An analysis to identify the important variables for the spread of COVID-19 using numerical techniques and data science

Estimation of the size of the COVID-19 pandemic using the epidemiological wavelength model: results from OECD countries

OBJECTIVES

This study aimed to simplify the previously developed epidemiological wavelength model and to expand the scope of the model with additional variables to estimate the magnitude of the COVID-19 pandemic. The applicability of the extended wavelength model was tested in Organisation for Economic Cooperation and Development (OECD) member countries.

STUDY DESIGN

The epidemiological wavelengths of OECD member countries for the years 2020, 2021 and 2022 were estimated comparatively, considering the cumulative number of COVID-19 cases.

METHODS

The size of the COVID-19 pandemic was estimated using the wavelength model. The scope of the wavelength model was expanded to include additional variables. The extended estimation model was improved by adding population density and human development index variables, in addition to the number of COVID-19 cases and number of days since the first case reported from the previous estimation model.

RESULTS

According to the findings obtained from the wavelength model, the country with the highest epidemiological wavelength for the years 2020, 2021 and 2022 was the United States (We = 29.96, We = 28.63 and We = 28.86, respectively), and the country with the lowest wavelength was Australia (We = 10.50, We = 13.14 and We = 18.44, respectively). The average wavelength score of OECD member countries was highest in 2022 (We = 24.32) and lowest in 2020 (We = 22.84). The differences in the periodic wavelengths of OECD countries were analysed with the dependent t-test for paired samples in two periods, 2020-2021 and 2021-2022. There was a statistically significant difference between wavelengths in the 2020-2021 and 2021-2022 groups (t(36) = -3.670; P < 0.001).

CONCLUSIONS

Decision-makers can use the extended wavelength model to easily follow the progress of the epidemic and to make quicker and more reliable decisions.

Self-regulation of light emission of an AlGaInP quantum well diode

When an AlGaInP quantum well (QW) diode is biased with a forward voltage and illuminated with an external shorter-wavelength light beam, the diode is in a superposition state of both light emission and detection. The two different states take place simultaneously, and both the injected current and the generated photocurrent begin to mix. Here, we make use of this intriguing effect and integrate an AlGaInP QW diode with a programmed circuit. The AlGaInP QW diode with the dominant emission peak wavelength centered around 629.5 nm is excited by a 620-nm red-light source. The photocurrent is then extracted as a feedback signal to regulate the light emission of the QW diode in real time without an external or monolithically integrated photodetector, paving a feasible way to autonomously adjust the brightness of the QW diode for intelligent illumination in response to changes in the environmental light condition.

Spatio-temporal modeling of COVID-19 prevalence and mortality using artificial neural network algorithms

The outbreak of coronavirus disease (COVID-19) has become one of the most challenging global concerns in recent years. Due to inadequate worldwide studies on spatio-temporal modeling of COVID-19, this research aims to examine the relative significance of potential explanatory variables (n = 75) concerning COVID-19 prevalence and mortality using multilayer perceptron artificial neural network topology. We utilized ten variable importance analysis methods to identify the relative importance of the explanatory variables. The main findings indicated that several variables were persistently among the most influential variables in all periods. Regarding COVID-19 prevalence, unemployment and population density were among the most influential variables with the highest importance scores. While for COVID-19 mortality, health-related variables such as diabetes prevalence and number of hospital beds were among the most significant variables. The obtained findings from this study might provide general insights for public health policymakers to monitor the spread of disease and support decision-making.