Pandemic management requires reliable and efficient dynamical simulation to predict and control disease spreading. The COVID-19 (SARS-CoV-2) pandemic is mitigated by several non-pharmaceutical interventions, but it is hard to predict which of these are the most effective for a given population. We developed the computationally effective and scalable, agent-based microsimulation framework PanSim, allowing us to test control measures in multiple infection waves caused by the spread of a new virus variant in a city-sized societal environment using a unified framework fitted to realistic data. We show that vaccination strategies prioritising occupational risk groups minimise the number of infections but allow higher mortality while prioritising vulnerable groups minimises mortality but implies an increased infection rate. We also found that intensive vaccination along with non-pharmaceutical interventions can substantially suppress the spread of the virus, while low levels of vaccination, premature reopening may easily revert the epidemic to an uncontrolled state. Our analysis highlights that while vaccination protects the elderly from COVID-19, a large percentage of children will contract the virus, and we also show the benefits and limitations of various quarantine and testing scenarios. The uniquely detailed spatio-temporal resolution of PanSim allows the design and testing of complex, specifically targeted interventions with a large number of agents under dynamically changing conditions.

Autophagy-dependent cellular survival is tightly regulated by both kinases and phosphatases. While mTORC1 inhibits autophagy by phosphorylating ULK1, PP2A is able to remove this phosphate group from ULK1 and promotes the key inducer of autophagosome formation. However, ULK1 inhibits mTORC1, mTORC1 is able to down-regulate PP2A. In addition, the active ULK1 promotes PP2A via phosphorylation. We claim that these double-negative (mTORC1 –| PP2A –| mTORC1, mTORC1 –| ULK1 –| mTORC1) and positive (ULK1 -> PP2A -> ULK1) feedback loops are all necessary for the robust, irreversible decision making process between the autophagy and non-autophagy states. We approach our scientific analysis from a systems biological perspective by applying both theoretical and molecular biological techniques. For molecular biological experiments, HEK293T cell line is used, meanwhile the dynamical features of the regulatory network are described by mathematical modelling. In our study, we explore the dynamical characteristic of mTORC1-ULK1-PP2A regulatory triangle in detail supposing that the positive feedback loops are essential to manage a robust cellular answer upon various cellular stress events (such as mTORC1 inhibition, starvation, PP2A inhibition or ULK1 silencing). We confirm that active ULK1 can up-regulate PP2A when mTORC1 is inactivated. By using theoretical analysis, we explain the importance of cellular PP2A level in stress response mechanism. We proved both experimentally and theoretically that PP2A down-regulation (via addition of okadaic acid) might generate a periodic repeat of autophagy induction. Understanding how the regulation of the cell survival occurs with the precise molecular balance of ULK1-mTORC1-PP2A in autophagy, is highly relevant in several cellular stress-related diseases (such as neurodegenerative diseases or diabetes) and might help to promote advanced therapies in the near future, too.

SummaryBackgroundPandemic management includes a variety of control measures, such as social distancing, testing/quarantining and vaccination applied to a population where the virus is circulating. The COVID-19 (SARS-CoV-2) pandemic is mitigated by several non-pharmaceutical interventions, but it is hard to predict which of these regulations are the most effective for a given population.MethodsWe developed a computationally effective and scalable, agent-based microsimulation framework. This unified framework was fitted to realistic data to enable us to test control measures (closures, quarantining, testing, vaccination) in multiple infection waves caused by the spread of a new virus variant in a city-sized societal environment. Our framework is capable of simulating nine billion agent-steps per minute, allowing us to model interactions in populations with up to 90 million individuals.FindingsWe show that vaccination strategies prioritising occupational risk groups minimise the number of infections but allow higher mortality while prioritising vulnerable groups minimises mortality but implies increased infection rate. We also found that intensive vaccination and non-pharmaceutical interventions can substantially suppress the spread of the virus, while low levels of vaccination and premature reopening may easily revert the epidemic to an uncontrolled state.InterpretationOur analysis highlights that while vaccination protects the elderly from COVID-19, a large percentage of children will contract and spread the virus, and we also show the benefits and limitations of various quarantine and testing scenarios.FundingThis work was carried out within the framework of the Hungarian National Development, Research, and Innovation (NKFIH) Fund 2020-2.1.1-ED-2020-00003.Research in contextEvidence before this studyWe still do not have an effective medical treatment against COVID-19 (SARS-CoV-2), thus the majority of the efforts to stop the pandemic focuses on non-pharmaceutical interventions. Each country came up with a local solution to stop the spread of the virus by increased testing, quarantining, lock-down of various events and institutions or early vaccination. There is no clear way how these interventions can be compared, and it is especially challenging to predict how combinations of interventions could influence the pandemic. Various mathematical modelling approaches helped decision-makers to foresee the effects of their decisions. Most of these models rely on classical, deterministic compartmental “SEIR” models, which can be solved easily but cannot take into account spatial effects and most non-pharmaceutical interventions affect the same parameters, so there is no way to analyse their separate or joint effects. Agent-based microsimulations are harder to solve but can consider far more details. Several models were developed on these lines focusing on questions about ideal vaccination, lock-down or other specific problems, but none of these studies evaluated and compared the individual and mixed effects of a wide variety of control measures.Added-value of this studyHere we present PanSim, a framework where we introduce a detailed infection event simulation step and the possibility to control specific workplaces individually (schools, hospitals, etc.), test various vaccination, testing and quarantine scenarios while considering preconditions, age, sex, residence and workplace of individuals and mutant viruses with various infectivity. The level of details and granularity of simulations allow our work to evaluate this wide range of scenarios and control measures accurately and directly compare them with one another to provide quantitative evidence to support decision-makers. Analysis of our simulations also provides emergent results on the risks children and non-vaccinated individuals face.Implications of all the available evidenceThe agent-based microsimulation framework allows us to evaluate the risk and possible consequences of particular interventions precisely. Due to the outstanding efficiency of the computations, it is possible to apply scenario-based analysis and control design methods which require a high number of simulation runs to obtain results on a given confidence level. This will enable us to design and quantitatively assess control measures in case of new waves of COVID-19 or new pandemic outbreaks.

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