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Journal ArticleDOI

How would Widespread Community Transmission of Covid-19 in Sri Lanka look like? A Population-based Simulation

TLDR
The simulation revealed that the number of patients requiring admissions, ICU care, and mechanical ventilation would peak at 1942, 583, and 388 per day, respectively, around 213 days from the onset of the Covid-19 outbreak.
Abstract
Abstract Widespread community transmission of Covid-19 can overwhelm the capacity of health systems; Sri Lanka is no exception. We simulated the widespread community transmission of Covid-19 in Sri Lanka, using the Susceptibility, Infected and Removed (SIR) model through the Penn State University CHIME Model incorporated to ArcGIS Pro, by introducing one case of Covid-19 to the current population in each of the 26 health districts and running the model for 365 days. The simulation revealed that the number of patients requiring admissions, ICU care, and mechanical ventilation would peak at 1942, 583, and 388 per day, respectively, around 213 days from the onset. The cumulative number of cases needing admission, ICU care, and ventilation will be 245,916, 73,775, and 49,183 after 365 days. Colombo and Gampaha districts will report the highest number of daily total numbers of hospitalized cases over 1680. Health authorities can use the results of such simulations to prepare to face the worst-case scenarios of the Covid-19 outbreak to minimize morbidity and mortality. Keywords: Covid-19, Community Transmission, SIR Model, CHIME, Outbreak, Simulation, Prediction

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References
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Journal ArticleDOI

Data-based analysis, modelling and forecasting of the COVID-19 outbreak.

TL;DR: Based on the publicly available epidemiological data for Hubei, China from January 11 to February 10, 2020, estimates of the main epidemiological parameters are provided, including an estimation of the case fatality and case recovery ratios, along with their 90% confidence intervals as the outbreak evolves.
Journal ArticleDOI

Why is it difficult to accurately predict the COVID-19 epidemic?

TL;DR: This study demonstrates that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations in model predictions on the COVID-19 epidemics in Wuhan and indicates that predictions using more complex models may not be more reliable compared to using a simpler model.
Journal ArticleDOI

Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic.

TL;DR: This modeling tool can inform preparations for capacity strain during the early days of a pandemic and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated.
Journal ArticleDOI

Dynamic behaviors of a modified SIR model in epidemic diseases using nonlinear incidence and recovery rates.

TL;DR: A modified SIR model with nonlinear incidence and recovery rates is established to understand the influence by any government intervention and hospitalization condition variation in the spread of diseases, and it is concluded that a sufficient number of the beds is critical to control the epidemic.
Journal ArticleDOI

When will the coronavirus outbreak peak

David Cyranoski
- 18 Feb 2020 - 
TL;DR: Officials want to know but predictions vary wildly, from now to after hundreds of millions of people are infected with norovirus, how widespread the virus is and how to prevent it.
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