Analysis and contrast of Chadian-Senegalese Covid-19 outbreaks

As COVID-19 is an emerging pandemic, analysing its evolution is necessary to understand it in order to find appropriate answers. In this paper, we aim to observe and analyse it at the Chadian-Senegalese level. Thus, we collect public data in order to present via curves, histograms and tables the main characteristics of this pandemic. In this way, we implement a python program to construct these. We focus only on extracting long-term data without predictive models. We observed that there are mainly two waves (outbreak) per year with stable or even decreasing infection and death rates. We also identified moments of growth and relaxation of the disease. These results can be used to identify times when treatment or prevention should be intensified.

Development of a Fast Fourier Transform-based Analytical Method for COVID-19 Diagnosis from Chest X-Ray Images Using GNU Octave.

Purpose: Many artificial intelligence-based computational procedures are developed to diagnose COVID-19 infection from chest X-ray (CXR) images, as diagnosis by CXR imaging is less time consuming and economically cheap compared to other detection procedures. Due to unavailability of skilled computer professionals and high computer architectural resource, majority of the employed methods are difficult to implement in rural and poor economic settings. Majority of such reports are devoid of codes and ignores related diseases (pneumonia). The absence of codes makes limitation in applying them widely. Hence, validation testing followed by evidence-based medical practice is difficult. The present work was aimed to develop a simple method that requires a less computational expertise and minimal level of computer resource, but with statistical inference. Materials and Methods: A Fast Fourier Transform-based (FFT) method was developed with GNU Octave, a free and open-source platform. This was employed to the images of CXR for further analysis. For statistical inference, two variables, i.e., the highest peak and number of peaks in the FFT distribution plot were considered. Results: The comparison of mean values among different groups (normal, COVID-19, viral, and bacterial pneumonia [BP]) showed statistical significance, especially when compared to normal, except between viral and BP groups. Conclusion: Parametric statistical inference from our result showed high level of significance (P < 0.001). This is comparable to the available artificial intelligence-based methods (where accuracy is about 94%). Developed method is easy, availability with codes, and requires a minimal level of computer resource and can be tested with a small sample size in different demography, and hence, be implemented in a poor socioeconomic setting.

Statistical analysis for the pitch of mask-wearing Arabic speech

According to Fourier analysis, any periodic function can be analyzed as an infinite series of trigonometric functions (sets of sines and cosines). The kernel of decay cosine yields an extension for the previous frequency-based, sieve-type detection algorithm by giving smooth peaks for decaying amplitudes with the harmonics of the signal correlation. The sequential outline of the RAPT algorithm is: 1) Providing speech samples with their sampling rate and with a reduced sampling rate. 2) Periodically, computing normalized cross-correlation function (NCCF) of the reduced sampling rate speech signal with lags in the F0 range. 3) Indicating the locations of maximum at the 1st pass of NCCF. 4) For the vicinity of the peaks in that 1st pass, calculate the NCCF for the original sampling rate. 5) Again, finding the maximum in that NCCF. Obtaining the location and amplitude of the modified peak. 6) For each peak obtained from the NCCF (high resolution), estimate the F0 of the processed frame. 7) The hypothesis of the frame for unvoiced/voiced is advanced for each frame. 8) Finding the group of the NCCF peaks via optimization process for the unvoiced/voiced hypotheses for all the frames which have the best match with the above characteristics. 9) Using the well-known speech pitch tracking algorithm (PTA), RAPT has the following differences: - PTA computes the NCCF in the linear prediction coding (LPC).

Practical tapered optical fiber system for in-situ label-free sensing of various antigens

A practical tapered optical fiber (TOF) biosensing system was developed for label-free detection using antigen-antibody pairs with repeatable results and a very high degree of sensitivity. This was done by attaching molecular recognition agents to a tapered fiber surface for augmenting sensitivity and specificity of analyte. The entire system included three main parts: a tunable laser, a tapered fiber, and an optical detector. Light from an unpolarized tunable fiber laser was introduced into the tapered fiber from one end, and the transmitted intensity was detected by a photodetector. In the tapered fiber area, the evanescent electromagnetic field, which extends outside the fiber, was able to detect minute changes in the refractive index caused by antigen-antibody pairs. Recorded data was analyzed using an innovative Fourier analysis method to find phase changes, which are directly related to the biomolecular concentration coated on fiber, from which antibody-antigen concentrations are obtained. Two experiments were performed to confirm the concept using two very different agents. The first was the protein Interleukin-8 (IL-8). Repeatable results with a sensitivity of 10 pg/mL were achieved. The second was human coronavirus OC43 (HCoV-OC43), a surrogate viral particle for SARS-CoV-2, with a sensitivity of 50 viruses/mL. Critical sources of error were identified and addressed for the purpose of using the device for real clinical diagnosis in various real-life environments, where viruses can reside in water, phosphate-buffer solution, or saliva, the most popular three environments in real clinical diagnosis. Our device was designed according to the principle that only one specific kind of antibody and antigen can be combined together. The device demonstrated good accuracy to chosen analyte(s) tailored to specific applications and offered the potential to develop a point-of-care device used in clinics, as well as for detecting a variety of viruses and biocontaminants. The reproducibility of TOFs was confirmed through multiple fabrications and consistent results. [ FROM AUTHOR] Copyright of Optical Engineering is the property of SPIE - International Society of Optical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Extracted features of national and continental daily biweekly growth rates of confirmed COVID-19 cases and deaths via Fourier analysis.

AIMS: By associating features with orthonormal bases, we analyse the values of the extracted features for the daily biweekly growth rates of COVID-19 confirmed cases and deaths on national and continental levels. METHODS: By adopting the concept of Fourier coefficients, we analyse the inner products with respect to temporal and spatial frequencies on national and continental levels. The input data are the global time series data with 117 countries over 109 days on a national level; and 6 continents over 447 days on a continental level. Next, we calculate the Euclidean distance matrices and their average variabilities, which measure the average discrepancy between one feature vector and all others. Then we analyse the temporal and spatial variabilities on a national level. By calculating the temporal inner products on a continental level, we derive and analyse the similarities between the continents. RESULTS: On the national level, the daily biweekly growth rates bear higher similarities in the time dimension than the ones in the space dimension. Furthermore, there exists a strong concurrency between the features for biweekly growth rates of cases and deaths. As far as the trends of the features are concerned, the features are stabler on the continental level, and less predictive on the national level. In addition, there are very high similarities between all the continents, except Asia. CONCLUSIONS: The features for daily biweekly growth rates of cases and deaths are extracted via orthonormal frequencies. By tracking the inner products for the input data and the orthonormal features, we could decompose the evolutionary results of COVID-19 into some fundamental frequencies. Though the frequency-based techniques are applied, the interpretation of the features should resort to other methods. By analysing the spectrum of the frequencies, we reveal hidden patterns of the COVID-19 pandemic. This would provide some preliminary research merits for further insightful investigations. It could also be used to predict future trends of daily biweekly growth rates of COVID-19 cases and deaths.

A kernel-modulated SIR model for Covid-19 contagious spread from county to continent.

The tempo-spatial patterns of Covid-19 infections are a result of nested personal, societal, and political decisions that involve complicated epidemiological dynamics across overlapping spatial scales. High infection "hotspots" interspersed within regions where infections remained sporadic were ubiquitous early in the outbreak, but the spatial signature of the infection evolved to affect most regions equally, albeit with distinct temporal patterns. The sparseness of Covid-19 infections in the United States was analyzed at scales spanning from 10 to 2,600 km (county to continental scale). Spatial evolution of Covid-19 cases in the United States followed multifractal scaling. A rapid increase in the spatial correlation was identified early in the outbreak (March to April). Then, the increase continued at a slower rate and approached the spatial correlation of human population. Instead of adopting agent-based models that require tracking of individuals, a kernel-modulated approach is developed to characterize the dynamic spreading of disease in a multifractal distributed susceptible population. Multiphase Covid-19 epidemics were reasonably reproduced by the proposed kernel-modulated susceptible-infectious-recovered (SIR) model. The work explained the fact that while the reproduction number was reduced due to nonpharmaceutical interventions (e.g., masks, social distancing, etc.), subsequent multiple epidemic waves still occurred; this was due to an increase in susceptible population flow following a relaxation of travel restrictions and corollary stay-at-home orders. This study provides an original interpretation of Covid-19 spread together with a pragmatic approach that can be imminently used to capture the spatial intermittency at all epidemiologically relevant scales while preserving the "disordered" spatial pattern of infectious cases.

Fourier analysis using the number of COVID-19 daily deaths in the US.

Fourier analysis can provide policymakers useful information for analysing the pandemic behaviours. This paper proposes a Fourier analysis approach for examining the cycle length and the power spectrum of the pandemic by converting the number of deaths due to coronavirus disease 2019 in the US to the frequency domain. Policymakers can control the pandemic by using observed cycle length whether they should strengthen their policy or not. The proposed Fourier method is useful for analysing waves in other medical applications.