BACKGROUND: Contact tracing is one of the most effective non-pharmaceutical interventions in the COVID-19 pandemic. This study uses a multi-agent model to investigate the impact of four types of contact tracing strategies to prevent the spread of COVID-19. METHODS: In order to analyse individual contact tracing in a reasonably realistic setup, we construct an agent-based model of a small municipality with about 60.000 inhabitants (nodes) and about 2.8 million social contacts (edges) in 30 different layers. Those layers reflect demographic, geographic, sociological and other patterns of the TTWA (Travel-to-work-area) Hodonín in Czechia. Various data sources such as census, land register, transport data or data reflecting the shopping behaviour, were employed to meet this purpose. On this multi-graph structure we run a modified SEIR model of the COVID-19 dynamics. The parameters of the model are calibrated on data from the outbreak in the Czech Republic in the period March to June 2020. The simplest type of contact tracing follows just the family, the second tracing version tracks the family and all the work contacts, the third type finds all contacts with the family, work contacts and friends (leisure activities). The last one is a complete (digital) tracing capable of recalling any and all contacts. We evaluate the performance of these contact tracing strategies in four different environments. First, we consider an environment without any contact restrictions (benchmark); second with strict contact restriction (replicating the stringent non-pharmaceutical interventions employed in Czechia in the spring 2020); third environment, where the measures were substantially relaxed, and, finally an environment with weak contact restrictions and superspreader events (replicating the situation in Czechia in the summer 2020). FINDINGS: There are four main findings in our paper. 1. In general, local closures are more effective than any type of tracing. 2. In an environment with strict contact restrictions there are only small differences among the four contact tracing strategies. 3. In an environment with relaxed contact restrictions the effectiveness of the tracing strategies differs substantially. 4. In the presence of superspreader events only complete contact tracing can stop the epidemic. INTERPRETATION: In situations, where many other non-pharmaceutical interventions are in place, the specific extent of contact tracing may not have a large influence on their effectiveness. In a more relaxed setting with few contact restrictions and larger events the effectiveness of contact tracing depends heavily on their extent.
- MeSH
- COVID-19 * epidemiologie MeSH
- epidemický výskyt choroby prevence a kontrola MeSH
- lidé MeSH
- pandemie prevence a kontrola MeSH
- SARS-CoV-2 MeSH
- trasování kontaktů MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Running across the globe for nearly 2 years, the Covid-19 pandemic keeps demonstrating its strength. Despite a lot of understanding, uncertainty regarding the efficiency of interventions still persists. We developed an age-structured epidemic model parameterized with epidemiological and sociological data for the first Covid-19 wave in the Czech Republic and found that (1) starting the spring 2020 lockdown 4 days earlier might prevent half of the confirmed cases by the end of lockdown period, (2) personal protective measures such as face masks appear more effective than just a realized reduction in social contacts, (3) the strategy of sheltering just the elderly is not at all effective, and (4) leaving schools open is a risky strategy. Despite vaccination programs, evidence-based choice and timing of non-pharmaceutical interventions remains an effective weapon against the Covid-19 pandemic.
- MeSH
- biologické modely MeSH
- COVID-19 * epidemiologie prevence a kontrola MeSH
- kontrola infekčních nemocí MeSH
- lidé MeSH
- masky * MeSH
- matematické pojmy MeSH
- pandemie prevence a kontrola MeSH
- SARS-CoV-2 MeSH
- senioři MeSH
- školy MeSH
- Check Tag
- lidé MeSH
- senioři MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Česká republika MeSH
- MeSH
- COVID-19 * epidemiologie MeSH
- epidemiologické modely MeSH
- lidé MeSH
- trasování kontaktů MeSH
- Check Tag
- lidé MeSH
Following initial optimism regarding potentially rapid vaccination, delays and shortages in vaccine supplies occurred in many countries during spring 2021. Various strategies to counter this gloomy reality and speed up vaccination have been set forth, of which the most popular has been to delay the second vaccine dose for a longer period than originally recommended by the manufacturers. Controversy has surrounded this strategy, and overly simplistic models have been developed to shed light on this issue. Here we use three different epidemic models, all accounting for then actual COVID-19 epidemic in the Czech Republic, including the real vaccination rollout, to explore when delaying the second vaccine dose by another 3 weeks from 21 to 42 days is advantageous. Using COVID-19-related deaths as a quantity to compare various model scenarios, we find that the way of vaccine action at the beginning of the infection course (preventing infection and symptoms appearance), mild epidemic and sufficient vaccine supply rate call for the original inter-dose period of 21 days regardless of vaccine efficacy. On the contrary, for the vaccine action at the end of infection course (preventing severe symptoms and death), severe epidemic and low vaccine supply rate, the 42-day inter-dose period is preferable, at any plausible vaccine efficacy.
- MeSH
- COVID-19 * epidemiologie prevence a kontrola MeSH
- lidé MeSH
- SARS-CoV-2 MeSH
- vakcinace MeSH
- vakcíny proti COVID-19 MeSH
- vakcíny * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
This paper deals with the vulnerability of machine learning models to adversarial examples and its implication for robustness and generalization properties. We propose an evolutionary algorithm that can generate adversarial examples for any machine learning model in the black-box attack scenario. This way, we can find adversarial examples without access to model's parameters, only by querying the model at hand. We have tested a range of machine learning models including deep and shallow neural networks. Our experiments have shown that the vulnerability to adversarial examples is not only the problem of deep networks, but it spreads through various machine learning architectures. Rather, it depends on the type of computational units. Local units, such as Gaussian kernels, are less vulnerable to adversarial examples.
- MeSH
- algoritmy MeSH
- lidé MeSH
- neuronové sítě * MeSH
- řízené strojové učení * trendy MeSH
- rozpoznávání automatizované metody trendy MeSH
- strojové učení trendy MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
A comparison of behavior-based and planning approaches of robot control is presented in this paper. We focus on miniature mobile robotic agents with limited sensory abilities. Two reactive control mechanisms for an agent are considered-a radial basis function neural network trained by evolutionary algorithm and a traditional reinforcement learning algorithm over a finite agent state space. The control architecture based on localization and planning is compared to the former method.
