scholarly journals Forecasting the Wuhan coronavirus (2019-nCoV) epidemics using a simple (simplistic) model

2020 ◽  
Author(s):  
Slav W. Hermanowicz
Keyword(s):  
Exponential Growth ◽  
Reproduction Number ◽  
Mainland China ◽  
Logistic Growth ◽  
Cumulative Number ◽  
Secondary Sources ◽  
Sources Of Infection ◽  
Logistic Growth Model ◽  
Simple Logistic ◽  
Simplistic Model

Abstract and FindingsConfirmed infection cases in mainland China were analyzed using the data up to January 28, 2020 (first 13 days of reliable confirmed cases). In addition, all available data up to February 3 were processed the same way. For the first period the cumulative number of cases followed an exponential function. However, from January 28, we discerned a downward deviation from the exponential growth. This slower-than-exponential growth was also confirmed by a steady decline of the effective reproduction number. A backtrend analysis suggested the original basic reproduction number R0 to be about 2.4 – 2.5. We used a simple logistic growth model that fitted very well with all data reported until the time of writing. Using this model and the first set of data, we estimate that the maximum cases will be about 21,000 reaching this level in mid-February. Using all available data the maximum number of cases is somewhat higher at 29,000 but its dynamics does not change. These predictions do not account for any possible other secondary sources of infection.

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

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.

scholarly journals Outbreak analysis with a logistic growth model shows COVID-19 suppression dynamics in China

2020 ◽  
Author(s):  
Yi Zou ◽  
Stephen Pan ◽  
Peng Zhao ◽  
Lei Han ◽  
Xiaoxiang Wang ◽  
...  
Keyword(s):  
Growth Model ◽  
Mainland China ◽  
Logistic Growth ◽  
Complete Suppression ◽  
Virus Growth ◽  
Rate Of Increase ◽  
Decline Curves ◽  
Major Outbreak ◽  
Logistic Growth Model ◽  
Novel Coronavirus

AbstractChina reported a major outbreak of a novel coronavirus, SARS-CoV2, from mid-January till mid-March 2020. The number of cases outside China is now growing fast, while in mainland China the virus outbreak is largely under control. We review the epidemic virus growth and decline curves in China using a phenomenological logistic growth model to summarize the outbreak dynamics using three parameters that characterize the epidemic’s timing, rate and peak. During the initial phase, the number of virus cases doubled every 2.7 (range 2.2 - 4.4) days. The rate of increase in the number of reported cases peaked approximately 10 days after suppression measures were started on 23-25 January 2020. The peak in the number of reported sick cases occurred on average 18 days after the start of measures. From the time of starting measures till the peak, the number of cases increased by a factor 39 in the province Hubei, and by a factor 9.5 for all of China (range: 6.2-20.4 in the other provinces). Complete suppression took up to 2 months (range: 23-57d.), during which period severe restrictions, social distancing measures, testing and isolation of cases were in place. The suppression of the disease in China has been successful, demonstrating that suppression is a viable strategy to contain SARS-CoV2.

scholarly journals Epidemic Potential of COVID-19 in Omsk Region during Anti-Epidemic Measures

2020 ◽  
pp. 36-42
Author(s):  
A. I. Blokh ◽  
N. A. Pen’evskaya ◽  
N. V. Rudakov ◽  
I. I. Lazarev ◽  
O. A. Mikhailova ◽  
...  
Keyword(s):  
Growth Rate ◽  
Exponential Growth ◽  
Reproduction Number ◽  
Herd Immunity ◽  
Case Fatality ◽  
Community Acquired Pneumonia ◽  
Cumulative Number ◽  
Exponential Growth Rate ◽  
Epidemic Size ◽  
Threshold Data

Aim. To study the spread of COVID-19 among the population of the Omsk Region during 24 weeks of the epidemic on the background of anti-epidemic measures.Materials and methods. A descriptive epidemiological study was carried out based on publically available data и data from the Center for Hygiene and Epidemiology in the Omsk Region on the official registration and epidemiological investigation of detected COVID-19 cases in the Omsk Region for the period from March 27 to September 10, 2020. To assess the potential of COVID-19 to spread, the following indicators were calculated: exponential growth rate (r), basic reproduction number (R0), effective reproduction number (Rt), expected natural epidemic size and herd immunity threshold. Data processing was performed using MS Excel 2010. The cartogram was built using the QGIS 3.12-Bukuresti application in the EPSG: 3576 coordinate system.Results and discussion. For the period from March 27 to September 10, 2020, a total of 9779 cases of COVID-19 were registered in the Omsk Region, the cumulative incidence was 507,6 per 100000 (95 % CI 497,5÷517,6), the case-fatality rate for completed cases was 2.9 %, for identified cases – 2.4 %. The most active spread of COVID-19 was noted in Omsk and 4 out of 32 districts of the region (Moskalensky, Azov German National, Mariyanovsky, Novovarshavsky). During the ongoing anti-epidemic measures, the exponential growth rate of the cumulative number of COVID-19 cases was 4.5 % per day, R0 – 1.4–1.5, Rt – 1.10, herd immunity threshold – 28.6 %. The expected size of the epidemic in case of sustained anti-epidemic measures can reach 58.0 % of the recovered population. A decrease in the number of detected virus carriers, incomplete detection of COVID-19 among patients with community-acquired pneumonia introduced additional risks for the latent spread of infection and complications of the epidemic situation. Maintaining restrictive  measures and increasing the proportion of the immune population (over 28.6 %) may significantly reduce the risks of increasing the spread of COVID-19 in the Omsk Region. 

scholarly journals Analysis and Prediction for Confirmed COVID-19 Cases in Czech Republic with Uncertain Logistic Growth Model

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2264
Author(s):  
Chunxiao Ding ◽  
Wenjian Liu
Keyword(s):  
Regression Analysis ◽  
Czech Republic ◽  
Growth Model ◽  
Hypothesis Test ◽  
Logistic Growth ◽  
Cumulative Number ◽  
Uncertain Variables ◽  
Stochastic Regression ◽  
Disturbance Term ◽  
Logistic Growth Model

This paper presents an uncertain logistic growth model to analyse and predict the evolution of the cumulative number of COVID-19 infection in Czech Republic. Some fundamental knowledge about the uncertain regression analysis are reviewed firstly. Stochastic regression analysis is invalid to model cumulative number of confirmed COVID-19 cases in Czech Republic, by considering the disturbance term as random variables, because that the normality test and the identical distribution test of residuals are not passed, and the residual plot does not look like a null plot in the sense of probability theory. In this case, the uncertain logistic growth model is applied by characterizing the disturbance term as uncertain variables. Then parameter estimation, residual analysis, the forecast value and confidence interval are studied. Additionally, the uncertain hypothesis test is proposed to evaluate the appropriateness of the fitted logistic growth model and estimated disturbance term. The analysis and prediction for the cumulative number of COVID-19 infection in Czech Republic can propose theoretical support for the disease control and prevention. Due to the symmetry and similarity of epidemic transmission, other regions of COVID-19 infections, or other diseases can be disposed in a similar theory and method.

scholarly journals Using different epidemiological models to modeling the epidemic dynamics in Brazil

2020 ◽  
Author(s):  
Lucas Ravellys Pyrrho de Alcantara ◽  
Lucio Silva ◽  
Anderson Rodrigues de Almeida ◽  
Maira Galdino da Rocha Pitta ◽  
Artur Paiva Coutinho
Keyword(s):  
Growth Model ◽  
Logistic Growth ◽  
Cumulative Number ◽  
Mean Square ◽  
Epidemiological Models ◽  
Epidemic Dynamics ◽  
Absolute Relative Error ◽  
Logistic Growth Model ◽  
Richards Growth Model

In this paper we provide forecasts of the cumulative number of confirmed reported cases in Brazil, specifically in Pernambuco and Ceara, by using the generalized logistic growth model, the Richards growth model and Susceptible, Un-quanrantined infected, Quarantined infected, Confirmed infected (SUQC) phenomenological model. We rely on the Nash-Sutcliffe efficiency (NSE), root-mean-square error (RMSE) and mean absolute relative error (MARE) to quantify the quality of the models fits during the calibrationAll of these analyzes have been valid until the present date, April 14, 2020. The different models provide insights of our scenario predictions.

Oscillation in the Simple Logistic Growth Model

Nature ◽  
1965 ◽  
Vol 207 (4994) ◽  
pp. 316-316 ◽  
Author(s):  
L. M. COOK
Keyword(s):  
Growth Model ◽  
Logistic Growth ◽  
Logistic Growth Model ◽  
Simple Logistic

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

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.

scholarly journals Agriculture Management Strategies Using Simple Logistic Growth Model

2020 ◽  
Vol 596 ◽  
pp. 012076
Author(s):  
Nurul Syaza Abdul Latif ◽  
Noor Asma’ Mohd Anuar Mushoddad ◽  
Norin Syerina Mior Azmai
Keyword(s):  
Growth Model ◽  
Management Strategies ◽  
Logistic Growth ◽  
Logistic Growth Model ◽  
Simple Logistic ◽  
Agriculture Management

scholarly journals Forecasting the 2020 COVID-19 Epidemic: A Multivariate Quasi-Poisson Regression to Model the Evolution of New Cases in Chile

2021 ◽  
Vol 9 ◽  
Author(s):  
María Ignacia Vicuña ◽  
Cristián Vásquez ◽  
Bernardo F. Quiroga
Keyword(s):  
Growth Model ◽  
Exponential Growth ◽  
Count Data ◽  
Poisson Regression ◽  
Logistic Growth ◽  
Supplementary Material ◽  
Logistic Growth Model ◽  
Policy Solutions

Objectives: To understand and forecast the evolution of COVID-19 (Coronavirus disease 2019) in Chile, and analyze alternative simulated scenarios to better predict alternative paths, in order to implement policy solutions to stop the spread and minimize damage.Methods: We have specified a novel multi-parameter generalized logistic growth model, which does not only look at the trend of the data, but also includes explanatory covariates, using a quasi-Poisson regression specification to account for overdispersion of the count data. We fitted our model to data from the onset of the disease (February 28) until September 15. Estimating the parameters from our model, we predicted the growth of the epidemic for the evolution of the disease until the end of October 2020. We also evaluated via simulations different fictional scenarios for the outcome of alternative policies (those analyses are included in the Supplementary Material).Results and Conclusions: The evolution of the disease has not followed an exponential growth, but rather, stabilized and moved downward after July 2020, starting to increase again after the implementation of the Step-by-Step policy. The lockdown policy implemented in the majority of the country has proven effective in stopping the spread, and the lockdown-relaxation policies, however gradual, appear to have caused an upward break in the trend.

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