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

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.

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

10.21203/rs.3.rs-70722/v2 ◽

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.

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

Military Medical Research ◽

10.1186/s40779-021-00325-4 ◽

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.

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Ongoing measles outbreak in Elche, Spain, 29 January to 9 March 2012

Eurosurveillance ◽

10.2807/ese.17.11.20119-en ◽

2012 ◽

Vol 17 (11) ◽

Author(s):

J A Delgado de los Reyes ◽

M Arencibia Jiménez ◽

J F Navarro Gracia ◽

E Alonso Echabe ◽

P García Puente ◽

...

Keyword(s):

Public Health ◽

Vaccination Coverage ◽

Surveillance System ◽

Preventive Services ◽

Epidemiological Surveillance ◽

Susceptible Population ◽

First Case ◽

Measles Outbreak ◽

Health Centres ◽

Epidemiological Surveillance System

On 29 January 2012, the first case of measles in Elche, Spain, since 2001 was notified through the epidemiological surveillance system of the Valencian Community. As of 9 March, 109 cases have been notified. The outbreak started in a neighbourhood where the vaccination coverage of the population is inadequate. This report highlights the need to vaccinate the susceptible population and also points to the importance of developing coordinated measures between public health centres and hospital preventive services.

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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

Journal of Medical Internet Research ◽

10.2196/21257 ◽

2020 ◽

Vol 22 (8) ◽

pp. e21257 ◽

Cited By ~ 4

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.

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

Applied Sciences ◽

10.3390/app10175895 ◽

2020 ◽

Vol 10 (17) ◽

pp. 5895 ◽

Cited By ~ 1

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.

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The SIR and SEIR Epidemiological Models Revisited

10.20944/preprints202005.0090.v1 ◽

2020 ◽

Author(s):

John P. Maassen

Keyword(s):

Public Health ◽

Monte Carlo ◽

Stochastic Model ◽

Monte Carlo Simulations ◽

Sir Model ◽

Epidemiological Models ◽

Seir Model ◽

Social Distancing

We review and assess the classic SIR and SEIR epidemiological models regarding possible applications to the COVID-19 pandemic. In spite of numerous more complicated models, we show how the qualitative features of the solution to the SIR and SEIR models continue to provide valuable public health insights in some scenarios. Using estimated COVID-19 data as of this date, the SEIR model shows that if it were possible to reduce R0 from 2.5 to 1.25 through social distancing and other measures, the maximum fraction of the population that would become infected at any particular time would drop from 17% to 4%, provided that all of the model assumptions are satisfied. Finally, we compare the classic SIR model with a recent stochastic model with favorable results. Since this comparison underscores the importance of underlying connectivity assumptions, we conclude with Monte-Carlo simulations with specific connectivity that reproduce the classical SIR model with standard incidence.

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

10.1101/2020.04.21.20073734 ◽

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.

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

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/4045064 ◽

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.

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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 (Preprint)

10.2196/preprints.21257 ◽

2020 ◽

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.

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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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