Detection and Isolation of Asymptomatic Individuals Can Make The Difference in COVID-19 Epidemic Management

Abstract Background Mathematical modelling of infectious diseases is a powerful tool for the design of management policies and a fundamental part of the arsenal currently deployed to deal with the COVID-19 pandemic. Methods We present a compartmental model for the disease where symptomatic and asymptomatic individuals move separately. We introduced healthcare burden parameters allowing to infer possible containment and suppression strategies. In addition, the model was scaled up to describe different interconnected areas, giving the possibility to trigger regionalized measures. It was specially adjusted to Mendoza-Argentina’s parameters, but is easily adaptable for elsewhere. Results Overall, the simulations we carried out were notably more effective when mitigation measures were not relaxed in between the suppressive actions. Since asymptomatics or very mildly affected patients are the vast majority, we studied the impact of detecting and isolating them. The removal of asymptomatics from the infectious pool remarkably lowered the effective reproduction number, healthcare burden and overall fatality. Furthermore, different suppression triggers regarding ICU occupancy were attempted. The best scenario was found to be the combination of ICU occupancy triggers (on: 50%, off: 30%) with the detection and isolation of asymptomatic individuals. In the ideal assumption that 45% of the asymptomatics could be detected and isolated, there would be no need for complete lockdown, and Mendoza’s healthcare system would not collapse. Conclusions Our model and its analysis inform that the detection and isolation of all infected individuals, without leaving aside the asymptomatic group is the key to surpass this pandemic.

Download Full-text

A modelling study highlights the power of detecting and isolating asymptomatic or very mildly affected individuals for COVID-19 epidemic management.

10.21203/rs.3.rs-33492/v3 ◽

2020 ◽

Author(s):

Lia Mayorga ◽

Clara García Samartino ◽

Gabriel Flores ◽

Sofía Masuelli ◽

María V. Sánchez ◽

...

Keyword(s):

Compartmental Model ◽

Reproduction Number ◽

Mitigation Measures ◽

Asymptomatic Group ◽

Healthcare Burden ◽

Fundamental Part ◽

Management Policies ◽

Asymptomatic Individuals ◽

The Impact ◽

The Ideal

Abstract Background: Mathematical modelling of infectious diseases is a powerful tool for the design of management policies and a fundamental part of the arsenal currently deployed to deal with the COVID-19 pandemic. Methods: We present a compartmental model for the disease where symptomatic and asymptomatic individuals move separately. We introduced healthcare burden parameters allowing to infer possible containment and suppression strategies. In addition, the model was scaled up to describe different interconnected areas, giving the possibility to trigger regionalized measures. It was specially adjusted to Mendoza-Argentina’s parameters, but is easily adaptable for elsewhere. Results: Overall, the simulations we carried out were notably more effective when mitigation measures were not relaxed in between the suppressive actions. Since asymptomatics or very mildly affected patients are the vast majority, we studied the impact of detecting and isolating them. The removal of asymptomatics from the infectious pool remarkably lowered the effective reproduction number, healthcare burden and overall fatality. Furthermore, different suppression triggers regarding ICU occupancy were attempted. The best scenario was found to be the combination of ICU occupancy triggers (on: 50%, off: 30%) with the detection and isolation of asymptomatic individuals. In the ideal assumption that 45% of the asymptomatics could be detected and isolated, there would be no need for complete lockdown, and Mendoza’s healthcare system would not collapse. Conclusions: Our model and its analysis inform that the detection and isolation of all infected individuals, without leaving aside the asymptomatic group is the key to surpass this pandemic.

Download Full-text

A modelling study highlights the power of detecting and isolating asymptomatic or very mildly affected individuals for COVID-19 epidemic management.

10.21203/rs.3.rs-33492/v2 ◽

2020 ◽

Author(s):

Lia Mayorga ◽

Clara García Samartino ◽

Gabriel Flores ◽

Sofía Masuelli ◽

María V. Sánchez ◽

...

Keyword(s):

Compartmental Model ◽

Reproduction Number ◽

Mitigation Measures ◽

Asymptomatic Group ◽

Healthcare Burden ◽

Fundamental Part ◽

Management Policies ◽

Asymptomatic Individuals ◽

The Impact ◽

The Ideal

Abstract Background: Mathematical modelling of infectious diseases is a powerful tool for the design of management policies and a fundamental part of the arsenal currently deployed to deal with the COVID-19 pandemic. Methods: We present a compartmental model for the disease where symptomatic and asymptomatic individuals move separately. We introduced healthcare burden parameters allowing to infer possible containment and suppression strategies. In addition, the model was scaled up to describe different interconnected areas, giving the possibility to trigger regionalized measures. It was specially adjusted to Mendoza-Argentina’s parameters, but is easily adaptable for elsewhere. Results: Overall, the simulations we carried out were notably more effective when mitigation measures were not relaxed in between the suppressive actions. Since asymptomatics or very mildly affected patients are the vast majority, we studied the impact of detecting and isolating them. The removal of asymptomatics from the infectious pool remarkably lowered the effective reproduction number, healthcare burden and overall fatality. Furthermore, different suppression triggers regarding ICU occupancy were attempted. The best scenario was found to be the combination of ICU occupancy triggers (on: 50%, off: 30%) with the detection and isolation of asymptomatic individuals. In the ideal assumption that 45% of the asymptomatics could be detected and isolated, there would be no need for complete lockdown, and Mendoza’s healthcare system would not collapse. Conclusions: Our model and its analysis inform that the detection and isolation of all infected individuals, without leaving aside the asymptomatic group is the key to surpass this pandemic.

Download Full-text

Detection and isolation of asymptomatic individuals can make the difference in COVID-19 epidemic management

10.1101/2020.04.23.20077255 ◽

2020 ◽

Author(s):

Lía Mayorga ◽

Clara García Samartino ◽

Gabriel Flores ◽

Sofía Masuelli ◽

María Victoria Sanchez ◽

...

Keyword(s):

Mathematical Modeling ◽

Infectious Diseases ◽

Compartmental Model ◽

Reproduction Number ◽

Healthcare Burden ◽

Fundamental Part ◽

Management Policies ◽

The Difference ◽

Asymptomatic Individuals ◽

Pharmaceutical Interventions

AbstractMathematical modeling of infectious diseases is a powerful tool for the design of management policies and a fundamental part of the arsenal currently deployed to deal with the COVID-19 pandemic. Here we present a compartmental model for the disease that can provide healthcare burden parameters allowing to infer possible containment and suppression strategies, explicitly including asymptomatic individuals. The main conclusion of our work is that efficient and timely detection and isolation of these asymptomatic individuals can have dramatic effects on the effective reproduction number and healthcare burden parameters. This intervention can provide a valuable tool complementary to other non-pharmaceutical interventions to contain the epidemic.

Download Full-text

Mitigating impact of spatial variance of turbulence in wind energy applications

Wind Energy Science ◽

10.5194/wes-5-439-2020 ◽

2020 ◽

Vol 5 (2) ◽

pp. 439-450

Author(s):

Jonas Kazda ◽

Jakob Mann

Keyword(s):

Wind Turbine ◽

Random Error ◽

Wind Farm ◽

Second Order ◽

Mitigation Measures ◽

Order Moment ◽

Spatial Variance ◽

Turbine Performance ◽

The Difference ◽

The Impact

Abstract. For the first time an analytical solution for the quantification of the spatial variance of the second-order moment of correlated wind speeds was developed in this work. The spatial variance is defined as random differences in the sample variance of wind speed between different points in space. The approach is successfully verified using simulation and field data. The impact of the spatial variance on three selected applications relevant to the wind energy sector is then investigated including mitigation measures. First, the difference of the second-order moment between front-row wind turbines of Lillgrund wind farm is investigated. The variance of the difference ranges between 25 % and 48 % for turbulence intensities ranging from 7 % to 10 % and a sampling period of 10 min. It is thus suggested to use the second-order moment measured at each individual turbine as input to flow models of wind farm controllers in order to mitigate random error. Second, the impact of the spatial variance of the measured second-order moment on the verification of wind turbine performance is investigated. Misalignment between the mean wind direction and the line connecting the meteorological mast and wind turbine is observed to result in an additional random error in the observed second-order moment of wind speed. In the investigated conditions the random error was up to 34 %. Such a random error adds uncertainty to the turbulence intensity-based classification of the fatigue loads and power output of a wind turbine. To mitigate the random error, it is suggested to either filter the measured data for low angles of misalignment or quantify wind turbine performance using the ensemble-averaged measurements of the same wind conditions. Third, the verification of sensors in wind farms was investigated with respect to the impact of distant reference measurements. In the case of a misalignment between the wind direction and the line connecting sensor and reference, an increased random error will hamper the comparison of the measured second-order moments. The suggested mitigation measures are equivalent to those for the verification of turbine performance.

Download Full-text

Using paired teaching for earthquake education in schools

Geoscience Communication ◽

10.5194/gc-4-281-2021 ◽

2021 ◽

Vol 4 (2) ◽

pp. 281-295

Author(s):

Solmaz Mohadjer ◽

Sebastian G. Mutz ◽

Matthew Kemp ◽

Sophie J. Gill ◽

Anatoly Ischuk ◽

...

Keyword(s):

Plate Tectonics ◽

Seismic Energy ◽

Classroom Culture ◽

Mitigation Measures ◽

Plate Boundaries ◽

Earthquake Hazards ◽

The Difference ◽

Earth’S Interior ◽

The Impact ◽

Earth's Interior

Abstract. In this study, we have created 10 geoscience video lessons that follow the paired-teaching pedagogical approach. This method is used to supplement the standard school curriculum with video lessons, instructed by geoscientists from around the world, coupled with activities carried out under the guidance of classroom teachers. The video lessons introduce students to the scientific concepts behind earthquakes (e.g. the Earth's interior, plate tectonics, faulting, and seismic energy), earthquake hazards, and mitigation measures (e.g. liquefaction, structural, and non-structural earthquake hazards). These concepts are taught through hands-on learning, where students use everyday materials to build models to visualize basic Earth processes that produce earthquakes and explore the effects of different hazards. To evaluate the effectiveness of these virtual lessons, we tested our videos in school classrooms in Dushanbe (Tajikistan) and London (United Kingdom). Before and after the video implementations, students completed questionnaires that probed their knowledge on topics covered by each video, including the Earth's interior, tectonic plate boundaries, and non-structural hazards. Our assessment results indicate that, while the paired-teaching video lessons appear to enhance student knowledge and understanding of some concepts (e.g. Earth's interior, earthquake location forecasting, and non-structural hazards), they bring little change to their views on the causes of earthquakes and their relation to plate boundaries. In general, the difference between UK and Tajik students' level of knowledge prior to and after video testing is more significant than the difference between pre- and post-knowledge for each group. This could be due to several factors affecting curriculum testing (e.g. level of teachers' participation and classroom culture) and students' learning of content (e.g. pre-existing hazards knowledge and experience). To maximize the impact of school-based risk reduction education, curriculum developers must move beyond innovative content and pedagogical approaches, take classroom culture into consideration, and instil skills needed for participatory learning and discovery.

Download Full-text

Mitigating Impact of Spatial Variance of Turbulence in Wind Energy Applications

10.5194/wes-2019-10 ◽

2019 ◽

Author(s):

Jonas Kazda ◽

Jakob Mann

Keyword(s):

Wind Turbine ◽

Random Error ◽

Wind Farm ◽

Second Order ◽

Mitigation Measures ◽

Order Moment ◽

Spatial Variance ◽

Turbine Performance ◽

The Difference ◽

The Impact

Abstract. The first analytical solution for the quantification of the spatial variance of the second-order moment of correlated wind speeds was developed in this work. The spatial variance is defined as random differences in the sample variance of wind speed between different points in space. The approach is successfully verified using simulation and field data. The impact of the spatial variance on three selected applications relevant to the wind energy sector is then investigated including mitigation measures. First, the difference of the second-order moment between front-row wind turbines of Lillgrund wind farm is investigated. The variance of the difference ranges between 25 % and 48 % for turbulence intensities ranging from 7 % to 10 % and a sampling period of 10 min. It is thus suggested to use the second-order moment measured at each individual turbine as input to flow models of wind farm controllers in order to mitigate random error. Second, the impact of the spatial variance of the measured second-order moment on the verification of wind turbine performance is investigated. Misalignment between the mean wind direction and the line connecting the meteorological mast and wind turbine is observed to result in an additional random error in the observed second-order moment of wind speed. In the investigated conditions the random error was up to 34 %. Such random error adds uncertainty to the turbulence intensity-based classification of the fatigue loads and power output of a wind turbine. To mitigate the random error it is suggested to either filter the measured data for low angles of misalignment, or to quantify wind turbine performance using the ensemble averaged measurements of the same wind conditions. Third, the verification of sensors in wind farms was investigated with respect to the impact of distant reference measurements. In case of a misalignment between the wind direction and the line connecting sensor and reference, an increased random error will hamper the comparison of the measured second-order moments. The suggested mitigation measures are equivalent to those for the verification of turbine performance.

Download Full-text

MATHEMATICAL STUDY OF THE IMPACT OF QUARANTINE, ISOLATION AND VACCINATION IN CURTAILING AN EPIDEMIC

Journal of Biological System ◽

10.1142/s0218339007002180 ◽

2007 ◽

Vol 15 (02) ◽

pp. 185-202 ◽

Cited By ~ 7

Author(s):

CHANDRA N. PODDER ◽

ABBA B. GUMEL ◽

CHRIS S. BOWMAN ◽

ROBERT G. MCLEOD

Keyword(s):

Disease Burden ◽

Compartmental Model ◽

Reproduction Number ◽

Control Measures ◽

Vaccination Program ◽

Reproductive Number ◽

Mathematical Study ◽

Asymptomatic Individuals ◽

Combined Impact ◽

The Impact

The quarantine of suspected cases and isolation of individuals with symptoms are two of the primary public health control measures for combating the spread of a communicable emerging or re-emerging disease. Implementing these measures, however, can inflict significant socio-economic and psychological costs. This paper presents a deterministic compartmental model for assessing the single and combined impact of quarantine and isolation to contain an epidemic. Comparisons are made with a mass vaccination program. The model is simulated using parameters for influenza-type diseases such as SARS. The study shows that even for an epidemic in which asymptomatic transmission does not occur, the quarantine of asymptomatically-infected individuals can be more effective than only isolating individuals with symptoms, if the associated reproductive number is high enough. For the case where asymptomatic transmission occurs, it is shown that isolation is more effective for a disease with a small basic reproduction number and transmission coefficient of asymptomatically-infected individuals. If asymptomatic individuals transmit at a rate that is at least 20% that of symptomatic individuals, quarantine is always more effective. The study further shows that the reduction in disease burden obtained from a combined quarantine and isolation program can be comparable to that obtained by a vaccination program, if the former is implemented quickly enough after the onset of the outbreak. If the implementation of such a quarantine/isolation program is delayed, however, even for a short while, its effectiveness decreases rapidly.

Download Full-text

The impact of asymptomatic individuals on the strength of public health interventions to prevent the second outbreak of COVID-19

10.21203/rs.3.rs-27142/v1 ◽

2020 ◽

Author(s):

Xiaochen Wang ◽

Shengfeng Wang ◽

Yueheng Lan ◽

Xiaofeng Tao ◽

Jinghua Xiao

Keyword(s):

Compartmental Model ◽

Economic Structure ◽

Health Interventions ◽

Control Measures ◽

Public Health Interventions ◽

Future Studies ◽

The Social ◽

Asymptomatic Individuals ◽

The Impact ◽

The Relationship

Abstract The pandemic of coronavirus disease 2019(COVID-19) has threatened the social and economic structure all around the world. Generally, COVID-19 has three possible transmission routes, including pre-symptomatic, symptomatic, and asymptomatic transmission, among which the last one has brought a severe challenge for the containment of the disease. One core scientific question is to understand the influence of asymptomatic individuals and of the strength of control measures on the evolution of the disease, particularly on a second outbreak of the disease. To explore these issues, we proposed a novel compartmental model that takes the infection of asymptomatic individuals into account. We get the relationship between asymptomatic individuals and critical strength of control measures theoretically. Furthermore, we verify the reliability of our model and the accuracy of the theoretical analysis by using the real confirmed cases of COVID-19 contamination. Our results, showing the importance of the asymptomatic population on the control measures, would provide useful theoretical reference to the policymakers and fuel future studies of COVID-19.

Download Full-text

The range of the Oxford Shoulder Score in the asymptomatic population: a marker for post-operative improvement

Annals of The Royal College of Surgeons of England ◽

10.1308/003588411x13165261994193 ◽

2011 ◽

Vol 93 (8) ◽

pp. 629-633 ◽

Cited By ~ 17

Author(s):

F Younis ◽

J Sultan ◽

S Dix ◽

PJ Hughes

Keyword(s):

Normal Population ◽

Adult Population ◽

Asymptomatic Group ◽

Oxford Shoulder Score ◽

Asymptomatic Volunteers ◽

Symptom Scores ◽

Asymptomatic Adult ◽

The Difference ◽

Asymptomatic Individuals ◽

Shoulder Score

INTRODUCTION The Oxford Shoulder Score (OSS) is a validated scoring system used to assess the degree of pain and disability caused by shoulder pathology. To date there is no knowledge of the range of the OSS in the healthy adult population. This study aimed to establish the range in asymptomatic individuals. METHODS The OSS of 100 asymptomatic volunteers was compared with the pre-operative OSS of 100 symptomatic individuals who had had elective shoulder surgery performed at the Royal Preston Hospital. RESULTS The difference in mean scores in the operated group (36.7) and the asymptomatic group (15.3) was statistically significant (p<0.0001). There was, however, a substantial overlap between the scores of the two groups (operated group range: 19–55, asymptomatic group range: 12–47). Factors such as age, sex, body mass index, co-morbidities and smoking did not have a statistically significant impact on the eventual score in the asymptomatic group. CONCLUSIONS This study has established the range of OSS in the asymptomatic adult population. Symptom scores can only be used effectively when the range in the asymptomatic population is known. This is so that disease severity can be gauged in the context of the normal population and post-operative improvements can be forecast more accurately.

Download Full-text