Prioritizing high-contact occupations raises effectiveness of vaccination campaigns

A twenty-year-old idea from network science is that vaccination campaigns would be more effective if high-contact individuals were preferentially targeted. Implementation is impeded by the ethical and practical problem of differentiating vaccine access based on a personal characteristic that is hard-to-measure and private. Here, we propose the use of occupational category as a proxy for connectedness in a contact network. Using survey data on occupation-specific contact frequencies, we calibrate a model of disease propagation in populations undergoing varying vaccination campaigns. We find that vaccination campaigns that prioritize high-contact occupational groups achieve similar infection levels with half the number of vaccines, while also reducing and delaying peaks. The paper thus identifies a concrete, operational strategy for dramatically improving vaccination efficiency in ongoing pandemics.

A stochastic contact network model for assessing outbreak risk of COVID-19 in workplaces

The COVID-19 pandemic has drastically shifted the way people work. While many businesses can operate remotely, a large number of jobs can only be performed on-site. Moreover as businesses create plans for bringing workers back on-site, they are in need of tools to assess the risk of COVID-19 for their employees in the workplaces. This study aims to fill the gap in risk modeling of COVID-19 outbreaks in facilities like offices and warehouses. We propose a simulation-based stochastic contact network model to assess the cumulative incidence in workplaces. First-generation cases are introduced as a Bernoulli random variable using the local daily new case rate as the success rate. Contact networks are established through randomly sampled daily contacts for each of the first-generation cases and successful transmissions are established based on a randomized secondary attack rate (SAR). Modification factors are provided for SAR based on changes in airflow, speaking volume, and speaking activity within a facility. Control measures such as mask wearing are incorporated through modifications in SAR. We validated the model by comparing the distribution of cumulative incidence in model simulations against real-world outbreaks in workplaces and nursing homes. The comparisons support the model’s validity for estimating cumulative incidences for short forecasting periods of up to 15 days. We believe that the current study presents an effective tool for providing short-term forecasts of COVID-19 cases for workplaces and for quantifying the effectiveness of various control measures. The open source model code is made available at github.com/abhineetgupta/covid-workplace-risk.

A Network Dynamics Model for the Transmission of COVID-19 in Diamond Princess and a Response to Reopen Large-Scale Public Facilities

Background: The current epidemic of COVID-19 has become the new normal. However, the novel coronavirus is constantly mutating. In public transportation or large entertainment venues, it can spread more quickly once an infected person is introduced. This study aims to discuss whether large public facilities can be opened and operated under the current epidemic situation. Methods: The dual Barabási–Albert (DBA) model was used to build a contact network. A dynamics compartmental modeling framework was used to simulate the COVID-19 epidemic with different interventions on the Diamond Princess. Results: The effect of isolation only was minor. Regardless of the transmission rate of the virus, joint interventions can prevent 96.95% (95% CI: 96.70–97.15%) of infections. Compared with evacuating only passengers, evacuating the crew and passengers can avoid about 11.90% (95% CI: 11.83–12.06%) of infections; Conclusions: It is feasible to restore public transportation services and reopen large-scale public facilities if monitoring and testing can be in place. Evacuating all people as soon as possible is the most effective way to contain the outbreak in large-scale public facilities.

Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China

The spread of viruses essentially occurs through the interaction and contact between people, which is closely related to the network of interpersonal relationships. Based on the epidemiological investigations of 1218 COVID-19 cases in eight areas of China, we use text analysis, social network analysis and visualization methods to construct a dynamic contact network of the epidemic. We analyze the corresponding demographic characteristics, network indicators, and structural characteristics of this network. We found that more than 65% of cases are likely to be infected by a strong relationship, and nearly 40% of cases have family members infected at the same time. The overall connectivity of the contact network is low, but there are still some clustered infections. In terms of the degree distribution, most cases’ degrees are concentrated between 0 and 2, which is relatively low, and only a few ones have a higher degree value. The degree distribution also conforms to the power law distribution, indicating the network is a scale-free network. There are 17 cases with a degree greater than 10, and these cluster infections are usually caused by local transmission. The first implication of this research is we find that the COVID-19 spread is closely related to social structures by applying computational sociological methods for infectious disease studies; the second implication is to confirm that text analysis can quickly visualize the spread trajectory at the beginning of an epidemic.

Mitigate SIR epidemic spreading via contact blocking in temporal networks

Progress has been made in how to suppress epidemic spreading on temporal networks via blocking all contacts of targeted nodes or node pairs. In this work, we develop contact blocking strategies that remove a fraction of contacts from a temporal (time evolving) human contact network to mitigate the spread of a Susceptible-Infected-Recovered epidemic. We define the probability that a contact c(i, j, t) is removed as a function of a given centrality metric of the corresponding link l(i, j) in the aggregated network and the time t of the contact. The aggregated network captures the number of contacts between each node pair. A set of 12 link centrality metrics have been proposed and each centrality metric leads to a unique contact removal strategy. These strategies together with a baseline strategy (random removal) are evaluated in empirical contact networks via the average prevalence, the peak prevalence and the time to reach the peak prevalence. We find that the epidemic spreading can be mitigated the best when contacts between node pairs that have fewer contacts and early contacts are more likely to be removed. A strategy tends to perform better when the average number contacts removed from each node pair varies less. The aggregated pruned network resulted from the best contact removal strategy tends to have a large largest eigenvalue, a large modularity and probably a small largest connected component size.

Reducing Power Losses by Voltage Stabilization at the DC Rolling Stock Current Collector

The invention relates to an energy-efficient method for voltage stabilization at the electric rolling stock current collector through traction substation control means which provide a nominal voltage value during the electric train movement by an interstation section. The dependence of potential distribution in the contact wire during the electric rolling stock movement by an interstation section was investigated. Also researched and developed are the new ways of voltage stabilization at the current collector of the electric rolling stock based on synchronous (the same for two adjacent traction substations) and asynchronous paths of voltage regulation at DC buses of traction substations related to one synchronous and two asynchronous ways of voltage stabilization in the contact network with obtaining the energy performance describing them. The energy performance of the investigated methods of voltage stabilization in the contact network is compared, and the energy efficiency of each of them is determined. It is proved that the use of modern types of semiconductor converters such as an active rectifier – voltage source in the power equipment of DC traction substations will enable to implement adaptive voltage stabilization systems at the rolling stock current collector, providing nominal voltage values of traction motors on the interstation section without using additional equipment on the rolling stock and, as a consequence, justification and application of these methods is suitable for upgrading the existing and designing new traction substations.

E-contact tracing of hospital infections – validation of SIRSZ automatic risk assessment

Aim. Contact networks play a crucial role in infectious disease propagation and position in the network mediate risk of acquiring or sending infections. We studied the spread of hospital-associated infections through computer simulations and validated our ‘computer assisted’ risk assessment with ‘human’ risk assessment in a prospective study.Concept. We collected time-varying structure of contacts and covariates reconstructed from Polish Hospitals:1. The organisational structure is mapped by a set of questionnaires, CAD maps integration, functional paths annotation and local vision. It is done mostly by surveys within medical staff through an interactive web application.2. The Cohabitation layer processes data from the registry of patient admissions and discharges from each hospital unit (wards, clinics, etc.) and medical shift register. With simulated infection paths, we were able to compute network centrality measures for patients. We obtained the risk of getting infected, based on the patient’s incoming connections, and the risk of spreading infections resulting from outgoing connections. We compare various standard centrality measures – position of patients and staff in contact networks (‘computer assisted’ risk assessment) of both contacts and paths networks, with a predictor of ‘human’ risk perception (based on 190 patients).Results. We showed that the best predictor of HAI risk is Adjusted Rage Rank on paths (r= 0.42, p < 0.01). However, surprisingly good predictive power in risk assessment was found in the betweenness centrality of the underlying network of contacts (r = 0.30, p < 0.01).Conclusion: We conclude that epidemiology of a given pathogen in a given place and time could be explained only with the contact network only to a large extent. However, further possibility of the collection, processing and storage of the data on individual persons, translated to mathematical modelling could lead in future to satisfactory improvement in risk assessment.

Revealing mechanisms of infectious disease spread through empirical contact networks

The spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling the dynamics of infectious disease spread through contact networks, however, can be challenging due to limited knowledge of how an infectious disease spreads and its transmission rate. We developed a novel statistical tool, INoDS (Identifying contact Networks of infectious Disease Spread) that estimates the transmission rate of an infectious disease outbreak, establishes epidemiological relevance of a contact network in explaining the observed pattern of infectious disease spread and enables model comparison between different contact network hypotheses. We show that our tool is robust to incomplete data and can be easily applied to datasets where infection timings of individuals are unknown. We tested the reliability of INoDS using simulation experiments of disease spread on a synthetic contact network and find that it is robust to incomplete data and is reliable under different settings of network dynamics and disease contagiousness compared with previous approaches. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumblebee colonies and Salmonella in wild Australian sleepy lizard populations. INoDS thus provides a novel and reliable statistical tool for identifying transmission pathways of infectious disease spread. In addition, application of INoDS extends to understanding the spread of novel or emerging infectious disease, an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints.

Contact network analysis of Covid-19 in tourist areas——Based on 333 confirmed cases in China

The spread of infectious diseases is highly related to the structure of human networks. Analyzing the contact network of patients can help clarify the path of virus transmission. Based on confirmed cases of COVID-19 in two major tourist provinces in southern China (Hainan and Yunnan), this study analyzed the epidemiological characteristics and dynamic contact network structure of patients in these two places. Results show that: (1) There are more female patients than males in these two districts and most are imported cases, with an average age of 45 years. Medical measures were given in less than 3 days after symptoms appeared. (2) The whole contact network of the two areas is disconnected. There are a small number of transmission chains in the network. The average values of degree centrality, betweenness centrality, and PageRank index are small. Few patients have a relatively high contact number. There is no superspreader in the network.