The late 2019 COVID-19 outbreak has put the health systems of many countries to the limit of their capacity. The most affected European countries are, so far, Italy and Spain. In both countries (and ...others), the authorities decreed a lockdown, with local specificities. The objective of this work is to evaluate the impact of the measures undertaken in Spain to deal with the pandemic.
We estimated the number of cases and the impact of lockdown on the reproducibility number based on the hospitalization reports up to April 15th 2020.
The estimated number of cases shows a sharp increase until the lockdown, followed by a slowing down and then a decrease after full quarantine was implemented. Differences in the basic reproduction ratio are also significant, dropping from 5.89 (95% confidence interval 95%CI: 5.46-7.09) before the lockdown to 0.48 (95%CI: 0.15-1.17) afterwards.
Handling a pandemic like COVID-19 is complex and requires quick decision making. The large differences found in the speed of propagation of the disease show us that being able to implement interventions at the earliest stage is crucial to minimise the impact of a potential infectious threat. Our work also stresses the importance of reliable up to date epidemiological data in order to accurately assess the impact of Public Health policies on viral outbreak.
El brote de COVID-19 a finales de 2019ha puesto los sistemas de salud de muchos países al límite de su capacidad. Los países europeos más afectados son, hasta ahora, Italia y España. En ambos (y en otros países), las autoridades decretaron un confinamiento, con especificidades locales. El objetivo de este trabajo es evaluar el impacto de las medidas adoptadas en España para hacer frente a la pandemia.
Estimamos el número de casos y el impacto del confinamiento en el número básico de reproducción según los informes de hospitalización hasta el 15 de abril de 2020.
El número estimado de casos muestra un fuerte aumento hasta el bloqueo, seguido de una desaceleración y luego una disminución tras la implementación del confinamiento total. Las diferencias en el número básico de reproducción también son muy significativas, cayendo de 5,89 (intervalo de confianza del 95% IC95%: 5,46-7,09) antes del bloqueo a 0,48 (IC95%: 0,15-1,17) después.
Gestionar una pandemia como la de COVID-19 es muy complejo y requiere una rápida toma de decisiones. Las grandes diferencias encontradas en la velocidad de propagación de la enfermedad muestran que poder implementar intervenciones en la etapa más temprana es crucial para minimizar el impacto de una potencial amenaza. Nuestro trabajo también indica la importancia de contar con datos epidemiológicos actualizados y confiables para evaluar con precisión el impacto de las políticas de salud pública en la pandemia.
Response to Giraudo, Ricceri and Rosso (2022) Moriña, David; Navarro, Albert
Communications in statistics. Simulation and computation,
06/2024, Letnik:
53, Številka:
6
Journal Article
Zero-inflated models are generally aimed to addressing the problem that arises from having two different sources that generate the zero values observed in a distribution. In practice, this is due to ...the fact that the population studied actually consists of two subpopulations: one in which the value zero is by default (structural zero) and the other is circumstantial (sample zero).
This work proposes a new methodology to fit zero inflated Bernoulli data from a Bayesian approach, able to distinguish between two potential sources of zeros (structural and non-structural).
The proposed methodology performance has been evaluated through a comprehensive simulation study, and it has been compiled as an R package freely available to the community. Its usage is illustrated by means of a real example from the field of occupational health as the phenomenon of sickness presenteeism, in which it is reasonable to think that some individuals will never be at risk of suffering it because they have not been sick in the period of study (structural zeros). Without separating structural and non-structural zeros one would be studying jointly the general health status and the presenteeism itself, and therefore obtaining potentially biased estimates as the phenomenon is being implicitly underestimated by diluting it into the general health status.
The proposed methodology is able to distinguish two different sources of zeros (structural and non-structural) from dichotomous data with or without covariates in a Bayesian framework, and has been made available to any interested researcher in the form of the bayesZIB R package ( https://cran.r-project.org/package=bayesZIB ).
We present an R package for the simulation of simple and complex survival data. It covers different situations, including recurrent events and multiple events. The main simulation routine allows the ...user to introduce an arbitrary number of distributions, each corresponding to a new event or episode, with its parameters, choosing between the Weibull (and exponential as a particular case), log-logistic and log-normal distributions.
The present paper introduces a new model used to study and analyse the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) epidemic-reported-data from Spain. This is a Hidden Markov Model ...whose hidden layer is a regeneration process with Poisson immigration, Po-INAR(1), together with a mechanism that allows the estimation of the under-reporting in non-stationary count time series. A novelty of the model is that the expectation of the unobserved process's innovations is a time-dependent function defined in such a way that information about the spread of an epidemic, as modelled through a Susceptible-Infectious-Removed dynamical system, is incorporated into the model. In addition, the parameter controlling the intensity of the under-reporting is also made to vary with time to adjust to possible seasonality or trend in the data. Maximum likelihood methods are used to estimate the parameters of the model.
The main goal of this work is to present a new model able to deal with potentially misreported continuous time series. The proposed model is able to handle the autocorrelation structure in continuous ...time series data, which might be partially or totally underreported or overreported. Its performance is illustrated through a comprehensive simulation study considering several autocorrelation structures and three real data applications on human papillomavirus incidence in Girona (Catalonia, Spain) and Covid-19 incidence in two regions with very different circumstances: the early days of the epidemic in the Chinese region of Heilongjiang and the most current data from Catalonia.
SARS-CoV-2 transmission within schools and its contribution to community transmission are still a matter of debate.
A retrospective cohort study in all public schools in Catalonia was conducted using ...publicly available data assessing the association between the number of reported SARS-CoV-2 cases among students and staff in weeks 1-2 (Sept 14-27th, 2020) of the academic year with school SARS-CoV-2 incidence among students in weeks 4-5. A multilevel Poisson regression model adjusted for the community incidence in the corresponding basic health area (BHA) and the type of school (primary or secondary), with random effects at the sanitary region and BHA levels, was performed.
A total of 2184 public schools opened on September 14th with 778,715 students. Multivariate analysis showed a significant association between the total number of SARS-CoV-2 cases in a centre in weeks 1-2 and the SARS-CoV-2 school incidence among students in weeks 4-5 (Risk Ratio (RR) 1.074, 95% CI 1.044-1.105, p-value <0.001). The adjusted BHA incidence in the first two weeks was associated with school incidence in weeks 4-5 (RR 1.002, 95% CI 1.002-1.003, p-value <0.001). Secondary schools showed an increased incidence in weeks 4 and 5 (RR primary vs secondary 1.709 95% CI 1.599-1.897, p-value <0.001).
Safety measures adopted by schools were not enough to stop related-to-school transmission in students and could be improved. The safest way to keep schools open is to reduce community transmission down to a minimum.
When dealing with recurrent events in observational studies it is common to include subjects who became at risk before follow-up. This phenomenon is known as left censoring, and simply ignoring these ...prior episodes can lead to biased and inefficient estimates. We aimed to propose a statistical method that performs well in this setting.
Our proposal was based on the use of models with specific baseline hazards. In this, the number of prior episodes were imputed when unknown and stratified according to whether the subject had been at risk of presenting the event before t = 0. A frailty term was also used. Two formulations were used for this "Specific Hazard Frailty Model Imputed" based on the "counting process" and "gap time." Performance was then examined in different scenarios through a comprehensive simulation study.
The proposed method performed well even when the percentage of subjects at risk before follow-up was very high. Biases were often below 10% and coverages were around 95%, being somewhat conservative. The gap time approach performed better with constant baseline hazards, whereas the counting process performed better with non-constant baseline hazards.
The use of common baseline methods is not advised when knowledge of prior episodes experienced by a participant is lacking. The approach in this study performed acceptably in most scenarios in which it was evaluated and should be considered an alternative in this context. It has been made freely available to interested researchers as R package miRecSurv.
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