Data analysis on Coronavirus spreading by macroscopic growth laws

Castorina, P.; Iorio, Alfredo; Lanteri, D.

doi:10.1142/s012918312050103x

Cited by 53 publications

(45 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such estimates are, of course, prone to high uncertainty: the less data available and the further in the future, the greater the uncertainty. Notwithstanding their inherent shortcomings, simple mathematical models provide valuable tools for quickly assessing the severity of an epidemic and help to guide the health and political authorities in defining or adjusting their national strategies to fight the disease (Crokidakis, 2020;Sameni, 2020;Castorina, Iorio & Lanteri, 2020;Dehkordi et al, 2020;Mair et al, 2016;Siegenfeld & Bar-Yam, 2020;Bastos & Cajueiro, 2020;Schulz, Coimbra-AraÃojo & Costiche, 2020;Manchein et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Modelling fatality curves of COVID-19 and the effectiveness of intervention strategies

Vasconcelos

Macêdo

Ospina

et al. 2020

PeerJ

View full text Add to dashboard Cite

The main objective of the present article is twofold: first, to model the fatality curves of the COVID-19 disease, as represented by the cumulative number of deaths as a function of time; and second, to use the corresponding mathematical model to study the effectiveness of possible intervention strategies. We applied the Richards growth model (RGM) to the COVID-19 fatality curves from several countries, where we used the data from the Johns Hopkins University database up to May 8, 2020. Countries selected for analysis with the RGM were China, France, Germany, Iran, Italy, South Korea, and Spain. The RGM was shown to describe very well the fatality curves of China, which is in a late stage of the COVID-19 outbreak, as well as of the other above countries, which supposedly are in the middle or towards the end of the outbreak at the time of this writing. We also analysed the case of Brazil, which is in an initial sub-exponential growth regime, and so we used the generalised growth model which is more appropriate for such cases. An analytic formula for the efficiency of intervention strategies within the context of the RGM is derived. Our findings show that there is only a narrow window of opportunity, after the onset of the epidemic, during which effective countermeasures can be taken. We applied our intervention model to the COVID-19 fatality curve of Italy of the outbreak to illustrate the effect of several possible interventions.

show abstract

Section: Introductionmentioning

confidence: 99%

Modelling fatality curves of COVID-19 and the effectiveness of intervention strategies

Vasconcelos

Macêdo

Ospina

et al. 2020

PeerJ

View full text Add to dashboard Cite

show abstract

“…Another research effort in [102] combined datasets collected from China, Singapore, South Korea, and Italy to build a comprehensive analytic model for virus spread tracking. Based on data learning and modelling, a macroscopic growth law is derived that allows us to estimate the maximum number of infected patients in a certain area.…”

Section: B Virus Spread Trackingmentioning

confidence: 99%

“…This is important for effective assessment of COVID-19 prevention and monitoring the potential spread of COVID-19 disease, especially the nearby regions of the central epidemic. Different from [102], the study in [103] proposed a temperature-based model that evaluates the relationship between the number of infected cases and the average temperature in different countries necessary for coronavirus tracking. A large dataset is collected from 42 countries which developed the epidemic earlier and built a dataset of 88 countries.…”

Section: B Virus Spread Trackingmentioning

confidence: 99%

Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts

Pham

Nguyen

Huynh‐The

et al. 2020

Preprint

104

View full text Add to dashboard Cite

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 215 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 14 April 2020, a cumulative total of 1,853,265 (118,854) infected (dead) COVID-19 cases were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify their applications in fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.

show abstract

“…Alert has been promptly evidenced by scientists after the first events in China [2,3]. Monitoring this number by a suitable statistical analysis [4][5][6][7] is an essential step in order to understand the phenomenon with the purpose to disentangle among possible existing models [8] and, consequently, to achieve a phenomenological description of the dynamical process [9][10][11]. In the simplest virological model [8], the S I S model, it is assumed that each individual could be infected and recovered, following the transitions: S → I → S → ..., for an illimited number of times, where S is the class of "non-infected" individual and I is the class of infected individuals.…”

Section: Introductionmentioning

confidence: 99%

A note on Corona-virus (Covid-19) Infection in Italy

Pagano

et al. 2020

Eur. Phys. J. Plus

View full text Add to dashboard Cite

A statistical analysis of the corona-virus (Covid-19) infective process has been performed by a cooperative action during the period February-June 2020. A good analysis has been obtained by using an entropic model typical of phenomena where statistical entropynegaentropy balance is expected to play a major role. A saturation value of the infected humans was observed, and the number of people potentially (asymptomatic) involved in the process was determined with an accuracy of 15% in the Italian case, as relevant example. The saturation value represents about 16% of the total (symptomatic + asymptomatic) involved population in the process. The stability of the observed saturation level with the time shows that the governmental lockdown prescriptions, guided by scientists (virologists) have been effective to contain the diffusion of the virus and the associated human mortality.

show abstract

Data analysis on Coronavirus spreading by macroscopic growth laws

Cited by 53 publications

References 12 publications

Modelling fatality curves of COVID-19 and the effectiveness of intervention strategies

Modelling fatality curves of COVID-19 and the effectiveness of intervention strategies

Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts

A note on Corona-virus (Covid-19) Infection in Italy

Contact Info

Product

Resources

About