Classification of the infection status of COVID-19 in 190 countries

Odagaki, Takashi; Suda, Reiji

doi:10.1101/2020.12.17.20248445

Cited by 6 publications

(8 citation statements)

References 9 publications

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“…This implies that when I becomes large, some policies are employed to reduce λ to the negative area and when I becomes small, then some measures are lifted and λ becomes positive. In fact, the plots of λ(t) against I(t) in many countries show similar loops [2].…”

Section: Model Countrymentioning

confidence: 86%

“…Namely, when the number of infected individuals approaches the maximum number acceptable in a country, a strong measure is introduced to make the net rate of change λ negative, and the measure will be lifted when the number of infected individuals is considered to be small enough which makes λ > 0. Therefore, the policy with I ℓ > 0 itself is considered to be the origin of the oscillation of the infection curve and that with I ℓ < 0 seems to have succeeded on controlling the pandemic [2].…”

Section: Discussionmentioning

confidence: 99%

“…Since the first outbreak in China in November 2019, COVID-19 has been spreading in all continents including Antarctica. According to a recent analysis of infection status of 186 countries 1 [1,2], the time dependence of the daily confirmed new cases in more than 80 countries show oscillations whose periods range from one to five months depending on the country. The period of the oscillation is much shorter than that of Spanish flu in 1918∼1919 which is the result of the mutation of virus, and it is an open question why the infection curve of COVID-19 shows oscillation in some countries.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Model Countrymentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Odagaki

2021

Preprint

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“…In the analysis of asymptomatic patients in Tokyo, I assumed that the infection status is stationary. The infection status can also be increasing or decreasing (4). However, if the condition 〈 〉 0 = 〈 〉 1 is satisfied, the relation Eq.…”

Section: Discussionmentioning

confidence: 99%

Estimation of the onset ratio 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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“…Our approach is based on a modification of the classic SIR model [47] and is motivated by our earlier research on the spread of COVID-19 in different communities in [9,8] and [23]. The number of infections over time in Japan exhibits a wavy pattern, pointing to the onset of secondary waves or surges and various types of infection curves have been observed during the spread of the virus [21,11,31,32]. Monitoring these onsets, surges, outbreaks or secondary waves and depending on the infection and transmission rates, governments and local authorities can decide to impose a range of measures to mitigate or slow down the spread of the virus.…”

Section: Introductionmentioning

confidence: 99%

Dynamical analysis of the infection status in diverse communities due to COVID-19 using a modified SIR model

Cooper

Mondal

Antonopoulos

et al. 2021

Preprint

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In this article, we model and study the spread of COVID-19 in Germany, Japan, India and highly impacted states in India, i.e., Delhi, Maharashtra, West Bengal, Kerala and Karnataka. We consider recorded data published in Worldometers and COVID-19 India websites from April 2020 to July 2021, including periods of interest where these countries and states were hit severely by the pandemic. Our methodology is based on the classic susceptible-infected-removed (SIR) model and can track the evolution of infections in communities, i.e., in countries, states or groups of individuals, where we (a) allow for the susceptible and infected populations to be reset at times where surges, outbreaks or secondary waves appear in the recorded data sets, (b) consider the parameters in the SIR model that represent the probability of the infection and recovery rates to be functions of time and (c) estimate the number of deaths by combining the model solutions with the recorded data sets to approximate them between consecutive surges, outbreaks or secondary waves, providing a more accurate estimate. We report on the status of the current infections in these countries and states, and the infections and deaths in India and Japan in the end of August 2021, assuming a major outbreak occurs in early August 2021 in India and in mid-July 2021 in Japan. Our model can adapt to the recorded data and can be used to explain them and importantly, to forecast the number of infected, recovered, removed and dead individuals, as well as to estimate the probability of the infection and recovery rates as functions of time, assuming an outbreak occurs at a given time. The latter information can be used to forecast the future basic reproduction number and together with the forecast on the number of infected and dead individuals, our approach can further be used to suggest the implementation of intervention strategies and mitigation policies to keep at bay the number of infected and dead individuals. This, in conjunction with the implementation of vaccination programs worldwide, can help reduce significantly the impact of the spread around the world and improve the wellbeing of people.

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Classification of the infection status of COVID-19 in 190 countries

Cited by 6 publications

References 9 publications

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

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

Estimation of the onset ratio and the number of asymptomatic patients of COVID-19 from the proportion of untraceable patients

Dynamical analysis of the infection status in diverse communities due to COVID-19 using a modified SIR model

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