Infection spreading in a population with evolving contacts.
Ontology highlight
ABSTRACT:
SUBMITTER: Zanette DH
PROVIDER: S-EPMC2577738 | biostudies-other | 2008 Apr
REPOSITORIES: biostudies-other
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Project description:Notable cross-country differences exist in the diffusion of the Covid-19 and in its lethality. Contact patterns in populations, and in particular intergenerational contacts, have been argued to be responsible for the most vulnerable, the elderly, getting infected more often and thus driving up mortality in some context, like in the southern European one. This paper asks a simple question: is it between whom contacts occur that matters or is it simply how many contacts people have? Due to the high number of confounding factors, it is extremely difficult to empirically assess the impact of single network features separately. This is why we rely on a simulation exercise in which we counterfactually manipulate single aspects of countries' age distribution and network structures. We disentangle the contributions of the kind and of the number of contacts while holding constant the age structure. More precisely, we isolate the respective effects of inter-age contact patterns, degree distribution and clustering on the virus propagation across age groups. We use survey data on face-to-face contacts for Great Britain, Italy, and Germany, to reconstruct networks that mirror empirical contact patterns in these three countries. It turns out that the number of social contacts (degree distribution) largely accounts for the higher infection rates of the elderly in the Italian context, while differences in inter-age contacts patterns are only responsible for minor differences. This suggests that policies specifically targeting inter-age contacts would be little effective.
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Project description:Carbonic anhydrase IX is a hypoxia-induced transmembrane enzyme linked with solid tumors. It catalyzes the reversible hydration of CO2 providing bicarbonate ions for intracellular neutralization and protons for extracellular acidosis, thereby supporting tumor cell survival and invasiveness. CA IX is the only human CA isoform containing the proteoglycan (PG) domain in its extracellular part. The PG domain appears to enhance the catalytic activity of CA IX and mediate its binding to the extracellular matrix. Moreover, manipulation of the CA IX level by siRNA or overexpression modulates cell adhesion pathway so that in the presence of CA IX, cells display an increased rate of adhesion and spreading. Here we show that deletion of the PG domain as well as treatment with the PG-binding monoclonal antibody M75 can impair this CA IX effect. Accordingly, CA IX-expressing cells show more prominent and elongated maturing paxillin-stained focal contacts (FC) than CA IX-negative controls, proving the role of CA IX in cell spreading. However, during active cell movement, CA IX is relocalized to lamellipodia and improves migration via its catalytic domain. Thus, we examined the influence of CA IX on FC turnover in these structures. While the lamellipodial regions lacking CA IX display dash-like adhesions, the CA IX-enriched neighboring regions exhibit dynamic dot-like FCs. These results suggest that CA IX can promote initial adhesion through its PG domain, but at the same time it facilitates formation of nascent adhesions at the leading edge of moving cells. Thereby it may allow for transmission of large forces and enhanced migration rate, presumably through catalytic activity and impact of pHe on FC dynamics. Thus, we provide the first evidence that CA IX protein localizes directly in focal adhesion (FA) structures and propose its functional relationship with the proteins involved in the regulation of FC turnover and maturation.
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Project description:Highlights • Epidemic curves created by using an agent-based model are presented.• The network of interactions, and thus the possibility of infection, is modeled with the scale-free Barabasi-Albert model. Modifications based on whether these interactions belong to a particular group are proposed.• A simple model of passing an infection via a direct interaction is proposed.• The influence of the network creation method, division into different numbers of groups and different times of intervention is presented.• The results are shown for two cases: the well-known influenza virus and the recently reported COVID-19. Background and Objective The currently active COVID-19 pandemic has increased, among others, public interest in the computational techniques enabling the study of disease-spreading processes. Thus far, numerous approaches have been used to study the development of epidemics, with special attention paid to the identification of crucial elements that can strengthen or weaken the dynamics of the process. The main thread of this research is associated with the use of the ordinary differential equations method. There also exist several approaches based on the analysis of flows in the Cellular Automata (CA) approach. Methods In this paper, we propose a new approach to disease-spread modeling. We start by creating a network that reproduces contacts between individuals in a community. This assumption makes the presented model significantly different from the ones currently dominant in the field. It also changes the approach to the act of infection. Usually, some parameters that describe the rate of new infections by taking into account those infected in the previous time slot are considered. With our model, we can individualize this process, considering each contact individually. Results The typical output from calculations of a similar type are epidemic curves. In our model, except of presenting the average curves, we show the deviations or ranges for particular results obtained in different simulation runs, which usually lead to significantly different results. This observation is the effect of the probabilistic character of the infection process, which can impact, in different runs, individuals with different significance to the community. We can also easily present the effects of different types of intervention. The effects are studied for different methods used to create the graph representing a community, which can correspond to different social bonds. Conclusions We see the potential usefulness of the proposition in the detailed study of epidemic development for specific environments and communities. The ease of entering new parameters enables the analysis of several specific scenarios for different contagious diseases.
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Project description:BackgroundProactive interventions have halted the pandemic of coronavirus infected disease in some regions. However, without reaching herd immunity, the return of epidemic is possible. We investigate the impact of population structure, case importation, asymptomatic cases, and the number of contacts on a possible second wave of epidemic through mathematical modeling.Methodswe built a modified Susceptible-exposed-Infectious-Removed (SEIR) model with parameters mirroring those of the COVID-19 pandemic and reported simulated characteristics of epidemics for incidence, hospitalizations and deaths under different scenarios.ResultsA larger percent of elderly people leads to higher number of hospitalizations, while a large percent of prior infection will effectively curb the epidemic. The number of imported cases and the speed of importation have small impact on the epidemic progression. However, a higher percent of asymptomatic cases slows the epidemic down and reduces the number of hospitalizations and deaths at the epidemic peak. Finally, reducing the number of contacts among young people alone has moderate effects on themselves, but little effects on the elderly population. However, reducing the number of contacts among elderly people alone can mitigate the epidemic significantly in both age groups, even though young people remain active within themselves.ConclusionReducing the number of contacts among high risk populations alone can mitigate the burden of epidemic in the whole society. Interventions targeting high risk groups may be more effective in containing or mitigating the epidemic.
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