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The COVID-19 pandemic brought to the forefront an unprecedented need for experts, as well as citizens, to visualize spatio-temporal disease surveillance data. Web application dashboards were quickly developed to fill this gap, including those built by JHU, WHO, and CDC, but all of these dashboards supported a particular niche view of the pandemic (ie, current status or specific regions). In this paper1, we describe our work developing our own COVID-19 Surveillance Dashboard, available at https://nssac.bii.virginia.edu/covid-19/dashboard/, which offers a universal view of the pandemic while also allowing users to focus on the details that interest them. From the beginning, our goal was to provide a simple visual way to compare, organize, and track near-real-time surveillance data as the pandemic progresses. Our dashboard includes a number of advanced features for zooming, filtering, categorizing and visualizing multiple time series on a single canvas. In developing this dashboard, we have also identified 6 key metrics we call the 6Cs standard which we propose as a standard for the design and evaluation of real-time epidemic science dashboards. Our dashboard was one of the first released to the public, and remains one of the most visited and highly used. Our group uses it to support federal, state and local public health authorities, and it is used by people worldwide to track the pandemic evolution, build their own dashboards, and support their organizations as they plan their responses to the pandemic. We illustrate the utility of our dashboard by describing how it can be used to support data story-telling – an important emerging area in data science.

We develop a general data-driven methodology that yields network representations of agricultural flows pertaining to the spread of invasive species. The methodology synthesizes sparse, diverse, noisy and incomplete data that is typically available to build realistic spatiotemporal network representations. We illustrate the methodology by modeling the seasonal flow of the tomato crop in Nepal between major domestic markets. Through dynamical analysis of the network, we study its role in the spread of a major pest of tomato, Tuta absoluta, an emerging outbreak in this country. In the absence of high-resolution pest distribution data, we apply a novel ranking-based inference approach to establish that tomato trade is a driving factor in the rapid spread of this pest.

Over the past decades, numerous techniques have been developed to forecast the temporal evolution of epidemic outbreaks. This paper proposes an approach that combines high resolution agent-based models using realistic social contact networks for simulating epidemic evolution with a particle filter based method for assimilation based forecasting. Agent-based modeling using realistic social contact networks provides two key advantages: (i) they capture the causal processes underlying the epidemic and hence are useful to understand the role of interventions on the course of the epidemics – typically time series models cannot capture this and as a result often do not perform well in such situations; (ii) they provide detailed forecast information – this allows us to produce forecast at high levels of temporal, spatial and social granularity. We also propose a new variation of particle filter technique called beam search particle filtering. The modification allows us to more efficiently search the parameter space which is necessitated by the fact that agent-based techniques are computationally expensive. We illustrate our methodology on the synthetic dataset of Ebola provided as a part of the NSF/NIH Ebola forecasting challenge. Our results show the efficacy of the proposed approach and suggest that agent-based causal models can be combined with filtering techniques to yield a new class of assimilation models for infectious disease forecasting.

Prophylactic interventions such as vaccine allocation are one of the most effective public health policy planning tools. The supply of vaccines is limited, and an importantproblem is when and how to allocate the available vaccination supply, referred to as the Vaccine Allocation Problem. The spread of epidemics is modeled by the SEIR process, which has a very complex dynamics, and depends on human contacts and mobility. This makes the design of efficient solutions tovaccine allocation problem to minimize the number of infections a very challenging problem. In particular, this requires good models for human mobility, and optimization tools for vaccine allocation.In this paper, we study the vaccine allocation problem in the context of seasonal Influenza spread inthe United States. We develop a novel national scale flu model that integrate both short andlong distance travel, which are known to be important determinants of the spread of Influenza. We also design a greedy algorithm for allocating the vaccine supply at a county level. Our results show significant improvement over the current baseline, whichinvolves allocating vaccines based on the state population.

We consider the competition between two competing content creators who can reach out to their potential consumers via two different online social networks. The efficiency of a network for information spread is characterized by two simple properties: the level of activity within the network and the popularity of the network among the population of consumers. We assume that the contents under our consideration are exclusive in nature, i.e., each consumer is interested in receiving only one of the competing contents. Each content creator optimizes the total budget spent across the two social networks. We study the non-cooperative game and characterize the best response functions for the content creators. From the best response functions we observe that there exists a hysteresis-like behavior when it comes to resource allocation across multiple networks, i.e., as a player responds to increasing budget of the competitor, there is an interval of the opponent's budget between when the player saturates the resources in the better network and when he begins to invest in the worse network. A similar behavior is also observed when the player begins reducing resources, responding to a much higher budget of the competitor. We also observe that the larger the difference between the networks' efficiency levels, the larger the interval. We then numerically evaluate the Nash equilibria from the best response functions and conclude with discussions on possible future scope of this work.

We provide new analytical results concerning the spread of information or influence under the linear threshold social network model introduced by Kempe et al. in [1], in the information dissemination context. The seeder starts by providing the message to a set of initial nodes and is interested in maximizing the number of nodes that will receive the message ultimately. A node's decision to forward the message depends on the set of nodes from which it has received the message. Under the linear threshold model, the decision to forward the information depends on the comparison of the total influence of the nodes from which a node has received the packet with its own threshold of influence. We derive analytical expressions for the expected number of nodes that receive the message ultimately, as a function of the initial set of nodes, for a generic network. We show that the problem can be recast in the framework of Markov chains. We then use the analytical expression to gain insights into information dissemination in some simple network topologies such as the star, ring, mesh and on acyclic graphs. We also derive the optimal initial set in the above networks, and also hint at general heuristics for picking a good initial set.