training was not a viable option. Alternatively, the students had to conduct online training. The assignment was the first-time experience for them to design and provide training. A question arose on how well the graduate students' experience was in designing, implementing, and evaluating online training. This article presents a qualitative study on implementing an online training program titled "Learning How to Make Halal Chinese Dumplings Online!" by graduate students in the Training Design and Management course at Universiti Putra Malaysia. This study aimed to explore the design and implementation of the online training program. Data collection was through an online focus group discussion (FGD) involving 11 respondents and a document analysis of a written report by the training implementers. The central theme that emerged from the data analysis is training design for online training. Meanwhile, the sub-themes are as follows: (a) training needs analysis and rationale for online training in making halal Chinese dumplings, (b) training design, (c) training evaluation, and (d) challenges in conducting online training. Methodologically, this study implies the feasibility of successful data collection via online platforms. The findings on the well-designed and implemented online training based on training needs analysis and continuous evaluation suggested that we could still gain access to