Multiple models for outbreak decision support in the face of uncertainty.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Propagation dynamics and control policies of COVID-19 pandemic at early stages: Implications on future resurgence response.

The spreading of novel coronavirus (SARS-CoV-2) has gravely impacted the world in the last year and a half. Understanding the spatial and temporal patterns of how it spreads at the early stage and the effectiveness of a governments' immediate response helps our society prepare for future COVID-19 waves or the next pandemic and contain it before the spreading gets out of control. In this article, a susceptible-exposed-infectious-removed model is used to model the city-to-city spreading patterns of the disease at the early stage of its emergence in China (from December 2019 to February 2020). Publicly available reported case numbers in 312 Chinese cities and between-city mobility data are leveraged to estimate key epidemiological characteristics, such as the transmission rate and the number of infectious people for each city. It is discovered that during any given time period, there are always only a few cities that are responsible for spreading the disease to other cities. We term these few cities as transmission centers. The spatial and temporal changes in transmission centers demonstrate predictable patterns. Moreover, rigorously designed experiments show that in controlling the disease spread in a city, non-pharmaceutical interventions (NPIs) implemented at transmission centers are more effective than the NPI implemented in the city itself. These findings have implications on the control of an infectious disease at the early stage of its spreading: implementing NPIs at transmission centers at early stages is effective in controlling the spread of infectious diseases.

A BEHAVIORALLY-INTEGRATED INDIVIDUAL-LEVEL STATE-TRANSITION MODEL THAT CAN PREDICT RAPID CHANGES IN EVACUATION DEMAND DAYS EARLIER.

Timely and accurate forecast of evacuation demand is key for emergency responders to plan and organize effective evacuation efforts during a disaster. The state-of-the-art in evacuation demand forecasting includes behavior-based models and dynamic flow-based models. Both lines of work have critical limitations: behavioral models are static, meaning that they cannot adjust model predictions by utilizing field observation in real time as the disaster is unfolding; and the flow-based models often have relatively short prediction windows ranging from minutes to hours. Consequently, both types of models fall short of being able to predict sudden changes (e.g., a surge or abrupt drop) of evacuation demand in advance. This paper develops a behaviorally-integrated individual-level state-transition model for online predictions of evacuation demand. On a daily basis, the model takes in observed evacuation data and updates its forecasted evacuation demand for the future. An individual-level survival model formulation is innovatively devised for the state-transition model to account for history-dependent transition probabilities and allow individual heterogeneity. A Bayesian updating approach is employed to assimilate observed evacuation data in real time. To enable a longer-term perspective on how evacuation demand may evolve over time so that rapid surges or drops in demand can be predicted days in advance, the model integrates insights from existing behavioral curves (either from past disasters or simply expert opinions). Using a likelihood-based approach, the state-transition model integrates the future trends of evacuation demand informed by the behavioral curve when updating its forecasts. Theoretical proof is also provided showing that the likelihood function guarantees a unique global solution to the state-transition model. The proposed model is tested in six scenarios using mobile app-based data for Hurricane Harvey that hit the US in 2017. The results demonstrate overall robustness of the proposed model: in all six scenarios, the model is able to predict accurately the occurrence of the rapid surges or drops in evacuation demand at least two days ahead. The current study contributes to the field of evacuation modeling by integrating the two lines of work (behavior-based and flow-based models) using mobile app-based data.

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support.

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

General methodology for inferring failure-spreading dynamics in networks.

A generic modeling framework to infer the failure-spreading process based on failure times of individual nodes is proposed and tested in four simulation studies: one for cascading failures in interdependent power and transportation networks, one for influenza epidemics, one benchmark test case for congestion cascade in a transportation network, and one benchmark test case for cascading power outages. Four general failure-spreading mechanisms-external, temporal, spatial, and functional-are quantified to capture what drives the spreading of failures. With the failure time of each node given, the proposed methodology demonstrates remarkable capability of inferring the underlying general failure-spreading mechanisms and accurately reconstructing the failure-spreading process in all four simulation studies. The analysis of the two benchmark test cases also reveals the robustness of the proposed methodology: It is shown that a failure-spreading process embedded by specific failure-spreading mechanisms such as flow redistribution can be captured with low uncertainty by our model. The proposed methodology thereby presents a promising channel for providing a generally applicable framework for modeling, understanding, and controlling failure spreading in a variety of systems.

Tracking the Evolution of Infrastructure Systems and Mass Responses Using Publically Available Data.

Networks can evolve even on a short-term basis. This phenomenon is well understood by network scientists, but receive little attention in empirical literature involving real-world networks. On one hand, this is due to the deceitfully fixed topology of some networks such as many physical infrastructures, whose evolution is often deemed unlikely to occur in short term; on the other hand, the lack of data prohibits scientists from studying subjects such as social networks that seem likely to evolve on a short-term basis. We show that both networks-the infrastructure network and social network-are able to demonstrate evolutionary dynamics at the system level even in the short-term, characterized by shifting between different phases as predicted in network science. We develop a methodology of tracking the evolutionary dynamics of the two networks by incorporating flows and the microstructure of networks such as motifs. This approach is applied to the human interaction network and two transportation networks (subway and taxi) in the context of Hurricane Sandy, using publically available Twitter data and transportation data. Our result shows that significant changes in the system-level structure of networks can be detected on a continuous basis. This result provides a promising channel for real-time tracking in the future.