Forecasting the impact of the containment measures for COVID-19 in France, Italy, and Spain (original) (raw)
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Estimation of COVID-19 prevalence in Italy, Spain, and France
Science of The Total Environment, 2020
Europe has become the epicentre of the virus and hit the continent harder than China. • The apparent mortality rate of COVID-19 is approximately 13% in Italy, 11% in Spain, and 15% in France. • Time series models are significant in predicting the prevalence of the COVID-19 pandemic. • ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) were chosen as the best models for Italy, Spain, and France, respectively.
A cross-country comparison of Covid-19 containment measures and their effects on the epidemic curves
BMC Public Health
Background European countries are still searching to eliminate or contain the Covid-19 pandemic. A variety of approaches have achieved different levels of success in limiting the spread of the disease early and preventing avoidable deaths. Governmental policy responses may explain these differences and this study aims to describe evidence about the effectiveness of containment measures throughout the course of the pandemic in five European countries (France, Germany, Italy, Spain and the UK). Methods The research approach adopted consisted of three steps: 1) Build a Containment Index (C.I.) that considers nine parameters to make an assessment on the strength of measures; 2) Develop dynamic epidemiological models for forecasting purposes; 3) Predict case numbers by assuming containment measures remain constant for a period of 30 days. Results Our analysis revealed that in the five European countries we compared, the use of different approaches definitively affected the effectiveness ...
Biostatistics & Epidemiology, 2021
The novel coronavirus SARS-CoV-2 was first identified in China in December 2019. In just over five months, the virus affected over 4 million people and caused about 300,000 deaths. This study aimed to model new COVID-19 cases in Italy using a new curve. A new empirical curve is proposed to model the number of new cases of COVID-19. It resembles a known exponential growth curve which has a straight line as an exponent, but in the growth curve proposed, the exponent is a logistic curve multiplied for a straight line. This curve shows an initial phase, the expected exponential growth; then rises to the maximum value and finally reaches zero. We characterized the epidemic growth patterns for the entire Italian nation and for each of the 20 Italian regions. The estimated growth curve has been used to calculate the expected time of the beginning, the time related to peak, and the end of the epidemics. Our analysis explores the development of the epidemics in Italy and the impact of the containment measures. Data obtained are useful to forecast future scenario and the possible end of the outbreak.
medRxiv (Cold Spring Harbor Laboratory), 2020
Predictions about the further development of the Corona pandemic are of great public interest but many approaches demand a large number of country specific parameters and are not easily transferable. A special interest of simulations on the pandemic is to trace the effect of politics for reducing the virus spread, since these measures have had an enormous impact on economy and daily life. Here a simple yet powerful algorithm is introduced for fitting the infection numbers by simple analytic functions. This way, the increase of the case numbers in periods with different regulations can be distinguished, and by extrapolating the fit functions, a forecast for the maximum numbers and time scales are possible. The effect of the restraints such as lock down are demonstrated by comparing the resulting infection history with the likely unconstrained virus spread, and it is shown that a delay of 1weeks before imposing measures aiming at social distancing could have led to a complete infection of the respective populations. The approach is simply transferable to many different states. Here data from six E.U. countries, the UK, Russia, two Asian countries, the USA and ten states inside the USA with significant case numbers are analyzed, and striking qualitative similarities are found.
Nowcasting COVID‐19 incidence indicators during the Italian first outbreak
Statistics in Medicine
A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided; this ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameters estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replicate the results are provided. COVID-19, Growth curves, Richards' equation, SARS-CoV-2, GLM
2020
We have recently introduced two novel mathematical models for characterizing the dynamics of the cumulative number of individuals in a given country reported to be infected with COVID-19. Here we show that these models can also be used for determining the time-evolution of the associated number of deaths. In particular, using data up to around the time that the rate of deaths reaches a maximum, these models provide estimates for the time that a plateau will be reached signifying that the epidemic is approaching its end, as well as for the cumulative number of deaths at that time. The plateau is defined to occur when the rate of deaths is 5% of the maximum rate. Results are presented for South Korea, Italy, Spain, France, UK, Germany, and USA. The number of COVID-19 deaths in other counties can be analyzed similarly.
Background an objective: In March 2020 the SARS-CoV-2 outbreak has been declared as global pandemic. Most countries have implemented numerous social distancing measures in order to limit its transmission and control the outbreak. This study aims to describe the impact of these control measures on the spread of the disease for Italy and Germany, forecast the epidemic trend of COVID-19 in both countries and estimate the medical capacity requirements in terms of hospital beds and intensive care units (ICUs) for optimal clinical treatment of severe and critical COVID-19 patients, for the Germany health system. Methods: We used an exponential decline function to model the trajectory of the daily growth rate of infections in Italy and Germany. A linear regression of the logarithmic growth rate functions of different stages allowed to describe the impact of the social distancing measures leading to a faster decline of the growth rate in both countries. We used the linear model to predict t...
Bangladesh Journal of Medical Science, 2020
Objective: The coronavirus, which originated in Wuhan, causing the disease called COVID-19, spread more than 200 countries and continents end of the March. In this study, it was aimed to model the outbreak with different time series models and also predict the indicators. Materials and Methods: The data was collected from 25 countries which have different process at least 20 days. ARIMA(p,d,q), Simple Exponential Smoothing, Holt’s Two Parameter, Brown’s Double Exponential Smoothing Models were used. The prediction and forecasting values were obtained for the countries. Trends and seasonal effects were also evaluated. Results and Discussion: China has almost under control according to forecasting. The cumulative death prevalence in Italy and Spain will be the highest, followed by the Netherlands, France, England, China, Denmark, Belgium, Brazil and Sweden respectively as of the first week of April. The highest daily case prevalence was observed in Belgium, America, Canada, Poland, Ir...
arXiv: Applications, 2020
Italy has been one of the first countries timewise strongly impacted by the COVID-19 pandemic. The adoption of social distancing and heavy lockdown measures is posing a heavy burden on the population and the economy. The timing of the measures has crucial policy-making implications. Using publicly available data for the pandemic progression in Italy, we quantitatively assess the effect of the intervention time on the pandemic expansion, with a methodology that combines a generalized susceptible-exposed-infectious-recovered (SEIR) model together with statistical learning methods. The modeling shows that the lockdown has strongly deviated the pandemic trajectory in Italy. However, the difference between the forecasts and real data up to 20 April 2020 can be explained only by the existence of a time lag between the actual issuance date and the full effect of the measures. To understand the relative importance of intervention with respect to other factors, a thorough uncertainty quantif...
An Empirical Inference of the Severity of Resurgence of COVID-19 in Europe
2020
In Europe the Corona Virus spread had started to retard months ago, but after some time it has started to accelerate again. In this article, we are going to analyze the current COVID-19 spread patterns in Italy, the UK, Germany, Russia, Spain and France. We have found that the current spread has perhaps been underestimated as just the second wave. As per our analysis, as on 7 October the resurgence is much more vigorous than the first wave of spread of the disease. It is going to be most serious in Russia, followed by Italy, Germany and the UK, while in Spain and France the patterns are yet to take inferable shapes.