Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the world

AbstractBackgroundthe COVID-19 has been successfully contained in China but is spreading all over the world. We use phenomenological models to dissect the development of the epidemics in China and the impact of the drastic control measures both at the aggregate level and within each province. We use the experience from China to analyze the calibration results on Japan, South Korea, Iran, Italy and Europe, and make future scenario projections.Methodswe calibrate the logistic growth model, the generalized logistic growth model, the generalized growth model and the generalized Richards model to the reported number of infected cases from Jan. 19 to March 10 for the whole of China, 29 provinces in China, four severely affected countries and Europe as a whole. The different models provide upper and lower bounds of our scenario predictions.ResultsWe quantitatively document four phases of the outbreak in China with a detailed analysis on the heterogenous situations across provinces. Based on Chinese experience, we identify a high risk in Japan with estimated total confirmed cases as of March 25 being 1574 (95% CI: [880, 2372]), and 5669 (95% CI: [988, 11340]) by June. For South Korea, we expect the number of infected cases to approach the ceiling, 7928 (95% CI: [6341, 9754]), in 20 days. We estimate 0.15% (95% CI: [0.03%, 0.30%]) of Italian population to be infected in a positive scenario. We would expect 114867 people infected in Europe in 10 days, in a negative but probable scenario, corresponding to 0.015% European population.ConclusionsThe extreme containment measures implemented by China were very effective with some instructive variations across provinces. For other countries, it is almost inevitable to see the continuation of the outbreak in the coming months. Japan and Italy are in serious situations with no short-term end to the outbreak to be expected. There is a significant risk concerning the upcoming July 2020 Summer Olympics in Tokyo. Iran’s situation is highly uncertain with unclear and negative future scenarios, while South Korea is approaching the end of the outbreak. Both Europe and the USA are at early stages of the outbreak, posing significant health and economic risks to the world in absence of serious measures.

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Generalized logistic growth modeling of the COVID-19 outbreak: comparing the dynamics in the 29 provinces in China and in the rest of the world

Nonlinear Dynamics ◽

10.1007/s11071-020-05862-6 ◽

2020 ◽

Vol 101 (3) ◽

pp. 1561-1581 ◽

Cited By ~ 6

Author(s):

Ke Wu ◽

Didier Darcet ◽

Qian Wang ◽

Didier Sornette

Keyword(s):

Growth Model ◽

Western Europe ◽

Control Measures ◽

Logistic Growth ◽

Global Pandemic ◽

The World ◽

Containment Measures ◽

Logistic Growth Model ◽

The Impact ◽

Scenario Projections

Abstract Started in Wuhan, China, the COVID-19 has been spreading all over the world. We calibrate the logistic growth model, the generalized logistic growth model, the generalized Richards model and the generalized growth model to the reported number of infected cases for the whole of China, 29 provinces in China, and 33 countries and regions that have been or are undergoing major outbreaks. We dissect the development of the epidemics in China and the impact of the drastic control measures both at the aggregate level and within each province. We quantitatively document four phases of the outbreak in China with a detailed analysis on the heterogeneous situations across provinces. The extreme containment measures implemented by China were very effective with some instructive variations across provinces. Borrowing from the experience of China, we made scenario projections on the development of the outbreak in other countries. We identified that outbreaks in 14 countries (mostly in western Europe) have ended, while resurgences of cases have been identified in several among them. The modeling results clearly show longer after-peak trajectories in western countries, in contrast to most provinces in China where the after-peak trajectory is characterized by a much faster decay. We identified three groups of countries in different level of outbreak progress, and provide informative implications for the current global pandemic.

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Forecasting the Stability of COVID-19 on Indian Dataset with Prophet Logistic Growth Model

10.21203/rs.3.rs-32472/v1 ◽

2020 ◽

Author(s):

Nishtha Phutela ◽

Arushi G Bakshi ◽

Sunil Gupta ◽

Goldie Gabrani

Keyword(s):

Infectious Disease ◽

Mathematical Models ◽

Growth Model ◽

Logistic Growth ◽

The World ◽

Logistic Growth Model ◽

The Stability

Abstract Recently COVID-2019, a highly infectious disease has been declared as Pandemic by WHO, and since then the researchers all over the world are making attempts to predict the likely progression of this pandemic using various mathematical models. In this paper, we are using logistic growth model to find out the stability of this pandemic and Prophet Model to forecast the total number of confirmed cases that would be caused by COVID-19 in India.

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COVID-19 outbreak in Mauritius: Logistic growth and SEIR modelling with quarantine and an effective reproduction number

10.1101/2020.09.22.20199364 ◽

2020 ◽

Author(s):

Antoine Gehin ◽

Smita Goorah ◽

Khemanand Moheeput ◽

Satish Ramchurn

Keyword(s):

Public Health ◽

Growth Model ◽

Reproduction Number ◽

Control Measures ◽

Logistic Growth ◽

Cumulative Number ◽

Public Health Response ◽

The Public ◽

Logistic Growth Model ◽

Outbreak Size

SUMMARY Background and Objectives The island of Mauritius experienced a COVID-19 outbreak from mid-March to end April 2020. The first three cases were reported on March 18 (Day 1) and the last locally transmitted case occurred on April 26 (Day 40). An island confinement was imposed on March 20 followed by a sanitary curfew on March 23. Supermarkets were closed as from March 25 (Day 8). There were a total of 332 cases including 10 deaths from Day 1 to Day 41. Control of the outbreak depended heavily on contact tracing, testing, quarantine measures and the adoption of personal protective measures (PPMs) such as social distancing, the wearing of face masks and personal hygiene by Mauritius inhabitants. Our objectives were to model and understand the evolution of the Mauritius outbreak using mathematical analysis, a logistic growth model and an SEIR compartmental model with quarantine and a reverse sigmoid effective reproduction number and to relate the results to the public health control measures in Mauritius. Methods The daily reported cumulative number of cases in Mauritius were retrieved from the Worldometer website at https://www.worldometers.info/coronavirus/country/mauritius/. A susceptible-exposed-infectious-quarantined-removed (SEIQR) model was introduced and analytically integrated under the assumption that the daily incidence of infectious cases evolved as the derivative of the logistic growth function. The cumulative incidence data was fitted using a logistic growth model. The SEIQR model was integrated computationally with an effective reproduction number (R_e) varying in time according to a three-parameter reverse sigmoid model. Results were compared with the retrieved data and the parameters were optimised using the normalised root mean square error (NRMSE) as a comparative statistic. Findings A closed-form analytical solution was obtained for the time-dependence of the cumulative number of cases. For a small final outbreak size, the solution tends to a logistic growth. The cumulative number of cases was well described by the logistic growth model (NRMSE = 0.0276, R^2=0.9945) and by the SEIQR model (NRMSE = 0.0270, R^2=0.9952) with the optimal parameter values. The value of R_e on the day of the reopening of supermarkets (Day 16) was found to be approximately 1.6. Interpretation A mathematical basis has been obtained for using the logistic growth model to describe the time evolution of outbreaks with a small final outbreak size. The evolution of the outbreak in Mauritius was consistent with one modulated by a time-varying effective reproduction number resulting from the epidemic control measures implemented by Mauritius authorities and the PPMs adopted by Mauritius inhabitants. The value of R_e≈1.6 on the reopening of supermarkets on Day 16 was sufficient for the outbreak to grow to large-scale proportions in case the Mauritius population did not comply with the PPMs. However, the number of cases remained contained to a small number which is indicative of a significant contribution of the PPMs in the public health response to the COVID-19 outbreak in Mauritius.

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Activities: Activities for the Logistic Growth Model; or, Invasion of the Killer Moths

Mathematics Teacher ◽

10.5951/mt.90.7.0588 ◽

1997 ◽

Vol 90 (7) ◽

pp. 588-597

Author(s):

Robert (Bob) Iovinelli

Keyword(s):

Growth Model ◽

Exponential Function ◽

Exponential Growth ◽

Rapid Growth ◽

Insect Population ◽

Logistic Growth ◽

The World ◽

Logistic Growth Model ◽

Teacher's Guide ◽

Better Than

Teacher's Guide: When students begin to study exponential growth and they see a model for the rapid growth of, say, an insect population, they may wonder, “Why has this little bug not taken over the world if it can grow so fast? It is a reasonable question that allows the introduction of a function that models the situation better than the straightforward exponential function. The growth of the population of a species, when first introduced into an environment, can be subdivided into several different stages only one of which is exponential.

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A chaotic growth model of agricultural population

Journal of Agricultural Sciences Belgrade ◽

10.2298/jas0201097j ◽

2002 ◽

Vol 47 (1) ◽

pp. 97-103

Author(s):

Vesna Jablanovic ◽

Nada Lakic

Keyword(s):

Growth Model ◽

Logistic Growth ◽

Population Movement ◽

Formal Framework ◽

The World ◽

Logistic Growth Model ◽

Autoregression Models ◽

Set Up ◽

The Stability ◽

Chaotic Growth

Using the autoregression models, the paper considers movement of agricultural population. Irregular movement of agricultural population can be analyzed within the formal framework of the chaotic growth model. The basic aims of this paper are: firstly, to set up a chaotic growth model of agricultural population; and secondly, to analyze the stability of agricultural population movement according to the presented logistic growth model in the world and eight group of countries in the period 1967-1997.

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Progression of COVID-19 in Indian States - Forecasting Endpoints Using SIR and Logistic Growth Models

10.1101/2020.05.15.20103028 ◽

2020 ◽

Cited By ~ 1

Author(s):

Bhoomika Malhotra ◽

Vishesh Kashyap

Keyword(s):

Growth Model ◽

Sir Model ◽

Logistic Growth ◽

Indian States ◽

Health Crisis ◽

Social Distancing ◽

First Case ◽

Logistic Growth Model ◽

The Government ◽

The Impact

COVID-19 has led to the most widespread public health crisis in recent history. The first case of the disease was detected in India on 31 January 2019, and confirmed cases stand at 74,281 as of 13 May 2020. Mathematical modeling can be utilized to forecast the final numbers as well as the endpoint of the disease in India and its states, as well as assess the impact of social distancing measures. In the present work, the Susceptible-Infected-Recovered (SIR) model and the Logistic Growth model have been implemented to predict the endpoint of COVID-19 in India as well as three states accounting for over 55% of the total cases - Maharashtra, Gujarat and Delhi. The results using the SIR model indicate that the disease will reach an endpoint in India on 12 September, while Maharashtra, Gujarat and Delhi will reach endpoints on 20 August, 30 July and 9 September respectively. Using the Logistic Regression model, the endpoint for India is predicted on 23 July, while that for Maharashtra, Gujarat and Delhi is 5 July, 23 June and 10 August respectively. It is also observed that the case numbers predicted by the SIR model are greater than those for the Logistic Growth model in each case. The results suggest that the lockdown enacted by the Government of India has had only a moderate impact on the spread of COVID-19, and emphasize the need for firm implementation of social distancing guidelines.

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Characterizing the effectiveness of COVID-19 mitigation measures: A data-centric approach (Preprint)

10.2196/preprints.21321 ◽

2020 ◽

Author(s):

Lukman Olagoke ◽

Ahmet E. Topcu

Keyword(s):

Control Measure ◽

Control Measures ◽

World Health ◽

Mitigation Measures ◽

Effective Control ◽

The World ◽

Decision Making Processes ◽

And Performance ◽

Health Organization ◽

The Impact

BACKGROUND COVID-19 represents a serious threat to both national health and economic systems. To curb this pandemic, the World Health Organization (WHO) issued a series of COVID-19 public safety guidelines. Different countries around the world initiated different measures in line with the WHO guidelines to mitigate and investigate the spread of COVID-19 in their territories. OBJECTIVE The aim of this paper is to quantitatively evaluate the effectiveness of these control measures using a data-centric approach. METHODS We begin with a simple text analysis of coronavirus-related articles and show that reports on similar outbreaks in the past strongly proposed similar control measures. This reaffirms the fact that these control measures are in order. Subsequently, we propose a simple performance statistic that quantifies general performance and performance under the different measures that were initiated. A density based clustering of based on performance statistic was carried out to group countries based on performance. RESULTS The performance statistic helps evaluate quantitatively the impact of COVID-19 control measures. Countries tend show variability in performance under different control measures. The performance statistic has negative correlation with cases of death which is a useful characteristics for COVID-19 control measure performance analysis. A web-based time-line visualization that enables comparison of performances and cases across continents and subregions is presented. CONCLUSIONS The performance metric is relevant for the analysis of the impact of COVID-19 control measures. This can help caregivers and policymakers identify effective control measures and reduce cases of death due to COVID-19. The interactive web visualizer provides easily digested and quick feedback to augment decision-making processes in the COVID-19 response measures evaluation. CLINICALTRIAL Not Applicable

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The Impact of COVID-19 on Surgical Training: the Past, the Present and the Future

Indian Journal of Surgery ◽

10.1007/s12262-021-02964-2 ◽

2021 ◽

Author(s):

Marina Yiasemidou

Keyword(s):

Infection Control ◽

Surgical Training ◽

New Technologies ◽

Negative Impact ◽

Elective Surgery ◽

Control Measures ◽

Negative Consequences ◽

Surgical Services ◽

The World ◽

The Impact

AbstractThe COVID-19 pandemic and infection control measures had an unavoidable impact on surgical services. During the first wave of the pandemic, elective surgery, endoscopy, and ‘face-to-face’ clinics were discontinued after recommendations from professional bodies. In addition, training courses, examinations, conferences, and training rotations were postponed or cancelled. Inadvertently, infection control and prevention measures, both within and outside hospitals, have caused a significant negative impact on training. At the same time, they have given space to new technologies, like telemedicine and platforms for webinars, to blossom. While the recovery phase is well underway in some parts of the world, most surgical services are not operating at full capacity. Unfortunately, some countries are still battling a second or third wave of the pandemic with severely negative consequences on surgical services. Several studies have looked into the impact of COVID-19 on surgical training. Here, an objective overview of studies from different parts of the world is presented. Also, evidence-based solutions are suggested for future surgical training interventions.

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