A computational tool for trend analysis and forecast of the COVID-19 pandemic.

PURPOSE: This paper proposes a methodology and a computational tool to study the COVID-19 pandemic throughout the world and to perform a trend analysis to assess its local dynamics. METHODS: Mathematical functions are employed to describe the number of cases and demises in each region and to predict their final numbers, as well as the dates of maximum daily occurrences and the local stabilization date. The model parameters are calibrated using a computational methodology for numerical optimization. Trend analyses are run, allowing to assess the effects of public policies. Easy to interpret metrics over the quality of the fitted curves are provided. Country-wise data from the European Centre for Disease Prevention and Control (ECDC) concerning the daily number of cases and demises around the world are used, as well as detailed data from Johns Hopkins University and from the Brasil.io project describing individually the occurrences in United States counties and in Brazilian states and cities, respectively. U. S. and Brazil were chosen for a more detailed analysis because they are the current focus of the pandemic. RESULTS: Illustrative results for different countries, U. S. counties and Brazilian states and cities are presented and discussed. CONCLUSION: The main contributions of this work lie in (i) a straightforward model of the curves to represent the data, which allows automation of the process without requiring interventions from experts; (ii) an innovative approach for trend analysis, whose results provide important information to support authorities in their decision-making process; and (iii) the developed computational tool, which is freely available and allows the user to quickly update the COVID-19 analyses and forecasts for any country, United States county or Brazilian state or city present in the periodic reports from the authorities.

COVID-19 Trend Analysis in Mexican States and Cities.

This paper presents a trend analysis of the COVID-19 pandemics in Mexico. The studies were run in a subnational basis because they are more useful that way, providing important information about the pandemic to local authorities. Unlike classic approaches in the literature, the trend analysis presented here is not based on the variations in the number of infections along time, but rather on the predicted value of the final number of infections, which is updated every day employing new data. Results for four states and four cities, selected among the most populated in Mexico, are presented. The model was able to suitably fit the local data for the selected regions under evaluation. Moreover, the trend analysis enabled one to assess the accuracy of the forecasts.

Study of the COVID-19 pandemic trending behavior in Israeli cities.

This paper studies the trending behavior of the COVID-19 dynamics in Israeli cities. The model employed is used to describe, for each city, the accumulated number of cases, the number of cases per day, and the predicted final number of cases. The innovative analysis adopted here is based on the daily evolution of the predicted final number of infections, estimated with data available until a given date. The results discussed here are illustrative for six cities in Israel, including Jerusalem and Tel Aviv. They show that the model employed fits well with the observed data and is able to suitably describe the COVID-19 dynamics in a country strongly impacted by the disease that holds one of the most successful vaccination programs in the world.

A data-driven model to describe and forecast the dynamics of COVID-19 transmission.

This paper proposes a dynamic model to describe and forecast the dynamics of the coronavirus disease COVID-19 transmission. The model is based on an approach previously used to describe the Middle East Respiratory Syndrome (MERS) epidemic. This methodology is used to describe the COVID-19 dynamics in six countries where the pandemic is widely spread, namely China, Italy, Spain, France, Germany, and the USA. For this purpose, data from the European Centre for Disease Prevention and Control (ECDC) are adopted. It is shown how the model can be used to forecast new infection cases and new deceased and how the uncertainties associated to this prediction can be quantified. This approach has the advantage of being relatively simple, grouping in few mathematical parameters the many conditions which affect the spreading of the disease. On the other hand, it requires previous data from the disease transmission in the country, being better suited for regions where the epidemic is not at a very early stage. With the estimated parameters at hand, one can use the model to predict the evolution of the disease, which in turn enables authorities to plan their actions. Moreover, one key advantage is the straightforward interpretation of these parameters and their influence over the evolution of the disease, which enables altering some of them, so that one can evaluate the effect of public policy, such as social distancing. The results presented for the selected countries confirm the accuracy to perform predictions.

Identification of fractional-order transfer functions using exponentially modulated signals with arbitrary excitation waveforms.

This paper proposes a new identification method based on an exponential modulation scheme for the determination of the coefficients and exponents of a fractional-order transfer function. The proposed approach has a broader scope of application compared to a previous method based on step response data, in that it allows for the use of arbitrary input signals. Moreover, it dispenses with the need for repeated simulations during the search for the best fractional exponents, which significantly reduces the computational workload involved in the identification process. Two examples involving measurement noise at the observed system output are presented to illustrate the effectiveness of the proposed method when compared to a conventional output-error optimization approach based on the polytope algorithm. In both examples, the proposed method is found to provide a better trade-off between computational workload and accuracy of the parameter estimates for different realizations of the noise.

Adaptive wavelet EMG compression based on local optimization of filter banks.

This paper presents an adaptive wavelet technique for compression of surface electromyographic signals. The technique employs an optimization algorithm to adjust the wavelet filter bank in order to minimize the distortion of the compressed signal. Orthogonality of the transform is ensured by using a restriction-free parametrization described elsewhere. A case study involving real-life isotonic and isometric electromyographic signals is presented for illustration. The results show that the proposed approach outperforms the standard non-optimized wavelet technique in terms of the percent residual difference for a given compression factor.

On the Effects of Artificial Feeding on Bee Colony Dynamics: A Mathematical Model.

This paper proposes a new mathematical model to evaluate the effects of artificial feeding on bee colony population dynamics. The proposed model is based on a classical framework and contains differential equations that describe the changes in the number of hive bees, forager bees, and brood cells, as a function of amounts of natural and artificial food. The model includes the following elements to characterize the artificial feeding scenario: a function to model the preference of the bees for natural food over artificial food; parameters to quantify the quality and palatability of artificial diets; a function to account for the efficiency of the foragers in gathering food under different environmental conditions; and a function to represent different approaches used by the beekeeper to feed the hive with artificial food. Simulated results are presented to illustrate the main characteristics of the model and its behavior under different scenarios. The model results are validated with experimental data from the literature involving four different artificial diets. A good match between simulated and experimental results was achieved.