Mathematical Model to Study Early COVID-19 Transmission Dynamics in Sri Lanka

Perera, S. S. N.; Ganegoda, Naleen Chaminda; Siriwardhana, Dhammika Deepani; Weerasinghe, Manuj C.

doi:10.1101/2020.04.27.20082537

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…Based on the current pattern of cases and the suppression measures, in place the model predicted some 872 active cases at the peak of transmission. These predictions are in line with some other local predictions done by Weerasinghe (2020) using a SIR model with an active caseload of 1000 peaking after 90 days of onset28 and a separate group predicting based on a logistic regression model a total case load of 998 (+/-6 cases) and ending by 20 July 19 . Similar findings are obtained from the model proposed by 29 also using a SIR model.…”

supporting

confidence: 90%

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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supporting

confidence: 90%

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

Jayatilleke

Dayarathne²,

Silva³

et al. 2020

Preprint

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“…The expanding human population and anthropogenic activities, which are also closely connected to the current and other recent zoonotic disease epidemics, have terribly harmed the ecosystem. Although the causes of zoonotic pandemics vary, most contributing environmental factors include fragmentation of the living environment, deforestation of habitat, loss of biodiversity, intensive livestock farming, unchecked urbanization, pollution increase, drastic changes in climate, and the trade and consumption of bush meat are among the main factors that contribute to their emergence and spread (Perera 2021).…”

Section: Environment-associated Zoonotic Diseasesmentioning

confidence: 99%

Molecular Techniques Used for Diagnosis of Zoonotic Diseases

2023

IJAB

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Zoonotic diseases are described as diseases that are transmitted between animals and humans through direct or indirect contact or by any other route. Many microorganisms causing recently identified zoonotic infections in humans were initially detected in animals (specifically, wildlife) or products of animal origin. Dogs and cats are the most common pets which can disseminate zoonotic diseases. Rabies, ringworms, Campylobacteriosis, Salmonellosis, and Leptospirosis are the most common diseases that can spread through dogs. All domesticated animals, including poultry, have the potential to harbor germs that cause food-borne diseases. Around 90% of bacteria-related food-borne infections are caused by Salmonella spp. and Campylobacter spp. Molecular biology methods are increasingly being used in small animal veterinary care to diagnose infectious diseases. Understanding of infectious disease agents has improved because of techniques like polymerase chain reaction (PCR), Real-time PCR, Restriction fragment length polymorphism (RFLP), ELISA and CRISPR-CAS. Approximately 50% of bacterial and 87% of viral genomes have been identified so far by different molecular techniques. To subtype organisms beyond phenotypic categories, genotyping techniques such as PFGE, RAPD, REP-PCR, and AFLP are especially applied. As DNA is the starting material, these molecular biology techniques are often frequently referred to as "DNA fingerprinting." In addition to epidemiology, these techniques are also employed in forensic medicine and evolutionary biology research. Compared to phenotyping procedures, genotyping techniques frequently offer better discriminating power. Another way to identify genomic sequences from various microbial communities is through metagenomics. Over the last few decades, metagenomics-based methodologies have been developed to assess, analyze, and utilize biodiversity across a wide range of various environmental niches. Metagenomics has helped characterize the microbiomes in many samples, such as the gastrointestinal tract of various creatures (e.g., feline, canine, human, mouse, and chicken), in addition to the discoveries of viral genomes. As technology continues to evolve, the integration of molecular diagnostics into routine surveillance and healthcare systems holds promise for more effective prevention and control of zoonotic diseases, ultimately safeguarding both humans and animals.

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“…In addition to the National Institute of Infectious Disease (NIID), several other hospitals were designated to treat COVID-19 patients and to observe suspected patients, increasing the capacity of curative services. Allocating new hospitals, building temporary treating facilities, and testing protocols were based on the epidemio-logical data and predictions 21 .…”

Section: Detection Isolation and Treatment Of Casesmentioning

confidence: 99%

Co-existing with COVID-19: Engaging the community to strengthen the public health response in Sri Lanka

Weerasinghe

2021

Gen. Dental Prac.

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COVID-19 was declared as a pandemic by the World Health Organization in early March 2020. Sri Lanka until mid-May experienced an epidemic contained within 32 clusters, largely due to a well-planned public health response. Following 2 months of partial shut-down, certain control measures are being relaxed to facilitate economic activities across the country. This paper details this public health response and explores how it could be strengthened to empower the community to co-exist with COVID- 19 in a “new normal” environment.Reprinting from Journal of the Ceylon College of Physicians, 2020, 51, 8-13 http://doi.org/10.4038/jccp.v51i1.7880

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Mathematical Model to Study Early COVID-19 Transmission Dynamics in Sri Lanka

Abstract: Background

Cited by 3 publications

References 10 publications

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

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

Molecular Techniques Used for Diagnosis of Zoonotic Diseases

Co-existing with COVID-19: Engaging the community to strengthen the public health response in Sri Lanka

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