Abstract:We developed an agent-based stochastic model, based on P Systems methodology, to decipher the effects of vaccination and contact tracing on the control of COVID-19 outbreak at population level under different control measures (social distancing, mask wearing and hand hygiene) and epidemiological scenarios. Our findings suggest that without the application of protection social measures, 56.1% of the Spanish population would contract the disease with a mortality of 0.4%. Assuming that 20% of the population was p… Show more
“…In addition, an increased coefficient of determination (R 2 ) is specified for the examples shown. Figure 1 Incidence values I [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] are shown as a function of the vaccination rates V of twice vaccinated people in German federal states and city states as examples for weeks as indicated according to the German RKÍs weekly status report ( https://www.rki.de ). Linear regression graphs are shown as examples for weeks as indicated.…”
Section: Resultsmentioning
confidence: 99%
“…The effect is reduced during December, which is also underlined by the curves shown in Figure 2 . Figure 3 The slope a of linear regression is shown as function of the time (A) and as function of the theoretical incidence [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] b for theoretical zero vaccination rate (B). …”
Section: Resultsmentioning
confidence: 99%
“… The slope a of linear regression is shown as function of the time (A) and as function of the theoretical incidence [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] b for theoretical zero vaccination rate (B). …”
Section: Resultsmentioning
confidence: 99%
“…The data within the 4th corona wave in Germany show a clear dependence of the incidence [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] on the vaccination rate. The incidence is strongly negatively correlated with the vaccination rate during the course of this wave.…”
“…In addition, an increased coefficient of determination (R 2 ) is specified for the examples shown. Figure 1 Incidence values I [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] are shown as a function of the vaccination rates V of twice vaccinated people in German federal states and city states as examples for weeks as indicated according to the German RKÍs weekly status report ( https://www.rki.de ). Linear regression graphs are shown as examples for weeks as indicated.…”
Section: Resultsmentioning
confidence: 99%
“…The effect is reduced during December, which is also underlined by the curves shown in Figure 2 . Figure 3 The slope a of linear regression is shown as function of the time (A) and as function of the theoretical incidence [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] b for theoretical zero vaccination rate (B). …”
Section: Resultsmentioning
confidence: 99%
“… The slope a of linear regression is shown as function of the time (A) and as function of the theoretical incidence [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] b for theoretical zero vaccination rate (B). …”
Section: Resultsmentioning
confidence: 99%
“…The data within the 4th corona wave in Germany show a clear dependence of the incidence [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] on the vaccination rate. The incidence is strongly negatively correlated with the vaccination rate during the course of this wave.…”
“…There have been many published works in the efforts to study the role of vaccination [71], social distancing [6], and household [6,72] in understanding the dynamic and control of the SARS-CoV-2 virus. Several authors have been designed and used simple models [74,73,75], complex models [71,77,78], and multiscale models [82,83] to simulate the trade-off between pharmaceutical (vaccination) and non-pharmaceutical (social distancing, stay-at-home restriction, decrease in working hours) intervention in the containment of COVID-19 pandemic. This study presents an agent-based model based on a time-dynamic network with stochastic transmission events to analyze the interplay between pharmaceutical and non-pharmaceutical interventions.…”
Background. Human behavior, economic activity, vaccination, and social distancing are inseparably entangled in epidemic management. This study aims to investigate the effects of various parameters such as stay-at-home restrictions, work hours, vaccination and social distance on the containment of pandemics such as COVID-19.
Methods. To achieve this, we developed an agent-based model based on a time-dynamic graph with stochastic transmission events. The graph is constructed from a real-world social network. The graph's edges have been categorized into three categories: home, workplaces, and social environment. The conditions needed to mitigate the spread of wild-type (WT) COVID-19 and the delta variant have been analyzed. Our purposeful agent-based model has carefully executed tens of thousands of individual-based simulations. We propose simple relationships for the trade-offs between effective reproduction number (Re), transmission rate, work hours, vaccination, and stay at home restrictions.
Results. For the WT, it has been found that a 13% increase in vaccination impacts the reproduction number, like the magnitude of decreasing nine hours of work to four and a single day of stay-at-home order. For the delta, 16% vaccination has the same effect. Also, since we can keep track of household and non-household infections, we observed that the change in household transmission rate does not significantly alter the Re. Household infections are not limited by transmission rate due to the high frequency of connections. For COVID-19's specifications, the Re depends on the non-household transmissions rate.
Conclusions. Vaccination and transmission reduction are almost interchangeable. Without vaccination or teaching people how to lower their transmission probability significantly, changing work hours or weekend restrictions will only make people more frustrated
Contact tracing is a non-pharmaceutical intervention (NPI) widely used in the control of the COVID-19 pandemic. Its effectiveness may depend on a number of factors including the proportion of contacts traced, delays in tracing, the mode of contact tracing (e.g. forward, backward or bidirectional contact training), the types of contacts who are traced (e.g. contacts of index cases or contacts of contacts of index cases), or the setting where contacts are traced (e.g. the household or the workplace). We performed a systematic review of the evidence regarding the comparative effectiveness of contact tracing interventions. 78 studies were included in the review, 12 observational (ten ecological studies, one retrospective cohort study and one pre-post study with two patient cohorts) and 66 mathematical modelling studies. Based on the results from six of the 12 observational studies, contact tracing can be effective at controlling COVID-19. Two high quality ecological studies showed the incremental effectiveness of adding digital contact tracing to manual contact tracing. One ecological study of intermediate quality showed that increases in contact tracing were associated with a drop in COVID-19 mortality, and a pre-post study of acceptable quality showed that prompt contact tracing of contacts of COVID-19 case clusters / symptomatic individuals led to a reduction in the reproduction number R. Within the seven observational studies exploring the effectiveness of contact tracing in the context of the implementation of other non-pharmaceutical interventions, contact tracing was found to have an effect on COVID-19 epidemic control in two studies and not in the remaining five studies. However, a limitation in many of these studies is the lack of description of the extent of implementation of contact tracing interventions. Based on the results from the mathematical modelling studies, we identified the following highly effective policies: (1) manual contact tracing with high tracing coverage and either medium-term immunity, highly efficacious isolation/quarantine and/ or physical distancing (2) hybrid manual and digital contact tracing with high app adoption with highly effective isolation/ quarantine and social distancing, (3) secondary contact tracing, (4) eliminating contact tracing delays, (5) bidirectional contact tracing, (6) contact tracing with high coverage in reopening educational institutions. We also highlighted the role of social distancing to enhance the effectiveness of some of these interventions in the context of 2020 lockdown reopening. While limited, the evidence from observational studies shows a role for manual and digital contact tracing in controlling the COVID-19 epidemic. More empirical studies accounting for the extent of contact tracing implementation are required.
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