1,721,148 research outputs found
Combining and comparing regional SARS-CoV-2 epidemic dynamics in Italy: Bayesian meta-analysis of compartmental models and global sensitivity analysis
Full text linkDuring autumn 2020, Italy faced a second important SARS-CoV-2 epidemic wave. We explored the time pattern of the instantaneous reproductive number, R(0)(t), and estimated the prevalence of infections by region from August to December calibrating SIRD models on COVID-19-related deaths, fixing at values from literature Infection Fatality Rate (IFR) and average infection duration. A Global Sensitivity Analysis (GSA) was performed on the regional SIRD models. Then, we used Bayesian meta-analysis and meta-regression to combine and compare the regional results and investigate their heterogeneity. The meta-analytic R(0)(t) curves were similar in the Northern and Central regions, while a less peaked curve was estimated for the South. The maximum R(0)(t) ranged from 2.15 (South) to 2.61 (North) with an increase following school reopening and a decline at the end of October. The predictive performance of the regional models, assessed through cross validation, was good, with a Mean Absolute Percentage Error of 7.2% and 10.9% when considering prediction horizons of 7 and 14 days, respectively. Average temperature, urbanization, characteristics of family medicine and healthcare system, economic dynamism, and use of public transport could partly explain the regional heterogeneity. The GSA indicated the robustness of the regional R(0)(t) curves to different assumptions on IFR. The infectious period turned out to have a key role in determining the model results, but without compromising between-region comparisons
Evaluating COVID-19 screening strategies based on serological tests
Full text linkBackground. Facing the SARS-CoV-2 epidemic requires intensive testing on the population to early identify and isolate infected subjects. Although RT-PCR is the most reliable technique to detect ongoing infections, serological tests are frequently proposed as tools in heterogeneous screening strategies. We analyze the performance of a screening strategy proposed in Tuscany (Italy), which first uses qualitative rapid tests for antibody detection, and then RT-PCR tests on the positive subjects. Methods. We simulate the number of RT-PCR tests required by the screening strategy and the undetected ongoing infections in a pseudo-population of 500000 subjects, under different prevalence scenarios and assuming a sensitivity of the serological test ranging from 0.50 to 0.80 (specificity=0.98). A compartmental model is used to predict the number of new infections generated by the false negatives two months after the screening, under different values of the infection reproduction number. Results. Assuming a sensitivity equal to 0.80 and a prevalence of 0.3%, the screening procedure would require on average 11167.6 RT-PCR tests and would produce 300 false negatives, responsible after two months of a number of contagions ranging from 526 to 1132, under the optimistic scenario of a reproduction number between 0.5 to 1. Costs and false negatives increase with the prevalence. Conclusions. The analyzed screening procedure should be avoided unless the prevalence and the rate of contagion are very low. The cost and effectiveness of the screening strategies should be evaluated in the actual context of the epidemic, accounting for the fact that it may change over time
Parametric and semi-parametric approaches in the analysis of short-term effects of air pollution on health
No full textSince mid-1990s, Generalised Additive Models (GAM) became very popular for the analysis of short-term effects of air pollution on health. Such approach involves specification of non parametric functions to adjust for confounding effect of unobserved variables with a systematic temporal behaviour and to model weather variables and influenza epidemics. Recently critical points in using commercial statistical software for fitting GAMs were stressed (Dominici et al., 2002; Ramsey et al., 2003) and some reanalyses of time series data on air pollution and health were performed. This new attention to semi-parametric models has led researchers to consider alternative estimation methods for GAMs and to wonder whether simpler parametric models can be a better choice than GAMs (Lumley and Sheppard, 2003). The purpose of this work is to show by simulation analyses some of the problems which we could find using GAMs, and to discuss real advantages of semi-parametric approach with respect to a fully parametric alternative, based on specification of Generalized Linear Models with natural cubic splines (GLM + NS). Here we considered the situation in which only the smooth function for time trend is included in the model. Generalized Additive Models were fitted by the direct methods implemented in R software (Wood, 2000). Different simulation analyses were performed, varying the "true" number of degrees of freedom for the smooth function, the concurvity amount in data and the "true" size of air pollutant effect. Our simulations show that GAM provide biased estimates of air pollutant effect, the bias being not negligible for moderate concurvity amount and small effect size. We found also that using semi-parametric approach a certain amount of undersmoothing is needed to obtain appropriated estimation of risk. Good performance was obtained selecting the smoothing parameter by Generalized Cross Validation. On the contrary analysis of partial autocorrelation of residuals from GAM brings to inappropriate model selection. GLM+NS is a good alternative to semi-parametric approach, resulting robust to misspecification of degrees of freedom for the spline. However the applicability of such approach should be considered carefully in presence of particular local variations of seasonality or in presence of outliers, because results could be sensitive to knots placement. Moreover the choice of knots positions could be a very important problem in smoothing other covariates like temperature
Approximate Bayesian Inference for Smoking Habit Dynamics in Tuscany
Full text linkSmoking is a major risk factor for lung cancer, as well as for many other chronic diseases, and understanding smoking habits is essential to evaluate and compare tobacco control policies. We developed a compartmental model to describe the evolution of smoking habits in Tuscany, a region of central Italy. Our model relies on flexible modelling of age and sex-dependent probabilities of starting, quitting, and relapsing from smoking. Furthermore, we considered smoking intensity as a risk factor affecting mortality. The resulting model has an intractable likelihood function, so we used Approximate Bayesian Computation, a powerful simulation-based inference method, to provide posterior estimates of the model’s parameters. Using these approximate posterior distributions, we predicted the prevalence of current, former, and never smokers in Tuscany up to 2043. The model results suggest that the prevalence of smokers will decrease over time
Bayesian probabilistic sensitivity analysis of Markov models for natural history of a disease: an application for cervical cancer
No full textCommunicating epidemiological results through alternative indicators: cognitive interviewing to assess a questionnaire on risk perception in a high environmental risk area
Full text linkParticipatory approaches to environmental research and decision-making require that all social stakeholders are involved from the onset of the debate. In such a setting, communication among different expertise is crucial, but language and technicalities may represent a barrier. In the clinical setting, decisions regarding treatment preferences may be influenced by the summary statistics used, but, according to the literature, no study has compared different statistical indicators for risk communication in environmental epidemiology. In this paper, we report on the qualitative results
of the cognitive interviews conducted for assessing two questionnaires devoted to investigating risk perception when selected epidemiological results are communicated, by using different statistical indicators of health impact and uncertainty. The initial questionnaires were tested on 15 people residing in the high environmental risk area of Livorno (Italy). Cognitive interviewing led to substantial revision of the initial drafts.
Moreover, it highlighted the difficulty of communicating statistical uncertainty and the need to account for the complex interaction between mathematical skills, affective factors and individual a priori knowledge on environmental risk perception
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