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

2021 ◽  
Author(s):  
NWANY Wijesekara ◽  
HDB Herath ◽  
KALC Kodituwakku ◽  
HMMNK Herath ◽  
BAMP Bulathsinghe ◽  
...  
Keyword(s):  
Sri Lanka ◽  
Sir Model ◽  
Population Based ◽  
Cumulative Number ◽  
State University ◽  
Worst Case ◽  
Health Authorities ◽  
Number Of Patients ◽  
Penn State ◽  
Community Transmission

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

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

2020 ◽  
Author(s):  
N.W.A.N.Y.Wijesekara ◽  
H.D.B. Herath ◽  
K.A.L.C. Kodithuwakku ◽  
H.M.M.N.K.Herath ◽  
B.A.M.P. Bulathsinhala ◽  
...  
Keyword(s):  
Sri Lanka ◽  
Viral Disease ◽  
Population Based ◽  
Cumulative Number ◽  
Public Health Response ◽  
Worst Case ◽  
Health Authorities ◽  
Penn State ◽  
Free Health Care ◽  
Community Transmission

Abstract Covid-19 is a viral disease which has briskly invaded the globe, Sri Lanka being no exception. If community transmission of Covid-19 occurs, it will have serious demands on Sri Lanka’s free health care system. Objective of this study was to simulate the widespread community transmission of Covid-19 in Sri Lanka. We used the Susceptibility, Infected and Removed (SIR) model through the Penn State University CHIME Model incorporated to ArcGIS Pro. We simulated introduction of one case of Covid-19 to each of the 26 health districts and ran the model for 365 days. During simulated scenario, the number patients requiring admissions, ICU care and mechanical ventilation will peak at 1942, 583 and 388 per day respectively around 213 days from the onset of widespread community transmission. 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, each which will be over 1680. Health authorities must be ready for the worst-case scenarios of the Covid-19 outbreak to sustain public health response to reduce morbidity and mortality.

scholarly journals Two-Way Contact Network Modeling for Identifying the Route of COVID-19 Community Transmission

Informatics ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 22
Author(s):  
Sung Jin Lee ◽  
Sang Eun Lee ◽  
Ji-On Kim ◽  
Gi Bum Kim
Keyword(s):  
Simulated Data ◽  
Contact Network ◽  
Transmission Route ◽  
Time Step ◽  
Source Of Infection ◽  
Health Authorities ◽  
Control And Prevention ◽  
Community Transmission ◽  
Centrality Analysis ◽  
Selection Of

In this study, we address the problem originated from the fact that “The Corona 19 Epidemiological Research Support System,” developed by the Korea Centers for Disease Control and Prevention, is limited to analyzing the Global Positioning System (GPS) information of the confirmed COVID-19 cases alone. Consequently, we study a method that the authority predicts the transmission route of COVID-19 between visitors in the community from a spatiotemporal perspective. This method models a contact network around the first confirmed case, allowing the health authorities to conduct tests on visitors after an outbreak of COVID-19 in the community. After securing the GPS data of community visitors, it traces back to the past from the time the first confirmed case occurred and creates contact clusters at each time step. This is different from other researches that focus on identifying the movement paths of confirmed patients by forward tracing. The proposed method creates the contact network by assigning weights to each contact cluster based on the degree of proximity between contacts. Identifying the source of infection in the contact network can make us predict the transmission route between the first confirmed case and the source of infection and classify the contacts on the transmission route. In this experiment, we used 64,073 simulated data for 100 people and extracted the transmission route and a top 10 list for centrality analysis. The contacts on the route path can be quickly designated as a priority for COVID-19 testing. In addition, it is possible for the authority to extract the subjects with high influence from the centrality theory and use them for additional COVID-19 epidemic investigation that requires urgency. This model is expected to be used in the epidemic investigation requiring the quick selection of close contacts.

scholarly journals Recent Trends in Youth Concussions: A Brief Report

2020 ◽  
Vol 11 ◽  
pp. 215013272098505
Author(s):  
Andrew T. Askow ◽  
Jacob L. Erickson ◽  
Andrew R. Jagim
Keyword(s):  
Incidence Rate ◽  
Brain Injuries ◽  
Population Based ◽  
Health Records ◽  
Medical Issues ◽  
Number Of Patients ◽  
Recent Trends ◽  
Males And Females ◽  
Large Health Care System

Objectives Concussions and mild traumatic brain injuries are important medical issues, particularly among youth as the long-term health consequences of these injuries can become increasingly problematic. The purpose of this study was to examine recent trends in diagnosed concussions among pediatric patients in a large health care system. Methods This was a retrospective, population-based epidemiology study design that queried all patient files (pediatrics included) using electronic medical health records and further stratified patients based on type of concussion, age, sex, and year from 2013 to 2018. Results Electronic health records from a cohort of 8 832 419 (nmales = 4 246 492; nfemales = 4 585 931) patient visits were assessed for concussion diagnosis and filtered for those whose concussive event led to a loss of consciousness (LOC) or not (nLOC). Of these patients, 12 068 were diagnosed with a concussion (LOC = 3 699; nLOC = 8 369) with an overall incidence rate of 1.37 concussions per 1000 patients. Overall, the number of patients diagnosed with a concussion increased by 5063 (LOC = 1351; nLOC = 3712) from 2013 to 2018. Males and females presented with similar rates of concussions 5919 (49.05%) and 6149 concussions (50.95%), respectively. Of total diagnosed concussions, 4972 (LOC = 815; nLOC = 4157) were under the age of 18 and represented 41.2% of all diagnosed concussions with an incidence rate of 6.79 per 1000 patients. Conclusion The number of concussions diagnosed appear to be on the rise with the largest number of concussions being diagnosed in those under the age of 18. Future studies should seek to determine primary causality and the long-term health implications of concussions with or without LOC.

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