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

Jayatilleke, Achala Upendra; Dayarathne, Sanjeewa; Silva, Padmal de; Siribaddana, Pandula; Abeygunawardana, R.A.B.; Nieveras, Olivia; Silva, Nilanthi de; Silva, Janaka de

doi:10.1101/2020.05.20.20103887

Cited by 11 publications

(13 citation statements)

References 13 publications

(13 reference statements)

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“…In Sri Lanka, the first COVID-19 case was reported on January 27, 2020; the patient was a Chinese woman from Hubei province who was touring Sri Lanka. 5 That incident drew the attention of the Health Ministry and other relevant authorities because even though the country had a plan in place for the ongoing pandemic, no actual incident had yet been reported. The first Sri Lankan patient, who was a tour guide working with a group of Italian tourists, tested positive for COVID-19 on March 11, 2020.…”

Section: Introductionmentioning

confidence: 99%

Assessing COVID-19 preventive behaviours using the health belief model: A Sri Lankan study

Mahindarathne

2021

Journal of Taibah University Medical Sciences

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Objective The novel coronavirus (COVID-19) is turning out to be one of the most severe public health crises in recent history. Promoting preventive behaviour among the public is of paramount importance to effectively contain the disease. Hence, this research attempts to identify factors that affect preventive behaviour against COVID-19. Methods The Health Belief Model (HBM), which outlines how perceived susceptibility, severity, benefits, barriers, and health motivation affect individuals’ health behaviour, served as the theoretical basis of the study. As the outcome measure of the study was cues to action against COVID-19, a regression analysis was conducted to explore how the aforementioned HBM constructs influence the cues to action. The data were collected using an online survey with a total of 307 respondents. Results The results revealed that perceived benefits (0.395, p < 0.001), self-efficacy (0.405, p < 0.001), and general health motivation (0.313, p < 0.001) had significant positive impacts on the cues to action taken to prevent COVID-19, whereas perceived barriers (−0.097, p < 0.05) had a significant negative impact. The statistical analysis further revealed that the cues to action taken to prevent COVID-19 were not significantly influenced by perceived susceptibility and perceived severity. Conclusion The study reinstates the usability of the HBM in exploring health behaviour. Importantly, the study findings suggest that by informing the public of the benefits of prevention and general health motivation, and by encouraging self-efficacy and eliminating the barriers to prevention, preventive actions against COVID-19 can be effectively promoted.

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Section: Introductionmentioning

confidence: 99%

Assessing COVID-19 preventive behaviours using the health belief model: A Sri Lankan study

Mahindarathne

2021

Journal of Taibah University Medical Sciences

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“…In Singapore, the initial interventions were related to the screening of the cases, testing, tracing, and treating the cases, followed by restrictions on flights from other countries [13]. Sri Lanka, which had also reported very few cases till that time, also acted in a similar way [14]. Both the countries shared similar interventions early in the period.…”

Section: Discussionmentioning

confidence: 99%

Impact of Nonpharmacological Public Health Interventions on Epidemiological Parameters of COVID-19 Pandemic in India

Kharya¹,

Koparkar²,

Dixit³

et al. 2021

Cureus

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BackgroundPublic health interventions are epidemiologically sound and cost-effective methods to control disease burden. Non-pharmacological public health interventions are the only mode to control diseases in the absence of medication. ObjectiveTo find the impact of public health interventions on the epidemiological indicators of disease progression. MethodsThis is a secondary data analysis done on COVID-19 data. The median doubling time and R0 were calculated for a rolling period of seven days. Interventions were scored from zero to three with an increasing level of stringency. Multivariate linear regression was performed to find the role of individual interventions on R0 and the median doubling time. ResultsThe highest intervention score was reported in the lockdown phase, which gradually decreased to the lowest level of 22. The R0 values settled to a level of 1.25, and the median doubling time increased to 20 days at the end of the study. Public awareness and public health laws were found to be related to both R0 and the median doubling time in the pre-lockdown phase only. ConclusionThe implementation of interventions at the ground level is one of the key factors in the success of public health interventions. Post implementation, poor effectiveness of many interventions is evident from the study. Further, studies related to the sequence of interventions are required to further analyze the poor effect of the interventions.

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“…The national GRSI has been used by researchers [ 16 ] and has also been used in a modified version applied to Brazil [ 17 ]. We build on and extend the contribution by the Oxford University Blavatnik School of Government.…”

Section: Introductionmentioning

confidence: 99%

Mitigation strategies and compliance in the COVID-19 fight; how much compliance is enough?

Mukerjee

Chow

2021

PLoS ONE

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The U.S. with only 4% of the world’s population, bears a disproportionate share of infections in the COVID-19 pandemic. To understand this puzzle, we investigate how mitigation strategies and compliance can work together (or in opposition) to reduce (or increase) the spread of COVID-19 infection. Building on the Oxford index, we create state-specific stringency indices tailored to U.S. conditions, to measure the degree of strictness of public mitigation measures. A modified time-varying SEIRD model, incorporating this Stringency Index as well as a Compliance Indicator is then estimated with daily data for a sample of 6 U.S. states: New York, New Hampshire, New Mexico, Colorado, Texas, and Arizona. We provide a simple visual policy tool to evaluate the various combinations of mitigation policies and compliance that can reduce the basic reproduction number to less than one, the acknowledged threshold in the epidemiological literature to control the pandemic. Understanding of this relationship by both the public and policy makers is key to controlling the pandemic. This tool has the potential to be used in a real-time, dynamic fashion for flexible policy options. Our methodology can be applied to other countries and has the potential to be extended to other epidemiological models as well. With this first step in attempting to quantify the factors that go into the “black box” of the transmission factor β, we hope that our work will stimulate further research in the dual role of mitigation policies and compliance.

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

Cited by 11 publications

References 13 publications

Assessing COVID-19 preventive behaviours using the health belief model: A Sri Lankan study

Assessing COVID-19 preventive behaviours using the health belief model: A Sri Lankan study

Impact of Nonpharmacological Public Health Interventions on Epidemiological Parameters of COVID-19 Pandemic in India

Mitigation strategies and compliance in the COVID-19 fight; how much compliance is enough?

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