Investigating Epidemic Growth of COVID-19 in Saudi Arabia based on Time Series Models

Torky, Mohamed; Sh, M.; Ahmed, Azza Tagelsir; Ella, Aboul; Said, W. K. El

doi:10.14569/ijacsa.2020.0111256

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…No studies consider all COVID-19 waves using integrated functions in the Peruvian case specifically. For Peruvian COVID-19 infection data, one research study used Mitchell's criteria [32], and there are studies using times series [33], which are different methods from those used in this work.…”

Section: Related Workmentioning

confidence: 99%

Analysis of the Epidemic Curve of the Waves of COVID-19 Using Integration of Functions and Neural Networks in Peru

Vilca Huayta,

Jimenez Chura,

Sosa Maydana

et al. 2024

Informatics

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The coronavirus (COVID-19) pandemic continues to claim victims. According to the World Health Organization, in the 28 days leading up to 25 February 2024 alone, the number of deaths from COVID-19 was 7141. In this work, we aimed to model the waves of COVID-19 through artificial neural networks (ANNs) and the sigmoidal–Boltzmann model. The study variable was the global cumulative number of deaths according to days, based on the Peru dataset. Additionally, the variables were adapted to determine the correlation between social isolation measures and death rates, which constitutes a novel contribution. A quantitative methodology was used that implemented a non-experimental, longitudinal, and correlational design. The study was retrospective. The results show that the sigmoidal and ANN models were reasonably representative and could help to predict the spread of COVID-19 over the course of multiple waves. Furthermore, the results were precise, with a Pearson correlation coefficient greater than 0.999. The computational sigmoidal–Boltzmann model was also time-efficient. Moreover, the Spearman correlation between social isolation measures and death rates was 0.77, which is acceptable considering that the social isolation variable is qualitative. Finally, we concluded that social isolation measures had a significant effect on reducing deaths from COVID-19.

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Section: Related Workmentioning

confidence: 99%

Analysis of the Epidemic Curve of the Waves of COVID-19 Using Integration of Functions and Neural Networks in Peru

Vilca Huayta,

Jimenez Chura,

Sosa Maydana

et al. 2024

Informatics

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“…As far as we know, there is no other work related to the integration of functions. On the other hand, there are several research works regarding the spread of Covid-19 [12]- [14].…”

Section: Related Workmentioning

confidence: 99%

Efficient Function Integration and a Case Study with Gompertz Functions for Covid-19 Waves

Huayta¹,

Tito²

2022

IJACSA

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Numerical algorithms are widely used in different applications, therefore, the execution time of the functions involved in numerical algorithms is important, and, in some cases, decisive, for example, in machine learning algorithms. Given a finite set of independent functions A(x), B(x), ..., Z(x) with domains defined by disjoint, consecutive, and not necessarily adjacent intervals, the main goal is to integrate into a single function F(x) = k1×A(x) + k2×B(x) + … + kn×Z(x), where each activation coefficient k, is one if x is in the interval of the respective domain and zero otherwise. The novelty of this work is the presentation and formal demonstration of two general forms of integration of functions in a single function: The first is the mathematical version and the second is the computational version (with the AND function at the bit level), which is characterized by its efficiency. The result is applied in a case study (Peru), where two regression functions were obtained that integrate all the waves of Covid-19, that is, the epidemic curve of the variable global number of deaths/infected per day, the adjustment provided a highly statistically significant measure of correlation, a Pearson's product-moment correlation of 0.96 and 0.98 respectively. Finally, the size of the epidemic was projected for the next 30 days.

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“…Nuo pandemijos pradžios publikuota daug mokslinių straipsnių, kuriuose pristatomi užsikrėtimų SARS-CoV-2 virusu prognozavimo remiantis įvairiais prognostiniais modeliais rezultatai (Wieczorek et al, 2020;Hawas, 2020;Chandra et al, 2021;Muhammad Adnan Khan, 2020;Papastefanopoulos et al, 2020). SARS-CoV-2 viruso plitimui prognozuoti naudojami klasikiniai epidemiologiniai modeliai (SIR, SEIR ir šių modelių modifikacijos (Bahloul et al, 2020;Torky et al, 2020;Leipus and Štikonienė, 2020), logistinis modelis (Wu et al, 2020;Aviv-Sharon and Aharoni, 2020), ARIMA modelis (Devaraj et al, 2021;Chakraborty and Ghosh, 2020), kelių kintamųjų tiesinė regresija (Suganya et al, 2020), taip pat neuroniniais tinklais pagrįsti modeliai (Holt-Winters 5 , TBAT (Livera et al, 2011), Prophet 6 (Taylor and Letham, 2017), Dee-pAR 7 (Salinas et al, 2019), N-Beats (Oreshkin et al, 2020) modeliai (Papastefanopoulos et al, 2020), ilgalaikės trumposios atminties tinklai (angl. Long Short-Term Memory (LSTM)) (Devaraj et al, 2021;Chandra et al, 2021;Chimmula and Zhang, 2020;Yudistira, 2020;Pal et al, 2020), rekurentiniai neuroniniai tinklai (angl.…”

Section: Didžiųjų Duomenų Ir Dirbtinio Intelekto Technologijų Naudojimo Viešojo Valdymo Ir Socialinės Politikos Srityje Galimybėsunclassified

The Analysis of Opportunities of the Application of Big Data and Artificial Intelligence Technologies in Public Governance and Social Policy

Rakšnys

Gudelis

Guogis

2021

STEPP

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This interdisciplinary article presents a concept of the 21st century and phenomena that are products of the 4th industrial revolution – big data and Artificial Intelligence technologies – as well as the opportunities of their application in public governance and social policy. This paper examines the advantages and disadvantages of big data, problems of data collection, its reliability and use. Big data can be used for the analysis and modeling of phenomena relevant to public governance and social policy. Big data consist of three main types: a) historical data, b) present data with little delay, c) prognostic data for future forecasting. The following categories of big data can be defined as: a) data from social networks, b) traditional data from business systems, c) machine-generated data, such as water extraction, pollution, satellite information. The article analyzes the advantages and disadvantages of big data. There are big data challenges such as data security, lack of cooperation in civil service and social work, in rare situations – data fragmentation, incompleteness and erroneous issues, as well as ethical issues regarding the analysis of data and its use in social policy and social administration. Big data, covered by Artificial Intelligence, can be used in public governance and social policy by identifying “the hot spots” of various phenomena, by prognosing the meanings of variables in the future on the basis of past time rows, and by calculating the optimal motion of actions in the situations where there are possible various alternatives. The technologies of Artificial Intelligence are used more profoundly in many spheres of public policy, and in the governance of COVID-19 pandemics too. The substantial advantages of the provided big data and Artificial Intelligence are a holistic improvement of public services, possibilities of personalization, the enhancement of citizen satisfaction, the diminishing of the costs of processing expenditure, the targeting of adopted and implemented decisions, more active involvement of citizens, the feedback of the preferences of policy formation and implementation, the observation of social phenomenas in real time, and possibilities for more detailed prognosing. Challenges to security of data, necessary resources and competences, the lack of cooperation in public service, especially rare instances of data fragmentation, roughness, falseness, and ethical questions regarding data analysis and application can be evaluated as the most significant problems of using big data and Artificial Intelligence technologies. Big data and their analytics conducted using Artificial Intelligence technologies can contribute to the adequacy and objectivity of decisions in public governance and social policy, effectively curbing corruption and nepotism by raising the authority and confidence of public sector organizations in governance, which is so lacking in the modern world.

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Investigating Epidemic Growth of COVID-19 in Saudi Arabia based on Time Series Models

Cited by 3 publications

References 18 publications

Analysis of the Epidemic Curve of the Waves of COVID-19 Using Integration of Functions and Neural Networks in Peru

Analysis of the Epidemic Curve of the Waves of COVID-19 Using Integration of Functions and Neural Networks in Peru

Efficient Function Integration and a Case Study with Gompertz Functions for Covid-19 Waves

The Analysis of Opportunities of the Application of Big Data and Artificial Intelligence Technologies in Public Governance and Social Policy

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