Self-organized wavy infection curve of COVID-19

Odagaki, Takashi

doi:10.1038/s41598-021-81521-z

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…1 we exhibit the fractions of Susceptible, Infected, Recovered and Dead individuals for the three areas 1, 2 and 3 as functions of time for C 1 = 1.0, C 2 = 0.4 and C 3 = 1.3, obtained from the numerical integration of Eqs. ( 5) - (8). Since the exposure coefficients are arbitrary parameters, we chose as unity the one for government controlled areas and modified the other two accordingly.…”

Section: Resultsmentioning

confidence: 99%

Crime and COVID-19 in Rio de Janeiro: How does organized crime shape the disease evolution?

Crokidakis,

Sigaud

2021

Preprint

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The city of Rio de Janeiro is one of the biggest cities in Brazil. Drug gangs and paramilitary groups called milícias control some regions of the city where the government is not present, specially in the slums. Due to the characteristics of such two distinct groups, it was observed that the evolution of COVID-19 is different in those two regions, in comparison with the regions controlled by the government. In order to understand qualitatively those observations, we divided the city in three regions controlled by the government, by the drug gangs and by the milícias, respectively, and we consider a SIRD-like epidemic model where the three regions are coupled. Considering different levels of exposure, the model is capable to reproduce qualitatively the distinct evolution of the COVID-19 disease in the three regions, suggesting that the organized crime shapes the COVID-19 evolution in the city of Rio de Janeiro. This case study suggests that the model can be used in general for any metropolitan region with groups of people that can be categorized by their level of exposure.

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Section: Resultsmentioning

confidence: 99%

Crime and COVID-19 in Rio de Janeiro: How does organized crime shape the disease evolution?

Crokidakis,

Sigaud

2021

Preprint

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“…The policy targeting on certain shops and opening hours has only limited effects on social distancing since people gather together in a park or on a street. Furthermore, the policy has been enforced and lifted every one or two months, which has caused the wavy infection curve (9,10). It could be possible to increase by, for example, promoting Telework, limiting working days, reducing crowd in commuter trains and banning gatherings.…”

Section: Effective Reproduction Number and Assessment Of Policiesmentioning

confidence: 99%

Estimation of the Onset Rate and the Number of Asymptomatic Patients of COVID-19 From the Proportion of Untraceable Patients

Odagaki

2021

Preprint

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A simple method is devised to estimate the onset rate of COVID-19 from the proportion of untraceable patients tested positive, which allows us to obtain the number of asymptomatic patients, the number of infectious patients and the effective reproduction number. The recent data in Tokyo indicate that there are about six times as many infectious patients in the city as the daily confirmed new cases. It is shown that a quarantine measure on non-symptomatic patients is critically important in controlling the pandemic.

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“…The SIQR model [10,11] separates quarantined patients (Q) as a compartment in the population and α in Eq. ( 1) is given by the quarantine rate q ≡ ∆Q(t)/I(t) where ∆Q(t) is the daily confirmed new cases [7]. In the application of the SIQR model to COVID-19, it has been shown that…”

Section: Model Countrymentioning

confidence: 99%

Self-organization of oscillation in an epidemic model for COVID-19

Odagaki

2021

Preprint

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On the basis of a compartment model, the epidemic curve is investigated when the net rate λ of change of the number of infected individuals I is given by an ellipse in the λ-I plane which is supported inwhen a < 1 or I ℓ > 0, oscillation of the infection curve is self-organized and the period of the oscillation is in proportion to the ratio of (I h − I ℓ ) and the geometric mean √ I h I ℓ of I h and I ℓ , (2) when a = 1, the infection curve shows a critical behavior where it decays obeying a power law function with exponent −2 in the long time limit after a peak, and (3) when a > 1, the infection curve decays exponentially in the long time limit after a peak. The present result indicates that the pandemic can be controlled by a measure which makes I ℓ < 0.

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Self-organized wavy infection curve of COVID-19

Cited by 11 publications

References 18 publications

Crime and COVID-19 in Rio de Janeiro: How does organized crime shape the disease evolution?

Crime and COVID-19 in Rio de Janeiro: How does organized crime shape the disease evolution?

Estimation of the Onset Rate and the Number of Asymptomatic Patients of COVID-19 From the Proportion of Untraceable Patients

Self-organization of oscillation in an epidemic model for COVID-19

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