Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSynchronized and mixed outbreaks of coupled recurrent epidemics
322
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Epidemic spreading has been studied for a long time and most of them are focused on the growing aspect of a single epidemic outbreak. Recently, we extended the study to the case of recurrent epidemics (Sci. Rep. {bf 5}, 16010 (2015)) but limited only to a single network. We here report from the real data of coupled regions or cities that the recurrent epidemics in two coupled networks are closely related to each other and can show either synchronized outbreak phase where outbreaks occur simultaneously in both networks or mixed outbreak phase where outbreaks occur in one network but do not in another one. To reveal the underlying mechanism, we present a two-layered network model of coupled recurrent epidemics to reproduce the synchronized and mixed outbreak phases. We show that the synchronized outbreak phase is preferred to be triggered in two coupled networks with the same average degree while the mixed outbreak phase is preferred for the case with different average degrees. Further, we show that the coupling between the two layers is preferred to suppress the mixed outbreak phase but enhance the synchronized outbreak phase. A theoretical analysis based on microscopic Markov-chain approach is presented to explain the numerical results. This finding opens a new window for studying the recurrent epidemics in multi-layered networks.
rate research
Read More
We analyze the paper of Nathan D. Grubaugh et al. (Nature 546, 401-405, 2017) and find that it does not offer a convincing quantitative explanation for what generated the temporal distribution of human Zika virus (ZIKV) cases shown in their paper (Fig. 1d). We criticize this aspect because it is this understanding of how human cases develop from day-today and week-to-week within an area such as these Ground Zeros, that policymakers need in order to mitigate future outbreaks. We present results that strongly suggest that the missing piece is everyday human visit-revisit behavior. These results reproduce the human outbreak data in the key areas of Miami in 2016 very well, and give policymakers specific predictions for how changes in human flow through these areas will affect, and hence can be used to mitigate, future ZIka outbreaks in Miami and beyond.
Assessing and managing the impact of large-scale epidemics considering only the individual risk and severity of the disease is exceedingly difficult and could be extremely expensive. Economic consequences, infrastructure and service disruption, as well as the recovery speed, are just a few of the many dimensions along which to quantify the effect of an epidemic on societys fabric. Here, we extend the concept of resilience to characterize epidemics in structured populations, by defining the system-wide critical functionality that combines an individuals risk of getting the disease (disease attack rate) and the disruption to the systems functionality (human mobility deterioration). By studying both conceptual and data-driven models, we show that the integrated consideration of individual risks and societal disruptions under resilience assessment framework provides an insightful picture of how an epidemic might impact society. In particular, containment interventions intended for a straightforward reduction of the risk may have net negative impact on the system by slowing down the recovery of basic societal functions. The presented study operationalizes the resilience framework, providing a more nuanced and comprehensive approach for optimizing containment schemes and mitigation policies in the case of epidemic outbreaks.
The COVID-19 pandemic poses challenges for continuing economic activity while reducing health risks. While these challenges can be mitigated through testing, testing budget is often limited. Here we study how institutions, such as nursing homes, should utilize a fixed test budget for early detection of an outbreak. Using an extended network-SEIR model, we show that given a certain budget of tests, it is generally better to test smaller subgroups of the population frequently than to test larger groups but less frequently. The numerical results are consistent with an analytical expression we derive for the size of the outbreak at detection in an exponential spread model. Our work provides a simple guideline for institutions: distribute your total tests over several batches instead of using them all at once. We expect that in the appropriate scenarios, this easy-to-implement policy recommendation will lead to earlier detection and better mitigation of local COVID-19 outbreaks.
The dynamics of epidemics depend on how peoples behavior changes during an outbreak. The impact of this effect due to control interventions on the morbidity rate is obvious and supported by numerous studies based on SIR-type models. However, the existing models do not explain the difference in outbreak profiles in countries with different intrinsic socio-cultural features and are rather specific for describing the complex dynamics of an outbreak. A system of models of the COVID-19 pandemic is proposed, combining the dynamics of social stress described by the tools of sociophysics8 with classical epidemic models. Even the combination of a dynamic SIR model with the classic triad of stages of general adaptation syndrome, Alarm-Resistance-Exhaustion, makes it possible to describe the available statistics for various countries of the world with a high degree of accuracy. The conceptualization of social stress leads to the division of the vulnerable population into different groups according to behavior mode, which can be tracked in detail. The sets of kinetic constants corresponding to optimal fit of model to data clearly characterize the society ability to focus efforts on protection against pandemic and keep this concentration for a considerable time. Such characterization can further help in the development of management strategies specific to a particular society: country, region, or social group.
We develop a minimalist compartmental model to study the impact of mobility restrictions in Italy during the Covid-19 outbreak. We show that an early lockdown shifts the epidemic in time, while that beyond a critical value of the lockdown strength, the epidemic tend to restart after lifting the restrictions. As a consequence, specific mitigation strategies must be introduced. We characterize the relative importance of different broad strategies by accounting for two fundamental sources of heterogeneity, i.e. geography and demography. First, we consider Italian regions as separate administrative entities, in which social interactions between age classs occur. Due to the sparsity of the inter-regional mobility matrix, once started the epidemics tend to develop independently across areas, justifying the adoption of solutions specific to individual regions or to clusters of regions. Second, we show that social contacts between age classes play a fundamental role and that measures which take into account the age structure of the population can provide a significant contribution to mitigate the rebound effects. Our model is general, and while it does not analyze specific mitigation strategies, it highlights the relevance of some key parameters on non-pharmaceutical mitigation mechanisms for the epidemics.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
