Wastewater-based modeling, reconstruction, and prediction for COVID-19 outbreaks in Hungary caused by highly immune evasive variants.

(MOTIVATION): Wastewater-based epidemiology (WBE) has emerged as a promising approach for monitoring the COVID-19 pandemic, since the measurement process is cost-effective and is exposed to fewer potential errors compared to other indicators like hospitalization data or the number of detected cases. Consequently, WBE was gradually becoming a key tool for epidemic surveillance and often the most reliable data source, as the intensity of clinical testing for COVID-19 drastically decreased by the third year of the pandemic. Recent results suggests that the model-based fusion of wastewater measurements with clinical data and other indicators is essential in future epidemic surveillance. (METHOD): In this work, we developed a wastewater-based compartmental epidemic model with a two-phase vaccination dynamics and immune evasion. We proposed a multi-step optimization-based data assimilation method for epidemic state reconstruction, parameter estimation, and prediction. The computations make use of the measured viral load in wastewater, the available clinical data (hospital occupancy, delivered vaccine doses, and deaths), the stringency index of the official social distancing rules, and other measures. The current state assessment and the estimation of the current transmission rate and immunity loss allow a plausible prediction of the future progression of the pandemic. (RESULTS): Qualitative and quantitative evaluations revealed that the contribution of wastewater data in our computational epidemiological framework makes predictions more reliable. Predictions suggest that at least half of the Hungarian population has lost immunity during the epidemic outbreak caused by the BA.1 and BA.2 subvariants of Omicron in the first half of 2022. We obtained a similar result for the outbreaks caused by the subvariant BA.5 in the second half of 2022. (APPLICABILITY): The proposed approach has been used to support COVID management in Hungary and could be customized for other countries as well.

Erythro-VLPs: Anchoring SARS-CoV-2 spike proteins in erythrocyte liposomes.

Novel therapeutic strategies are needed to control the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic. Here, we present a protocol to anchor the SARS-CoV-2 spike (S-)protein in the cytoplasmic membranes of erythrocyte liposomes. A surfactant was used to stabilize the S-protein's structure in the aqueous environment before insertion and to facilitate reconstitution of the S-proteins in the erythrocyte membranes. The insertion process was studied using coarse grained Molecular Dynamics (MD) simulations. Liposome formation and S-protein anchoring was studied by dynamic light scattering (DLS), ELV-protein co-sedimentation assays, fluorescent microcopy and cryo-TEM. The Erythro-VLPs (erythrocyte based virus like particles) have a well defined size of ∼200 nm and an average protein density on the outer membrane of up to ∼300 proteins/µm2. The correct insertion and functional conformation of the S-proteins was verified by dose-dependent binding to ACE-2 (angiotensin converting enzyme 2) in biolayer interferometry (BLI) assays. Seroconversion was observed in a pilot mouse trial after 14 days when administered intravenously, based on enzyme-linked immunosorbent assays (ELISA). This red blood cell based platform can open novel possibilities for therapeutics for the coronavirus disease (COVID-19) including variants, and other viruses in the future.

Microsimulation based quantitative analysis of COVID-19 management strategies.

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.