Household secondary attack rates of SARS-CoV-2 by variant and vaccination status: an updated systematic review and meta-analysis

We previously reported a household secondary attack rate (SAR) for SARS-CoV-2 of 18.9% through June 17, 2021. To examine how emerging variants and increased vaccination have affected transmission rates, we searched PubMed from June 18, 2021, through January 7, 2022. Meta-analyses used generalized linear mixed models to obtain SAR estimates and 95%CI, disaggregated by several covariates. SARs were used to estimate vaccine effectiveness based on the transmission probability for susceptibility (VE_S,p), infectiousness (VE_I,p), and total vaccine effectiveness (VE_T,p). Household SAR for 27 studies with midpoints in 2021 was 35.8% (95%CI, 30.6%-41.3%), compared to 15.7% (95%CI, 13.3%-18.4%) for 62 studies with midpoints through April 2020. Household SARs were 38.0% (95%CI, 36.0%-40.0%), 30.8% (95%CI, 23.5%-39.3%), and 22.5% (95%CI, 18.6%-26.8%) for Alpha, Delta, and Beta, respectively. VE_I,p, VE_S,p, and VE_T,p were 56.6% (95%CI, 28.7%-73.6%), 70.3% (95%CI, 59.3%-78.4%), and 86.8% (95%CI, 76.7%-92.5%) for full vaccination, and 27.5% (95%CI, -6.4%-50.7%), 43.9% (95%CI, 21.8%-59.7%), and 59.9% (95%CI, 34.4%-75.5%) for partial vaccination, respectively. Household contacts exposed to Alpha or Delta are at increased risk of infection compared to the original wild-type strain. Vaccination reduced susceptibility to infection and transmission to others.

Investigation of features for prediction modeling of nano-scale conduction with time-dependent calculation of electron wave packet

The model to predict the electron transmission probability from the random impurity distribution in a two-dimensional nanowire system by combining the time evolution of the electron wave function and machine learning is proposed. We have shown that the intermediate state of the time evolution calculation is a great advantage for efficient modeling by machine learning. The features for machine learning are extracted by analyzing the time variation of the electron density distribution using time evolution calculations. Consequently, the prediction error of the model is improved by performing machine learning based on the features. The proposed method provides a useful perspective for analyzing the motion of electrons in nanoscale semiconductors.

ESTIMATION OF THE MESSAGE TRANSMISSION PROBABILITY IN CHANNELS WITH ERRORS PACKETS

Рассмотрена передача сообщений в каналах с пачками ошибок. Реализована модель дискретного канала с формированием как одиночных, так и пачек ошибок с заданной вероятностью. Модель позволяет получить оценки вероятности потери сообщений для различных условий. Представлены результаты моделирования. The paper deals with the transmission of messages over channels with error packets. A model of a discrete channel has been designed with the formation of both single errors and packets of errors with a given probability. The model provides estimates of the message error rate for various conditions. The results of the numerical simulation are presented.

Controlling Virus Spread Using an Intermittent Quarantine Strategy on Multiplex Networks

Virus spreading on the Internet will negatively affect cybersecurity. An intermittent quarantine immunization strategy to control virus spreading when containing information diffusion is proposed herein. In this model, information and virus spread on different subnetworks and interact with each other. We further develop a heterogeneous mean-field approach with time delays to investigate this model and use Monte Carlo simulations to systematically investigate the spreading dynamics. For a relatively short intermittent period, the optimal information transmission probability of the virus will be significantly suppressed. However, when the intermittent period is extremely long; increasing the probability of information transmission can control the virus spreading as well as suppress the increase in the intermittent period. Finally, it is shown that the average degree of the two subnetworks does not qualitatively affect the spreading dynamics.

Transmission probability through zigzag monolayer phosphorene superlattice

The transportation of charge carriers for monolayer phosphorene superlattice has been investigated utilizing a transfer matrix method. Also, the efficacy of structural parameters has been studied on the transmission of charge carriers for the system. Our findings demonstrated that the barrier number at the superlattice structures performs an essential role in the transportation probability, that can be utilized in the design of nanoelectronic sets. Further, it can be comprehended that the transmission probability of one for the normal incident has occurred in twenty obstacles. On the other hand, the transmission probability of close to one has occurred in lower landing energies by increasing the obstacle number. As well, it has been understood the transmission probability of close to one by enhancing the barrier number can happen in barriers with a smaller width. According to the results, phosphorene can be used in the novel advances of two dimensional semiconductor devices in electronic applications.

Micro-climate to macro-risk: Mapping fine scale differences in mosquito-borne disease risk using remote sensing

Mosquito-borne diseases (MBD) threaten over 80% of the world’s population, and are increasing in intensity and shifting in geographical range with land use and climate change. Mitigation hinges on understanding disease-specific risk profiles, but current risk maps are severely limited in spatial resolution. One important determinant of MBD risk is temperature, and though the relationships between temperature and risk have been extensively studied, maps are often created using sparse data that fail to capture microclimatic conditions. Here, we leverage high resolution land surface temperature (LST) measurements, in conjunction with established relationships between air temperature and MBD risk factors like mosquito biting rate and transmission probability, to produce fine resolution (70 m) maps of MBD risk components. We focus our case study on West Nile virus (WNV) in the San Joaquin Valley of California, where temperatures vary widely across the day and the diverse agricultural/urban landscape. We first use field measurements to establish a relationship between LST and air temperature, and apply it to Ecosystem Spaceborne Thermal Radiometer Experiment (ECOSTRESS) data (2018-2020) in peak WNV transmission months (June-September). We then use the previously derived equations to estimate spatially explicit mosquito biting and WNV transmission rates. We use these maps to uncover significant differences in risk across land cover types, and identify the times of day which contribute to high risk for different land covers. Additionally, we evaluate the value of high resolution spatial and temporal data in avoiding biased risk estimates due to Jensen’s inequality, and find that using aggregate data leads to significant biases of up to 40.5% in the possible range of risk values. Through this analysis, we show that the synergy between novel remote sensing technology and fundamental principles of disease ecology can unlock new insights into the spatio-temporal dynamics of mosquito-borne diseases.

Achieving proportional fairness in WiFi networks via bandit convex optimization

In this paper, we revisit proportional fair channel allocation in IEEE 802.11 networks. Traditional approaches are either based on the explicit solution of the optimization problem or use iterative solvers to converge to the optimum. Instead, we propose an algorithm able to learn the optimal slot transmission probability only by monitoring the throughput of the network. We have evaluated this algorithm both (i) using the true value of the function to optimize and (ii) considering estimation errors. We provide a comprehensive performance evaluation that includes assessing the sensitivity of the algorithm to different learning and network parameters as well as its reaction to network dynamics. We also evaluate the effect of noisy estimates on the convergence rate and propose a method to alleviate them. We believe our approach is a practical solution to improve the performance of wireless networks as it does not require knowing the network parameters in advance. Yet, we conclude that the setting of the parameters of the algorithm is crucial to guarantee fast convergence.

The impact of heating, ventilation, and air conditioning design features on the transmission of viruses, including the 2019 novel coronavirus: a systematic review of ventilation and coronavirus

Aerosol transmission has been a pathway for virus spread for many viruses. Similarly, emerging evidence regarding SARS-CoV-2, and the resulting pandemic as declared by WHO in March 2020, determined aerosol transmission for SARS-CoV-2 to be significant. As such, public health officials and professionals have sought data regarding the effect of Heating, Ventilation, and Air Conditioning (HVAC) features to control and mitigate viruses, particularly coronaviruses. A systematic review was conducted using international standards to identify and comprehensively synthesize research examining the effectiveness of ventilation for mitigating transmission of coronaviruses. The results from 32 relevant studies showed that: increased ventilation rate was associated with decreased transmission, transmission probability/risk, infection probability/risk, droplet persistence, virus concentration, and increased virus removal and virus particle removal efficiency; increased ventilation rate decreased risk at longer exposure times; some ventilation was better than no ventilation; airflow patterns affected transmission; ventilation feature (e.g., supply/exhaust, fans) placement influenced particle distribution. Some studies provided qualitative recommendations; however, few provided specific quantitative ventilation parameters suggesting a significant gap in current research. Adapting HVAC ventilation systems to mitigate virus transmission is not a one-solution-fits-all approach but instead requires consideration of factors such as ventilation rate, airflow patterns, air balancing, occupancy, and feature placement.Practical ImplicationsIncreasing ventilation, whether through ventilation rates (ACH, m3/h, m3/min, L/min) or as determined by CO2 levels (ppm), is associated with decreased transmission, transmission probability/risk, infection probability/risk, droplet persistence, and virus concentration, and increased virus removal and efficiency of virus particle removal. As well, professionals should consider the fact that changing ventilation rate or using mixing ventilation is not always the only way to mitigate and control viruses as varying airflow patterns and the use of ventilation resulted in better outcomes than situations without ventilation. Practitioners also need to consider occupancy, ventilation feature (supply/exhaust and fans) placement, and exposure time in conjunction with both ventilation rates and airflow patterns. Some recommendations with quantified data were made, including using an air change rate of 9 h-1 for a hospital ward; waiting six air changes or 2.5 hours before allowing different individuals into an unfiltered office with ∼2 fresh air changes (FCH) and one air change for a high-efficiency MERV or HEPA filtered laboratory; and using a pressure difference between -2 and -25 Pa in negative pressure isolation spaces. Other recommendations for practice included using or increasing ventilation, introducing fresh air, using maximum supply rates, avoiding poorly ventilated spaces, assessing fan placement and potentially increasing ventilation locations, and employing ventilation testing and air balancing checks.

Quantifying the relationship between SARS-CoV-2 viral load and infectiousness

The relationship between SARS-CoV-2 viral load and infectiousness is poorly known. Using data from a cohort of cases and high-risk contacts, we reconstructed viral load at the time of contact and inferred the probability of infection. The effect of viral load was larger in household contacts than in non-household contacts, with a transmission probability as large as 48% when the viral load was greater than 1010 copies per mL. The transmission probability peaked at symptom onset, with a mean probability of transmission of 29%, with large individual variations. The model also projects the effects of variants on disease transmission. Based on the current knowledge that viral load is increased by two- to eightfold with variants of concern and assuming no changes in the pattern of contacts across variants, the model predicts that larger viral load levels could lead to a relative increase in the probability of transmission of 24% to 58% in household contacts, and of 15% to 39% in non-household contacts.

Network assessment and modeling the management of an epidemic on a college campus with testing, contact tracing, and masking

There remains a great challenge to minimize the spread of epidemics, especially in high-density communities such as colleges and universities. This is particularly true on densely populated, residential college campuses. To construct class and residential networks data from a four-year, residential liberal arts college with 5539 students were obtained from SUNY College at Geneseo, a rural, residential, undergraduate institution in western NY, USA. Equal-sized random networks also were created for each day. Different levels of compliance with mask use (none to 100%), mask efficacy (50% to 100%), and testing frequency (daily, or every 2, 3, 7, 14, 28, or 105 days) were assessed. Tests were assumed to be only 90% accurate and positive results were used to isolate individuals. The effectiveness of contact tracing, and the effect of quarantining neighbors of infectious individuals, was tested. The structure of the college course enrollment and residence networks greatly influenced the dynamics of the epidemics, as compared to the random networks. In particular, average path lengths were longer in the college networks compared to random networks. Students in larger majors generally had shorter average path lengths than students in smaller majors. Average transitivity (clustering) was lower on days when students most frequently were in class (MWF). Degree distributions were generally large and right skewed, ranging from 0 to 719. Simulations began by inoculating twenty students (10 exposed and 10 infectious) with SARS-CoV-2 on the first day of the fall semester and ended once the disease was cleared. Transmission probability was calculated based on an R0 = 2.4. Without interventions epidemics resulted in most students becoming infected and lasted into the second semester. On average students in the college networks experienced fewer infections, shorter duration, and lower epidemic peaks when compared to the dynamics on equal-sized random networks. The most important factors in reducing case numbers were the proportion masking and the frequency of testing, followed by contact tracing and mask efficacy. The paper discusses further high-order interactions and other implications of non-pharmaceutical interventions for disease transmission on a residential college campus.