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.

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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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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.

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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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Assessing the lockdown effect from excess mortalities

10.1101/2021.01.22.21250312 ◽

2021 ◽

Author(s):

Alexej Weber

Keyword(s):

Growth Rate ◽

South Korea ◽

Growth Rates ◽

Excess Mortality ◽

Reproduction Number ◽

Average Growth Rate ◽

Average Growth ◽

Reproduction Numbers ◽

The Difference ◽

Pharmaceutical Interventions

AbstractBackground and AimsThe reported case numbers of COVID-19 are often used to estimate the reproduction number or the growth rate. We use the excess mortality instead, showing the difference between most restrictive non-pharmaceutical interventions (mrNPIs) and less restrictive NPIs (lrNPIs) with respect to the growth rate and death counts.MethodsWe estimate the COVID-19 growth rate for Sweden, South Korea, Italy and Germany from the excess mortality. We use the average growth rate obtained for Sweden and South Korea, two countries with lrNPIs, to estimate additional death numbers in Germany and Italy (two countries with mrNPIs) in a hypothetic lrNPIs scenario.ResultsThe growth rate estimated from excess mortality decreased faster for Germany and Italy than for Sweden and South Korea, suggesting that the mrNPIs have a non-negligible effect. This is not visible when the growth rate is calculated using the reported case numbers of COVID-19. This results in approximately 4 500 and 12 000 more death numbers for Germany and Italy, respectively.ConclusionThe reproduction numbers or growth rates obtained from reported COVID-19 cases are most likely biased. Expanding testing capacity led to an overestimation of the growth rate across all countries analyzed, masking the true decrease already visible in the excess mortality. Using our method, a more realistic estimate of the growth rate is obtained. Conclusions made for the reproduction number derived from the reported case numbers like the insignificance of most restrictive non-pharmaceutical interventions (lockdowns) might be wrong and have to be reevaluated using the growth rates obtained with our method.

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Analysis and Prediction of COVID-19 Characteristics Using a Birth-and-Death Model

10.1101/2020.06.23.20138719 ◽

2020 ◽

Author(s):

Narayanan C. Viswanath

Keyword(s):

Mathematical Modeling ◽

Infectious Diseases ◽

Explicit Expression ◽

Basic Reproduction Number ◽

Stationary Point ◽

Reproduction Number ◽

Sir Model ◽

Expected Number ◽

Infection Period ◽

The Basic Reproduction Number

AbstractIts spreading speed together with the risk of fatality might be the main characteristic that separates COVID-19 from other infectious diseases in our recent history. In this scenario, mathematical modeling for predicting the spread of the disease could have great value in containing the disease. Several very recent papers have contributed to this purpose. In this study we propose a birth-and-death model for predicting the number of COVID-19 active cases. It relation to the Susceptible-Infected-Recovered (SIR) model has been discussed. An explicit expression for the expected number of active cases helps us to identify a stationary point on the infection curve, where the infection ceases increasing. Parameters of the model are estimated by fitting the expressions for active and total reported cases simultaneously. We analyzed the movement of the stationary point and the basic reproduction number during the infection period up to the 20th of April 2020. These provide information about the disease progression path and therefore could be really useful in designing containment strategies.

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Can medication mitigate the need for a strict lock down?: A mathematical study of control strategies for COVID-19 infection

10.21203/rs.3.rs-35440/v1 ◽

2020 ◽

Author(s):

Mohsin Ali ◽

Mudassar Imran ◽

Adnan Khan

Keyword(s):

Reproduction Number ◽

Virus Disease ◽

Control Strategies ◽

Effective Control ◽

Control Mechanisms ◽

Mathematical Study ◽

Epidemic Curve ◽

The World ◽

Asymptomatic Individuals ◽

Pharmaceutical Interventions

Abstract BackgroundCOVID-19 is a pandemic that has swept across the world in 2020. To date the only effective control mechanisms were non-pharmaceutical interventions, however there have been encouraging reports regarding possible medication in the literature, with emergency approval given to some drugs in various countries.MethodsWe formulate a deterministic epidemic model to study the effects of medication on the transmission dynamics of Corona Virus Disease (COVID-19). We are especially interested in how the availability of medication could change the necessary quarantine measures for effective control of the disease. We model the transmission by extending the SEIR model to include asymptomatic, quarantined, isolated and medicated population compartments.ResultsWe calculate the basic reproduction number R0 and show that for R0<1 the disease dies out and for R0>1 the disease is endemic. Using sensitivity analysis we establish that R0 is most sensitive to the rates of quarantine and medication. We also study how the effectiveness and the rate of medication along with the quarantine rate affect R0. We devise optimal quarantine, medication and isolation strategies, noting that availability of medication reduces the duration and severity of the lock-down needed for effective disease control.ConclusionOur study also reinforces the idea that with the availability of medication, while the severity of the lock downs can be eased over time some social distancing protocols need to be observed, at least till a vaccine is found. We also analyze the COVID-19 outbreak data for four different countries, in two of these, India and Pakistan the curve is still rising, and in he other two, Italy and Spain, the epidemic curve is now falling due to effective quarantine measures. We provide estimates of R0 and the proportion of asymptomatic individuals in the population for these countries.

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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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Modeling the Effects of Non-Pharmaceutical Interventions on COVID-19 Spread in Kenya

10.1101/2020.05.14.20102087 ◽

2020 ◽

Author(s):

Duncan K. Gathungu ◽

Viona N. Ojiambo ◽

Mark E. M. Kimathi ◽

Samuel M. Mwalili

Keyword(s):

Mathematical Modeling ◽

Fourth Order ◽

Reproduction Number ◽

Compartment Model ◽

Time Dependent ◽

Matrix Approach ◽

Next Generation Matrix ◽

Fifth Order ◽

Pharmaceutical Interventions ◽

The Basic Reproduction Number

Mathematical modeling of non-pharmaceutical interventions (NPIs) of COVID-19 in Kenya is presented. An SEIR compartment model is considered with additional compartments of hospitalized population whose condition is severe or critical and also the fatalities compartment. The basic reproduction number (R_0) is computed by next generation matrix approach and later expressed as a time-dependent function so as to incorporate the NPIs into the model. The resulting system of ordinary differential equations (ODEs) are solved using fourth-order and fifth-order Runge-Kutta methods. Different intervention scenarios are considered and results show that, implementation of closure of education insitutions, curfew and partial lockdown yield predicted delayed peaks of the overall infections, severe cases and fatalities and subsequently containement of the pandemic in the country.

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Mathematical Modeling Predicts That Strict Social Distancing Measures Would Be Needed to Shorten the Duration of Waves of COVID-19 Infections in Vietnam

Frontiers in Public Health ◽

10.3389/fpubh.2020.559693 ◽

2021 ◽

Vol 8 ◽

Author(s):

Anass Bouchnita ◽

Abdennasser Chekroun ◽

Aissam Jebrane

Keyword(s):

Mathematical Modeling ◽

Urban Areas ◽

Reproduction Number ◽

Global Public Health ◽

Social Distancing ◽

Multi Scale ◽

Major Threat ◽

Reported Data ◽

The Impact ◽

Pharmaceutical Interventions

Coronavirus disease 2019 (COVID-19) emerged in Wuhan, China in 2019, has spread throughout the world and has since then been declared a pandemic. As a result, COVID-19 has caused a major threat to global public health. In this paper, we use mathematical modeling to analyze the reported data of COVID-19 cases in Vietnam and study the impact of non-pharmaceutical interventions. To achieve this, two models are used to describe the transmission dynamics of COVID-19. The first model belongs to the susceptible-exposed-infectious-recovered (SEIR) type and is used to compute the basic reproduction number. The second model adopts a multi-scale approach which explicitly integrates the movement of each individual. Numerical simulations are conducted to quantify the effects of social distancing measures on the spread of COVID-19 in urban areas of Vietnam. Both models show that the adoption of relaxed social distancing measures reduces the number of infected cases but does not shorten the duration of the epidemic waves. Whereas, more strict measures would lead to the containment of each epidemic wave in one and a half months.

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