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.

The analysis of hepatitis B virus (HBV) transmission using an epidemic model

In this article, we propose an epidemic problem of hepatitis B with vaccination. So to do this, first we presents the model formulation and prove that the solutions are bounded and positive. We obtain the disease free equilibrium and calculate the basic reproduction number (R0). The reproductive number will be used to find the endemic state of the model. We discuss the qualitative analysis of the proposed problem and show that whenever, R0 < 1 then the disease free equilibrium is stable locally and globally. Moreover, whenever, R0 > 1, then the endemic state is asymptotically stable. We derive sufficient conditions for both the equilibria and its stabilities. Further more numerical simulation are carried out to illustrate the feasibility of the obtained results and verified that with actual data, we are in the position to put down the hepatitis B infection form the community. We also highlight the role of epidemic parameters in the disease propagation. Our numerical works verified the analytical results. Finally some important conclusion are given at the end of the article.

A novel hybrid SEIQR model incorporating the effect of quarantine and lockdown regulations for COVID-19

Mitigating the devastating effect of COVID-19 is necessary to control the infectivity and mortality rates. Hence, several strategies such as quarantine of exposed and infected individuals and restricting movement through lockdown of geographical regions have been implemented in most countries. On the other hand, standard SEIR based mathematical models have been developed to understand the disease dynamics of COVID-19, and the proper inclusion of these restrictions is the rate-limiting step for the success of these models. In this work, we have developed a hybrid Susceptible-Exposed-Infected-Quarantined-Removed (SEIQR) model to explore the influence of quarantine and lockdown on disease propagation dynamics. The model is multi-compartmental, and it considers everyday variations in lockdown regulations, testing rate and quarantine individuals. Our model predicts a considerable difference in reported and actual recovered and deceased cases in qualitative agreement with recent reports.

Intercellular Propagation and Aggregate Seeding of Mutant Ataxin-1

Intercellular propagation of aggregated protein inclusions along actin-based tunneling nanotubes (TNTs) has been reported as a means of pathogenic spread in Alzheimer’s, Parkinson’s, and Huntington’s diseases. Propagation of oligomeric-structured polyglutamine-expanded ataxin-1 (Atxn1[154Q]) has been reported in the cerebellum of a Spinocerebellar ataxia type 1 (SCA1) knock-in mouse to correlate with disease propagation. In this study, we investigated whether a physiologically relevant polyglutamine-expanded ATXN1 protein (ATXN1[82Q]) could propagate intercellularly. Using a cerebellar-derived live cell model, we observed ATXN1 aggregates form in the nucleus, subsequently form in the cytoplasm, and finally, propagate to neighboring cells along actin-based intercellular connections. Additionally, we observed the facilitation of aggregate-resistant proteins into aggregates given the presence of aggregation-prone proteins within cells. Taken together, our results support a pathogenic role of intercellular propagation of polyglutamine-expanded ATXN1 inclusions.

Understanding components of mobility during the COVID-19 pandemic

Travel restrictions have proven to be an effective strategy to control the spread of the COVID-19 epidemics, in part because they help delay disease propagation across territories. The question, however, as to how different types of travel behaviour, from commuting to holiday-related travel, contribute to the spread of infectious diseases remains open. Here, we address this issue by using factorization techniques to decompose the temporal network describing mobility flows throughout 2020 into interpretable components. Our results are based on two mobility datasets: the first is gathered from Danish mobile network operators; the second originates from the Facebook Data-For-Good project. We find that mobility patterns can be described as the aggregation of three mobility network components roughly corresponding to travel during workdays, weekends and holidays, respectively. We show that, across datasets, in periods of strict travel restrictions the component corresponding to workday travel decreases dramatically. Instead, the weekend component, increases. Finally, we study how each type of mobility (workday, weekend and holiday) contributes to epidemics spreading, by measuring how the effective distance, which quantifies how quickly a disease can travel between any two municipalities, changes across network components. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

Activity-based epidemic propagation and contact network scaling in auto-dependent metropolitan areas

We build on recent work to develop a fully mechanistic, activity-based and highly spatio-temporally resolved epidemiological model which leverages person-trajectories obtained from an activity-based model calibrated for two full-scale prototype cities, consisting of representative synthetic populations and mobility networks for two contrasting auto-dependent city typologies. We simulate the propagation of the COVID-19 epidemic in both cities to analyze spreading patterns in urban networks across various activity types. Investigating the impact of the transit network, we find that its removal dampens disease propagation significantly, suggesting that transit restriction is more critical for mitigating post-peak disease spreading in transit dense cities. In the latter stages of disease spread, we find that the greatest share of infections occur at work locations. A statistical analysis of the resulting activity-based contact networks indicates that transit contacts are scale-free, work contacts are Weibull distributed, and shopping or leisure contacts are exponentially distributed. We validate our simulation results against existing case and mortality data across multiple cities in their respective typologies. Our framework demonstrates the potential for tracking epidemic propagation in urban networks, analyzing socio-demographic impacts and assessing activity- and mobility-specific implications of both non-pharmaceutical and pharmaceutical intervention strategies.

An Approach to the Modeling and Simulation of Intra-Hospital Diseases

This publication presents an approach to a simulator to recreate a large number of scenarios and to make agile decisions in the planning of a real emergency room system. A modeling and simulation focused on the point prevalence of intrahospital infections in an emergency room and how it is affected by different factors related to hospital management. To carry out the simulator modeling, the Agent-based Modeling and Simulation (ABMS) paradigm was used. Thus, different intervening agents in the emergency room environment — patients and doctors, among others— were classified. The user belonging to the health system has different data to configure the simulation, such as the number of patients, the number of available beds, etc. Based on the tests carried out and the measurements obtained, it is concluded that the disease propagation model relative to the time and contact area of the patients has greater precision than the purely statistical model of the intensive care unit.

Simulations of Infectious Disease Propagation II, Focusing on Herd Immunity

Introduction and Objectives: The results of simulations of the propagation of an infectious disease are presented. In managing and controlling the spread of an infectious disease, such as Covid-19, the concept of Herd Immunity (HI) is often invoked as to when the disease’s propagation will dwindle to acceptable levels. We have extended a previous work with explicit attention on the usefulness of this concept. The objectives of this research was to track the propagation of an infectious disease as a function of population density, time, and to evaluate HI. The population was divided into two groups. One group was protected from the infection. The second group was unprotected. The results are given as a percentage of the unprotected population that is infected as a function of time. Methods: The method used here was to use computer simulation on a person level to follow the progress of the diseases infection across the population. In the beginning, the people are uniformly distributed in a square. Each person performed a random walk, which simulated the movement of the people. Infection rates are given for the unprotected portion of the population as a function of time. The disease was transferred from an infected person to an uninfected person if the two people are closer together than a given distance. Results and Discussion: These simulations show the unprotected portion of the population was at total risk if proper measures are not taken early. For 400 unprotected people the infection rate is 100% after approximately 100,000 iterations. We give the results from one dual simulation in which protection was afforded for a significant part of the population and carried out until all of the unprotected were infected. In the second part the protection was lifted to see how fast the total population was infected. For the case of 50% protected it took 400,000 iterations to infect the unprotected people. After the restrictions were lifted it took 150,000 to infect the other half. The simulations here were people based which has the advantage of seeing individual personal involvement. Results of infection rates were calculated for 1,000, 2,500, 5,000, and 10,000 people. Conclusions: The propagation of the disease can be fast and depends on population density. Protection is vital to containing the disease. Restrictions must be lifted carefully and slowly or the total population is again at risk. According to the results obtained here the concept of HI is not a viable concept in controlling or managing the spread of the disease.

Detection of spreader nodes in human-SARS-CoV protein-protein interaction network

The entire world is witnessing the coronavirus pandemic (COVID-19), caused by a novel coronavirus (n-CoV) generally distinguished as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 promotes fatal chronic respiratory disease followed by multiple organ failure, ultimately putting an end to human life. International Committee on Taxonomy of Viruses (ICTV) has reached a consensus that SARS-CoV-2 is highly genetically similar (up to 89%) to the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), which had an outbreak in 2003. With this hypothesis, current work focuses on identifying the spreader nodes in the SARS-CoV-human protein–protein interaction network (PPIN) to find possible lineage with the disease propagation pattern of the current pandemic. Various PPIN characteristics like edge ratio, neighborhood density, and node weight have been explored for defining a new feature spreadability index by which spreader proteins and protein–protein interaction (in the form of network edges) are identified. Top spreader nodes with a high spreadability index have been validated by Susceptible-Infected-Susceptible (SIS) disease model, first using a synthetic PPIN followed by a SARS-CoV-human PPIN. The ranked edges highlight the path of entire disease propagation from SARS-CoV to human PPIN (up to level-2 neighborhood). The developed network attribute, spreadability index, and the generated SIS model, compared with the other network centrality-based methodologies, perform better than the existing state-of-art.