Forecasting Models for Coronavirus (COVID-19): A Survey of the State-of-the-Art

Mahalle, Parikshit N.; Kalamkar, Asmita Balasaheb; Dey, Nilanjan; Chaki, Jyotismita; Hassanien, Aboul Ella; Shinde, Gitanjali Rahul

doi:10.36227/techrxiv.12101547.v1

Cited by 14 publications

(8 citation statements)

References 30 publications

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“…Mahalle and Kalamkar [5] categorized forecasting models as mathematical models and machine learning techniques, using WHO and social media communications as datasets. Significant parameters including death count, metrological parameters, quarantine period, medical resources, and mobility were also studied [5].…”

Section: Other Reviewsmentioning

confidence: 99%

A review on COVID-19 forecasting models

Rahimi

Fang

Gandomi

2021

Neural Comput & Applic

207

125

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The novel coronavirus (COVID-19) has spread to more than 200 countries worldwide, leading to more than 36 million confirmed cases as of October 10, 2020. As such, several machine learning models that can forecast the outbreak globally have been released. This work presents a review and brief analysis of the most important machine learning forecasting models against COVID-19. The work presented in this study possesses two parts. In the first section, a detailed scientometric analysis presents an influential tool for bibliometric analyses, which were performed on COVID-19 data from the Scopus and Web of Science databases. For the above-mentioned analysis, keywords and subject areas are addressed, while the classification of machine learning forecasting models, criteria evaluation, and comparison of solution approaches are discussed in the second section of the work. The conclusion and discussion are provided as the final sections of this study.

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Section: Other Reviewsmentioning

confidence: 99%

A review on COVID-19 forecasting models

Rahimi

Fang

Gandomi

2021

Neural Comput & Applic

207

125

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“…Ancient [1,4,5] studies, as well as characteristics of the covid-19 disease compared to past corona infections like the SARS one [3,[6][7][8], suggest that the spread of covid-19 could diminish in warm weather, particularly at the start of the epidemic, and may have a low temperature threshold under which it could spread fastest. These seasonal changes could occur in exactly the same way as for other pathogens, like the common cold or influenza [9][10][11][12]. This phenomenon can be modelled and the deterministic as well as stochastic models [2,[13][14][15][16][17][18][19][20][21][22][23][24][25] include potentially temperature-dependent parameters, like the contagion coefficient increasing with cold, dry weather because of faster evaporation of aerosol droplets.…”

Section: Introductionmentioning

confidence: 99%

Temperature Decreases Spread Parameters of the New Covid-19 Case Dynamics

Demongeot

Flet-Berliac

Seligmann

2020

Biology

137

152

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1) Background: The virulence of coronavirus diseases due to viruses like SARS-CoV or MERS-CoV decreases in humid and hot weather. The putative temperature dependence of infectivity by the new coronavirus SARS-CoV-2 or covid-19 has a high predictive medical interest. (2) Methods: External temperature and new covid-19 cases in 21 countries and in the French administrative regions were collected from public data. Associations between epidemiological parameters of the new case dynamics and temperature were examined using an ARIMA model. (3) Results: We show that, in the first stages of the epidemic, the velocity of contagion decreases with country-or region-wise temperature. (4) Conclusions: Results indicate that high temperatures diminish initial contagion rates, but seasonal temperature effects at later stages of the epidemy remain questionable. Confinement policies and other eviction rules should account for climatological heterogeneities, in order to adapt the public health decisions to possible geographic or seasonal gradients.

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“…As a retrospective, it is important to retrieve, via inferential statistical methods, the factors that have contributed significantly in controlling the propagation of the novel coronavirus in Mauritius. Besides, since the COVID-19 data is in the form of an integer-valued time series [2] layout, it is of research importance to develop an integer-valued regression time series process that can provide insights on the significance of explanatory variables as well set a platform to forecast the possible number of COVID-19 infected cases which is quite tedious as illustrated in [3,4,5]. Admittedly, research findings in [6] indicate that the COVID-19 data exhibits lots of dynamic and latent features and hence, to cater for such volatilities, we introduce random effects in our model specification.…”

Section: Introductionmentioning

confidence: 99%

Studying the trend of the novel coronavirus series in Mauritius and its implications

Khan

Soobhug²,

Khan

2020

PLoS ONE

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Mauritius stands as one of the few countries in the world to have controlled the current pandemic, the novel coronavirus 2019 (COVID-19) to a significant extent in a relatively short lapse of time. Owing to uncertainties and crisis amid the pandemic, as an emergency announcement, the World Health Organization (WHO) solicits the help of health authorities, especially, researchers to conduct in-depth research on the evolution and treatment of COVID-19. This paper proposes an integer-valued time series model to analyze the series of COVID-19 cases in Mauritius wherein the corresponding innovation term accommodates for covariate specification. In this setup , sanitary curfew followed by sanitization and sensitization campaigns, time factor and safe shopping guidelines have been tested as the most significant variables, unlike climatic conditions. The over-dispersion estimates and the serial auto-correlation parameter are also statistically significant. This study also confirms the presence of some unobservable effects like the pathological genesis of the novel coronavirus and environmental factors which contribute to rapid propagation of the zoonotic virus in the community. Based on the proposed COM-Poisson mixture models, we could predict the number of COVID-19 cases in Mauritius. The forecasting results provide satisfactory mean squared errors. Such findings will subsequently encourage the policymakers to implement strict precautionary measures in terms of constant upgrading of the current health care and wellness system and re-enforcement of sanitary obligations.

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Forecasting Models for Coronavirus (COVID-19): A Survey of the State-of-the-Art

Cited by 14 publications

References 30 publications

A review on COVID-19 forecasting models

A review on COVID-19 forecasting models

Temperature Decreases Spread Parameters of the New Covid-19 Case Dynamics

Studying the trend of the novel coronavirus series in Mauritius and its implications

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