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This viewpoint note discusses the covid-19 pandemic from the lens of complexity thinking and resilience engineering (RE). It intends to raise questions and encourage critical thinking on the underlying theoretical foundations of the responses adopted so far to cope with the pandemic. Insights arising from this analysis can be useful for the refinement of complexity and RE theory and practice, as well as for the further development of non-medical practices to address the pandemic and its effects.

The covidBR R package was built to extract data from the Brazilian COVID-19 Portal (Portal do COVID-19) and exported as CSV files.

Open peer-reviews on 'National Economic and Health Recovery in the Disruptive Pandemic: A Proposal for Indonesia' (Abraham, Buhaerah, Aji, Onie, & Dalimunthe, 2021; Academia Letters, Article 1345, June 2021). The full article can be accessed via https://ssrn.com/abstract=3899576 and https://doi.org/10.20935/AL1345

README This repository provides the script to reproduce the fitting analysis and figures from Coutinho et al. 2021 Notes: This code runs best on R version 4.0.3 or above. The code is also optimized for [linux]. When you first run the code from Run_analysis.R, R will install all required packages. Running all.params.exploration() and fit_minima() takes a large number of hours to complete. For convenience, we also provide the tables resulting from these function as .csv files. Run the file Run_analysis.R to reproduce the paper`s main analysis and sensitivity analysis. The following values correspond to the parameters presented in Table 1: REL.BETA.RATE2: Relative transmission rate for the new variant PROB.REINFEC.V2: Relative force of reinfection of P.1 prevalence: Prevalence of previous infection (2020-11-01) (%) INIT.V2.FRAC: Initial fraction of the new variant (2020-11-01) r: Intrinsic growth rate IHR.V2.PROP: odds ratio of P.1 parameters relative to wild variant References: Coutinho, R. M., Marquitti, F. M. D., Ferreira, L. S., Borges, M. E., da Silva, R. L. P., Canton, O., … & Prado, P. I. (2021). Model-based estimation of transmissibility and reinfection of SARS-CoV-2 P. 1 variant. medRxiv.

Background Since the onset of the COVID-19 pandemic, mathematical models have been widely used to inform public health recommendations regarding COVID-19 control in healthcare settings. Objectives To systematically review SARS-CoV-2 transmission models in healthcare settings, and summarise their contributions to understanding nosocomial COVID-19. Methods Systematic search and review. Data sources Published articles indexed in PubMed. Study eligibility criteria Modelling studies describing dynamic inter-individual transmission of SARS-CoV-2 in healthcare settings, published by mid-February 2022. Participants and interventions Any population and intervention described by included models. Assessment of risk of bias Not appropriate for modelling studies. Methods of data synthesis Structured narrative review. Results Models have mostly focused on acute care and long-term care facilities in high-income countries. Models have quantified outbreak risk across different types of individuals and facilities, showing great variation across settings and pandemic periods. Regarding surveillance, routine testing – rather than symptom-based testing – was highlighted as essential for COVID-19 prevention due to high rates of silent transmission. Surveillance impacts were found to depend critically on testing frequency, diagnostic sensitivity, and turn-around time. Healthcare re-organization was also found to have large epidemiological impacts: beyond obvious benefits of isolating cases and limiting inter-individual contact, more complex strategies such as staggered staff scheduling and immune-based cohorting reduced infection risk. Finally, vaccination impact, while highly effective for limiting COVID-19 burden, varied substantially depending on assumed mechanistic impacts on infection acquisition, symptom onset and transmission. Studies were inconsistent regarding which individuals to prioritize for interventions, probably due to the high diversity of settings and populations investigated. Conclusions Modelling results form an extensive evidence base that may inform control strategies for future waves of SARS-CoV-2 and other viral respiratory pathogens. We propose new avenues for future models of healthcare-associated outbreaks, with the aim of enhancing their efficiency and contributions to decision-making.

ROSS (Rotordynamic Open Source Software) is a library written in Python for rotordynamic analysis. It allows the construction of rotor models and their numerical simulation.

The binding affinity between the Spike (S) glycoprotein and its host cell receptors, angiotensin-converting enzyme 2 (ACE2) and Furin plays a critical role in determining SARS-CoV-2 replication rates. Variants of the S protein, including Alpha, Beta, Gamma, Delta, Omicron BA.1, BA.2, BA.4, and BA.5, have been associated with shifting levels of COVID-19 susceptibility and severity. However, the mechanisms underlying these virus-host protein interactions are not fully understood. The current research employs molecular modeling, protein-protein interaction, and molecular dynamics (MD) simulations of Wild-Type (WT) and variants of virus-host complexes ACE2-Spike-Furin to investigate interactions and stability. The objective is to gain a comprehensive understanding of protein complex dynamics and their relevance to COVID-19. Protein 3D structures were obtained from the Protein Data Bank, while MODELLER was employed to model S protein variants. Glycans were incorporated using the Glycan Reader & Modeller tool. Complexes were chosen based on HADDOCK scores, with lower scores signifying greater reliability. Interaction free energies were calculated using the PRODIGY server. MD simulations were conducted using GROMACS version 2019.3 with the CHARMM36 force field. The complexes were solvated, neutralized, minimized, and equilibrated under an NVT and NPT ensemble. The production phase was performed at 300K for 100ns. Complex selection was based on both the lowest HADDOCK scores and the free energies of each interaction. ACE2-Spike interactions displayed binding affinities ranging from -11.4 to -15.1 kcal/mol for WT Spike and its variants. Spike-Furin interactions exhibited affinities ranging from -10.5 to -12.6 kcal/mol. 28 glycans were incorporated into each complex. Throughout the MD simulations, all complexes demonstrated stability, as corroborated by RMSD, RMSF, Gyration, and SASA analyses, though with specific variations related to each variant. This project uniquely explores the interactions among three proteins and glycans, yielding high-fidelity complex models. These findings are an integral component of a comprehensive analysis that will additionally investigate the interactions of the TMPRSS2 protein and employ machine learning techniques to discern differences among SARS-CoV-2 lineages, aiming to understand genetic variations and gain insights into S protein dynamics, thereby enhancing our comprehension of SARS-CoV-2's protein-protein biology.

Code used during the article "Internet search query data as disease surveillance: the analysis of Covid-19 in Brazil " to perform correlations between searches of certain categories in Google Trends with Covid-19 cases and deaths in the different states of Brazil. In this code, besides the moving window correlation, there is also the inclusion of a lag between the time series to be correlated, in order to verify the existence of a predictive potential in the time series of Google Trends searches.

RNA viruses have been responsible for causing severe epidemics and pandemics all across the world in recent decades. This scenario has been constantly studied by viral epidemiological surveillance and molecular evolution methodologies to investigate the appearance of new viral agents in order to respond to these new threats before they reach the human population. In this context, bats have received notable attention for harboring several viral pathogens of zoonotic importance, such as Rabies, Ebola, Marburg, Nipah, SARS-CoV, MERS-CoV, SARS-CoV-2 pandemic, among others. Given this, the present project will apply techniques involving metatranscriptomics, Bayesian inference and machine-learning to investigate the presence, characterize, identify genetic signatures and map the diversity of RNA viruses at the interface of vertical transmission of different bats species inhabiting Brazilian Northeast. Through our approach, we also aim to test the hypothesis of vertical transmission of different viral agents through intra-host gene expression patterns, in addition to studying their evolution through phylodynamics, phylogeographic and phyloanatomy methods, approaches not undertaken in earlier investigations. We hope that such contributions will help to clarify mechanisms behind viral intra-host evolutionary dynamics and spread over time, as well as will promote measures on behalf of Public Health strategies aimed at preventing future epidemics and pandemics episodes.

Abstract The COVID-19 pandemic is expected to have significant consequences for wildlife populations and conservation efforts, particularly in driving an increase in illegal extraction at the local and regional level. Here, we report a shift in illegal catch by both commercial and recreational fishing vessels inside and outside the Alcatrazes Archipelago marine protected area, a biodiversity hotspot in the southwest Atlantic Ocean, for the three years before and two years during the pandemic. We witnessed an increase in both the total illegal catch and numbers of species caught since the pandemic, particularly by amateur fishers. Both types of vessels targeted larger, more valuable apex predators during the pandemic, including many threatened and endangered species. Based on a functional trait analysis, removal of these species is likely to have significant ecosystem consequences into the future. Our study provides new evidence that the pandemic can significantly reduce the effectiveness of marine conservation.