Search Results (3)

Lyme borreliosis (LB) is not notifiable in many European countries, and accurate data on the incidence are often lacking. This study aimed to determine the seroprevalence of Borrelia burgdorferi sensu lato (s.l.)-specific antibodies in the general population of The Netherlands, and to determine risk factors associated with seropositivity. Sera and questionnaires were obtained from participants (n = 5592, aged 0–88 years) enrolled in a nationwide serosurveillance study. The sera were tested for B. burgdorferi s.l.-specific IgM and IgG antibodies using ELISA and immunoblot. Seroprevalence was estimated controlling for the survey design. Risk factors for seropositivity were analyzed using a generalized linear mixed-effect model. In 2016/2017, the seroprevalence in The Netherlands was 4.4% (95% CI 3.5–5.2). Estimates were higher in men (5.7% [95% CI 4.4–7.2]) than in women (3.1% [95% CI 2.0–4.0]), and increased with age from 2.6% (95% CI 1.4–4.4) in children to 7.7% (95% CI 5.9–7.9) in 60- to 88-year-olds. The seroprevalence for B. burgdorferi s.l. in the general population in The Netherlands was comparable to rates reported in European countries. The main risk factors for seropositivity were increasing age, being male and the tick bite frequency. The dynamics of LB infection are complex and involve variables from various disciplines. This could be further elucidated using infectious disease modelling. Full article

Estimation of COVID-19 dynamics and its evolution is a multidisciplinary effort, which requires the unification of heterogeneous disciplines (scientific, mathematics, epidemiological, biological/bio-chemical, virologists and health disciplines to mention the most relevant) to work together towards a better understanding of this pandemic. Time series analysis is of great importance to determine both the similarity in the behavior of COVID-19 in certain countries/states and the establishment of models that can analyze and predict the transmission process of this infectious disease. In this contribution, an analysis of the different states of the United States will be carried out to measure the similarity of COVID-19 time series, using dynamic time warping distance (DTW) as a distance metric. A parametric methodology is proposed to jointly analyze infected and deceased persons. This metric allows comparison of time series that have a different time length, making it very appropriate for studying the United States, since the virus did not spread simultaneously in all the states/provinces. After a measure of the similarity between the time series of the states of United States was determined, a hierarchical cluster was created, which makes it possible to analyze the behavioral relationships of the pandemic between different states and to discover interesting patterns and correlations in the underlying data of COVID-19 in the United States. With the proposed methodology, nine different clusters were obtained, showing a different behavior in the eastern zone and western zone of the United States. Finally, to make a prediction of the evolution of COVID-19 in the states, Logistic, Gompertz and SIR models were computed. With these mathematical models, it is possible to have a more precise knowledge of the evolution and forecast of the pandemic. Full article

The outbreak of coronavirus disease 2019 (COVID-19) has become a global public health crisis due to its high contagious characteristics. In this article, we propose a new epidemic-dynamics model combining the transmission characteristics of COVID-19 and then use the reported epidemic data from 15 February to 30 June to simulate the spread of the Italian epidemic. Numerical simulations showed that (1) there was a remarkable amount of asymptomatic individuals; (2) the lockdown measures implemented by Italy effectively controlled the spread of the outbreak; (3) the Italian epidemic has been effectively controlled, but SARS-CoV-2 will still exist for a long time; and (4) the intervention of the government is an important factor that affects the spread of the epidemic. Full article