scholarly journals COVID-19 Case Prediction and Outbreak Control of Navy Cluster in Sri Lanka: Effectiveness of SIR Model

2020 ◽  
Author(s):  
N.W.A.N.Y. Wijesekara ◽  
Nayomi Herath ◽  
K.A.L.C. Kodituwakku ◽  
H.D.B. Herath ◽  
Samitha Ginige ◽  
...  
Keyword(s):  
Sri Lanka ◽  
Sir Model ◽  
Effective Control ◽  
Infectious Period ◽  
Base Line ◽  
Susceptible Population ◽  
Outbreak Management ◽  
Social Distancing ◽  
Peak Number ◽  
First Case

Abstract Introduction: Infectious diseases such as coronavirus disease 2019 (COVID-19) can spread contagiously fast in semi-confined places, which demand prompt public health interventions such as isolation and quarantine for their effective control. An outbreak of COVID-19 was reported within a cluster of Navy personnel in the Western Province of Sri Lanka commencing from 22nd April 2020. In response, an aggressive outbreak management program was launched by the Epidemiology Unit of the Ministry of Health supported by the Sri Lanka Navy. The objective of this research was to predict possible number of cases within the susceptible population in Sri Lanka Navy. Methods: COVID-19 Hospital Impact Model for Epidemics (CHIME) developed by Predictive Health Care Team at Penn Medicine, Philadelphia, USA, which was a Susceptibility, Infected and Removed (SIR) model was used. The model was run on 20.05.2020 for a susceptible population of 10400, with number of hospitalized patients on the day of running the model being 357, first case hospitalized on 22.04.2020 and social distancing being implemented on 26.04.2020. Social distancing scenarios of 0, 25, 50 and 74% were run with 10 days of infectious period and 30 days of projection period. Results: With increasing social distancing measures, the peak number of infected persons decreased, and the duration of the curve extended. With increasing social distancing from 0% to 74%, the date on which the peak number of infected cases was reported increased from 49th day to the 54th day, the doubling time increased from 3.1 days to 4.1 days, the Ro decreased from 3.54 to 2.83, and expected daily growth rate decreased from 25.38% to 18.53%. The number of COVID-19 cases prevented as per the model ranged from 2.3 – 21.1 %, compared to the base line prediction of no social distancing. When comparing the observed number of cases with the baseline model with no social distancing, a 90.3% reduction was observed. Conclusion: The research demonstrated the practical use of a prediction model made readily available through an online open-source platform for the operational aspects of controlling outbreaks such as COVID-19 in a closed community. Predictive modelling is a useful tool for outbreak management.

scholarly journals COVID-19 Case Prediction and Outbreak Control of Navy Cluster in Sri Lanka: Effectiveness of SIR Model

2020 ◽  
Author(s):  
N.W.A.N.Y. Wijesekara ◽  
Nayomi Herath ◽  
K.A.L.C Kodituwakku ◽  
H.D.B. Herath ◽  
Samitha Ginige ◽  
...  
Keyword(s):  
Public Health ◽  
Sri Lanka ◽  
Sir Model ◽  
Epidemiological Surveillance ◽  
Infectious Period ◽  
Base Line ◽  
Ministry Of Health ◽  
Susceptible Population ◽  
Social Distancing ◽  
First Case

Abstract Introduction: Infectious diseases such as coronavirus disease 2019 (COVID-19) can spread dangerously fast in semi-confined places. Nevertheless, it has been found that rapid public health interventions such as isolation and quarantine could successfully curtail such outbreaks. An outbreak of COVID-19 was reported within a cluster of Navy personnel in the Western Province of Sri Lanka commencing from 22nd April 2020. An epidemiological investigation followed by aggressive public health measures were implemented by the Epidemiology Unit of the Ministry of Health with the support of the Sri Lanka Navy in response to the above outbreak. The objective of this research was to predict possible number of cases within the susceptible population in Sri Lanka Navy, to be used primarily for operational planning purpose by the Ministry of Health in control of outbreak in Sri Lanka.Methods: COVID-19 Hospital Impact Model for Epidemics (CHIME) developed by Predictive Health Care Team at Penn Medicine, which was a Susceptibility, Infected and Removed (SIR) model was used. The model was run on 20.05.2020 for a susceptible population of 10400, with number of hospitalized patients on the day of running the model being 357, first case hospitalized on 22.04.2020 and social distancing being implemented on 26.04.2020. Social distancing scenarios of 0, 25, 50 and 74% were run with 10 days of infectious period and 30 days of projection period.Results: With increasing social distancing measures, the peak number of infected persons decreased, as well as the duration of the curve extended. The number of infected cases from the first case ranged from 49th day to 54th day under social distancing scenarios from 0% to 74%. The doubling time increased from 3.1 days to 4.1 days from no social distancing to application of 74% social distancing, with corresponding decrease of Ro from 3.54 to 2.83. Expected daily growth rate of COVID-19 cases has decreased from 25.38 % to 18.53% under aforementioned increasing social distancing scenarios. The observed or actually experienced number of cases were well above the projected number of cases up to 07.05.2020, however, since this date the reported number of cases were lower than the projected number of cases from the model under four social distancing scenarios considered. Similar pattern was noted for the observed or actually experienced number of cases until the 20.05.2020, however, since then it was continuing at a very low intensity until the end of the modelling period. The number of COVID-19 cases prevented as per the model ranged from 2.3 – 21.1 %, compared to the base line prediction of no social distancing. However, based on the observed number of cases and the baseline model with no social distancing, 90.3% reduction was observed by the time of the model application date.Conclusion: The research demonstrated the practical use of a prediction model made readily available through an online open source platform for the operational aspects of controlling a COVID-19 or similar communicable disease outbreaks in a closed community such as armed forces. While comprehensive epidemiological surveillance, contact tracing, case isolation and case management should be the cornerstone of outbreak management, predictive modelling could supplement above efforts.

scholarly journals Predictive modelling for COVID-19 outbreak control: lessons from the navy cluster in Sri Lanka

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
N. W. A. N. Y. Wijesekara ◽  
Nayomi Herath ◽  
K. A. L. C. Kodituwakku ◽  
H. D. B. Herath ◽  
Samitha Ginige ◽  
...  
Keyword(s):  
Sri Lanka ◽  
Predictive Modelling ◽  
Management Program ◽  
Impact Model ◽  
Ministry Of Health ◽  
Susceptible Population ◽  
Outbreak Management ◽  
Social Distancing ◽  
The Right ◽  
Navy Personnel

AbstractIn response to an outbreak of coronavirus disease 2019 (COVID-19) within a cluster of Navy personnel in Sri Lanka commencing from 22nd April 2020, an aggressive outbreak management program was launched by the Epidemiology Unit of the Ministry of Health. To predict the possible number of cases within the susceptible population under four social distancing scenarios, the COVID-19 Hospital Impact Model for Epidemics (CHIME) was used. With increasing social distancing, the epidemiological curve flattened, and its peak shifted to the right. The observed or actually reported number of cases was above the projected number of cases at the onset; however, subsequently, it fell below all predicted trends. Predictive modelling is a useful tool for the control of outbreaks such as COVID-19 in a closed community.

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 COVID-19 epidemic in Sri Lanka: A mathematical and computational modelling approach to control

2020 ◽  
Author(s):  
WPTM Wickramaarachchi ◽  
SSN Perera ◽  
S Jayasignhe
Keyword(s):  
Sri Lanka ◽  
Computational Modelling ◽  
Control Measures ◽  
Critical Parameters ◽  
World Population ◽  
Sri Lankan ◽  
Social Distancing ◽  
First Case ◽  
Local Case ◽  
The Government

AbstractThe ongoing COVID19 outbreak originated in the city of Wuhan, China has caused a significant damage to the world population and the global economy. It has claimed more than 50,000 lives worldwide and more than one million of people have been infected as of 04th April 2020.In Sri Lanka, the first case of COVI19 was reported late January 2020 was a Chinese national and the first local case was identified in the second week of March. Since then, the government of Sri Lanka introduced various sequential measures to improve social distancing such as closure of schools and education institutes, introducing work from home model to reduce the public gathering, introducing travel bans to international arrivals and more drastically, imposed island wide curfew expecting to minimize the burden of the disease to the Sri Lankan health system and the entire community. Currently, there are 159 cases with five fatalities and also reported that 24 patients are recovered and discharged from hospitals.In this study, we use the SEIR conceptual model and its modified version by decomposing infected patients into two classes; patients who show mild symptoms and patients who tend to face severe respiratory problems and are required to treat in intensive care units. We numerically simulate the models for about five months period considering three critical parameters of COVID transmission mainly in the Sri Lankan context; efficacy of control measures, rate of overseas imported cases and time to introduce social distancing measures by the respective authorities.

scholarly journals Estimating the basic reproduction number for the 2015 bubonic plague outbreak in Nyimba district of Eastern Zambia

2020 ◽  
Vol 14 (11) ◽  
pp. e0008811
Author(s):  
Joseph Sichone ◽  
Martin C. Simuunza ◽  
Bernard M. Hang’ombe ◽  
Mervis Kikonko
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Cost Effective ◽  
Control Measures ◽  
Effective Control ◽  
Infectious Period ◽  
Bubonic Plague ◽  
Infectious Individual ◽  
Susceptible Population ◽  
The Basic Reproduction Number

Background Plague is a re-emerging flea-borne infectious disease of global importance and in recent years, Zambia has periodically experienced increased incidence of outbreaks of this disease. However, there are currently no studies in the country that provide a quantitative assessment of the ability of the disease to spread during these outbreaks. This limits our understanding of the epidemiology of the disease especially for planning and implementing quantifiable and cost-effective control measures. To fill this gap, the basic reproduction number, R0, for bubonic plague was estimated in this study, using data from the 2015 Nyimba district outbreak, in the Eastern province of Zambia. R0 is the average number of secondary infections arising from a single infectious individual during their infectious period in an entirely susceptible population. Methodology/Principal findings Secondary epidemic data for the most recent 2015 Nyimba district bubonic plague outbreak in Zambia was analyzed. R0 was estimated as a function of the average epidemic doubling time based on the initial exponential growth rate of the outbreak and the average infectious period for bubonic plague. R0 was estimated to range between 1.5599 [95% CI: 1.382–1.7378] and 1.9332 [95% CI: 1.6366–2.2297], with average of 1.7465 [95% CI: 1.5093–1.9838]. Further, an SIR deterministic mathematical model was derived for this infection and this estimated R0 to be between 1.4 to 1.5, which was within the range estimated above. Conclusions/Significance This estimated R0 for bubonic plague is an indication that each bubonic plague case can typically give rise to almost two new cases during these outbreaks. This R0 estimate can now be used to quantitatively analyze and plan measurable interventions against future plague outbreaks in Zambia.

scholarly journals Transmission dynamics and control strategies of COVID-19: a modelling study

2021 ◽  
Vol 102 (2) ◽  
pp. 92-105
Author(s):  
U.T. Mustapha ◽  
◽  
E. Hincal ◽  
A. Yusuf ◽  
S. Qureshi ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Recovery Rate ◽  
Transmission Rate ◽  
Face Mask ◽  
Basic Reproductive Number ◽  
Effective Control ◽  
Reproductive Number ◽  
Model Parameters ◽  
Susceptible Population ◽  
Social Distancing

In this paper a mathematical model is proposed, which incorporates quarantine and hospitalization to assess the community impact of social distancing and face mask among the susceptible population. The model parameters are estimated and fitted to the model with the use of laboratory confirmed COVID-19 cases in Turkey from March 11 to October 10, 2020. The partial rank correlation coefficient is employed to perform sensitivity analysis of the model, with basic reproduction number and infection attack rate as response functions. Results from the sensitivity analysis reveal that the most essential parameters for effective control of COVID-19 infection are recovery rate from quarantine individuals (δ1), recovery rate from hospitalized individuals (δ4), and transmission rate (β). Some simulation results are obtained with the aid of mesh plots with respect to the basic reproductive number as a function of two different biological parameters randomly chosen from the model. Finally, numerical simulations on the dynamics of the model highlighted that infections from the compartments of each state variables decreases with time which causes an increase in susceptible individuals. This implies that avoiding contact with infected individuals by means of adequate awareness of social distancing and wearing face mask are vital to prevent or reduce the spread of COVID-19 infection.

scholarly journals COVID-19 Epidemic in Sri Lanka: A Mathematical and Computational Modelling Approach to Control

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
W. P. T. M. Wickramaarachchi ◽  
S. S. N. Perera ◽  
S. Jayasinghe
Keyword(s):  
Sri Lanka ◽  
Early Stage ◽  
Computational Modelling ◽  
Control Measures ◽  
Critical Parameters ◽  
World Population ◽  
Sri Lankan ◽  
Social Distancing ◽  
First Case ◽  
Local Case

The ongoing COVID-19 outbreak that originated in the city of Wuhan, China, has caused a significant damage to the world population and the global economy. It has claimed more than 0.8 million lives worldwide, and more than 27 million people have been infected as of 07th September 2020. In Sri Lanka, the first case of COVID-19 was reported late January 2020 which was a Chinese national and the first local case was identified in the second week of March. Since then, the government of Sri Lanka introduced various sequential measures to improve social distancing such as closure of schools and education institutes, introducing work from home model to reduce the public gathering, introducing travel bans to international arrivals, and more drastically, imposed island wide curfew expecting to minimize the burden of the disease to the Sri Lankan health system and the entire community. Currently, there are 3123 cases with 12 fatalities and also, it was reported that 2925 patients have recovered and are discharged from hospitals, according to the Ministry of Health, Sri Lanka. In this study, we use the SEIR conceptual model and its modified version by decomposing infected patients into two classes: patients who show mild symptoms and patients who tend to face severe respiratory problems and are required to be treated in intensive care units. We numerically simulate the models for about a five-month period reflecting the early stage of the epidemic in the country, considering three critical parameters of COVID-19 transmission mainly in the Sri Lankan context: efficacy of control measures, rate of overseas imported cases, and time to introduce social distancing measures by the respective authorities.

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 Flattening the Curve: The effects of intervention strategies during COVID-19

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Kelly Reagan ◽  
Rachel Pryor ◽  
Gonzalo Bearman ◽  
David Chan
Keyword(s):  
Personal Protective Equipment ◽  
Transmission Rate ◽  
State Level ◽  
Sir Model ◽  
Intervention Strategies ◽  
Time Dependent ◽  
Protective Equipment ◽  
Social Distancing ◽  
Peak Number ◽  
Number Of Individuals

COVID-19 has plagued countries worldwide due to its infectious nature. Social distancing and the use of personal protective equipment (PPE) are two main strategies employed to prevent its spread. A SIR model with a time-dependent transmission rate is implemented to examine the effect of social distancing and PPE use in hospitals. These strategies’ effect on the size and timing of the peak number of infectious individuals are examined as well as the total number of individuals infected by the epidemic. The effect on the epidemic of when social distancing is relaxed is also examined. Overall, social distancing was shown to cause the largest impact in the number of infections. Studying this interaction between social distancing and PPE use is novel and timely. We show that decisions made at the state level on implementing social distancing and acquiring adequate PPE have dramatic impact on the health of its citizens.

scholarly journals A Model for the Testing and Tracing Needed to Suppress COVID-19

2020 ◽  
Author(s):  
Victor Wang
Keyword(s):  
Reproduction Number ◽  
Infectious Period ◽  
Simple Mathematical Model ◽  
Susceptible Population ◽  
Seir Model ◽  
Social Distancing ◽  
Health Practitioners ◽  
Public Health Practitioners ◽  
The Basic Reproduction Number ◽  
Close Contacts

AbstractThis paper presents a simple mathematical model that answers how much testing and tracing we need to do to suppress new surges of COVID-19 infections after reopening. We derived the model by modifying the SEIR model taking into the effects of testing and tracing. The following equation is one of the essential outcomes of the model: Where ρ is the percentage of infectious people that have to be detected per day, R0 is the basic reproduction number, S/N is the percentage of the susceptible population over the entire population, D is the length of the infectious period, and η is the percentage of close contacts that have to be traced. If the above equation is satisfied, we can bring the effective reproduction number Re to below 1 to get the transmission suppressed. This model demonstrates that together with social-distancing measures such as wearing masks in public, with a reasonable amount of testing and tracing, we may suppress the COVID-19 transmission for good. For example, if social distancing measures can bring R0 to below 1.2, for D being 10 days, in places where 15% people have developed antibodies, we can suppress the transmission by detecting only 0.13% of the infectious population daily while tracing 50% of their close contacts. The model provides intuitive insights and quantitative guidance for policymakers and public health practitioners to deploy the testing and tracing resources optimally.

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