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.

Social Distancing and SARS-CoV-2 Transmission Potential Early in the Epidemic in Finland.

BACKGROUND: Information about social mixing patterns under heavy social distancing is needed to model the impact of nonpharmaceutical interventions on SARS-CoV-2 transmission. METHODS: We conducted a survey on daily person-to-person contacts during the early phase of the SARS-CoV-2 epidemic in Finland, one month after strong social distancing measures had been introduced nationwide. We defined a contact as exchange of at least a few words in proximity of another person. We also considered physical ("skin-to-skin") contacts separately. Based on 3,171 reported contacts by 1,320 participants of 1-79 years of age, we estimated age-stratified contact matrices essential in modeling virus transmission. RESULTS: Compared with contacts during prepandemic conditions, as learned from the Finnish part of the Polymod study, there was a 72% (95% credible interval, CI = 71, 74) reduction in the daily number of all contacts and a 69% (95% CI = 66, 73) reduction in the daily number of physical contacts in April 2020. The largest reduction, of almost 90%, occurred in physical contacts by individuals more than 70 years of age. The estimated reduction in the transmission potential of the virus attributable solely to reduced contact frequencies varied between 59% (whole population; physical contacts; 95% CI = 52, 68) and 77% (over 20-year olds; physical contacts; 95% CI = 70, 89). CONCLUSIONS: We surmise that the large reduction in the daily numbers of social contacts in the early part of the SARS-CoV-2 epidemic in Finland was likely a major contributor to the steady decline of the epidemic in the country since early April.

Contrasting the impact of cytotoxic and cytostatic drug therapies on tumour progression.

A tumour grows when the total division (birth) rate of its cells exceeds their total mortality (death) rate. The capability for uncontrolled growth within the host tissue is acquired via the accumulation of driver mutations which enable the tumour to progress through various hallmarks of cancer. We present a mathematical model of the penultimate stage in such a progression. We assume the tumour has reached the limit of its present growth potential due to cell competition that either results in total birth rate reduction or death rate increase. The tumour can then progress to the final stage by either seeding a metastasis or acquiring a driver mutation. We influence the ensuing evolutionary dynamics by cytotoxic (increasing death rate) or cytostatic (decreasing birth rate) therapy while keeping the effect of the therapy on net growth reduction constant. Comparing the treatments head to head we derive conditions for choosing optimal therapy. We quantify how the choice and the related gain of optimal therapy depends on driver mutation, metastasis, intrinsic cell birth and death rates, and the details of cell competition. We show that detailed understanding of the cell population dynamics could be exploited in choosing the right mode of treatment with substantial therapy gains.

Revealing the True Incidence of Pandemic A(H1N1)pdm09 Influenza in Finland during the First Two Seasons - An Analysis Based on a Dynamic Transmission Model.

The threat of the new pandemic influenza A(H1N1)pdm09 imposed a heavy burden on the public health system in Finland in 2009-2010. An extensive vaccination campaign was set up in the middle of the first pandemic season. However, the true number of infected individuals remains uncertain as the surveillance missed a large portion of mild infections. We constructed a transmission model to simulate the spread of influenza in the Finnish population. We used the model to analyse the two first years (2009-2011) of A(H1N1)pdm09 in Finland. Using data from the national surveillance of influenza and data on close person-to-person (social) contacts in the population, we estimated that 6% (90% credible interval 5.1 - 6.7%) of the population was infected with A(H1N1)pdm09 in the first pandemic season (2009/2010) and an additional 3% (2.5 - 3.5%) in the second season (2010/2011). Vaccination had a substantial impact in mitigating the second season. The dynamic approach allowed us to discover how the proportion of detected cases changed over the course of the epidemic. The role of time-varying reproduction number, capturing the effects of weather and changes in behaviour, was important in shaping the epidemic.

Parallel independent evolution of pathogenicity within the genus Yersinia.

The genus Yersinia has been used as a model system to study pathogen evolution. Using whole-genome sequencing of all Yersinia species, we delineate the gene complement of the whole genus and define patterns of virulence evolution. Multiple distinct ecological specializations appear to have split pathogenic strains from environmental, nonpathogenic lineages. This split demonstrates that contrary to hypotheses that all pathogenic Yersinia species share a recent common pathogenic ancestor, they have evolved independently but followed parallel evolutionary paths in acquiring the same virulence determinants as well as becoming progressively more limited metabolically. Shared virulence determinants are limited to the virulence plasmid pYV and the attachment invasion locus ail. These acquisitions, together with genomic variations in metabolic pathways, have resulted in the parallel emergence of related pathogens displaying an increasingly specialized lifestyle with a spectrum of virulence potential, an emerging theme in the evolution of other important human pathogens.

The impact of host metapopulation structure on the population genetics of colonizing bacteria.

Many key bacterial pathogens are frequently carried asymptomatically, and the emergence and spread of these opportunistic pathogens can be driven, or mitigated, via demographic changes within the host population. These inter-host transmission dynamics combine with basic evolutionary parameters such as rates of mutation and recombination, population size and selection, to shape the genetic diversity within bacterial populations. Whilst many studies have focused on how molecular processes underpin bacterial population structure, the impact of host migration and the connectivity of the local populations has received far less attention. A stochastic neutral model incorporating heightened local transmission has been previously shown to fit closely with genetic data for several bacterial species. However, this model did not incorporate transmission limiting population stratification, nor the possibility of migration of strains between subpopulations, which we address here by presenting an extended model. We study the consequences of migration in terms of shared genetic variation and show by simulation that the previously used summary statistic, the allelic mismatch distribution, can be insensitive to even large changes in microepidemic and migration rates. Using likelihood-free inference with genotype network topological summaries we fit a simpler model to commensal and hospital samples from the common nosocomial pathogens Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecalis and Enterococcus faecium. Only the hospital data for E. faecium display clearly marked deviations from the model predictions which may be attributable to its adaptation to the hospital environment.

Efficacy and effectiveness of case isolation and quarantine during a growing phase of the COVID-19 epidemic in Finland.

Based on data collected as part of the contact tracing activity of the City of Helsinki Epidemiological Operations Unit, we evaluated the efficacy and effectiveness of isolating SARS-CoV-2 cases and quarantining their exposed contacts during a mildly growing phase of the COVID-19 epidemic in Finland in autumn 2020. Based on the observed symptom-to-symptom intervals in 1016 pairs of primary and secondary cases, we estimated that without case isolation or quarantine 40[Formula: see text] (90[Formula: see text] credible interval, CI 25-59) of transmission would have occurred on the day of or after symptom onset. One third of SARS-CoV-2 cases (N = 1521) had initially been quarantined, with a self-reported time until isolation (quarantine) of 0.8 days before symptom onset. This delay translates into an efficacy of 50[Formula: see text] (90[Formula: see text] CI 40-63) of averting secondary infections per quarantined case. Due to later isolation (mean 2.6 days after symptoms), the efficacy was smaller (24[Formula: see text]; 90[Formula: see text] CI 12-41) in those two third of the cases (N = 3101) whose isolation was prompted by their symptoms, i.e. without being previously quarantined. At the population level, we evaluated the effectiveness of case isolation and quarantine on the growth rate of the COVID-19 epidemic in the autumn of 2020. Under a wide range of underlying assumptions, the rate would have been at least 2 times higher without case isolation and quarantine. The numbers needed to isolate or quarantine to prevent one secondary case were 2 and 20, respectively.

Identifying Multiple Potential Metabolic Cycles in Time-Series from Biolog Experiments.

Biolog Phenotype Microarray (PM) is a technology allowing simultaneous screening of the metabolic behaviour of bacteria under a large number of different conditions. Bacteria may often undergo several cycles of metabolic activity during a Biolog experiment. We introduce a novel algorithm to identify these metabolic cycles in PM experimental data, thus increasing the potential of PM technology in microbiology. Our method is based on a statistical decomposition of the time-series measurements into a set of growth models. We show that the method is robust to measurement noise and captures accurately the biologically relevant signals from the data. Our implementation is made freely available as a part of an R package for PM data analysis and can be found at www.helsinki.fi/bsg/software/Biolog_Decomposition.

Novel R pipeline for analyzing Biolog Phenotypic MicroArray data.

Data produced by Biolog Phenotype MicroArrays are longitudinal measurements of cells' respiration on distinct substrates. We introduce a three-step pipeline to analyze phenotypic microarray data with novel procedures for grouping, normalization and effect identification. Grouping and normalization are standard problems in the analysis of phenotype microarrays defined as categorizing bacterial responses into active and non-active, and removing systematic errors from the experimental data, respectively. We expand existing solutions by introducing an important assumption that active and non-active bacteria manifest completely different metabolism and thus should be treated separately. Effect identification, in turn, provides new insights into detecting differing respiration patterns between experimental conditions, e.g. between different combinations of strains and temperatures, as not only the main effects but also their interactions can be evaluated. In the effect identification, the multilevel data are effectively processed by a hierarchical model in the Bayesian framework. The pipeline is tested on a data set of 12 phenotypic plates with bacterium Yersinia enterocolitica. Our pipeline is implemented in R language on the top of opm R package and is freely available for research purposes.