Correction: Convergent evolution of diverse Bacillus anthracis outbreak strains toward altered surface oligosaccharides that modulate anthrax pathogenesis.

[This corrects the article DOI: 10.1371/journal.pbio.3001052.].

Convergent evolution of diverse Bacillus anthracis outbreak strains toward altered surface oligosaccharides that modulate anthrax pathogenesis.

Bacillus anthracis, a spore-forming gram-positive bacterium, causes anthrax. The external surface of the exosporium is coated with glycosylated proteins. The sugar additions are capped with the unique monosaccharide anthrose. The West African Group (WAG) B. anthracis have mutations rendering them anthrose deficient. Through genome sequencing, we identified 2 different large chromosomal deletions within the anthrose biosynthetic operon of B. anthracis strains from Chile and Poland. In silico analysis identified an anthrose-deficient strain in the anthrax outbreak among European heroin users. Anthrose-deficient strains are no longer restricted to West Africa so the role of anthrose in physiology and pathogenesis was investigated in B. anthracis Sterne. Loss of anthrose delayed spore germination and enhanced sporulation. Spores without anthrose were phagocytized at higher rates than spores with anthrose, indicating that anthrose may serve an antiphagocytic function on the spore surface. The anthrose mutant had half the LD50 and decreased time to death (TTD) of wild type and complement B. anthracis Sterne in the A/J mouse model. Following infection, anthrose mutant bacteria were more abundant in the spleen, indicating enhanced dissemination of Sterne anthrose mutant. At low sample sizes in the A/J mouse model, the mortality of ΔantC-infected mice challenged by intranasal or subcutaneous routes was 20% greater than wild type. Competitive index (CI) studies indicated that spores without anthrose disseminated to organs more extensively than a complemented mutant. Death process modeling using mouse mortality dynamics suggested that larger sample sizes would lead to significantly higher deaths in anthrose-negative infected animals. The model was tested by infecting Galleria mellonella with spores and confirmed the anthrose mutant was significantly more lethal. Vaccination studies in the A/J mouse model showed that the human vaccine protected against high-dose challenges of the nonencapsulated Sterne-based anthrose mutant. This work begins to identify the physiologic and pathogenic consequences of convergent anthrose mutations in B. anthracis.

An investigation of the seasonal relationships between meteorological factors, water quality, and sporadic cases of Legionnaires' disease in Washington, DC.

Since the discovery of Legionnaires' disease (LD), limited progress has been made in understanding the epidemiology of sporadic cases of LD. Outbreaks have confirmed that air conditioning and potable water systems can be sources of community-acquired LD. However, studying the association between water quality and LD incidence has been challenging due to the heterogeneity of water systems across large geographic areas. Furthermore, although seasonal trends in incidence have been linked to increased rainfall and temperatures, the large geographic units have posed similar difficulties. To address this issue, a retrospective ecological study was conducted in Washington, DC, from 2001 to 2019. The study identified aseasonal pattern of LD incidence, with the majority of cases occurring between June and December, peaking in August, October, and November. Increased temperature was found to be associated with LD incidence. In surface water, higher concentrations of manganese, iron, and strontium were positively associated with LD, while aluminum and orthophosphate showed a negative association. Intreatment plant water, higher concentrations of total organic carbon, aluminum, barium, and chlorine were positively associated with LD, while strontium, zinc, and orthophosphate showed a negative association. The results for orthophosphates and turbidity were inconclusive, indicating the need for further research.

Influence of Preseason Antibodies Against Influenza Virus on Risk of Influenza Infection Among Healthcare Personnel.

BACKGROUND: The association of hemagglutination inhibition (HAI) antibodies with protection from influenza among healthcare personnel (HCP) with occupational exposure to influenza viruses has not been well-described. METHODS: The Respiratory Protection Effectiveness Clinical Trial was a cluster-randomized, multisite study that compared medical masks to N95 respirators in preventing viral respiratory infections among HCP in outpatient healthcare settings for 5180 participant-seasons. Serum HAI antibody titers before each influenza season and influenza virus infection confirmed by polymerase chain reaction were studied over 4 study years. RESULTS: In univariate models, the risk of influenza A(H3N2) and B virus infections was associated with HAI titers to each virus, study year, and site. HAI titers were strongly associated with vaccination. Within multivariate models, each log base 2 increase in titer was associated with 15%, 26% and 33%-35% reductions in the hazard of influenza A(H3N2), A(H1N1), and B infections, respectively. Best models included preseason antibody titers and study year, but not other variables. CONCLUSIONS: HAI titers were associated with protection from influenza among HCP with routine exposure to patients with respiratory illness and influenza season contributed to risk. HCP can be reassured about receiving influenza vaccination to stimulate immunity.

Kinetic Analysis of Hepatic Metabolism Using Hyperpolarized Dihydroxyacetone.

Hyperpolarized carbon-13 magnetic resonance (HP-MR) is a new metabolic imaging method the does not use ionizing radiation. Due to the inherent chemical specificity of MR, not only tracer uptake but also downstream metabolism of the agent is detected in a straightforward manner. HP [2-13C] dihydroxyacetone (DHA) is a promising new agent that directly interrogates hepatic glucose metabolism. DHA has three metabolic fates in the liver: glucose production, glycerol production and potential inclusion into triglycerides, and oxidation in the tricarboxylic acid cycle. Each pathway is regulated by flux through multiple enzymes. Using Duhamel's formula, the kinetics of DHA metabolism is modeled, resulting in estimates of specific reaction rate constants. The multiple enzymatic steps that control DHA metabolism make more simplified methods for extracting kinetic data less than satisfactory. The described modeling paradigm effectively identifies changes in metabolism between gluconeogenic and glycogenolytic models of hepatic function.

SECIMTools: a suite of metabolomics data analysis tools.

BACKGROUND: Metabolomics has the promise to transform the area of personalized medicine with the rapid development of high throughput technology for untargeted analysis of metabolites. Open access, easy to use, analytic tools that are broadly accessible to the biological community need to be developed. While technology used in metabolomics varies, most metabolomics studies have a set of features identified. Galaxy is an open access platform that enables scientists at all levels to interact with big data. Galaxy promotes reproducibility by saving histories and enabling the sharing workflows among scientists. RESULTS: SECIMTools (SouthEast Center for Integrated Metabolomics) is a set of Python applications that are available both as standalone tools and wrapped for use in Galaxy. The suite includes a comprehensive set of quality control metrics (retention time window evaluation and various peak evaluation tools), visualization techniques (hierarchical cluster heatmap, principal component analysis, modular modularity clustering), basic statistical analysis methods (partial least squares - discriminant analysis, analysis of variance, t-test, Kruskal-Wallis non-parametric test), advanced classification methods (random forest, support vector machines), and advanced variable selection tools (least absolute shrinkage and selection operator LASSO and Elastic Net). CONCLUSIONS: SECIMTools leverages the Galaxy platform and enables integrated workflows for metabolomics data analysis made from building blocks designed for easy use and interpretability. Standard data formats and a set of utilities allow arbitrary linkages between tools to encourage novel workflow designs. The Galaxy framework enables future data integration for metabolomics studies with other omics data.

A comprehensive analysis of myocardial substrate preference emphasizes the need for a synchronized fluxomic/metabolomic research design.

The heart oxidizes fatty acids, carbohydrates, and ketone bodies inside the tricarboxylic acid (TCA) cycle to generate the reducing equivalents needed for ATP production. Competition between these substrates makes it difficult to estimate the extent of pyruvate oxidation. Previously, hyperpolarized pyruvate detected propionate-mediated activation of carbohydrate oxidation, even in the presence of acetate. In this report, the optimal concentration of propionate for the activation of glucose oxidation was measured in mouse hearts perfused in Langendorff mode. This study was performed with a more physiologically relevant perfusate than the previous work. Increasing concentrations of propionate did not cause adverse effects on myocardial metabolism, as evidenced by unchanged O2 consumption, TCA cycle flux, and developed pressures. Propionate at 1 mM was sufficient to achieve significant increases in pyruvate dehydrogenase flux (3×), and anaplerosis (6×), as measured by isotopomer analysis. These results further demonstrate the potential of propionate as an aid for the correct estimation of total carbohydrate oxidative capacity in the heart. However, liquid chromotography/mass spectroscopy-based metabolomics detected large changes (~30-fold) in malate and fumarate pool sizes. This observation leads to a key observation regarding mass balance in the TCA cycle; flux through a portion of the cycle can be drastically elevated without changing the O2 consumption.

Bone mineral density in glycogen storage disease type Ia and Ib.

Purpose:The aim of this study was to characterize the pathogenesis of low bone mineral density in glycogen storage disease type Ia and Ib.Methods:A retrospective chart review performed at the University of Florida Glycogen Storage Disease Program included patients with glycogen storage disease type Ia and Ib for whom dual-energy X-ray absorptiometry analysis was performed. A Z-score less than -2 SD was considered low. Analysis for association of bone mineral density with age, gender, presence of complications, mean triglyceride and 25-hydroxyvitamin D concentrations, erythrocyte sedimentation rate, duration of granulocyte colony-stimulating factor therapy, and history of corticosteroid use was performed.Results:In glycogen storage disease Ia, 23/42 patients (55%) had low bone mineral density. Low bone mineral density was associated with other disease complications (P = 0.02) and lower mean serum 25-hydroxyvitamin D concentration (P = 0.03). There was a nonsignificant trend toward lower mean triglyceride concentration in the normal bone mineral density group (P = 0.1).In patients with glycogen storage disease type Ib, 8/12 (66.7%) had low bone mineral density. We did not detect an association with duration of granulocyte colony-stimulating factor therapy (P = 0.68), mean triglyceride level (P = 0.267), erythrocyte sedimentation rate (P = 0.3), or 25-hydroxyvitamin D (P = 0.63) concentration, and there was no evidence that corticosteroid therapy was associated with lower bone mineral density (P = 1).Conclusion:In glycogen storage disease type Ia, bone mineral density is associated with other complications and 25-hydroxyvitamin D status. In glycogen storage disease type Ib, bone mineral density was not associated with any covariates analyzed, suggesting multifactorial etiology or reflecting a small sample.Genet Med advance online publication 5 April 2012.

Retrospective evaluation of short-term forecast performance of ensemble sub-epidemic frameworks and other time-series models: The 2022-2023 mpox outbreak across multiple geographical scales, July 14th, 2022, through February 26th, 2023.

In May 2022, public health officials noted an unprecedented surge in mpox cases in non-endemic countries worldwide. As the epidemic accelerated, multi-model forecasts of the epidemic's trajectory were critical in guiding the implementation of public health interventions and determining policy. As the case levels have significantly decreased as of early September 2022, evaluating model performance is essential to advance the growing field of epidemic forecasting. Using laboratory-confirmed mpox case data from the Centers for Disease Control and Prevention (CDC) and Our World in Data (OWID) teams through the week of January 26th, 2023, we generated retrospective sequential weekly forecasts (e.g., 1-4-weeks) for Brazil, Canada, France, Germany, Spain, the United Kingdom, the USA, and at the global scale using models that require minimal input data including the auto-regressive integrated moving average (ARIMA), general additive model (GAM), simple linear regression (SLR), Facebook's Prophet model, as well as the sub-epidemic wave (spatial-wave) and n-sub-epidemic modeling frameworks. We assess forecast performance using average mean squared error (MSE), mean absolute error (MAE), weighted interval score (WIS), 95% prediction interval coverage (95% PI coverage), and skill scores. Average Winkler scores were used to calculate skill scores for 95% PI coverage. Overall, the n-sub-epidemic modeling framework outcompeted other models across most locations and forecasting horizons, with the unweighted ensemble model performing best across all forecasting horizons for most locations regarding average MSE, MAE, WIS, and 95% PI coverage. However, many locations had multiple models performing equally well for the average 95% PI coverage. The n-sub-epidemic and spatial-wave frameworks improved considerably in average MSE, MAE, and WIS, and Winkler scores (95% PI coverage) relative to the ARIMA model. Findings lend further support to sub-epidemic frameworks for short-term forecasting epidemics of emerging and re-emerging infectious diseases.

Excess mortality in Ukraine during the course of COVID-19 pandemic in 2020-2021.

In this work, the COVID-19 pandemic burden in Ukraine is investigated retrospectively using the excess mortality measures during 2020-2021. In particular, the epidemic impact on the Ukrainian population is studied via the standardized both all-cause and cause-specific mortality scores before and during the epidemic. The excess mortality counts during the pandemic were predicted based on historic data using parametric and nonparametric modeling and then compared with the actual reported counts to quantify the excess. The corresponding standardized mortality P-score metrics were also compared with the neighboring countries. In summary, there were three "waves" of excess all-cause mortality in Ukraine in December 2020, April 2021 and November 2021 with excess of 32%, 43% and 83% above the expected mortality. Each new "wave" of the all-cause mortality was higher than the previous one and the mortality "peaks" corresponded in time to three "waves" of lab-confirmed COVID-19 mortality. The lab-confirmed COVID-19 mortality constituted 9% to 24% of the all-cause mortality during those three peak months. Overall, the mortality trends in Ukraine over time were similar to neighboring countries where vaccination coverage was similar to that in Ukraine. For cause-specific mortality, the excess observed was due to pneumonia as well as circulatory system disease categories that peaked at the same times as the all-cause and lab-confirmed COVID-19 mortality, which was expected. The pneumonias as well as circulatory system disease categories constituted the majority of all cases during those peak times. The seasonality in mortality due to the infectious and parasitic disease category became less pronounced during the pandemic. While the reported numbers were always relatively low, alcohol-related mortality also declined during the pandemic.

Genomic and Phylogenetic Analysis of Bacillus cereus Biovar anthracis Isolated from Archival Bone Samples Reveals Earlier Natural History of the Pathogen.

(1) Background: Bacillus cereus biovar anthracis (Bcbva) was the causative agent of an anthrax-like fatal disease among wild chimpanzees in 2001 in Côte d'Ivoire. Before this, there had not been any description of an anthrax-like disease caused by typically avirulent Bacillus cereus. Genetic analysis found that B. cereus had acquired two anthrax-like plasmids, one a pXO1-like toxin producing plasmid and the other a pXO2-like plasmid encoding capsule. Bcbva caused animal fatalities in Cameroon, Democratic Republic of Congo, and the Central African Republic between 2004 and 2012. (2) Methods: The pathogen had acquired plasmids in the wild and that was discovered as the cause of widespread animal fatalities in the early 2000s. Primate bones had been shipped out of the endemic zone for anthropological studies prior to the realized danger of contamination with Bcbva. Spores were isolated from the bone fragments and positively identified as Bcbva. Strains were characterized by classical microbiological methods and qPCR. Four new Bcbva isolates were whole-genome sequenced. Chromosomal and plasmid phylogenomic analysis was performed to provide temporal and spatial context to these new strains and previously sequenced Bcbva. Tau and principal component analyses were utilized to identify genetic and spatial case patterns in the Taï National Park anthrax zone. (3) Results: Preliminary studies positively identified Bcbva presence in several archival bone fragments. The animals in question died between 1994 and 2010. Previously, the earliest archival strains of Bcbva were identified in 1996. Though the pathogen has a homogeneous genome, spatial analyses of a subset of mappable isolates from Taï National Park revealed strains found closer together were generally more similar, with strains from chimpanzees and duikers having the widest distribution. Ancestral strains were located mostly in the west of the park and had lower spatial clustering compared to more recent isolates, indicating a local increase in genetic diversity of Bcbva in the park over space and time. Global clustering analysis indicates patterns of genetic diversity and distance are shared between the ancestral and more recently isolated type strains. (4) Conclusions: Our strains have the potential to unveil historical genomic information not available elsewhere. This information sheds light on the evolution and emergence of a dangerous anthrax-causing pathogen.

Beyond the spore, the exosporium sugar anthrose impacts vegetative Bacillus anthracis gene regulation in cis and trans.

The Bacillus anthracis exosporium nap is the outermost portion of spore that interacts with the environment and host systems. Changes to this layer have the potential to impact wide-ranging physiological and immunological processes. The unique sugar, anthrose, normally coats the exosporium nap at its most distal points. We previously identified additional mechanisms rendering B. anthracis anthrose negative. In this work, several new ant - B. anthracis strains are identified and the impact of anthrose negativity on spore physiology is investigated. We demonstrate that live-attenuated Sterne vaccines as well as culture filtrate anthrax vaccines generate antibodies targeting non-protein components of the spore. The role of anthrose as a vegetative B. anthracis Sterne signaling molecule is implicated by luminescent expression strain assays, RNA-seq experiments, and toxin secretion analysis by western blot. Pure anthrose and the sporulation-inducing nucleoside analogue decoyinine had similar effects on toxin expression. Co-culture experiments demonstrated gene expression changes in B. anthracis depend on intracellular anthrose status (cis) in addition to anthrose status of extracellular interactions (trans). These findings provide a mechanism for how a unique spore-specific sugar residue affects physiology, expression and genetics of vegetative B. anthracis with impacts on the ecology, pathogenesis, and vaccinology of anthrax.

A multi-reference poly-conformational method for in silico design, optimization, and repositioning of pharmaceutical compounds illustrated for selected SARS-CoV-2 ligands.

Background: This work presents a novel computational multi-reference poly-conformational algorithm for design, optimization, and repositioning of pharmaceutical compounds. Methods: The algorithm searches for candidates by comparing similarities between conformers of the same compound and identifies target compounds, whose conformers are collectively close to the conformers of each compound in the reference set. Reference compounds may possess highly variable MoAs, which directly, and simultaneously, shape the properties of target candidate compounds. Results: The algorithm functionality has been case study validated in silico, by scoring ChEMBL drugs against FDA-approved reference compounds that either have the highest predicted binding affinity to our chosen SARS-CoV-2 targets or are confirmed to be inhibiting such targets in-vivo. All our top scoring ChEMBL compounds also turned out to be either high-affinity ligands to the chosen targets (as confirmed in separate studies) or show significant efficacy, in-vivo, against those selected targets. In addition to method case study validation, in silico search for new compounds within two virtual libraries from the Enamine database is presented. The library's virtual compounds have been compared to the same set of reference drugs that we used for case study validation: Olaparib, Tadalafil, Ergotamine and Remdesivir. The large reference set of four potential SARS-CoV-2 compounds has been selected, since no drug has been identified to be 100% effective against the virus so far, possibly because each candidate drug was targeting only one, particular MoA. The goal here was to introduce a new methodology for identifying potential candidate(s) that cover multiple MoA-s presented within a set of reference compounds.

Automatic scoring of COVID-19 severity in X-ray imaging based on a novel deep learning workflow.

In this study, we propose a two-stage workflow used for the segmentation and scoring of lung diseases. The workflow inherits quantification, qualification, and visual assessment of lung diseases on X-ray images estimated by radiologists and clinicians. It requires the fulfillment of two core stages devoted to lung and disease segmentation as well as an additional post-processing stage devoted to scoring. The latter integrated block is utilized, mainly, for the estimation of segment scores and computes the overall severity score of a patient. The models of the proposed workflow were trained and tested on four publicly available X-ray datasets of COVID-19 patients and two X-ray datasets of patients with no pulmonary pathology. Based on a combined dataset consisting of 580 COVID-19 patients and 784 patients with no disorders, our best-performing algorithm is based on a combination of DeepLabV3 + , for lung segmentation, and MA-Net, for disease segmentation. The proposed algorithms' mean absolute error (MAE) of 0.30 is significantly reduced in comparison to established COVID-19 algorithms; BS-net and COVID-Net-S, possessing MAEs of 2.52 and 1.83 respectively. Moreover, the proposed two-stage workflow was not only more accurate but also computationally efficient, it was approximately 11 times faster than the mentioned methods. In summary, we proposed an accurate, time-efficient, and versatile approach for segmentation and scoring of lung diseases illustrated for COVID-19 and with broader future applications for pneumonia, tuberculosis, pneumothorax, amongst others.

Excess mortality in Belarus during the COVID-19 pandemic as the case study of a country with limited non-pharmaceutical interventions and limited reporting.

Public health intervention to contain the ongoing COVID-19 pandemic significantly differed by country since the SARS-CoV-2 spread varied regionally in time and in scale. Since vaccinations were not available until the end of 2020 non-pharmaceutical interventions (NPIs) remained the only strategies to mitigate the pandemic spread at that time. Belarus in Europe is one of a few countries with a high Human Development Index where no lockdowns have ever been implemented and only limited NPIs have taken place for a period of time. Therefore, the Belarusian case was evaluated and compared in terms of the mortality burden. Since the COVID-19 mortality was low, the excess overall mortality was studied for Belarus. Since no overall mortality data have been reported past June 2020 the analysis was complemented by the study of Google Trends funeral-related search queries up until August 2021. Depending on the model, the Belarusian mortality for June of 2020 was 29 to 39% higher than otherwise expected with the corresponding estimated excess death was from 2953 to 3690 while the reported COVID-19 mortality for June 2020 was only 157 cases. The Belarusian excess mortality for June 2020 was higher than for all neighboring countries with an excess of 5% for Poland, 5% for Ukraine, 8% for Russia, 11% for Lithuania and 11% for Latvia. The relationship between Google Trends and mortality time series was studied using Granger's test and the results were statistically significant. The results for Google Trends searches did vary by key phrase with the largest excess of 138% for April 2020 and 148% for September 2020 was observed for a key phrase "coffin", while the largest excess of 218% for January 2021 was observed for "funeral services". In summary, there are indications of the excess overall mortality in Belarus, which is larger than the reported COVID-19-related mortality.

Indirect supervision applied to COVID-19 and pneumonia classification.

The novel coronavirus 19 (COVID-19) continues to have a devastating effect around the globe, leading many scientists and clinicians to actively seek to develop new techniques to assist with the tackling of this disease. Modern machine learning methods have shown promise in their adoption to assist the healthcare industry through their data and analytics-driven decision making, inspiring researchers to develop new angles to fight the virus. In this paper, we aim to develop a CNN-based method for the detection of COVID-19 by utilizing patients' chest X-ray images. Developing upon the inclusion of convolutional units, the proposed method makes use of indirect supervision based on Grad-CAM. This technique is used in the training process where Grad-CAM's attention heatmaps support the network's predictions. Despite recent progress, scarcity of data has thus far limited the development of a robust solution. We extend upon existing work by combining publicly available data across 5 different sources and carefully annotate the comprising images across three categories: normal, pneumonia, and COVID-19. To achieve a high classification accuracy, we propose a training pipeline based on indirect supervision of traditional classification networks, where the guidance is directed by an external algorithm. With this method, we observed that the widely used, standard networks can achieve an accuracy comparable to tailor-made models, specifically for COVID-19, with one network in particular, VGG-16, outperforming the best of the tailor-made models.

Novel Efficient Multistage Lead Optimization Pipeline Experimentally Validated for DYRK1B Selective Inhibitors.

In addition to general challenges in drug discovery such as the identification of lead compounds in time- and cost-effective ways, specific challenges also exist. Particularly, it is necessary to develop pharmacological inhibitors that effectively discriminate between closely related molecular targets. DYRK1B kinase is considered a valuable target for cancer-specific mono- or combination chemotherapy; however, the inhibition of its closely related DYRK1A kinase is not beneficial. Existing inhibitors target both kinases with essentially the same efficiency, and the unavailability of the DYRK1B crystal structure makes the discovery of DYRK1B-specific inhibitors even more challenging. Here, we propose a novel multi-stage compound discovery pipeline aimed at in silico identification of both potent and selective small molecules from a large set of initial candidates. The method uses structure-based docking and ligand-based quantitative structure-activity relationship modeling. This approach allowed us to identify lead and runner-up small-molecule compounds targeting DYRK1B with high efficiency and specificity.

An investigation of spatial-temporal patterns and predictions of the coronavirus 2019 pandemic in Colombia, 2020-2021.

Colombia announced the first case of severe acute respiratory syndrome coronavirus 2 on March 6, 2020. Since then, the country has reported a total of 5,002,387 cases and 127,258 deaths as of October 31, 2021. The aggressive transmission dynamics of SARS-CoV-2 motivate an investigation of COVID-19 at the national and regional levels in Colombia. We utilize the case incidence and mortality data to estimate the transmission potential and generate short-term forecasts of the COVID-19 pandemic to inform the public health policies using previously validated mathematical models. The analysis is augmented by the examination of geographic heterogeneity of COVID-19 at the departmental level along with the investigation of mobility and social media trends. Overall, the national and regional reproduction numbers show sustained disease transmission during the early phase of the pandemic, exhibiting sub-exponential growth dynamics. Whereas the most recent estimates of reproduction number indicate disease containment, with Rt<1.0 as of October 31, 2021. On the forecasting front, the sub-epidemic model performs best at capturing the 30-day ahead COVID-19 trajectory compared to the Richards and generalized logistic growth model. Nevertheless, the spatial variability in the incidence rate patterns across different departments can be grouped into four distinct clusters. As the case incidence surged in July 2020, an increase in mobility patterns was also observed. On the contrary, a spike in the number of tweets indicating the stay-at-home orders was observed in November 2020 when the case incidence had already plateaued, indicating the pandemic fatigue in the country.

Investigating the first stage of the COVID-19 pandemic in Ukraine using epidemiological and genomic data.

The novel coronavirus SARS-CoV-2 was first detected in China in December 2019 and has rapidly spread around the globe. The World Health Organization declared COVID-19 a pandemic in March 2020 just three months after the introduction of the virus. Individual nations have implemented and enforced a variety of social distancing interventions to slow the virus spread, that had different degrees of success. Understanding the role of non-pharmaceutical interventions (NPIs) on COVID-19 transmission in different settings is highly important. While most such studies have focused on China, neighboring Asian counties, Western Europe, and North America, there is a scarcity of studies for Eastern Europe. The aim of this epidemiological study is to fill this gap by analyzing the characteristics of the first months of the epidemic in Ukraine using agent-based modelling and phylodynamics. Specifically, first we studied the dynamics of COVID-19 incidence and mortality and explored the impact of epidemic NPIs. Our stochastic model suggests, that even a small delay of weeks could have increased the number of cases by up to 50%, with the potential to overwhelm hospital systems. Second, the genomic data analysis suggests that there have been multiple introductions of SARS-CoV-2 into Ukraine during the early stages of the epidemic. Our findings support the conclusion that the implemented travel restrictions may have had limited impact on the epidemic spread. Third, the basic reproduction number for the epidemic that has been estimated independently from case counts data and from genomic data suggest sustained intra-country transmissions.

Investigating the first stage of the COVID-19 pandemic in Ukraine using epidemiological and genomic data.

The novel coronavirus SARS-CoV-2 was first detected in China in December 2019 and has rapidly spread around the globe. The World Health Organization declared COVID-19 a pandemic in March 2020 just three months after the introduction of the virus. Individual nations have implemented and enforced a variety of social distancing interventions to slow the virus spread, that had different degrees of success. Understanding the role of non-pharmaceutical interventions (NPIs) on COVID-19 transmission in different settings is highly important. While most such studies have focused on China, neighboring Asian counties, Western Europe, and North America, there is a scarcity of studies for Eastern Europe. The aim of this study is to contribute to filling this gap by analyzing the characteristics of the first months of the epidemic in Ukraine using agent-based modelling and phylodynamics. Specifically, first we studied the dynamics of COVID-19 incidence and mortality and explored the impact of epidemic NPIs. Our stochastic model suggests, that even a small delay of weeks could have increased the number of cases by up to 50%, with the potential to overwhelm hospital systems. Second, the genomic data analysis suggests that there have been multiple introductions of SARS-CoV-2 into Ukraine during the early stages of the epidemic. Our findings support the conclusion that the implemented travel restrictions may have had limited impact on the epidemic spread. Third, the basic reproduction number for the epidemic that has been estimated independently from case counts data and from genomic data suggest sustained intra-country transmissions.