No abstract

Objectives The ability to predict COVID-19 dynamic has been very low, reflected in unexpected changes in the number of cases in different settings. Here the objective was to study the temporal memory of the reported daily incidence time series and propose a simple model for short-term forecast of the incidence. Methods We propose a new concept called incidence moments that allows exploring the memory of the reported incidence time series, based on successive products of the incidence and the reproductive number that allow a short term forecast of the future incidence. We studied the correlation between the predictions of and the reported incidence determining the best predictor. We compared the predictions and observed COVID-19 incidences with the mean arctangent absolute percentage error (MAAPE) analyses for the world, 43 countries and for Chile and its regions. Results The best predictor was the third moment of incidence, determining a short temporal prediction window of 15 days. After 15 days the absolute percentage error of the prediction increases significantly. The method perform better for larger populations and presents distortions in contexts of abrupt changes in incidence. Conclusions The epidemic dynamics of COVID 19 had a very short prediction window, probably associated with an intrinsic chaotic behavior of its dynamics. The incident moment modeling approach could be useful as a tool whose simplicity is appealing, since it allows rapid implementation in different settings, even with limited epidemiological technical capabilities and without requiring a large amount of computational data.

Percolation of influenza AH1N1 epidemic in the world: Usefulness of the spatial-connectivity models Background: The 2009 AH1N1 epidemics expanded rapidly around the world by the current connectivity conditions. The spread of epidemics can be described by the phenomenon of percolation, that allows the estimation of the threshold conditions that produce connectivity between different regions and that has been used to describe physical and ecological phenomena. Aim: To analyze the spread of AH1N1 epidemic based on information from the WHO. Material and Methods: The world was considered as composed of a set of countries and regular cells. The moment when the percolation occurred was analyzed and logistic regressions were adjusted to the change in the proportion of infected units versus time, comparing predicted and observed rates. Results: Percolation occurred in America on day 15, in Eurasia on day 32 and in the world on day 74. The models showed adequate predictive capacity. The predictions for the percolation of the epidemic in the world varied between days 66 and 75. The prediction based on countries was better than that based on cells. Conclusions: These results show that percolation theory fits well to the spread of epidemics. Predictions based only on data on-off (infected non infected) and in the progression of the proportion of infected cells are a good way of predicting the spread of an epidemic and when this crosses a region geographically.

In prostatic carcinoma, the glandular architecture is replaced by cancer cells producing barriers to water motion, anomaly that can be studied through diffusion-enhanced MRI technique. To assess the contribution of these sequences in the prostate cancer exploration, we conducted a descriptive and inferential study using diffusion-enhanced MRI technique in 26 patients with abnormal digital rectal examination (DRE) and increased prostate specific antigen (PSA) values. We analyzed sensitivity, specificity and ROC curves based on apparent diffusion coefficient (ADC). Seven out of 14 biopsies were positive in patients undergoing prostate biopsy. When applying ADC <1000 _m2/se, high sensitivity with low specificity levels, as well as moderate predictive values were obtained. By incorporating T2-weighted images, improved diagnostic accuracy, specificity and predictive values were achieved. When comparing ADC values in tissues with and without cancer, average and minimum ADC appeared to exhibit different values. ROC curves depicted increased and significant values, suggesting cutoff values of 1059 µm 2 /s and 969 µm 2 /s for healthy and malignant tissues, respectively; LR (+) for cutoff value: 6.97 and 5.23, respectively. Our results enable us to propose that improved diagnostic outcomes are attained through combined interpretation of T2-weighted images and diffusion-weighted sequences and that the ADC permits discrimination between normal and malignant tissues. Therefore, we strongly support that these criteria should be taken into account when performing prostate explorations.