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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.

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.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological agent of the ongoing pandemic of 2019 CoV disease (COVID-19), which is already responsible for far more deaths than were reported during the previous public health emergencies of international concern provoked by two related pathogenic coronaviruses (CoVs) from 2002 and 2012. The identification of any clinically approved drug that could be repurposed to combat COVID-19 would allow the rapid implementation of potentially life-saving procedures to complement social distancing and isolation protocols. The major protease (Mpro) of SARS-CoV-2 is considered a promising target for drug interventions, based on results from related CoVs with lopinavir (LPV) an HIV protease inhibitor, that that can inhibit the Mpro of 2002 SARS-CoV. However, limited evidence exists for other clinically approved anti-retroviral protease inhibitors that may bind more efficiently to Mpro from SARS-CoV-2 and block its replication. Of high interest is atazanavir (ATV) due to its documented bioavailability within the respiratory tract, which motivated our evaluation on its ability to impair SARS-CoV-2 replication through a series of in vitro experiments. A molecular dynamic analysis showed that ATV could dock in the active site of SARS-CoV-2 Mpro with greater strength than LPV and occupied the substrate cleft on the active side of the protease throughout the entire molecular dynamic analysis. In a cell-free protease assay, ATV was determined to block Mpro activity at a concentration of 10 μM. Next, a series of assays with in vitro models of virus infection/replications were performed using three cell types, Vero cells, a human pulmonary epithelial cell line and primary human monocytes, which confirmed that ATV could inhibit SARS-CoV-2 replication, alone or in combination with ritonavir (RTV). In addition, the virus-induced levels of IL-6 and TNF-α were reduced in the presence of these drugs, which performed better than chloroquine, a compound recognized for its anti-viral and anti-inflammatory activities. Together, our data strongly suggest that ATV and ATV/RTV should be considered among the candidate repurposed drugs undergoing clinical trials in the fight against COVID-19.

Set of supplementary tables and figures obtained for the analysis of topics in space and time about the Brazilian Immunization Program against COVID-19. These elements are: Table S1: Parameters used in the scraping script; Table S2: Example of the georeferenced corpus, containing the first five rows; Table S3: Cities with at least 300 tweets retrieved; Table S4: The 23 topics extracted using LDA algorithm; Table S5: The 23 topics terms translations (or approximated translations) to English; Table S6: Possible interpretations for each topic; Table S7: Tweets amounts according to each topic; Figure S1: Topic 1 distribution on the Brazilian territory; Figure S2: Topic 2 distribution on the Brazilian territory; Figure S3: Topic 4 distribution on the Brazilian territory; Figure S4: Topic 5 distribution on the Brazilian territory; Figure S5: Topic 6 distribution on the Brazilian territory; Figure S6: Topic 7 distribution on the Brazilian territory; Figure S7: Topic 8 distribution on the Brazilian territory; Figure S9: Topic 10 distribution on the Brazilian territory; Figure S10: Topic 11 distribution on the Brazilian territory; Figure S11: Topic 12 distribution on the Brazilian territory; Figure S12: Topic 14 distribution on the Brazilian territory; Figure S13: Topic 15 distribution on the Brazilian territory; Figure S14: Topic 16 distribution on the Brazilian territory; Figure S15: Topic 17 distribution on the Brazilian territory; Figure S16: Topic 18 distribution on the Brazilian territory; Figure S17: Topic 19 distribution on the Brazilian territory; Figure S18: Topic 20 distribution on the Brazilian territory; Figure S19: Topic 21 distribution on the Brazilian territory; Figure S20: Topic 22 distribution on the Brazilian territory; Figure S21: Topic 23 distribution on the Brazilian territory.

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.