Background The rapid spread of COVID-19 means that government and health services providers have little time to plan and design effective response policies. It is therefore important to quickly provide accurate predictions of how vulnerable geographic regions such as counties are to the spread of this virus. Objective The aim of this study is to develop county-level prediction around near future disease movement for COVID-19 occurrences using publicly available data. Methods We estimated county-level COVID-19 occurrences for the period March 14 to 31, 2020, based on data fused from multiple publicly available sources inclusive of health statistics, demographics, and geographical features. We developed a three-stage model using XGBoost, a machine learning algorithm, to quantify the probability of COVID-19 occurrence and estimate the number of potential occurrences for unaffected counties. Finally, these results were combined to predict the county-level risk. This risk was then used as an estimated after-five-day-vulnerability of the county. Results The model predictions showed a sensitivity over 71% and specificity over 94% for models built using data from March 14 to 31, 2020. We found that population, population density, percentage of people aged >70 years, and prevalence of comorbidities play an important role in predicting COVID-19 occurrences. We observed a positive association at the county level between urbanicity and vulnerability to COVID-19. Conclusions The developed model can be used for identification of vulnerable counties and potential data discrepancies. Limited testing facilities and delayed results introduce significant variation in reported cases, which produces a bias in the model.

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In a high‐mix and low‐volume manufacturing facility, heterogeneous jobs introduce frequent reconfiguration of machines which increases the chance of unplanned machine breakdowns. As machines are often nonidentical and their performance degrades over time, it is critical to consider the heterogeneity and non‐stationarity of the machines during scheduling. We propose a reinforcement learning‐based framework with a novel sampling method to train the agent to schedule heterogeneous jobs on non‐stationary unreliable parallel machines to minimize weighted tardiness. The results indicate that the new sampling approach expedites the learning process and the resulting policy significantly outperforms static dispatching rules.

In order to analyze the evacuation behaviors and optimize evacuation strategies for rail transit system, an evacuation agent centered simulation model was proposed. Firstly, by considering the attributes, status and decision-making behaviors of evacuation personnel, the evacuation agent model was established, and the running principle as well as construction process of multi-agent simulation model was discussed. Then, the specific definition and design for the agent attributes and evacuation behavior protocol were provided. Finally, based on the simulation model proposed, an evacuation simulation platform for the military museum station of Beijing subway line 9 was established by using REPAST and JAVA, several evacuation strategies were tested and optimized.

Importance:The rapid spread of COVID-19 means that government and health services providers have little time to plan and design effective response policies. It is therefore important to rapidly provide accurate predictions of how vulnerable geographic regions such as counties are to the spread.Objective: Developing county level prediction around near future disease movement for Main Outcome(s) and Measure(s):We developed a 3-stage model to quantify, firstly the probability of COVID-19 occurrence for unaffected counties using XGBoost classifier and secondly, the number of potential occurrences of a county via XGBoost regression. Thirdly, these results are combined to compute the county level risk. This risk is then used as an estimated after-five-day-vulnerability of the county. Results:Using data from March 14-31, 2020, the model shows a sensitivity over 71.5% and specificity over 94%. Conclusions and Relevance:We found that population, population density, percentage of people aged 70 or greater and prevalence of comorbidities play an important role in predicting COVID-19 occurrences. We found a positive association between affected and urban counties as well as less vulnerable and rural counties. The developed model can be used for