scholarly journals Using Proper Mean Generation Intervals in Modeling of COVID-19

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiujuan Tang ◽  
Salihu S. Musa ◽  
Shi Zhao ◽  
Shujiang Mei ◽  
Daihai He
Keyword(s):  
United Arab Emirates ◽  
Time Lag ◽  
Basic Reproductive Number ◽  
Infectious Period ◽  
Reproductive Number ◽  
Secondary Case ◽  
Primary Case ◽  
The Mean ◽  
Proper Estimation ◽  
Parameter Values

In susceptible–exposed–infectious–recovered (SEIR) epidemic models, with the exponentially distributed duration of exposed/infectious statuses, the mean generation interval (GI, time lag between infections of a primary case and its secondary case) equals the mean latent period (LP) plus the mean infectious period (IP). It was widely reported that the GI for COVID-19 is as short as 5 days. However, many works in top journals used longer LP or IP with the sum (i.e., GI), e.g., >7 days. This discrepancy will lead to overestimated basic reproductive number and exaggerated expectation of infection attack rate (AR) and control efficacy. We argue that it is important to use suitable epidemiological parameter values for proper estimation/prediction. Furthermore, we propose an epidemic model to assess the transmission dynamics of COVID-19 for Belgium, Israel, and the United Arab Emirates (UAE). We estimated a time-varying reproductive number [R0(t)] based on the COVID-19 deaths data and we found that Belgium has the highest AR followed by Israel and the UAE.

scholarly journals Using proper mean generation intervals in modelling of COVID-19

2021 ◽  
Author(s):  
Xiujuan Tang ◽  
Salihu S Musa ◽  
Shi Zhao ◽  
Daihai He
Keyword(s):  
Time Lag ◽  
Basic Reproductive Number ◽  
Infectious Period ◽  
Reproductive Number ◽  
Secondary Case ◽  
Primary Case ◽  
Control Efficacy ◽  
The Mean ◽  
And Control ◽  
Parameter Values

In susceptible-exposed-infectious-recovered (SEIR) epidemic models, with the exponentially distributed duration of exposed/infectious statuses, the mean generation interval (GI, time lag between infections of a primary case and its secondary case) equals the mean latent period (LP) plus the mean infectious period (IP). It was widely reported that the GI for COVID-19 is as short as 5 days. However, many works in top journals used longer LP or IP with the sum (i.e., GI), e.g., > 7 days. This discrepancy will lead to overestimated basic reproductive number, and exaggerated expectation of infectious attack rate and control efficacy, since all these quantities are functions of basic reproductive number. We argue that it is important to use suitable epidemiological parameter values.

scholarly journals Viral Dynamics of Acute HIV-1 Infection

1999 ◽  
Vol 190 (6) ◽  
pp. 841-850 ◽  
Author(s):  
Susan J. Little ◽  
Angela R. McLean ◽  
Celsa A. Spina ◽  
Douglas D. Richman ◽  
Diane V. Havlir
Keyword(s):  
Antiretroviral Therapy ◽  
Hiv Infection ◽  
Doubling Time ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Target Cells ◽  
Viral Dynamics ◽  
Acute Hiv Infection ◽  
Acute Hiv ◽  
The Mean

Viral dynamics were intensively investigated in eight patients with acute HIV infection to define the earliest rates of change in plasma HIV RNA before and after the start of antiretroviral therapy. We report the first estimates of the basic reproductive number (R0), the number of cells infected by the progeny of an infected cell during its lifetime when target cells are not depleted. The mean initial viral doubling time was 10 h, and the peak of viremia occurred 21 d after reported HIV exposure. The spontaneous rate of decline (α) was highly variable among individuals. The phase 1 viral decay rate (δI = 0.3/day) in subjects initiating potent antiretroviral therapy during acute HIV infection was similar to estimates from treated subjects with chronic HIV infection. The doubling time in two subjects who discontinued antiretroviral therapy was almost five times slower than during acute infection. The mean basic reproductive number (R0) of 19.3 during the logarithmic growth phase of primary HIV infection suggested that a vaccine or postexposure prophylaxis of at least 95% efficacy would be needed to extinguish productive viral infection in the absence of drug resistance or viral latency. These measurements provide a basis for comparison of vaccine and other strategies and support the validity of the simian immunodeficiency virus macaque model of acute HIV infection.

Estimation of the basic reproductive number (R0) for epidemic, highly pathogenic avian influenza subtype H5N1 spread

2008 ◽  
Vol 137 (2) ◽  
pp. 219-226 ◽  
Author(s):  
M. P. WARD ◽  
D. MAFTEI ◽  
C. APOSTU ◽  
A. SURU
Keyword(s):  
Avian Influenza ◽  
Highly Pathogenic Avian Influenza ◽  
Control Strategies ◽  
Basic Reproductive Number ◽  
Infectious Period ◽  
Reproductive Number ◽  
Highly Pathogenic ◽  
Pathogenic Avian Influenza ◽  
True Basic ◽  
Road Distance

SUMMARYThree different methods were used for estimating the basic reproductive number (R0) from data on 110 outbreaks of highly pathogenic avian influenza (HPAI) subtype H5N1 that occurred in village poultry in Romania, 12 May to 6 June 2006. We assumed a village-level infectious period of 7 days. The methods applied were GIS-based identification of nearest infectious neighbour (based on either Euclidean or road distance), the method of epidemic doubling time, and a susceptible–infectious (SI) modelling approach. In general, the estimated basic reproductive numbers were consistent: 2·14, 1·95, 2·68 and 2·21, respectively. Although the true basic reproductive number in this epidemic is unknown, results suggest that the use of a range of methods might be useful for characterizing epidemics of infectious diseases. Once the basic reproductive number has been estimated, better control strategies and targeted surveillance programmes can be designed.

scholarly journals An Epidemiological Model Considering Isolation to Predict COVID-19 Trends in Tokyo, Japan: Numerical Analysis (Preprint)

2020 ◽  
Author(s):  
Motoaki Utamura ◽  
Makoto Koizumi ◽  
Seiichi Kirikami
Keyword(s):  
Public Health ◽  
Transmission Rate ◽  
Time Lag ◽  
Sir Model ◽  
Epidemiological Model ◽  
Basic Reproductive Number ◽  
Polymerase Chain Reaction Test ◽  
Series Data ◽  
Reproductive Number ◽  
Alternative Formulation

BACKGROUND COVID-19 currently poses a global public health threat. Although Tokyo, Japan, is no exception to this, it was initially affected by only a small-level epidemic. Nevertheless, medical collapse nearly happened since no predictive methods were available to assess infection counts. A standard susceptible-infectious-removed (SIR) epidemiological model has been widely used, but its applicability is limited often to the early phase of an epidemic in the case of a large collective population. A full numerical simulation of the entire period from beginning until end would be helpful for understanding COVID-19 trends in (separate) counts of inpatient and infectious cases and can also aid the preparation of hospital beds and development of quarantine strategies. OBJECTIVE This study aimed to develop an epidemiological model that considers the isolation period to simulate a comprehensive trend of the initial epidemic in Tokyo that yields separate counts of inpatient and infectious cases. It was also intended to induce important corollaries of governing equations (ie, effective reproductive number) and equations for the final count. METHODS Time-series data related to SARS-CoV-2 from February 28 to May 23, 2020, from Tokyo and antibody testing conducted by the Japanese government were adopted for this study. A novel epidemiological model based on a discrete delay differential equation (apparent time-lag model [ATLM]) was introduced. The model can predict trends in inpatient and infectious cases in the field. Various data such as daily new confirmed cases, cumulative infections, inpatients, and PCR (polymerase chain reaction) test positivity ratios were used to verify the model. This approach also derived an alternative formulation equivalent to the standard SIR model. RESULTS In a typical parameter setting, the present ATLM provided 20% less infectious cases in the field compared to the standard SIR model prediction owing to isolation. The basic reproductive number was inferred as 2.30 under the condition that the time lag <i>T</i> from infection to detection and isolation is 14 days. Based on this, an adequate vaccine ratio to avoid an outbreak was evaluated for 57% of the population. We assessed the date (May 23) that the government declared a rescission of the state of emergency. Taking into consideration the number of infectious cases in the field, a date of 1 week later (May 30) would have been most effective. Furthermore, simulation results with a shorter time lag of <i>T</i>=7 and a larger transmission rate of α=1.43α0 suggest that infections at large should reduce by half and inpatient numbers should be similar to those of the first wave of COVID-19. CONCLUSIONS A novel mathematical model was proposed and examined using SARS-CoV-2 data for Tokyo. The simulation agreed with data from the beginning of the pandemic. Shortening the period from infection to hospitalization is effective against outbreaks without rigorous public health interventions and control.

scholarly journals A mathematical model of COVID-19 transmission between frontliners and the general public

2020 ◽  
Author(s):  
Christian Alvin H. Buhat ◽  
Monica C. Torres ◽  
Yancee H. Olave ◽  
Maica Krizna A. Gavina ◽  
Edd Francis O. Felix ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Customer Service ◽  
General Public ◽  
Food Service ◽  
The Philippines ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Primary Case ◽  
Epidemic Curve ◽  
Community Effort

ABSTRACTThe number of COVID-19 cases is continuously increasing in different countries (as of March 2020) including the Philippines. It is estimated that the basic reproductive number of COVID-19 is around 1.5 to 4. The basic reproductive number characterizes the average number of persons that a primary case can directly infect in a population full of susceptible individuals. However, there can be superspreaders that can infect more than this estimated basic reproductive number. In this study, we formulate a conceptual mathematical model on the transmission dynamics of COVID-19 between the frontliners and the general public. We assume that the general public has a reproductive number between 1.5 to 4, and frontliners (e.g. healthcare workers, customer service and retail personnel, food service crews, and transport or delivery workers) have a higher reproduction number. Our simulations show that both the frontliners and the general public should be protected or resilient against the disease. Protecting only the frontliners will not result in flattening the epidemic curve. Protecting only the general public may flatten the epidemic curve but the infection risk faced by the frontliners is still high, which may eventually affect their work. Our simple model does not consider all factors involved in COVID-19 transmission in a community, but the insights from our model results remind us of the importance of community effort in controlling the transmission of the disease. All in all, the take-home message is that everyone in the community, whether a frontliner or not, should be protected or should implement preventive measures to avoid being infected.

scholarly journals Different Transmission Dynamics of Coronavirus Disease 2019 (COVID-19) and Influenza Suggest the Relative Efficiency of Isolation/Quarantine and Social Distancing Against COVID-19 in China

2020 ◽  
Author(s):  
Hao Lei ◽  
Xifeng Wu ◽  
Xiao Wang ◽  
Modi Xu ◽  
Yu Xie ◽  
...  
Keyword(s):  
Relative Efficiency ◽  
Seasonal Influenza ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Social Distancing ◽  
Relative Age ◽  
Age Dependent ◽  
The Mean ◽  
Age Structured ◽  
Nonpharmaceutical Interventions

Abstract Background Nonpharmaceutical interventions (NPIs) against coronavirus disease 2019 (COVID-19) are vital to reducing transmission risks. However, the relative efficiency of social distancing against COVID-19 remains controversial, since social distancing and isolation/quarantine were implemented almost at the same time in China. Methods In this study, surveillance data of COVID-19 and seasonal influenza in 2018–2020 were used to quantify the relative efficiency of NPIs against COVID-19 in China, since isolation/quarantine was not used for the influenza epidemics. Given that the relative age-dependent susceptibility to influenza and COVID-19 may vary, an age-structured susceptible/infected/recovered model was built to explore the efficiency of social distancing against COVID-19 under different population susceptibility scenarios. Results The mean effective reproductive number, Rt, of COVID-19 before NPIs was 2.12 (95% confidence interval [CI], 2.02–2.21). By 11 March 2020, the overall reduction in Rt of COVID-19 was 66.1% (95% CI, 60.1–71.2%). In the epidemiological year 2019–20, influenza transmissibility was reduced by 34.6% (95% CI, 31.3–38.2%) compared with transmissibility in epidemiological year 2018–19. Under the observed contact pattern changes in China, social distancing had similar efficiency against COVID-19 in 3 different scenarios. By assuming the same efficiency of social distancing against seasonal influenza and COVID-19 transmission, isolation/quarantine and social distancing could lead to 48.1% (95% CI, 35.4–58.1%) and 34.6% (95% CI, 31.3–38.2%) reductions of the transmissibility of COVID-19, respectively. Conclusions Though isolation/quarantine is more effective than social distancing, given that the typical basic reproductive number of COVID-19 is 2–3, isolation/quarantine alone could not contain the COVID-19 pandemic effectively in China.

scholarly journals A case study of 2019-nCOV cases in Argentina with the real data based on daily cases from March 03, 2020 to March 29, 2021 using classical and fractional derivatives

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Pushpendra Kumar ◽  
Vedat Suat Erturk ◽  
Marina Murillo-Arcila ◽  
Ramashis Banerjee ◽  
A. Manickam
Keyword(s):  
Fractional Order ◽  
Trust Region ◽  
Simulated Data ◽  
Real Data ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Order Model ◽  
The Real ◽  
The Stability ◽  
Parameter Values

AbstractIn this study, our aim is to explore the dynamics of COVID-19 or 2019-nCOV in Argentina considering the parameter values based on the real data of this virus from March 03, 2020 to March 29, 2021 which is a data range of more than one complete year. We propose a Atangana–Baleanu type fractional-order model and simulate it by using predictor–corrector (P-C) method. First we introduce the biological nature of this virus in theoretical way and then formulate a mathematical model to define its dynamics. We use a well-known effective optimization scheme based on the renowned trust-region-reflective (TRR) method to perform the model calibration. We have plotted the real cases of COVID-19 and compared our integer-order model with the simulated data along with the calculation of basic reproductive number. Concerning fractional-order simulations, first we prove the existence and uniqueness of solution and then write the solution along with the stability of the given P-C method. A number of graphs at various fractional-order values are simulated to predict the future dynamics of the virus in Argentina which is the main contribution of this paper.

scholarly journals Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: framework and applications to the novel coronavirus (SARS-CoV-2) outbreak

2020 ◽  
Author(s):  
Sang Woo Park ◽  
Benjamin M. Bolker ◽  
David Champredon ◽  
David J. D. Earn ◽  
Michael Li ◽  
...  
Keyword(s):  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Similar Data ◽  
Primary Case ◽  
Susceptible Population ◽  
Generation Interval ◽  
Statistical Framework ◽  
Wide Range ◽  
Novel Coronavirus ◽  
Global Threat

AbstractA novel coronavirus (SARS-CoV-2) has recently emerged as a global threat. As the epidemic progresses, many disease modelers have focused on estimating the basic reproductive number ℛ0– the average number of secondary cases caused by a primary case in an otherwise susceptible population. The modeling approaches and resulting estimates of ℛ0 vary widely, despite relying on similar data sources. Here, we present a novel statistical framework for comparing and combining different estimates of ℛ0 across a wide range of models by decomposing the basic reproductive number into three key quantities: the exponential growth rate r, the mean generation interval , and the generation-interval dispersion κ. We then apply our framework to early estimates of ℛ0 for the SARS-CoV-2 outbreak. We show that many early ℛ0 estimates are overly confident. Our results emphasize the importance of propagating uncertainties in all components of ℛ0, including the shape of the generation-interval distribution, in efforts to estimate ℛ0 at the outset of an epidemic.

scholarly journals A Simulation on Potential Secondary Spread of Novel Coronavirus in an Exported Country Using a Stochastic Epidemic SEIR Model

2020 ◽  
Author(s):  
Kentaro Iwata ◽  
Chisato Miyakoshi
Keyword(s):  
Healthcare Professionals ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Secondary Case ◽  
Hospital Visit ◽  
Seir Model ◽  
Stochastic Epidemic ◽  
Novel Coronavirus ◽  
The Impact ◽  
Multiple Scenarios

Ongoing outbreak of pneumonia caused by novel coronavirus (2019-nCoV) began in December 2019 in Wuhan, China, and the number of new patients continues to increase. On the contrary to ongoing outbreak in China, however, there are limited secondary outbreaks caused by exported case outside the country. We here conducted simulations to estimate the impact of potential secondary outbreaks at a community outside China. Simulations using stochastic SEIR model was conducted, assuming one patient was imported to a community. Among 45 possible scenarios we prepared, the worst scenario resulted in total number of persons recovered or removed to be 997 (95% CrI 990-1,000) at day 100 and maximum number of symptomatic infectious patients per day of 335 (95% CrI 232-478). Calculated mean basic reproductive number (R0) was 6.5 (Interquartile range, IQR 5.6-7.2). However, with good case scenarios with different parameter led to no secondary case. Altering parameters, especially time to hospital visit could change the impact of secondary outbreak. With this multiple scenarios with different parameters, healthcare professionals might be able to prepare for this viral infection better.

scholarly journals Behavior of a Discrete Fractional Order SIR Epidemic Model

2018 ◽  
Vol 7 (4.10) ◽  
pp. 675 ◽  
Author(s):  
A. George Maria Selvam ◽  
D. Abraham Vianny
Keyword(s):  
Equilibrium Point ◽  
Fractional Order ◽  
Epidemic Model ◽  
Dynamical Behavior ◽  
Basic Reproductive Number ◽  
Phase Portraits ◽  
Reproductive Number ◽  
Sir Epidemic Model ◽  
Sir Epidemic ◽  
Parameter Values

In this paper we investigate the dynamical behavior of a SIR epidemic model of fractional order. Disease Free Equilibrium point, Endemic Equilibrium point and basic reproductive number are obtained. Time series plots, phase portraits and bifurcation diagrams are presented for suitable parameter values. Also some numerical examples are provided to illustrate the dynamics of the system.  

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