An Epidemiological Model to Aid Decision-Making for Covid-19 Control in Sri Lanka

Ediriweera, Dileepa Senajith; Silva, Nilanthi R. de; Malavige, N.; Silva, H Janaka de

doi:10.1101/2020.04.11.20061481

Cited by 11 publications

(10 citation statements)

References 7 publications

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“…in [18] mathematically model and evaluate numerous non-pharmaceutical interventions of Sri Lanka for a limited time period of 8 months. Similar work which mathematically model spreading using Susceptible-Exposed-Infectious-Recovered (SEIR) model [49,55] considers the spreading of the disease in the first 6 months [26]. But such work does not take into account some factors such as varieties of the COVID variants, immunization due to vaccination which had taken place recently.…”

Section: Motivationmentioning

confidence: 99%

An Accurate Mathematical Epidemiological Model (SEQIJRDS) to Recommend Public Health Interventions Related to COVID-19 in Sri Lanka.

Wijesekara

2021

Preprint

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COVID-19 has been causing negative impacts on various sectors in Sri Lanka as a result of the public health interventions that government had to implement in order to reduce the spreading of the disease. Equivalent work carried out in this context is outdated and close to ideal models. This research is carried out in a crucial time which the daily deaths are rapidly increasing which arise the requirement for an accurate and practical model to predict the mortality in order to take decisions regarding public health interventions. This paper presents a mathematical epidemiological model called SEQIJRDS to predict on COVID-19. The model has been validated for the COVID 19 pandemic in Sri Lanka. The results show that the model outstands many of the state-of-the-art SEIR epidemiological models such as Imperial, IHME once properly parameterized. At the end; this work recommends public health interventions at this crucial time to save people's lives based on the predictions of the proposed model. Specifically, 3 recommendations called minimal, sub-optimal and optimal recommendations are provided for public health interventions.

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Section: Motivationmentioning

confidence: 99%

An Accurate Mathematical Epidemiological Model (SEQIJRDS) to Recommend Public Health Interventions Related to COVID-19 in Sri Lanka.

Wijesekara

2021

Preprint

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“…Based on the SIR-model, by adopting that the entire population of the country should be in this group, then the outbreak will stop just if the Majority of people have been infected (28,29). Furthermore, the SIR-models do not beg the question of the critical point of People infected, after reaching the early exponential phase.…”

Section: Implement Distributed Delay Models With Koyck Transformationmentioning

confidence: 99%

Modeling and forecasting hospitalized mortality rate of COVID-19 according to delay effect of daily hospitalization

Rastaghi

Shark

Saki

2020

Preprint

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Introduction: The COVID-19 infectious epidemic has become a serious worry all over the world, including Iran. The high outbreak of disease ranked Iran as second in Asia and 11th in the world. Given the growing progress of this epidemic in infecting and killing individuals, it is essential to forecast the delay effect of the number of hospitalized upon the hospitalized mortality rate. Methods: In this study, we used the daily Hospitalization cases of COVID-19 of IRAN for the period of 15-May 2020 to 5-Oct 2020 which were obtained from the "Iran-COVID-19-Data[1]" database. Five distribution delay models were compared for estimating and forecasting. Results: based on measurement errors DDM selected as the best model for forecasting the number of death. According to this model, the long-run effects show that observing the effect of hospitalization counts on death counts takes an average of five days and the overall hospitalized mortality rate was 12%.Conclusion: The overall hospitalized mortality rate of COVID-19 in Iran is less than the global rate of 15%. The mean of delay effect of daily hospitalization on mortality is approximately 5 days. Our findings showed distributed delay model (DDM) has better performance in the forecasting of the future behavior of the Coronavirus mortality, and providing to government and health care decision- makers the possibility to predict the outcomes of their decision on public health.

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“…The number of COVID-19 cases reported since then showed a gradual increase and included both imported and locally transmitted cases. Since the country followed an aggressive policy of contact tracing and isolation the health authorities were able to contain the situation [4][5][6] . However, on 22 April 2020, a sailor from a Sri Lanka Navy camp outside the capital, Colombo, who was actively engaged in contact tracing, tested positive for COVID -19, and 19 more sailors tested positive on the same day 7 .…”

Section: Introductionmentioning

confidence: 99%

COVID-19 case forecasting model for Sri Lanka based on Stringency Index

Jayatilleke

Dayarathne²,

Silva³

et al. 2020

Preprint

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Introduction Sri Lanka has been able to contain COVID-19 transmission through very stringent suppression measures implemented from the onset. The country had been under a strict lockdown since 20 March 2020, and currently the lockdown is being relaxed gradually. The objective of this paper is to describe a projection model for COVID-19 cases in Sri Lanka applied to different scenarios after lockout, utilizing the stringency index as a proxy of total government response. Methods COVID-19 Stringency Index (C19SI) is published and updated real time by a research group from Oxford university on 17 selected mitigation and suppression measures employed by different countries. We have mapped and validated the stringency index for Sri Lanka and subsequently validated the projection model in two phases. Predictions for the base-case scenario, less stringent scenarios, advanced relaxation scenarios, and high-risk districts were done with 95% confidence intervals (95%CI) using the validated model, utilizing data up to 10th May. Results C19SI was able to accommodate all of the government responses. The model using validated C19SI could predict number of cases with 95% confidence for a period of two weeks. The model predicted base-case scenario of 815 (95%CI, 753-877) active cases by 17 May 2020 and 648 (95%CI, 599-696) cases for the high-risk districts by 18 May 2020. The model further predicted 3,159 (95%CI, 2,928-3,391) cases for 75% stringency and 928,824 (95%CI, 861,772-995,877) cases for 50% stringency. Advancing normalcy by three weeks resulted in the case load increasing to 4526 (95%CI, 4309-4744) by 30 June 2020. Conclusion The proposed prediction model based on C19SI provides policy makers an evidence based scientific method for identifying suppression measures that can be relaxed at the most appropriate time. Key words: COVID-19, Projections, Stringency Index, SIR Model, Sri Lanka

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An Epidemiological Model to Aid Decision-Making for Covid-19 Control in Sri Lanka

Cited by 11 publications

References 7 publications

An Accurate Mathematical Epidemiological Model (SEQIJRDS) to Recommend Public Health Interventions Related to COVID-19 in Sri Lanka.

An Accurate Mathematical Epidemiological Model (SEQIJRDS) to Recommend Public Health Interventions Related to COVID-19 in Sri Lanka.

Modeling and forecasting hospitalized mortality rate of COVID-19 according to delay effect of daily hospitalization

COVID-19 case forecasting model for Sri Lanka based on Stringency Index

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