When an influenza pandemic emerges, temporary school closures and antiviral treat-ment may slow virus spread, reduce the overall disease burden, and provide time for vaccine development, distribution, and administration while keeping a larger portion of the general population infection free. The impact of such measures will depend on the transmissibility and severity of the virus and the timing and extent of their imple-mentation. To provide robust assessments of layered pandemic intervention strategies, the Centers for Disease Control and Prevention (CDC) funded a network of academic groups to build a framework for the development and comparison of multiple pandemic influenza models. Research teams from Columbia University, Imperial College London/ Princeton University, Northeastern University, the University of Texas at Austin/Yale University, and the University of Virginia independently modeled three prescribed sets of pandemic influenza scenarios developed collaboratively by the CDC and network members. Results provided by the groups were aggregated into a mean- based ensemble. The ensemble and most component models agreed on the ranking of the most and least effective intervention strategies by impact but not on the magnitude of those impacts. In the scenarios evaluated, vaccination alone, due to the time needed for development, approval, and deployment, would not be expected to substantially reduce the numbers of illnesses, hospitalizations, and deaths that would occur. Only strategies that included early implementation of school closure were found to substantially mitigate early spread and allow time for vaccines to be developed and administered, especially under a highly transmissible pandemic scenario.

Community detection in networks is extensively studied from a structural perspective, but very few works characterize communities with respect to dynamics on networks. We propose a generic framework based on Moore-Shannon network reliability for defining and discovering communities with respect to a variety of dynamical processes. This approach extracts communities in directed edge-weighted networks which satisfy strong connectivity properties as well as strong mutual influence between pairs of nodes through the dynamical process. We apply this framework to food networks. We compare our results with modularity-based approach, and analyze community structure across commodities, evolution over time, and with regard to dynamical system properties.

Synthetic populations for regions of the World (SPW) | IcelandDataset informationA 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). LicenseCC-BY-4.0 AcknowledgmentThis project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541). Contact informationHenning.Mortveit@virginia.edu Identifiers Region name Iceland Region ID isl Model coarse Version 0_9_0 Statistics Name Value Population 293477.0 Average age 36.8 Households 115570.0 Average household size 2.5 Residence locations 115570.0 Activity locations 48082.0 Average number of activities 5.8 Average travel distance 53.2 Sources Description Name Version Url Activity template data World Bank 2021 https://data.worldbank.org Administrative boundaries ADCW 7.6 https://www.adci.com/adc-worldmap Curated POIs based on OSM SLIPO/OSM POIs http://slipo.eu/?p=1551 https://www.openstreetmap.org/ Population count with demographic attributes GPW v4.11 https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11 Files descriptionBase data files (isl_data_v_0_9.zip) Filename Description isl_person_v_0_9.csv Data for each person including attributes such as age, gender, and household ID. isl_household_v_0_9.csv Data at household level. isl_residence_locations_v_0_9.csv Data about residence locations isl_activity_locations_v_0_9.csv Data about activity locations, including what activity types are supported at these locations isl_activity_location_assignment_v_0_9.csv For each person and for each of their activities, this file specifies the location where the activity takes place Derived data files Filename Description isl_contact_matrix_v_0_9.csv A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model. Validation and measures files Filename Description isl_household_grouping_validation_v_0_9.pdf Validation plots for household construction isl_activity_durations_{adult,child}_v_0_9.pdf Comparison of time spent on generated activities with survey data isl_activity_patterns_{adult,child}_v_0_9.pdf Comparison of generated activity patterns by the time of day with survey data isl_location_construction_0_9.pdf Validation plots for location construction isl_location_assignement_0_9.pdf Validation plots for location assignment, including travel distribution plots isl_isl_ver_0_9_0_avg_travel_distance.pdf Choropleth map visualizing average travel distance isl_isl_ver_0_9_0_travel_distr_combined.pdf Travel distance distribution isl_isl_ver_0_9_0_num_activity_loc.pdf Choropleth map visualizing number of activity locations isl_isl_ver_0_9_0_avg_age.pdf Choropleth map visualizing average age isl_isl_ver_0_9_0_pop_density_per_sqkm.pdf Choropleth map visualizing population density isl_isl_ver_0_9_0_pop_size.pdf Choropleth map visualizing population size

The probabilistic satisfiability of a logical expression is a fundamental concept known as the partition function in statistical physics and field theory, an evaluation of a related graph's Tutte polynomial in mathematics, and the Moore-Shannon network reliability of that graph in engineering. It is the crucial element for decision-making under uncertainty. Not surprisingly, it is provably hard to compute exactly or even to approximate. Many of these applications are concerned only with a subset of problems for which the solutions are monotonic functions. Here we extend the weak- and strong-coupling methods of statistical physics to heterogeneous satisfiability problems and introduce a novel approach to constructing lower and upper bounds on the approximation error for monotonic problems. These bounds combine information from both perturbative analyses to produce bounds that are tight in the sense that they are saturated by some problem instance that is compatible with all the information contained in either approximation.

Synthetic populations for regions of the World (SPW) | KenyaDataset informationA 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). LicenseCC-BY-4.0 AcknowledgmentThis project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541). Contact informationHenning.Mortveit@virginia.edu Identifiers Region name Kenya Region ID ken Model coarse Version 0_9_0_adm1 Statistics Name Value Population 39129968.0 Average age 21.6 Households 10938486.0 Average household size 3.6 Residence locations 10938486.0 Activity locations 838758.0 Average number of activities 5.4 Average travel distance 134.7 Sources Description Name Version Url Activity template data World Bank 2021 https://data.worldbank.org Administrative boundaries ADCW 7.6 https://www.adci.com/adc-worldmap Curated POIs based on OSM SLIPO/OSM POIs http://slipo.eu/?p=1551 https://www.openstreetmap.org/ Household data DHS https://dhsprogram.com Population count with demographic attributes GPW v4.11 https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11 Files descriptionBase data files (ken_data_v_0_9.zip) Filename Description ken_person_v_0_9.csv Data for each person including attributes such as age, gender, and household ID. ken_household_v_0_9.csv Data at household level. ken_residence_locations_v_0_9.csv Data about residence locations ken_activity_locations_v_0_9.csv Data about activity locations, including what activity types are supported at these locations ken_activity_location_assignment_v_0_9.csv For each person and for each of their activities, this file specifies the location where the activity takes place Derived data files Filename Description ken_contact_matrix_v_0_9.csv A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model. Validation and measures files Filename Description ken_household_grouping_validation_v_0_9.pdf Validation plots for household construction ken_activity_durations_{adult,child}_v_0_9.pdf Comparison of time spent on generated activities with survey data ken_activity_patterns_{adult,child}_v_0_9.pdf Comparison of generated activity patterns by the time of day with survey data ken_location_construction_0_9.pdf Validation plots for location construction ken_location_assignement_0_9.pdf Validation plots for location assignment, including travel distribution plots ken_ken_ver_0_9_0_avg_travel_distance.pdf Choropleth map visualizing average travel distance ken_ken_ver_0_9_0_travel_distr_combined.pdf Travel distance distribution ken_ken_ver_0_9_0_num_activity_loc.pdf Choropleth map visualizing number of activity locations ken_ken_ver_0_9_0_avg_age.pdf Choropleth map visualizing average age ken_ken_ver_0_9_0_pop_density_per_sqkm.pdf Choropleth map visualizing population density ken_ken_ver_0_9_0_pop_size.pdf Choropleth map visualizing population size

We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Using a realistic representation of a social contact network for the Commonwealth of Virginia, we study how a limited number of vaccine doses can be strategically distributed to individuals to reduce the overall burden of the pandemic. We show that allocation of vaccines based on individuals' degree (number of social contacts) and total social proximity time is significantly more effective than the usually used age-based allocation strategy in reducing the number of infections, hospitalizations and deaths. The overall strategy is robust even: (i) if the social contacts are not estimated correctly; (ii) if the vaccine efficacy is lower than expected or only a single dose is given; (iii) if there is a delay in vaccine production and deployment; and (iv) whether or not non-pharmaceutical interventions continue as vaccines are deployed. For reasons of implementability, we have used degree, which is a simple structural measure and can be easily estimated using several methods, including the digital technology available today. These results are significant, especially for resource-poor countries, where vaccines are less available, have lower efficacy, and are more slowly distributed.

This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; (ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis; (iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC; (iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences.

Background: This study explores how the Roma in Romania, the EU’s most concentrated population, are faring in terms of a number of quality of life indicators, including poverty levels, healthcare, education, water, sanitation, and hygiene. It further explores the role of synthetic populations and modelling in identifying at-risk populations and delivering targeted aid. Methods: 135 surveys were conducted across five geographically diverse Romanian communities. Household participants were selected through a comprehensive random walk method. Analyses were conducted on all data using Pandas for Python. Combining land scan data, time-use survey analyses, interview data, and ArcGIS, the resulting synthetic population was analysed via classification and regression tree (CART) analysis to identify hot-spots of need, both ethnically and geographically. Results: These data indicate that the Roma in Romania face significant disparities in education, with Roma students less likely to progress beyond 8 th grade. In addition, the Roma population remains significantly disadvantaged with regard to safe and secure housing, poverty, and healthcare status, particularly in connection to diarrheal disease. In contrast, however, both Roma and non-Roma in rural areas face difficulties regarding full-time employment, sanitation, and water, sanitation, and hygiene infrastructure. In addition, the use of a Open Peer Review

We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Even optimistic estimates suggest that most countries will likely take 6 to 24 months to vaccinate their citizens. These time estimates and the emergence of new viral strains urge us to find quick and effective ways to allocate the vaccines and contain the pandemic. While current approaches use combinations of age-based and occupation-based prioritizations, our strategy marks a departure from such largely aggregate vaccine allocation strategies. We propose a novel approach motivated by recent advances in (i) science of real-world networks that point to efficacy of certain vaccination strategies and (ii) digital technologies that improve our ability to estimate some of these structural properties. Using a realistic representation of a social contact network for the Commonwealth of Virginia, combined with accurate surveillance data on spatiotemporal cases and currently accepted models of within- and between-host disease dynamics, we study how a limited number of vaccine doses can be strategically distributed to individuals to reduce the overall burden of the pandemic. We show that allocation of vaccines based on individuals' degree (number of social contacts) and total social proximity time is significantly more effective than the currently used age-based allocation strategy in terms of number of infections, hospitalizations and deaths. Our results suggest that in just two months, by March 31, 2021, compared to age-based allocation, the proposed degree-based strategy can result in reducing an additional 56-110k infections, 3.2- 5.4k hospitalizations, and 700-900 deaths just in the Commonwealth of Virginia. Extrapolating these results for the entire US, this strategy can lead to 3-6 million fewer infections, 181-306k fewer hospitalizations, and 51-62k fewer deaths compared to age-based allocation. The overall strategy is robust even: (i) if the social contacts are not estimated correctly; (ii) if the vaccine efficacy is lower than expected or only a single dose is given; (iii) if there is a delay in vaccine production and deployment; and (iv) whether or not non-pharmaceutical interventions continue as vaccines are deployed. For reasons of implementability, we have used degree, which is a simple structural measure and can be easily estimated using several methods, including the digital technology available today. These results are significant, especially for resource-poor countries, where vaccines are less available, have lower efficacy, and are more slowly distributed.

Beginning in April 2020, we gathered partial county-level data on non-pharmaceutical interventions (NPIs) implemented in response to the COVID-19 pandemic in the United States, using both volunteer and paid crowdsourcing. In this report, we document the data collection process and summarize our results, to increase the utility of our open data and inform the design of future rapid crowdsourcing data collection efforts.

Computational epidemiology aims to develop computer models and decision support systems that understand, predict, and control a disease’s spatiotemporal diffusion throughout a population. Researchers can use these models to forecast an epidemic’s future course, allocate scarce resources and assess depletion of current resources, infer disease parameters, and evaluate various interventions. Individual behavior and public policy are critical in understanding and controlling infectious diseases, and computational techniques provide a potentially powerful study tool. The COVID-19 pandemic has had significant social, health, economic, and political ramifications worldwide, and its impact will undoubtedly continue to grow in the coming months. Here we outline an approach to support the COVID-19 response with examples that are rooted in network science and data-driven modeling.

This research measures the epidemiological and economic impact of COVID-19 spread in the US under different mitigation scenarios, comprising of non-pharmaceutical interventions. A detailed disease model of COVID-19 is combined with a model of the US economy to estimate the direct impact of labor supply shock to each sector arising from morbidity, mortality, and lockdown, as well as the indirect impact caused by the interdependencies between sectors. During a lockdown, estimates of jobs that are workable from home in each sector are used to modify the shock to labor supply. Results show trade-offs between economic losses, and lives saved and infections averted are non-linear in compliance to social distancing and the duration of the lockdown. Sectors that are worst hit are not the labor-intensive sectors such as the Agriculture sector and the Construction sector, but the ones with high valued jobs such as the Professional Services, even after the teleworkability of jobs is accounted for. Additionally, the findings show that a low compliance to interventions can be overcome by a longer shutdown period and vice versa to arrive at similar epidemiological impact but their net effect on economic loss depends on the interplay between the marginal gains from averting infections and deaths, versus the marginal loss from having healthy workers stay at home during the shutdown.

Global airline networks play a key role in the global importation of emerging infectious diseases. Detailed information on air traffic between international airports has been demonstrated to be useful in retrospectively validating and prospectively predicting case emergence in other countries. In this paper, we use a well-established metric known as effective distance on the global air traffic data from IATA to quantify risk of emergence for different countries as a consequence of direct importation from China, and compare it against arrival times for the first 24 countries. Using this model trained on official first reports from WHO, we estimate time of arrival (ToA) for all other countries. We then incorporate data on airline suspensions to recompute the effective distance and assess the effect of such cancellations in delaying the estimated arrival time for all other countries. Finally we use the infectious disease vulnerability indices to explain some of the estimated reporting delays.

We use an individual based model and national level epidemic simulations to estimate the medical costs of keeping the US economy open during COVID-19 pandemic under different counterfactual scenarios. We model an unmitigated scenario and 12 mitigation scenarios which differ in compliance behavior to social distancing strategies and in the duration of the stay-home order. Under each scenario we estimate the number of people who are likely to get infected and require medical attention, hospitalization, and ventilators. Given the per capita medical cost for each of these health states, we compute the total medical costs for each scenario and show the tradeoffs between deaths, costs, infections, compliance and the duration of stay-home order. We also consider the hospital bed capacity of each Hospital Referral Region (HRR) in the US to estimate the deficit in beds each HRR will likely encounter given the demand for hospital beds. We consider a case where HRRs share hospital beds among the neighboring HRRs during a surge in demand beyond the available beds and the impact it has in controlling additional deaths.

Models of urban mobility patterns are key inputs to urban analytics, e.g., planning the transportation infrastructure. Typically, only limited data about mobility is available, due to privacy and data collection challenges. Here, we study the problem of reconstructing mobility traces for all individuals from flow measurements on transportation network links. We formalize this as the constrained label sequence problem (denoted by CLS). CLS is the problem of constructing labeled sequences with constraints that correspond to flows. The proposed formulation is quite general and allows us to consider bounds on the total flow through a set of links, instead of an individual link. We show that CLS is computationally hard to solve exactly, and design an algorithm with rigorous approximation guarantees. We also study the complexity of counting and sampling from the set of mobility traces consistent with such measurements. Finally, we demonstrate our results using transportation flows across zones around Washington DC and data from the American Community Survey Commuting Flows.

The paper describes a systematic approach for validating real-world biological, information, social and technical (BIST) networks. BIST systems are usually represented using agent-based models and computer simulations are used to study their dynamical (state-space) properties. Here, we use a formal representation called a graph dynamical system (GDS). We present two types of results. First we describe two real-world validation studies spanning a variety of BIST networks. Various types of validation are considered and unique challenges presented by each domain are discussed. Each system is represented using the GDS formalism. This illustrates the power of the formalism and enables a unified approach for validation. We complement the case studies by presenting new theoretical results on validating BIST systems represented as GDSs. These theoretical results delineate computationally intractable and efficiently solvable versions of validation problems.

Understanding the structural and dynamical properties of food networks is critical for food security and social welfare. Here, we analyze international trade networks corresponding to four solanaceous crops obtained using the Food and Agricultural Organization trade database using Moore-Shannon network reliability. We present a novel approach to identify important dynamics-induced clusters of highly-connected nodes in a directed weighted network. Our analysis shows that the structure and dynamics can greatly vary across commodities. However, a consistent pattern that we observe in these commodity-specific networks is that almost all clusters that are formed are between adjacent countries in regions where liberal bilateral trade relations exist. Our analysis of networks of different years shows that intensification of trade has led to increased size of clusters, which implies that the number of countries spared from the network effects of disruption is reducing. Finally, applying this method to the aggregate network obtained by combining the four networks reveals clusters very different from those found in the constituent networks.

Accurate and timely influenza (flu) forecasting has gained significant traction in recent times. If done well, such forecasting can aid in deploying effective public health measures. Unlike other statistical or machine learning problems, however, flu forecasting brings unique challenges and considerations stemming from the nature of the surveillance apparatus and the end utility of forecasts. This article presents a set of considerations for flu forecasters to take into account prior to applying forecasting algorithms.

We develop a formalism for addressing many common questions about network structure. The formalism is based on Shannon-Moore network reliability and on the Birnbaum definition of importance in terms of reliability. The computational methods we suggest based on this formalism are designed to answer specific questions about how network structure affects particular aspects of the dynamics, and can be applied to a wide variety of dynamical systems on interaction networks. The formalism and methods bring well-understood analytical techniques from statistical physics and computer science to bear on current issues in network science. We introduce the concepts that underpin the formalism, explore methods of applying these concepts, and demonstrate applications to problems such as edge ranking and graph reduction.