This dataset consists of 3 components: a synthetic population representing a demographically realistic population, including synthetic activities for each individual denoting the different locations they visited, when they visited, and the duration of that visit; a contact network derived from these activities; and a set of disease states, the result of simulating the effects of a COVID-like disease within this population. There are two datasets available. One is a representation of the state of Virginia (~7.7 million individuals) and the other is a representation of the United Kingdom (~62 million individuals).

This is a collection of data sets, each data set being a synthetic population for a country, or state, both of which will be referred to as a region in the following. At a high level, a synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

We gathered data on non-pharmaceutical interventions (NPIs) against COVID-19 from counties and independent cities in Virginia. NPIs are methods for reducing the spread of a disease that do not involve vaccines or drug treatments. Specifically, we look for dates when closures or mandates were implemented or lifted in the following five categories: masks, businesses, pre-K-12 schools, colleges, religious organizations.

We have developed a national-scale metapopulation model for the spread of influenza by integrating both local and long-distance mobility data. For the example below, we combined data on commuter mobility from the American Community Survey (ACS) with domestic airline passenger data from the Bureau of Transportation Statistics (BTS) to capture human mobility across the country. Next, we adopted a metapopulation approach to simulate epidemic spread at the spatial resolution of counties (patches) wherein the temporal travel matrix is used to represent the flow of people between these patches: PatchSim.

COPASI is a software application for the simulation and analysis of biochemical networks and their dynamics.

dismolib is an interactive library for simulating the numerical outcomes of published mathematical epidemiology models including two types of COVID-19 models. For a disease model in this library, you can vary the parameters, simulate the time course, perform stability analysis, and more.

The Net.Science WebApp will allow for the discovery of data sets, networks, and tasks of interest to users. Users can submit tasks to perform analyses on this discovered data, user-uploaded data, and other's shared data.

EXCEADS (EXperiments in Computational Epidemiology for Action and Decision Support) is an application that allows the design, execution, and analysis of computational epidemiology experiments. The user can simulate the spread of infectious diseases under different initial conditions and intervention strategies to better understand disease dynamics to inform actions and support decisions.

The BV-BRC (Bacterial and Viral Bioinformatics Resource Center) is an information system designed to support research on bacterial and viral infectious diseases.

This is a C program that samples RNA sequences from a given RNA secondary structure with a given Boltzmann distribution as well as with Hamming distance filtration. It takes a secondary structure, a reference sequence, and a given distance as input, and generates RNA sequences that are Boltzmann distributed having the specified fixed Hamming distance to the reference sequence.

This is a C program that samples RNA sequences for two structures. It takes two RNA secondary structures having the same length as input and generates sequences that are thermodynamically stable to the two input structures.

This is a software package for identifying the target secondary structure from an RNA structure ensemble. We present a demo of the ensemble tree.

This Python package ingests a (monotone) weighted simplicial complex from a file
and computes its weighted homology over the ring of power series over the rational numbers with uniformizer π\piπ.). Weighted simplicial homology is a deformation of classical simplicial homology for which now the boundary operator takes into account simplex weights.