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Title
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A contact-based modelling framework for contagious disease dynamics
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Author
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Abstract
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| Contagious diseases are severe threats to human societies, endangering global health and affecting the economy of countries. The diffusion of the infection can be prevented, controlled, and mitigated by employing pharmaceutical and non-pharmaceutical control measures as recently demonstrated in the case of the COVID-19 pandemic. Strict city lockdown, contact tracing, isolation and quarantine, and vaccination campaigns have been employed worldwide, reducing SARS-CoV-2 transmissions, thereby releasing the pressure on health care facilities. The adequacy of intervention measures depends on the spreading characteristics of contagious diseases and relies on the epidemiological parameter values inferred by using mathematical and statistical models. However, epidemiological quantities depend on ongoing transmission dynamics affected by specific human-to-human interactions and interventions that limit social interactions or decrease individual infectivity. To represent variations of the transmission dynamics, we introduced a theoretical framework that describes both between-individual interactions and within-individual viral progression. Based on this approach, we developed an individual-based model to simulate specific characteristics of the transmission dynamics. We defined competition among infectors and depletion of susceptibles and we set a simulation study to measure these interaction dynamics in different epidemiological contexts. While varying basic reproduction number, infectious period distribution, infectiousness profile and population size, we computed competition and depletion intensity, relating their values to the contraction of the realized generation time. We showed that competition and depletion are interaction dynamics that occur during an epidemic outbreak and that the higher their intensities, the higher the generation time contraction. Furthermore, we investigated the effect of non-pharmaceutical control measures on the realized serial and generation intervals. We showed that both these epidemiological quantities have higher contraction when interventions are more effective and that their mean values depend on the prevalence of undiagnosed individuals. In particular, we noted that the mean serial interval differs from the mean generation time when control measures are in place, highlighting the different nature of such quantities and suggesting that using the one as a proxy for the other can result in biased estimates of epidemiological quantities. Last, we presented a control strategy based on the use of antiviral drugs to mitigate local outbreaks, in addition to non-pharmaceutical interventions. By reducing and shortening the viral shedding period, antivirals may effectively reduce the spreading of contagious diseases. However, to increase the efficacy of these drugs, the administration should take place soon in the infectious period, even before individuals are diagnosed. Therefore, we proposed to use contact tracing to find infected individuals early in their infectious period and to administered antiviral drugs to limit infections caused by imperfect isolation. With such a mitigation strategy, both transmissions at the population level and peak incidence are shown to reduce during an outbreak. The presented framework results in a flexible simulation approach where variations of the transmission dynamic can be defined and investigated. While we focused on control measures and individual interactions, other aspects of the epidemic dynamic and human behavior can be investigated using this theoretical approach. In addition, other epidemiological quantities can be considered, making the presented investigations the foundations on which further study can be based. |
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Language
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English
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Publication
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Antwerp
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University of Antwerp, Faculty of Medicine and Health Sciences
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2021
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Volume/pages
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138 p.
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Note
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:
Hens, Niel [Supervisor]
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Full text (open access)
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