scholarly journals Estimation of the Transmission Risk of the 2019-nCoV and Its Implication for Public Health Interventions

2020 ◽  
Vol 9 (2) ◽  
pp. 462 ◽  
Author(s):  
Biao Tang ◽  
Xia Wang ◽  
Qian Li ◽  
Nicola Luigi Bragazzi ◽  
Sanyi Tang ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Transmission Risk ◽  
Sensitivity Analyses ◽  
Contact Tracing ◽  
The Novel ◽  
Intervention Measures ◽  
Novel Coronavirus ◽  
Restrictive Measures ◽  
And Control ◽  
Travel Restriction

Since the emergence of the first cases in Wuhan, China, the novel coronavirus (2019-nCoV) infection has been quickly spreading out to other provinces and neighboring countries. Estimation of the basic reproduction number by means of mathematical modeling can be helpful for determining the potential and severity of an outbreak and providing critical information for identifying the type of disease interventions and intensity. A deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals, and intervention measures. The estimations based on likelihood and model analysis show that the control reproduction number may be as high as 6.47 (95% CI 5.71–7.23). Sensitivity analyses show that interventions, such as intensive contact tracing followed by quarantine and isolation, can effectively reduce the control reproduction number and transmission risk, with the effect of travel restriction adopted by Wuhan on 2019-nCoV infection in Beijing being almost equivalent to increasing quarantine by a 100 thousand baseline value. It is essential to assess how the expensive, resource-intensive measures implemented by the Chinese authorities can contribute to the prevention and control of the 2019-nCoV infection, and how long they should be maintained. Under the most restrictive measures, the outbreak is expected to peak within two weeks (since 23 January 2020) with a significant low peak value. With travel restriction (no imported exposed individuals to Beijing), the number of infected individuals in seven days will decrease by 91.14% in Beijing, compared with the scenario of no travel restriction.

scholarly journals Exploring the role and mechanism of COVID-19 apps in fighting the current pandemic

2020 ◽  
Vol 0 ◽  
pp. 1-6
Author(s):  
Karthikeyan P. Iyengar ◽  
Rachit Jain ◽  
David Ananth Samy ◽  
Vijay Kumar Jain ◽  
Raju Vaishya ◽  
...  
Keyword(s):  
Data Privacy ◽  
Viral Transmission ◽  
Contact Tracing ◽  
The Novel ◽  
Test Track ◽  
The World ◽  
Novel Coronavirus ◽  
And Control ◽  
Second Wave ◽  
Transmission Test

As COVID-19 pandemic spread worldwide, policies have been developed to contain the disease and prevent viral transmission. One of the key strategies has been the principle of “‘test, track, and trace” to minimize spread of the virus. Numerous COVID-19 contact tracing applications have been rolled around the world to monitor and control the spread of the disease. We explore the characteristics of various COVID-19 applications and especially the Aarogya Setu COVID-19 app from India in its role in fighting the current pandemic. We assessed the current literature available to us using conventional search engines, including but not limited to PubMed, Google Scholar, and Research Gate in May 2020 till the time of submission of this article. The search criteria used MeSH keywords such as “COVID-19,” “pandemics,” “contact tracing,” and “mobile applications.” A variable uptake of different COVID-19 applications has been noted with increasing enrolment around the world. Security concerns about data privacy remain. The various COVID-19 applications will complement manual contact tracing system to assess and prevent viral transmission. Test, track, trace, and support policy will play a key role in avoidance of a “second wave” of the novel coronavirus severe acute respiratory syndrome coronavirus 2 outbreak.

scholarly journals Effective Reproduction Number and Dispersion under Contact Tracing and Lockdown on COVID-19 in Karnataka

2020 ◽  
Author(s):  
Siva Athreya ◽  
Nitya Gadhiwala ◽  
Abhiti Mishra
Keyword(s):  
Individual Variation ◽  
Reproduction Number ◽  
Contact Tracing ◽  
The Novel ◽  
Secondary Infections ◽  
The Government ◽  
Novel Coronavirus

We analyze the data provided in the Novel Coronavirus (COVID-19) media bulletins of the Government of Karnataka. We classify the patients of COVID-19 into clusters and study the Reproduction number and Dispersion for eight specific clusters. We find that it is uniformly less than one, indicating the benefits of contact tracing, lockdown and quarantine measures. However, the Dispersion is low indicating individual variation in secondary infections and the occurrence of Super-spreading events. Finally, we analyze the surge in infections after 27th June and find it unlikely that it was caused solely by the large Migration in May and June 2020.

scholarly journals Analysis of Potential Risk of COVID-19 Infections in China Based on a Pairwise Epidemic Model

2020 ◽  
Author(s):  
Xiaofeng Luo ◽  
Shanshan Feng ◽  
Junyuan Yang ◽  
Xiao-Long Peng ◽  
Xiaochun Cao ◽  
...  
Keyword(s):  
Epidemic Model ◽  
Reproduction Number ◽  
Economic Benefits ◽  
Control Measures ◽  
Contact Tracing ◽  
The Novel ◽  
The Public ◽  
Disease Propagation ◽  
Long Run ◽  
Novel Coronavirus

The ongoing outbreak of the novel coronavirus pneumonia (also known as COVID-19) has triggered a series of stringent control measures in China, such as city closure, traffic restrictions, contact tracing and household quarantine. These containment efforts often lead to changes in the contact pattern among individuals of the population. Many existing compartmental epidemic models fail to account for the effects of contact structure. In this paper, we devised a pairwise epidemic model to analyze the COVID-19 outbreak in China based on confirmed cases reported during the period February 3rd--17th, 2020. By explicitly incorporating the effects of family clusters and contact tracing followed by household quarantine and isolation, our model provides a good fit to the trajectory of COVID-19 infections and is useful to predict the epidemic trend. We obtained the average of the reproduction number $R=1.494$ ($95\%$ CI: $1.483-1.507$) for Hubei province and $R=1.178$ ($95\%$ CI: $1.145-1.158$) for China (except Hubei), suggesting that some existing studies may have overestimated the reproduction number by neglecting the dynamical correlations and clustering effects. We forecasted that the COVID-19 epidemic would peak on February 13th ($95\%$ CI: February $9-17$th) in Hubei and 6 days eariler in the regions outside Hubei. Moreover the epidemic was expected to last until the middle of March in China (except Hubei) and late April in Hubei. The sensitivity analysis shows that ongoing exposure for the susceptible and population clustering play an important role in the disease propagation. With the enforcement of household quarantine measures, the reproduction number $R$ effectively reduces and epidemic quantities decrease accordingly. Furthermore, we gave an answer to the public concern on how long the stringent containment strategies should maintain. Through numerical analysis, we suggested that the time for the resumption of work and production in China (except Hubei) and Hubei would be the middle of March and the end of April, 2020, respectively. These constructive suggestions may bring some immeasurable social-economic benefits in the long run.

scholarly journals Early dynamics of transmission and control of COVID-19: a mathematical modelling study

2020 ◽  
Author(s):  
Adam J Kucharski ◽  
Timothy W Russell ◽  
Charlie Diamond ◽  
Yang Liu ◽  
John Edmunds ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Control Measures ◽  
Transmission Model ◽  
The Novel ◽  
Transmission Potential ◽  
Travel Restrictions ◽  
Gates Foundation ◽  
Novel Coronavirus ◽  
And Control ◽  
Over Time

AbstractBackgroundAn outbreak of the novel coronavirus SARS-CoV-2 has led to 46,997 confirmed cases as of 13th February 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas.MethodsWe combined a stochastic transmission model with data on cases of novel coronavirus disease (COVID-19) in Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January and February 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas.FindingsWe estimated that the median daily reproduction number, Rt, declined from 2.35 (95% CI: 1.15-4.77) one week before travel restrictions were introduced on 23rd January to 1.05 (95% CI: 0.413-2.39) one week after. Based on our estimates of Rt,we calculated that in locations with similar transmission potential as Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population.InterpretationOur results show that COVID-19 transmission likely declined in Wuhan during late January 2020, coinciding with the introduction of control measures. As more cases arrive in international locations with similar transmission potential to Wuhan pre-control, it is likely many chains of transmission will fail to establish initially, but may still cause new outbreaks eventually.FundingWellcome Trust (206250/Z/17/Z, 210758/Z/18/Z), HDR UK (MR/S003975/1), Gates Foundation (INV-003174), NIHR (16/137/109)

scholarly journals Social network analysis of COVID-19 transmission in Karnataka, India

2020 ◽  
Vol 148 ◽  
Author(s):  
S. Saraswathi ◽  
A. Mukhopadhyay ◽  
H. Shah ◽  
T. S. Ranganath
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Cluster Formation ◽  
Contact Tracing ◽  
The Novel ◽  
Monitoring And Control ◽  
Healthcare Administrators ◽  
Novel Coronavirus ◽  
And Control

Abstract We used social network analysis (SNA) to study the novel coronavirus (COVID-19) outbreak in Karnataka, India, and to assess the potential of SNA as a tool for outbreak monitoring and control. We analysed contact tracing data of 1147 COVID-19 positive cases (mean age 34.91 years, 61.99% aged 11–40, 742 males), anonymised and made public by the Karnataka government. Software tools, Cytoscape and Gephi, were used to create SNA graphics and determine network attributes of nodes (cases) and edges (directed links from source to target patients). Outdegree was 1–47 for 199 (17.35%) nodes, and betweenness, 0.5–87 for 89 (7.76%) nodes. Men had higher mean outdegree and women, higher mean betweenness. Delhi was the exogenous source of 17.44% cases. Bangalore city had the highest caseload in the state (229, 20%), but comparatively low cluster formation. Thirty-four (2.96%) ‘super-spreaders’ (outdegree ⩾ 5) caused 60% of the transmissions. Real-time social network visualisation can allow healthcare administrators to flag evolving hotspots and pinpoint key actors in transmission. Prioritising these areas and individuals for rigorous containment could help minimise resource outlay and potentially achieve a significant reduction in COVID-19 transmission.

scholarly journals Estimating the reproduction number of COVID-19 in Iran using epidemic modeling

2020 ◽  
Author(s):  
Ebrahim Sahafizadeh ◽  
Samaneh Sartoli
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Transmission Model ◽  
The Novel ◽  
Epidemic Spreading ◽  
Social Distancing ◽  
Novel Coronavirus ◽  
And Control ◽  
Second Wave ◽  
The Basic Reproduction Number

AbstractBackgroundAs reported by Iranian governments, the first cases of coronavirus (COVID-19) infections confirmed in Qom, Iran on February 19, 2020 (30 Bahman 1398). The number of identified cases afterward increased rapidly and the novel coronavirus spread to all provinces of the country. This study aimed to fit an epidemic model to the reported cases data to estimate the basic reproduction number (R0) of COVID-19 in Iran.MethodsWe used data from February 21, 2020, to April 21, 2020, on the number of cases reported by Iranian governments and we employed the SIR (Susceptible-Infectious-Removed) epidemic spreading model to fit the transmission model to the reported cases data by tuning the parameters in order to estimate the basic reproduction number of COVID-19 in Iran.ResultsThe value of reproduction number was estimated 4.86 in the first week and 4.5 in the second week. it decreased from 4.29 to 2.37 in the next four weeks. At the seventh week of the outbreak the reproduction number was reduced below one.ConclusionsThe results indicate that the basic reproduction number of COVID-19 was significantly larger than one in the early stages of the outbreak. However, implementing social distancing and preventing travelling on Nowruz (Persian New Year) effectively reduced the reproduction number. Although the results indicate that reproduction number is below one, it is necessary to continue social distancing and control travelling to prevent causing a second wave of outbreak.

scholarly journals A deterministic epidemic model for the emergence of COVID-19 in China

2020 ◽  
Author(s):  
Meng Wang ◽  
Jingtao Qi
Keyword(s):  
Prevention And Control ◽  
Reproduction Number ◽  
Chinese Mainland ◽  
Hubei Province ◽  
Control Measures ◽  
The Novel ◽  
Health Authorities ◽  
Prevention And Control Measures ◽  
Novel Coronavirus ◽  
And Control

AbstractCoronavirus disease (COVID-19) broke out in Wuhan, Hubei province, China, in December 2019 and soon after Chinese health authorities took unprecedented prevention and control measures to curb the spreading of the novel coronavirus-related pneumonia. We develop a mathematical model based on daily updates of reported cases to study the evolution of the epidemic. With the model, on 95% confidence level, we estimate the basic reproduction number, R0 = 2.82 ± 0.11, time between March 19 and March 21 when the effective reproduction number becoming less than one, the epidemic ending after April 2 and the total number of confirmed cases approaching 14408 ± 429 on the Chinese mainland excluding Hubei province.

scholarly journals Multi-Objective Optimization of Integrated Civilian-Military Scheduling of Medical Supplies for Epidemic Prevention and Control

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 126
Author(s):  
Hai-Feng Ling ◽  
Zheng-Lian Su ◽  
Xun-Lin Jiang ◽  
Yu-Jun Zheng
Keyword(s):  
Prevention And Control ◽  
Large Scale ◽  
Medical Services ◽  
The Novel ◽  
Multi Objective ◽  
Medical Supplies ◽  
Problem Instances ◽  
Novel Coronavirus ◽  
And Control ◽  
Minimum Ratio

In a large-scale epidemic, such as the novel coronavirus pneumonia (COVID-19), there is huge demand for a variety of medical supplies, such as medical masks, ventilators, and sickbeds. Resources from civilian medical services are often not sufficient for fully satisfying all of these demands. Resources from military medical services, which are normally reserved for military use, can be an effective supplement to these demands. In this paper, we formulate a problem of integrated civilian-military scheduling of medical supplies for epidemic prevention and control, the aim of which is to simultaneously maximize the overall satisfaction rate of the medical supplies and minimize the total scheduling cost, while keeping a minimum ratio of medical supplies reservation for military use. We propose a multi-objective water wave optimization (WWO) algorithm in order to efficiently solve this problem. Computational results on a set of problem instances constructed based on real COVID-19 data demonstrate the effectiveness of the proposed method.

scholarly journals COVID-19 Pandemic Response Simulation in a Large City: Impact of Nonpharmaceutical Interventions on Reopening Society

2021 ◽  
pp. 0272989X2110030
Author(s):  
Serin Lee ◽  
Zelda B. Zabinsky ◽  
Judith N. Wasserheit ◽  
Stephen M. Kofsky ◽  
Shan Liu
Keyword(s):  
Large City ◽  
Face Mask ◽  
School Closure ◽  
Contact Tracing ◽  
The Novel ◽  
Agent Based ◽  
Social Distancing ◽  
Variable Compliance ◽  
Novel Coronavirus ◽  
Nonpharmaceutical Interventions

As the novel coronavirus (COVID-19) pandemic continues to expand, policymakers are striving to balance the combinations of nonpharmaceutical interventions (NPIs) to keep people safe and minimize social disruptions. We developed and calibrated an agent-based simulation to model COVID-19 outbreaks in the greater Seattle area. The model simulated NPIs, including social distancing, face mask use, school closure, testing, and contact tracing with variable compliance and effectiveness to identify optimal NPI combinations that can control the spread of the virus in a large urban area. Results highlight the importance of at least 75% face mask use to relax social distancing and school closure measures while keeping infections low. It is important to relax NPIs cautiously during vaccine rollout in 2021.

scholarly journals Digital Tracing during the COVID-19 Pandemic: User Appraisal, Emotion, and Continuance Intention

2021 ◽  
Vol 13 (2) ◽  
pp. 608
Author(s):  
Ayoung Suh ◽  
Mengjun Li
Keyword(s):  
Mobile Apps ◽  
Contact Tracing ◽  
The Novel ◽  
Personal Privacy ◽  
Continuance Intention ◽  
Infectious Disease Outbreak ◽  
Using Data ◽  
Novel Coronavirus ◽  
Technological Affordances ◽  
Method Approach

This study explores how people appraise the use of contact tracing apps during the novel coronavirus (COVID-19) pandemic in South Korea. Despite increasing attention paid to digital tracing for health disasters, few studies have empirically examined user appraisal, emotion, and their continuance intention to use contact tracing apps for disaster management during an infectious disease outbreak. A mixed-method approach combining qualitative and quantitative inquiries was employed. In the qualitative study, by conducting interviews with 25 people who have used mobile apps for contact tracing, the way users appraise contact tracing apps for COVID-19 was explored. In the quantitative study, using data collected from 506 users of the apps, the interplay among cognitive appraisal (threats and opportunities) and its association with user emotion, and continuance intention was examined. The findings indicate that once users experience loss emotions, such as anger, frustration, and disgust, they are not willing to continue using the apps. App designers should consider providing technological affordances that enable users to have a sense of control over the technology so that they do not experience loss emotions. Public policymakers should also consider developing measures that can balance public health and personal privacy.

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