An epidemic model for correlated information diffusion in crowd intelligence networks

Li, Yuejiang; Zhao, Haoyu; Chen, Yan

doi:10.1108/ijcs-01-2019-0005

Cited by 6 publications

(6 citation statements)

References 32 publications

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“…Therefore, a study of the crowd cyber system requires systemic analysis of the network connectivity among intelligent agents to assess the value they create (Li et al , 2019a, 2019 b). Measuring both profitability and risk is not straightforward; account needs to be taken of operating costs, customer availability, transaction costs related to transparent information and optimization models.…”

Section: Discussionmentioning

confidence: 99%

“…Our results contribute to the notion of crowd science and engineering and are in line with the recent work on crowd cyber systems (Chai et al , 2017; Liu et al , 2018). This multidisciplinary framework (Li et al , 2019a, 2019 b; Han et al , 2019) is based on several theories that include methods and tools able to capture the emergent concept of crowd cyber systems. Our research on sharing economy logistics and crowd logistics provides some answers to questions posed in the context of crowd cyber systems regarding crowd intelligence in the logistics sector.…”

Section: Introductionmentioning

confidence: 99%

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Bibliometric analysis of sharing economy logistics and crowd logistics

Saglietto

2021

IJCS

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Purpose This study aims to review the literature on sharing economy logistics and crowd logistics to answer the three following questions: How is the literature on sharing economy logistics structured? What are the main trends in sharing economy logistics and crowd logistics? What are the future research options? Design/methodology/approach Bibliometric analysis is used to evaluate 85 articles published over the past 12 years; it identifies the top academic journals, authors and research topics contributing to the field. Findings The sharing economy logistics and crowd logistics literature is structured around several disciplines and highlights that some are more scientifically advanced than others in their subject definitions, designs, modelling and innovative solutions. The main trends are organized around three clusters: Cluster 1 refers to the optimal allocation of costs, prices, distribution and supplier relationships; Cluster 2 corresponds to business related crowdsourcing and international industry practices; and Cluster 3 includes the impact of transport on last-mile delivery, crowd shipping and the environment. Research limitations/implications The study is based on data from peer-reviewed scientific journals and conferences. A broader overview could include other data sources such as books, book chapters, working papers, etc. Originality/value Future research directions are discussed in the context of the evolution from crowd logistics to crowd intelligence, and the complexities of crowd logistics such as understanding how the social crowd can be integrated into the logistics process. Our results are part of the crowd science and engineering concept and provide some answers about crowd cyber-system questions regarding crowd intelligence in logistic sector.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bibliometric analysis of sharing economy logistics and crowd logistics

Saglietto

2021

IJCS

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“…In equations (5)(6)(7)(8), there are 4-set of ODE equations based on susceptible population (S), infected population (I), recovered population (R), deceased population (D). Dotproducts are time derivatives such as ̇= ⁄ .The transmission rate ( ) represent growth rate of infected disease.…”

Section: B Sird Modelmentioning

confidence: 99%

“…Zhang et al [6] studied the duration and outbreak of COVID-19 in several countries in Europe and America, using power-law and exponential law models. Li et al [7] applied SIR mixture model to tackle double peak epidemy situations. The method is cumbersome and not easy to implement.…”

Section: Introductionsmentioning

confidence: 99%

Coronavirus (COVID-19) Outbreak Prediction Using Epidemiological Models of Richards Gompertz Logistic Ratkowsky and SIRD

Sedaghat

Oloomi

Malayer

et al. 2020

Preprint

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On 30 July 2020, a total number of 301,530 diagnosed COVID-19 cases were reported in Iran, with 261,200 recovered and 16,569 dead. The COVID-19 pandemic started with 2 patients in Qom city in Iran on 20 February 2020. Accurate prediction of the end of the COVID-19 pandemic and the total number of populations affected is challenging. In this study, several widely used models, including Richards, Gompertz, Logistic, Ratkowsky, and SIRD models, are used to project dynamics of the COVID-19 pandemic in the future of Iran by fitting the present and the past clinical data. Iran is the only country facing a second wave of COVID-19 infections, which makes its data difficult to analyze. The present study’s main contribution is to forecast the near-future of COVID-19 trends to allow non-pharmacological interventions (NPI) by public health authorities and/or government policymakers. We have divided the COVID-19 pandemic in Iran into two waves, Wave I, from February 20, 2020 to May 4, 2020, and Wave II from May 5, 2020, to the present. Two statistical methods, i.e., Pearson correlation coefficient (R) and the coefficient of determination (R2), are used to assess the accuracy of studied models. Results for Wave I Logistic, Ratkowsky, and SIRD models have correctly fitted COVID-19 data in Iran. SIRD model has fitted the first peak of infection very closely on April 6, 2020, with 34,447 cases (The actual peak day was April 7, 2020, with 30,387 active infected patients) with the re-production number R0=3.95. Results of Wave II indicate that the SIRD model has precisely fitted with the second peak of infection, which was on June 20, 2020, with 19,088 active infected cases compared with the actual peak day on June 21, 2020, with 17,644 cases. In Wave II, the re-production number R0=1.45 is reduced, indicating a lower transmission rate. We aimed to provide even a rough project future trends of COVID-19 in Iran for NPI decisions. Between 180,000 to 250,000 infected cases and a death toll of between 6,000 to 65,000 cases are expected in Wave II of COVID-19 in Iran. There is currently no analytical method to project more waves of COVID-19 beyond Wave II.

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“…= 399,997; (0) = 3; (0) = 0; (0) = 0 (11)The 4-set of ODE equations(5)(6)(7)(8) are solved using MATLAB for time intervals of one day. The set of ODE equations (5-8) are solved using initial conditions(11) in MATLAB for total time duration of contagious disease.In Wave I, SIRD model 4-set of ODE equations requires initial population of COVID-19 in Iran on February 20, 2020(the first day of infection) as follows: (0) = 95,000; (0) = 2; (0) = 0; (0) = 2 (12) The initial susceptible population, (0) , was assumed, infectious, (0), recovered, (0) , and dead, (0) were adopted from COVID-19 data of Iran on February 20, 2020[3].We consider 5 May 2020 as the start of the Wave II when the infected cases raise to 1,223 on the second wave of COVID-19 in Iran.…”

mentioning

confidence: 99%

Coronavirus (COVID-19) Outbreak Prediction Using Epidemiological Models of Richards Gompertz Logistic Ratkowsky and SIRD

Sedaghat¹,

Oloomi²,

Malayer³

et al. 2020

Preprint

View full text Add to dashboard Cite

On 30 July 2020, a total number of 301,530 diagnosed COVID-19 cases were reported in Iran, with 261,200 recovered and 16,569 dead. The COVID-19 pandemic started with 2 patients in Qom city in Iran on 20 February 2020. Accurate prediction of the end of the COVID-19 pandemic and the total number of populations affected is challenging. In this study, several widely used models, including Richards, Gompertz, Logistic, Ratkowsky, and SIRD models, are used to project dynamics of the COVID-19 pandemic in the future of Iran by fitting the present and the past clinical data. Iran is the only country facing a second wave of COVID-19 infections, which makes its data difficult to analyze. The present study's main contribution is to forecast the near-future of COVID-19 trends to allow non-pharmacological interventions (NPI) by public health authorities and/or government policymakers. We have divided the COVID-19 pandemic in Iran into two waves, Wave I, from February 20, 2020 to May 4, 2020, and Wave II from May 5, 2020, to the present. Two statistical methods, i.e., Pearson correlation coefficient (R) and the coefficient of determination (R2), are used to assess the accuracy of studied models. Results for Wave I Logistic, Ratkowsky, and SIRD models have correctly fitted COVID-19 data in Iran. SIRD model has fitted the first peak of infection very closely on April 6, 2020, with 34,447 cases (The actual peak day was April 7, 2020, with 30,387 active infected patients) with the re-production number R0=3.95. Results of Wave II indicate that the SIRD model has precisely fitted with the second peak of infection, which was on June 20, 2020, with 19,088 active infected cases compared with the actual peak day on June 21, 2020, with 17,644 cases. In Wave II, the re-production number R0=1.45 is reduced, indicating a lower transmission rate. We aimed to provide even a rough project future trends of COVID-19 in Iran for NPI decisions. Between 180,000 to 250,000 infected cases and a death toll of between 6,000 to 65,000 cases are expected in Wave II of COVID-19 in Iran. There is currently no analytical method to project more waves of COVID-19 beyond Wave II.

show abstract

An epidemic model for correlated information diffusion in crowd intelligence networks

Cited by 6 publications

References 32 publications

Bibliometric analysis of sharing economy logistics and crowd logistics

Bibliometric analysis of sharing economy logistics and crowd logistics

Coronavirus (COVID-19) Outbreak Prediction Using Epidemiological Models of Richards Gompertz Logistic Ratkowsky and SIRD

Coronavirus (COVID-19) Outbreak Prediction Using Epidemiological Models of Richards Gompertz Logistic Ratkowsky and SIRD

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