The impact of public health interventions in the Nordic countries during the first year of SARS-CoV-2 transmission and evolution

The landscape of the COVID-19 pandemic in Poland emerging from epidemiological and genomic data

The COVID-19 pandemic has profoundly affected all aspects of our lives. Through real-time monitoring and rapid vaccine implementation, we succeeded in suppressing the spread of the disease and mitigating its consequences. Finally, conclusions can be summarized and drawn. Here, we use the example of Poland, which was seriously affected by the pandemic. Compared to other countries, Poland has not achieved impressive results in either testing or vaccination, which may explain its high mortality (case fatality rate, CFR 1.94%). Through retrospective analysis of data collected by the COVID-19 Data Portal Poland, we found significant regional differences in the number of tests performed, number of cases detected, number of COVID-19-related deaths, and vaccination rates. The Masovian, Greater Poland, and Pomeranian voivodeships, the country's leaders in vaccination, reported high case numbers but low death rates. In contrast, the voivodeships in the eastern and southern parts of Poland (Subcarpathian, Podlaskie, Lublin, Opole), which documented low vaccination levels and low case numbers, had higher COVID-19-related mortality rates. The strong negative correlation between the CFR and the percentage of the population that was vaccinated in Poland supports the validity of vaccination. To gain insight into virus evolution, we sequenced more than 500 genomes and analyzed nearly 80 thousand SARS-CoV-2 genome sequences deposited in GISAID by Polish diagnostic centers. We showed that the SARS-CoV-2 variant distribution over time in Poland reflected that in Europe. Haplotype network analysis allowed us to follow the virus transmission routes and identify potential superspreaders in each pandemic wave.

The influence of cross-border mobility on the COVID-19 epidemic in Nordic countries

Restrictions of cross-border mobility are typically used to prevent an emerging disease from entering a country in order to slow down its spread. However, such interventions can come with a significant societal cost and should thus be based on careful analysis and quantitative understanding on their effects. To this end, we model the influence of cross-border mobility on the spread of COVID-19 during 2020 in the neighbouring Nordic countries of Denmark, Finland, Norway and Sweden. We investigate the immediate impact of cross-border travel on disease spread and employ counterfactual scenarios to explore the cumulative effects of introducing additional infected individuals into a population during the ongoing epidemic. Our results indicate that the effect of inter-country mobility on epidemic growth is non-negligible essentially when there is sizeable mobility from a high prevalence country or countries to a low prevalence one. Our findings underscore the critical importance of accurate data and models on both epidemic progression and travel patterns in informing decisions related to inter-country mobility restrictions.

Risk factors identification of COVID-19 patients with chronic obstructive pulmonary disease: A retrospective study in Punjab-Pakistan

BACKGROUND

Accessibility to the immense collection of studies on noncommunicable diseases related to coronavirus disease of 2019 (COVID-19) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an immediate focus of researchers. However, there is a scarcity of information about chronic obstructed pulmonary disease (COPD), which is associated with a high rate of infection in COVID-19 patients. Moreover, by combining the effects of the SARS-CoV-2 on COPD patients, we may be able to overcome formidable obstacles factors, and diagnosis influencers.

MATERIALS AND METHODS

A retrospective study of 280 patients was conducted at DHQ Hospital Muzaffargarh in Punjab, Pakistan. Negative binomial regression describes the risk of fixed successive variables. The association is described by the Cox proportional hazard model and the model coefficient is determined through log-likelihood observation. Patients with COPD had their survival and mortality plotted on Kaplan-Meier curves.

RESULTS

The increased risk of death in COPD patients was due to the effects of variables such as cough, lower respiratory tract infection (LRTI), tuberculosis (TB), and body-aches being 1.369, 0.693, 0.170, and 0.217 times higher at (95% confidence interval [CI]: 0.747-1.992), (95% CI: 0.231-1.156), (95% CI: 0.008-0.332), and (95% CI: -0.07 to 0.440) while it decreased 0.396 in normal condition.

CONCLUSION

We found that the symptoms of COPD (cough, LRTI, TB, and bodyaches) are statistically significant in patients who were most infected by SARS-CoV-2.

The importance of utilizing travel history metadata for informative phylogeographical inferences: a case study of early SARS-CoV-2 introductions into Australia

Inferring the spatiotemporal spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via Bayesian phylogeography has been complicated by the overwhelming sampling bias present in the global genomic dataset. Previous work has demonstrated the utility of metadata in addressing this bias. Specifically, the inclusion of recent travel history of SARS-CoV-2-positive individuals into extended phylogeographical models has demonstrated increased accuracy of estimates, along with proposing alternative hypotheses that were not apparent using only genomic and geographical data. However, as the availability of comprehensive epidemiological metadata is limited, many of the current estimates rely on sequence data and basic metadata (i.e. sample date and location). As the bias within the SARS-CoV-2 sequence dataset is extensive, the degree to which we can rely on results drawn from standard phylogeographical models (i.e. discrete trait analysis) that lack integrated metadata is of great concern. This is particularly important when estimates influence and inform public health policy. We compared results generated from the same dataset, using two discrete phylogeographical models: one including travel history metadata and one without. We utilized sequences from Victoria, Australia, in this case study for two unique properties. Firstly, the high proportion of cases sequenced throughout 2020 within Victoria and the rest of Australia. Secondly, individual travel history was collected from returning travellers in Victoria during the first wave (January to May) of the coronavirus disease 2019 (COVID-19) pandemic. We found that the implementation of individual travel history was essential for the estimation of SARS-CoV-2 movement via discrete phylogeography models. Without the additional information provided by the travel history metadata, the discrete trait analysis could not be fit to the data due to numerical instability. We also suggest that during the first wave of the COVID-19 pandemic in Australia, the primary driving force behind the spread of SARS-CoV-2 was viral importation from international locations. This case study demonstrates the necessity of robust genomic datasets supplemented with epidemiological metadata for generating accurate estimates from phylogeographical models in datasets that have significant sampling bias. For future work, we recommend the collection of metadata in conjunction with genomic data. Furthermore, we highlight the risk of applying phylogeographical models to biased datasets without incorporating appropriate metadata, especially when estimates influence public health policy decision making.

A scalable analytical approach from bacterial genomes to epidemiology

Recent years have seen a remarkable increase in the practicality of sequencing whole genomes from large numbers of bacterial isolates. The availability of this data has huge potential to deliver new insights into the evolution and epidemiology of bacterial pathogens, but the scalability of the analytical methodology has been lagging behind that of the sequencing technology. Here we present a step-by-step approach for such large-scale genomic epidemiology analyses, from bacterial genomes to epidemiological interpretations. A central component of this approach is the dated phylogeny, which is a phylogenetic tree with branch lengths measured in units of time. The construction of dated phylogenies from bacterial genomic data needs to account for the disruptive effect of recombination on phylogenetic relationships, and we describe how this can be achieved. Dated phylogenies can then be used to perform fine-scale or large-scale epidemiological analyses, depending on the proportion of cases for which genomes are available. A key feature of this approach is computational scalability and in particular the ability to process hundreds or thousands of genomes within a matter of hours. This is a clear advantage of the step-by-step approach described here. We discuss other advantages and disadvantages of the approach, as well as potential improvements and avenues for future research. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.

Non-pharmaceutical interventions in response to the COVID-19 pandemic in 30 European countries: the ECDC-JRC Response Measures Database

In response to the COVID-19 pandemic, the European Union/European Economic Area (EU/EEA) countries implemented a wide set of non-pharmaceutical interventions (NPIs), sometimes with limited knowledge on their effect and impact on population. The European Centre for Disease Prevention and Control (ECDC) and the European Commission's Joint Research Centre (JRC) developed a Response Measures Database (ECDC-JRC RMD) to archive NPIs in 30 EU/EEA countries from 1 January 2020 to 30 September 2022. We aimed to introduce a tool for the wider scientific community to assess COVID-19 NPIs effect and impact in the EU/EEA. We give an overview of the ECDC-JRC RMD rationale and structure, including a brief analysis of the main NPIs applied in 2020, before the roll-out of the COVID-19 vaccination campaigns. The ECDC-JRC RMD organises NPIs through a three-level hierarchical structure and uses four additional parameters ('status', 'implementation', 'target group' and 'geographical representation') to provide further information on the implementation of each measure. Features including the ready-for-analysis, downloadable format and its agile taxonomy and structure highlight the potential of the ECDC-JRC RMD to facilitate further NPI analysis and optimise decision making on public health response policies.

Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications

Phylodynamics requires an interdisciplinary understanding of phylogenetics, epidemiology, and statistical inference. It has also experienced more intense application than ever before amid the SARS-CoV-2 pandemic. In light of this, we present a review of phylodynamic models beginning with foundational models and assumptions. Our target audience is public health researchers, epidemiologists, and biologists seeking a working knowledge of the links between epidemiology, evolutionary models, and resulting epidemiological inference. We discuss the assumptions linking evolutionary models of pathogen population size to epidemiological models of the infected population size. We then describe statistical inference for phylodynamic models and list how output parameters can be rearranged for epidemiological interpretation. We go on to cover more sophisticated models and finish by highlighting future directions.

Phylodynamic Dispersal of SARS-CoV-2 Lineages Circulating across Polish-German Border Provinces

Introduction: The emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has evolved into a worldwide outbreak, with significant molecular evolution over time. Large-scale phylodynamic studies allow to map the virus spread and inform preventive strategies. Aim: This study investigates the extent of binational dispersal and dynamics of SARS-CoV-2 lineages between seven border provinces of the adjacent countries of Poland and Germany to reconstruct SARS-CoV-2 transmission networks. Methods: Following three pandemic waves from March 2020 to the end of May 2021, we analysed a dataset of 19,994 sequences divided into B.1.1.7|Alpha and non-Alpha lineage groups. We performed phylogeographic analyses using the discrete diffusion models to identify the pathways of virus spread. Results: Based on population dynamics inferences, in total, 673 lineage introductions (95% HPD interval 641−712) for non-Alpha and 618 (95% HPD interval 599−639) for B.1.1.7|Alpha were identified in the area. For non-Alpha lineages, 5.05% binational, 86.63% exclusively German, and 8.32% Polish clusters were found, with a higher frequency of international clustering observed for B.1.1.7|Alpha (13.11% for binational, 68.44% German and 18.45% Polish, p < 0.001). We identified key transmission hubs for the analysed lineages, namely Saxony, West Pomerania and Lower Silesia. Conclusions: Clustering patterns between Poland and Germany reflect the viral variant transmission dynamics at the international level in the borderline area. Tracing the spread of the virus between two adjacent large European countries may provide a basis for future intervention policies in cross-border cooperation efforts against the spread of the pandemics.

Assessment of COVID-19 intervention strategies in the Nordic countries using genomic epidemiology

We explored how the duration, size and number of virus transmission clusters, defined as country-specific monophyletic groups in a SARS-CoV-2 phylogenetic tree, differed between the Nordic countries of Norway, Sweden, Denmark, Finland and Iceland. Our results suggest that although geographical connectivity, population density and openness influence the spread and the size of SARS-CoV-2 transmission clusters, the differing country-specific intervention strategies had the largest impact. We also found a significant positive association between the size and duration of transmission clusters in the Nordic countries, suggesting that the rapid deployment of contact tracing is a key response measure in reducing virus transmission.