A modelling study highlights the power of detecting and isolating asymptomatic or very mildly affected individuals for 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.

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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.

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

10.21203/rs.3.rs-33492/v1 ◽

2020 ◽

Author(s):

Lia Mayorga ◽

Clara García Samartino ◽

Gabriel Flores ◽

Sofía Masuelli ◽

María V. Sánchez ◽

...

Keyword(s):

Compartmental Model ◽

Mitigation Measures ◽

Asymptomatic Group ◽

Healthcare Burden ◽

Fundamental Part ◽

Management Policies ◽

The Difference ◽

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.

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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.

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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.

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Are we there yet? An adaptive SIR model for continuous estimation of COVID-19 infection rate and reproduction number in the United States (Preprint)

10.2196/preprints.24389 ◽

2020 ◽

Author(s):

Mark Shapiro ◽

Fazle Karim ◽

Guido Muscioni ◽

Abel Saju Augustine

Keyword(s):

Infection Rate ◽

Reproduction Number ◽

Infected Individual ◽

Local Population ◽

The United States ◽

Mitigation Measures ◽

Simple Method ◽

Wide Range ◽

Continuous Estimation ◽

The Impact

BACKGROUND The dynamics of the COVID-19 epidemic vary due to local population density and policy measures. When making decisions, policy makers consider an estimate of the effective reproduction number R_t which is the expected number of secondary infections by a single infected individual. OBJECTIVE We propose a simple method for estimating the time-varying infection rate and reproduction number R_t . METHODS We use a sliding window approach applied to a Susceptible-Infectious-Removed model. The infection rate is estimated using the reported cases for a seven-day window to obtain continuous estimation of R_t. The proposed adaptive SIR (aSIR) model was applied to data at the state and county levels. RESULTS The aSIR model showed an excellent fit for the number of reported COVID-19 positive cases, a one-day forecast MAPE was less than 2.6% across all states. However, a seven-day forecast MAPE reached 16.2% and strongly overestimated the number of cases when the reproduction number was high and changing fast. The maximal R_t showed a wide range of 2.0 to 4.5 across all states, with the highest values for New York (4.4) and Michigan (4.5). We demonstrate that the aSIR model can quickly adapt to an increase in the number of tests and associated increase in the reported cases of infections. Our results also suggest that intensive testing may be one of the effective methods of reducing R_t. CONCLUSIONS The aSIR model provides a simple and accurate computational tool to obtain continuous estimation of the reproduction number and evaluate the impact of mitigation measures.

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Trajectory of individual immunity and vaccination required for SARS-CoV-2 community immunity: a conceptual investigation

Journal of The Royal Society Interface ◽

10.1098/rsif.2020.0683 ◽

2021 ◽

Vol 18 (175) ◽

pp. 20200683

Author(s):

Chadi M. Saad-Roy ◽

Simon A. Levin ◽

C. Jessica E. Metcalf ◽

Bryan T. Grenfell

Keyword(s):

Reproduction Number ◽

Infected Individual ◽

Mitigation Measures ◽

Simple Description ◽

Vaccination Rates ◽

Epidemic Dynamics ◽

Low Levels ◽

Analytical Relations ◽

The Impact ◽

Individual Immunity

SARS-CoV-2 is an international public health emergency; high transmissibility and morbidity and mortality can result in the virus overwhelming health systems. Combinations of social distancing, and test, trace, and isolate strategies can reduce the number of new infections per infected individual below 1, thus driving declines in case numbers, but may be both challenging and costly. These interventions must also be maintained until development and (now likely) mass deployment of a vaccine (or therapeutics), since otherwise, many susceptible individuals are still at risk of infection. We use a simple analytical model to explore how low levels of infection, combined with vaccination, determine the trajectory to community immunity. Understanding the repercussions of the biological characteristics of the viral life cycle in this scenario is of considerable importance. We provide a simple description of this process by modelling the scenario where the effective reproduction number R eff is maintained at 1. Since the additional complexity imposed by the strength and duration of transmission-blocking immunity is not yet clear, we use our framework to probe the impact of these uncertainties. Through intuitive analytical relations, we explore how the necessary magnitude of vaccination rates and mitigation efforts depends crucially on the durations of natural and vaccinal immunity. We also show that our framework can encompass seasonality or preexisting immunity due to epidemic dynamics prior to strong mitigation measures. Taken together, our simple conceptual model illustrates the importance of individual and vaccinal immunity for community immunity, and that the quantification of individuals immunized against SARS-CoV-2 is paramount.

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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.

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Study of the Dependence of Effective Reproduction Number of COVID-19 on the Temperature and Humidity: A Case Study with the Indian States

10.1101/2020.07.05.20146324 ◽

2020 ◽

Cited By ~ 1

Author(s):

Manoj Mandal ◽

Subhradeep Patra ◽

Sabyasachi Pal ◽

Suman Acharya ◽

Mangal Hazra

Keyword(s):

Relative Humidity ◽

Reproduction Number ◽

World Health ◽

Mitigation Measures ◽

Negative Slope ◽

Maximum Temperature ◽

Linear Regression Method ◽

Indian States ◽

Temperature And Humidity ◽

The Impact

Corona Virus Disease 2019 (COVID-19) started in Wuhan province of China in November 2019 and within a short time, it was declared as a worldwide pandemic by World Health Organisation due to very fast worldwide spread of the virus. In the absence of any vaccine, various mitigation measures were used. In the past, the effect of temperature and humidity on the spread of the virus was studied for a very early phase of the data with mixed results. We are studying the impact of COVID-19 on the maximum temperature and relative humidity of a place using Indian states as test cases for SIR, SIRD, and SEIR models. We used a linear regression method to look for any dependency between effective reproduction number with maximum temperature and relative humidity. Most of the states show a correlation with the negative slope between the effective reproduction number with the maximum temperature and the relative humidity. It indicates that the effective reproduction number goes down as maximum temperature or relative humidity rise. But, the regression coefficient R2 is low for these correlations which means that the correlation is not strong.

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The Impact of the Industrial Processing of Oil on the Rainfall Water Quality

Revista de Chimie ◽

10.37358/rc.17.1.5414 ◽

2017 ◽

Vol 68 (1) ◽

pp. 175-179

Author(s):

Oana Roxana Chivu ◽

Augustin Semenescu ◽

Claudiu Babis ◽

Catalin Amza ◽

Gabriel Iacobescu ◽

...

Keyword(s):

Water Quality ◽

Sulfuric Acid ◽

Experimental Research ◽

Air Purification ◽

High Toxicity ◽

Industrial Processing ◽

Mobile Sources ◽

Specific Activities ◽

The Impact ◽

The Ideal

Rainfall is a major component of the environment and the main source of the air purification becouse of many pollutants increases who have the most varied sources: various human activities including industry and agriculture, and some household duties. Air purification by means of precipitation is achieved by numerous highly complex mechanisms. The final products of degradation of the pollutant in the air, which are generally harmless, can be reacted with each other in the presence of water, giving rise to the final compounds with a high toxicity. Thus, exhaust, mobile sources of noxious almost identical to those specific activities in the industrial processing of oil, contain lead which is the ideal catalyst for converting SO2 to sulfuric acid in the presence of rainwater, with all the disadvantages that they create. This paper will present an experimental research oabout how rainfall water quality is influenced by the activity of the industrial processing of oil, in a chemical plant in Constanta County.

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