Viral Infection Spreading and Mutation in Cell Culture

A new model of viral infection spreading in cell cultures is proposed taking into account virus mutation. This model represents a reaction-diffusion system of equations with time delay for the concentrations of uninfected cells, infected cells and viral load. Infection progression is characterized by the virus replication number Rv, which determines the total viral load. Analytical formulas for the speed of propagation and for the viral load are obtained and confirmed by numerical simulations. It is shown that virus mutation leads to the emergence of a new virus variant. Conditions of the coexistence of the two variants or competitive exclusion of one of them are found, and different stages of infection progression are identified.

A packet replication algorithm for delay tolerant networks based on multi-metric

Utility function of packet is often used to decide how to replicate packet in DTN networks. But this method has more uncertainty and relies on a single performance metric. For this problem, in order to reduce the impact of a single metric uncertainty, multiple utility metrics are introduced in the new algorithm. A packet replication probability calculation method based on entropy weight is designed. By calculating the entropy weight of each metric, the algorithm obtains the replication probability of each packet and takes the probability as the priority of packet replication. Because of considering the two metrics of packet expected transmission delay and node encounter possibility, the algorithm effectively reduces the influence of encounter time distribution problem and direction prediction problem in the original algorithm, and reduces the uncertainty of utility function. Simulation results show that the algorithm reduces the packet replication number and the average delay, improves the successful packet delivery rate. The overall performance of the network is further improved.

Time And Mode Of Epidemic Hcv-2 Subtypes Dispersal In Europe: Phylodynamics Of Hcv-2c In Italy And Albania

Newly characterising 245 Italian and Albanian HCV-2 NS5B sequences collected between 2001 and 2016 was used to reconstruct the origin and dispersion pathways of HCV-2c. The tree of a subset of these sequences aligned with 247 publicly available sequences was reconstructed in spatio-temporal scale using the Bayesian approach, and the effective replication number (Re) was estimated using the birth-death model. Our findings show that HCV-2c was the most prevalent subtype in Italy and Albania, and that GT2 originated in Guinea Bissau in the XVI century and spread to Europe in the XX century. The HCV-2c subtype had two internal nodes respectively dating back to the 1930s and 1950s having as most probable locations Ghana and Italy, respectively. Phylodynamic analysis revealed an exponential increase in the effective number of infections and Re in both Italy between the 1950s and 1980s, and Albania between the 1990s and the early 2000s. It seems very likely that HCV-2c reached Italy from Africa at the time of the second Italian colonisation (1936-1941), but did not reach Albania until the period of dramatic migration to Italy in the 1990s.

Social Distancing with Movement Restrictions and the Effective Replication Number of COVID-19: Multi-Country Analysis Based on Phone Mobility Data

Linking phone mobility data to the effective replication number (Rt) could help evaluation of the impact of social distancing on the coronavirus disease 2019 (COVID-19) spread and estimate the time lag (TL) needed for the effect of movement restrictions to appear. We used a time-series analysis to discover how patterns of five indicators of mobility data relate to changes in Rt of 125 countries distributed over three groups based on Rt-mobility correlation. Group 1 included 71 countries in which Rt correlates negatively with residential and positively with other mobility indicators. Group 2 included 25 countries showing an opposite correlation pattern to Group 1. Group 3 included the 29 remaining countries. We chose the best-fit TL based on forecast and linear regression models. We used linear mixed models to evaluate how mobility indicators and the stringency index (SI) relate with Rt. SI reflects the strictness of governmental responses to COVID-19. With a median of 14 days, TLs varied across countries as well as across groups of countries. There was a strong negative correlation between SI and Rt in most countries belonging to Group 1 as opposed to Group 2. SI (units of 10%) associated with decreasing Rt in Group 1 [β −0.15, 95% CI −0.15 – (−0.14)] and Group 3 [-0.05, −0.07 – (−0.03)], whereas, in Group 2, SI associated with increasing Rt (0.13, 0.11 – 0.16). Mobile phone mobility data could contribute evaluations of the impact of social distancing with movement restrictions on the spread of the COVID-19.

Estimates of regional infectivity of COVID-19 in the United Kingdom following imposition of social distancing measures

We describe regional variation in the reproduction number of SARS-CoV-2 infections observed using publicly reported data in the UK, with a view to understanding both if there are clear hot spots in viral spread in the country, or other spatial patterns. Based on case data up to the 9th April, we estimate that the viral replication number remains above 1 overall in the UK but that its trend is to decrease. This suggests the peak of the first wave of COVID-19 patients is imminent. We find that there is significant regional variation in the UK and that this is changing over time. Within England currently the reproductive ratio is lowest in the Midlands (1.11 95% CI 1.07; 1.14), and highest in the North East of England (1.38 95% CI 1.33-1.42). There are long and variable time delays between infection and detection of cases, and thus it remains unclear whether the reduction in the reproductive number is a result of social distancing measures. If we are to prevent further outbreaks, it is critical that we both reduce the time taken for detection and improve our ability to predict the regional spread of outbreaks.

Effects of latency on estimates of the COVID-19 replication number

It is not currently known how long it takes a person infected by the COVID-19 virus to become infectious. Models of the spread of COVID-19 use very different lengths for this latency period, leading to very different estimates of the replication number R, even when models work from the same underlying data sets. In this paper we quantify how much varying the length of the latency period affects estimates of R, and thus the fraction of the population that is predicted to be infected in the first wave of the pandemic. This variation underscores the uncertainty in our understanding of R and raises the possibility that R may be considerably greater than has been assumed by those shaping public policy.

MODELLING THE EFFECTS OF HCV PLUS-STRAND RNA INFLUX INTO A CELL DURING HCV REPLICATION

This paper presents a sub-genomic Hepatitis C Virus (HCV) replication model which incorporates the rate of influx of HCV plus-strand RNA into Huh-7 cell and monitored its effects. The schematic diagram of HCV replication has been simplified. The model exhibits three equilibrium, namely: trivial equilibrium, healthy equilibrium and endemic equilibrium. Stability analysis of the model shows that the healthy equilibrium is globally asymptotically stable under certain condition. It is shown that increase in the rate of influx, increases the steady state level of total plus strand RNA, synthesized plus strand RNA, replicated plus strand RNA and NS5B in the system. Sensitivity and uncertainty analyses of the model (using the \textit{basic replication number} (${\mathcal R}_0$) as the response function) show that the top three PRCC-ranked parameters are the rate of influx, $k_0$ of HCV plus-strand RNA, the rate of production of translation complex ($T_c$) and the rate of degradation, $\mu_{p}^{cyt}$, of plus-strand RNA $R_{P}^{cyt}$. Furthermore, the distribution of $\mathcal{R}_0$ is between $[0.9999,\, 1.0008]$ with a mean of $\mathcal{R}_{0}=1.0003$.}}