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

2020 ◽  
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.

scholarly journals An adaptive social distancing SIR model for COVID-19 disease spreading and forecasting

2021 ◽  
Vol 10 (s1) ◽  
Author(s):  
Said Gounane ◽  
Yassir Barkouch ◽  
Abdelghafour Atlas ◽  
Mostafa Bendahmane ◽  
Fahd Karami ◽  
...  
Keyword(s):  
Mathematical Theory ◽  
Real Data ◽  
Sir Model ◽  
Epidemic Threshold ◽  
Social Distancing ◽  
Sir Epidemic ◽  
Proposed Model ◽  
Disease Spreading ◽  
The Government ◽  
The Impact

Abstract Recently, various mathematical models have been proposed to model COVID-19 outbreak. These models are an effective tool to study the mechanisms of coronavirus spreading and to predict the future course of COVID-19 disease. They are also used to evaluate strategies to control this pandemic. Generally, SIR compartmental models are appropriate for understanding and predicting the dynamics of infectious diseases like COVID-19. The classical SIR model is initially introduced by Kermack and McKendrick (cf. (Anderson, R. M. 1991. “Discussion: the Kermack–McKendrick Epidemic Threshold Theorem.” Bulletin of Mathematical Biology 53 (1): 3–32; Kermack, W. O., and A. G. McKendrick. 1927. “A Contribution to the Mathematical Theory of Epidemics.” Proceedings of the Royal Society 115 (772): 700–21)) to describe the evolution of the susceptible, infected and recovered compartment. Focused on the impact of public policies designed to contain this pandemic, we develop a new nonlinear SIR epidemic problem modeling the spreading of coronavirus under the effect of a social distancing induced by the government measures to stop coronavirus spreading. To find the parameters adopted for each country (for e.g. Germany, Spain, Italy, France, Algeria and Morocco) we fit the proposed model with respect to the actual real data. We also evaluate the government measures in each country with respect to the evolution of the pandemic. Our numerical simulations can be used to provide an effective tool for predicting the spread of the disease.

scholarly journals Characterizing Two Outbreak Waves of COVID-19 in Spain Using Phenomenological Epidemic Modelling

2021 ◽  
Author(s):  
Miguel López ◽  
Alberto Peinado ◽  
Andrés Ortiz
Keyword(s):  
Growth Model ◽  
Logistic Growth ◽  
Data Series ◽  
The Novel ◽  
Short Term ◽  
Growth Trend ◽  
Global Pandemic ◽  
First Case ◽  
Logistic Growth Model ◽  
Novel Coronavirus

AbstractSince the first case reported of SARS-CoV-2 the end of December 2019 in China, the number of cases quickly climbed following an exponential growth trend, demonstrating that a global pandemic is possible. As of December 3, 2020, the total number of cases reported are around 65,527,000 contagions worldwide, and 1,524,000 deaths affecting 218 countries and territories. In this scenario, Spain is one of the countries that has suffered in a hard way, the ongoing epidemic caused by the novel coronavirus SARS-CoV-2, namely COVID-19 disease. In this paper, we present the utilization of phenomenological epidemic models to characterize the two first outbreak waves of COVID-19 in Spain. The study is driven using a two-step phenomenological epidemic approach. First, we use a simple generalized growth model to fit the main parameters at the early epidemic phase; later, we apply our previous finding over a logistic growth model to that characterize both waves completely. The results show that even in the absence of accurate data series, it is possible to characterize the curves of case incidence, and even construct short-term forecast in the near time horizon.

scholarly journals Reconstructing and Forecasting the COVID-19 Epidemic in the US Using a 5-Parameter Logistic Growth Model

2020 ◽  
Author(s):  
Ding-Geng Chen ◽  
Xinguang Chen ◽  
Jenny Ke Chen
Keyword(s):  
Growth Rate ◽  
Growth Model ◽  
Early Period ◽  
Logistic Growth ◽  
Daily Growth ◽  
First Case ◽  
The Us ◽  
Logistic Growth Model ◽  
Using Data ◽  
Public Policy Decisions

Abstract Background: Many studies have modeled and predicted the epidemic of COVID-19 in the US using data that starts from the first reported cases. However, because of the shortage of test services to detect the infected, this approach is subject to error due to under-detection in the early period of the epidemic. We attempted a new approach to overcome this limitation and to provide data supporting the public policy decisions against the life-threatening COVID-19 epidemic.Methods: Documented data by CDC were used, including daily new and cumulative cases of confirmed COVID-19 in the US from January 22 to April 6, 2020. A 5-parameter logistic growth model was used to reconstruct the epidemic. Instead of all data in the whole study period, we fitted data in a 2-week window from March 21 to April 4 (approximately one incubation period) during which massive testing services were in position. With parameters obtained from the modeling, we reconstructed and predicted the epidemic and evaluated the under-detection.Results: The data fit the model satisfactorily. The estimated daily growth rate was 16.8% (95% CI: 15.95%, 17.76%) overall, with 4 consecutive days having a doubling growth rate. Based on the modeling result, the tipping point for new cases to decline will be on April 7 th , 2020, with 32,860 new cases. By the end of the epidemic, a total of 792,548 (95% CI: 789,162-795,934) will be infected. Based on the model, a total of 12,029 cases were not detected from the first case from January 22 to April 4.Conclusions: Study findings suggest the usage of a 5-parameter logistic growth model with reliable data that comes from a specified window period, where governmental interventions are appropriately implemented. In addition to informing decision-making, this model adds one tool for use to capture the underlying COVID-19 epidemic caused by a novel pathogen.

scholarly journals Impact of Wearing Masks, Hand Hygiene, and Social Distancing on Influenza, Enterovirus, and All-Cause Pneumonia During the Coronavirus Pandemic: Retrospective National Epidemiological Surveillance Study

10.2196/21257 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e21257 ◽  
Author(s):  
Nan-Chang Chiu ◽  
Hsin Chi ◽  
Yu-Lin Tai ◽  
Chun-Chih Peng ◽  
Cheng-Yin Tseng ◽  
...  
Keyword(s):  
Influenza Virus ◽  
Hand Hygiene ◽  
Viral Infections ◽  
Control Strategies ◽  
Epidemiological Surveillance ◽  
Regression Analyses ◽  
Health Crisis ◽  
Social Distancing ◽  
First Case ◽  
The Impact

Background The coronavirus disease (COVID-19) pandemic is an important health crisis worldwide. Several strategies were implemented to combat COVID-19, including wearing masks, hand hygiene, and social distancing. The impact of these strategies on COVID-19 and other viral infections remains largely unclear. Objective We aim to investigate the impact of implemented infectious control strategies on the incidences of influenza, enterovirus infection, and all-cause pneumonia during the COVID-19 pandemic. Methods We utilized the electronic database of the Taiwan National Infectious Disease Statistics System and extracted incidences of COVID-19, influenza virus, enterovirus, and all-cause pneumonia. We compared the incidences of these diseases from week 45 of 2016 to week 21 of 2020 and performed linear regression analyses. Results The first case of COVID-19 in Taiwan was reported in late January 2020 (week 4). Infectious control strategies have been promoted since late January. The influenza virus usually peaks in winter and decreases around week 14. However, a significant decrease in influenza was observed after week 6 of 2020. Regression analyses produced the following results: 2017, R2=0.037; 2018, R2=0.021; 2019, R2=0.046; and 2020, R2=0.599. A dramatic decrease in all-cause pneumonia was also reported (R2 values for 2017-2020 were 0.435, 0.098, 0.352, and 0.82, respectively). Enterovirus had increased by week 18 in 2017-2019, but this was not observed in 2020. Conclusions Using this national epidemiological database, we found a significant decrease in cases of influenza, enterovirus, and all-cause pneumonia during the COVID-19 pandemic. Wearing masks, hand hygiene, and social distancing may contribute not only to the prevention of COVID-19 but also to the decline of other respiratory infectious diseases. Further studies are warranted to elucidate the causal relationship.

scholarly journals Epidemiological Modeling of COVID-19 in Saudi Arabia: Spread Projection, Awareness, and Impact of Treatment

2020 ◽  
Vol 10 (17) ◽  
pp. 5895 ◽  
Author(s):  
Yousef Alharbi ◽  
Abdulrahman Alqahtani ◽  
Olayan Albalawi ◽  
Mohsen Bakouri
Keyword(s):  
Saudi Arabia ◽  
Information Criterion ◽  
Sir Model ◽  
Cumulative Number ◽  
Epidemiological Modeling ◽  
Social Distancing ◽  
Epidemic Size ◽  
Statistical Indices ◽  
First Case ◽  
The Impact

The first case of COVID-19 originated in Wuhan, China, after which it spread across more than 200 countries. By 21 July 2020, the rapid global spread of this disease had led to more than 15 million cases of infection, with a mortality rate of more than 4.0% of the total number of confirmed cases. This study aimed to predict the prevalence of COVID-19 and to investigate the effect of awareness and the impact of treatment in Saudi Arabia. In this paper, COVID-19 data were sourced from the Saudi Ministry of Health, covering the period from 31 March 2020 to 21 July 2020. The spread of COVID-19 was predicted using four different epidemiological models, namely the susceptible–infectious–recovered (SIR), generalized logistic, Richards, and Gompertz models. The assessment of models’ fit was performed and compared using four statistical indices (root-mean-square error (RMSE), R squared (R2), adjusted R2 ( Radj2), and Akaike’s information criterion (AIC)) in order to select the most appropriate model. Modified versions of the SIR model were utilized to assess the influence of awareness and treatment on the prevalence of COVID-19. Based on the statistical indices, the SIR model showed a good fit to reported data compared with the other models (RMSE = 2790.69, R2 = 99.88%, Radj2 = 99.98%, and AIC = 1796.05). The SIR model predicted that the cumulative number of infected cases would reach 359,794 and that the pandemic would end by early September 2020. Additionally, the modified version of the SIR model with social distancing revealed that there would be a reduction in the final cumulative epidemic size by 9.1% and 168.2% if social distancing were applied over the short and long term, respectively. Furthermore, different treatment scenarios were simulated, starting on 8 July 2020, using another modified version of the SIR model. Epidemiological modeling can help to predict the cumulative number of cases of infection and to understand the impact of social distancing and pharmaceutical intervention on the prevalence of COVID-19. The findings from this study can provide valuable information for governmental policymakers trying to control the spread of this pandemic.

scholarly journals 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

2020 ◽  
Vol 101 (3) ◽  
pp. 1561-1581 ◽  
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.

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

2020 ◽  
Author(s):  
Ke Wu ◽  
Didier Darcet ◽  
Qian Wang ◽  
Didier Sornette
Keyword(s):  
South Korea ◽  
Growth Model ◽  
Significant Risk ◽  
Control Measures ◽  
Upper And Lower Bounds ◽  
Logistic Growth ◽  
Italian Population ◽  
The World ◽  
Logistic Growth Model ◽  
The Impact

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.

scholarly journals Forecasting Novel Corona Positive Cases in India using Truncated Information: A Mathematical Approach

2020 ◽  
Author(s):  
Brijesh P. Singh
Keyword(s):  
United States ◽  
Growth Model ◽  
United States Of America ◽  
Initial Phase ◽  
The United States ◽  
The Other ◽  
Logistic Growth ◽  
Mathematical Approach ◽  
First Case ◽  
Logistic Growth Model

AbstractNovel corona virus is declared as pandemic and India is struggling to control this from a massive attack of death and destruction, similar to the other countries like China, Europe, and the United States of America. India reported 2545 cases novel corona confirmed cases as of April 2, 2020 and out of which 191 cases were reported recovered and 72 deaths occurred. The first case of novel corona is reported in India on January 30, 2020. The growth in the initial phase is following exponential. In this study an attempt has been made to model the spread of novel corona infection. For this purpose logistic growth model with minor modification is used and the model is applied on truncated information on novel corona confirmed cases in India. The result is very exiting that till date predicted number of confirmed corona positive cases is very close to observed on. The time of point of inflexion is found in the end of the April, 2020 means after that the increasing growth will start decline and there will be no new case in India by the end of July, 2020.

scholarly journals Characterizing two outbreak waves of COVID-19 in Spain using phenomenological epidemic modelling

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253004
Author(s):  
Miguel López ◽  
Alberto Peinado ◽  
Andrés Ortiz
Keyword(s):  
Growth Model ◽  
Total Duration ◽  
Logistic Growth ◽  
Data Series ◽  
The Novel ◽  
Growth Trend ◽  
Global Pandemic ◽  
First Case ◽  
Logistic Growth Model ◽  
Novel Coronavirus

Since the first case reported of SARS-CoV-2 the end of December 2019 in China, the number of cases quickly climbed following an exponential growth trend, demonstrating that a global pandemic is possible. As of December 3, 2020, the total number of cases reported are around 65,527,000 contagions worldwide, and 1,524,000 deaths affecting 218 countries and territories. In this scenario, Spain is one of the countries that has suffered in a hard way, the ongoing epidemic caused by the novel coronavirus SARS-CoV-2, namely COVID-19 disease. In this paper, we present the utilization of phenomenological epidemic models to characterize the two first outbreak waves of COVID-19 in Spain. The study is driven using a two-step phenomenological epidemic approach. First, we use a simple generalized growth model to fit the main parameters at the early epidemic phase; later, we apply our previous finding over a logistic growth model to that characterize both waves completely. The results show that even in the absence of accurate data series, it is possible to characterize the curves of case incidence, and construct a short-term forecast of 60 days in the near time horizon, in relation to the expected total duration of the pandemic.

scholarly journals Application Of The Hybrid Method Nonlinear Regression With Modified Logistic Growth Model-Exponential Double Smoothing For Forecasting Covid-19 Cases In Indonesia And Armenia

Khazanah ◽  
2020 ◽  
Vol 12 (2) ◽  
Author(s):  
Andy Rezky Pratama Syam ◽  
Keyword(s):  
Growth Model ◽  
Nonlinear Regression ◽  
Hybrid Method ◽  
Good Method ◽  
Logistic Growth ◽  
Forecasting Method ◽  
Logistic Growth Model ◽  
Double Smoothing ◽  
The Government ◽  
Second Wave

Since the first cases of Covid-19 infection were officially recognized and recorded in Indonesia on March 2, 2020 and March 1, 2020 in Armenia, the addition of new cases has not shown any indication of sloping. The relatively high number of new cases indicates that Indonesia has not yet passed the peak of the pandemic. As for Armenia, the addition of new cases indicates a new pandemic peak to be faced. In these conditions, an important question for decision makers (the Government) to find answers to is when and at what level of total cases will the COVID-19 pandemic end in Indonesia or the second wave in Armenia. Forecasting method of Hybrid Nonlinear Regression With Modified Logistic Growth Model - Double Smoothing Exponential and Classical methods is used to predict the Covid-19 cases that occur in Indonesia and Armenia. Based on the model formed, the peak of Covid-19 cases in Indonesia is predicted to occur on November 26, 2020, with the number of cases reaching 5968 cases. As for Armenia, the peak of Covid-19 cases will occur on November 15, 2020, with the number of cases reaching 3098 cases. Covid-19 in both countries is predicted to decline and be constant in 2021. For the country, Indonesia is predicted to begin to stabilize and be controlled in July - August 2021. As for Armenia, Covid-19 is predicted to be under control and approaching 0 cases in February - March 2021. Forecasting models for the Covid-19 cases in Indonesia and Armenia are different, where for the Covid-19 case in Indonesia the Nonlinear Regression with Logistic Growth Model can be used and for the country of Armenia, the Nonlinear Regression with Modified Logistic Growth Model must be used because it has 2 peak cases. Hybrid method is a very good method for optimizing forecast results. Its application in the Covid-19 case in Indonesia and in Armenia shows that the Hybrid method produces a better MAPE value than the Nonlinear Regression with Logistic Growth Model alone or the Exponential Double Smoothing method alone

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