Modelling the evolution of COVID-19 in high-incidence European countries and regions: estimated number of infections and impact of past and future intervention measures

Abstract

A previously developed mechanistic model of COVID-19 transmission has been adapted and applied here to study the evolution of the disease and the effect of intervention measures in some European countries and territories where the disease had major impact. A clear impact of the major intervention measures on the reproduction number (R t ) has been found in all studied countries and territories, as already suggested by the drop in the number of deaths over time. Interestingly, the impact of such major intervention measures seems to be the same in most of these countries. The model has also provided realistic estimates of the total number of infections, active cases and future outcome. While the predictive capabilities of the model are much more uncertain before the peak of the outbreak, we could still reliably predict the evolution of the disease after a major intervention by assuming the afterwards reproduction number from current study. More challenging is to foresee the long-term impact of softer intervention measures, but this model can estimate the outcome of different scenarios and help planning changes in the implementation of control measures in a given country or region.

Chasing the ghost of infection past: identifying thresholds of change during the COVID-19 infection in Spain

“…Hence, an outreach strategy that increases awareness in the general public and disseminates good practice in personal hygiene may have significant effects on the spread of airborne diseases such as COVID-19. In the case of Spain, it seems fair to argue that during the second week of March (8)(9)(10)(11)(12)(13)(14)(15), the population responded swiftly to the voluntary prevention and distancing measures suggested by the authorities and the media [23] and these were effective to slow down the early spread of the pandemic. Interestingly, a similar slowdown of COVID-19 growth rates before the enforcement of the population lockdown was not observed in China [28], probably due to the initial lack of information about the potential for easy transmission of the disease.…”

“…An increasing number of countries was progressively affected, and they responded differently depending on the international (WHO) and local expert advice available at the moment, the structure and resources of their public health systems, their R&D capacity (which determined the number of PCRs available for testing contagions from swab samples, among other things) and their ability to implement social distancing measures [e.g. [6][7][8][9]. The diversity of policy responses, together with the pre-existing differences in spatial aggregation, social behaviour and age structure of their populations, provides a unique array of test cases to understand how different levels and combinations of preventive quarantine and socialdistancing measures affected the spread of the pandemic.…”

“…Luis Santamaría and Joaquín HortalMarch, broadly understood in the Spanish media as a reflection of the success of the social distancing measures, was actually caused by the combined impact of these two sets of previous events: the reduction in the number of infections starting on 2-3 March (phase 2), and the reduction in the number of deaths starting on 27 February (phase 1)underscoring the decoupling of cause and perception (see below). (3) A third phase showing a highly consistent pattern of decreases in the growth rates of both the number of cases and the number of fatalities at the three geographical extents (49-57% and 21-40% reductions for cases and deaths, respectively), corresponding to infections starting on[5][6][7][8] March. This cluster of breakpoints is the second most consistent and supported, coinciding closely in time for five out of six of the time series evaluated.…”

COVID-19 effective reproduction number dropped during Spain's nationwide dropdown, then spiked at lower-incidence regions

“… Gatto et al, 2020 ). These differences were partly due to their different responses to the WHO alert, based on the resources and structure of their health and R&D systems, and the ability to implement social distance measures ( Fernández-Recio, 2020 ; Dehning et al, 2020 ). But also to their international connectivity ( Coelho et al, 2020 ), the specific strains reaching each territory (see updated data at nextstrain; Hadfield et al, 2018 ) and the age and spatial structure of their human populations ( Gatto et al, 2020 ).…”

Prediction Models for COVID-19 Integrating Age Groups, Gender, and Underlying Conditions

“…Incorporating the government's interventions in a prediction model may potentially make it highly useful for accurate predictions. For example, [27] considered intervention measures to analyze the evolution of COVID-19 in European countries. The data from Spain, Italy, UK, Germany, and France were used to t the model for predicting infections and the infectionsto-deaths rates.…”