Distinguishing Viruses Responsible for Influenza-Like Illness

Spencer, Jeanne P; Shutt, Deborah P.; Moser, S Kane; Clegg, Hannah; Wearing, Helen J.; Mukundan, Harshini; Manore, Carrie

doi:10.1101/2020.02.04.20020404

Cited by 28 publications

(19 citation statements)

References 141 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The values of the transmission constants β E 0 and β I 0 can be found in a recent study [ 13 ]. The incubation period of the infection ranges between 2–14 days, with a mean of 5–7 days [ 31 ], and we take the value of 7 days in our model. The average recovery period is about 15 days [ 31 ], and so we set the disease recovery rate as γ = 1/15 per day.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The incubation period of the infection ranges between 2–14 days, with a mean of 5–7 days [ 31 ], and we take the value of 7 days in our model. The average recovery period is about 15 days [ 31 ], and so we set the disease recovery rate as γ = 1/15 per day. Members of the coronavirus family can survive in the environment from a few hours to several days [ 19 ], and we take the value of 1 day which results in a virus removal rate σ = 1 per day.…”

Section: Numerical Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A mathematical model for the novel coronavirus epidemic in Wuhan, China

Yang¹,

Wang²

2020

Mathematical Biosciences and Engineering

410

402

View full text Add to dashboard Cite

We propose a mathematical model to investigate the current outbreak of the coronavirus disease 2019 (COVID-19) in Wuhan, China. Our model describes the multiple transmission pathways in the infection dynamics, and emphasizes the role of the environmental reservoir in the transmission and spread of this disease. Our model also employs non-constant transmission rates which change with the epidemiological status and environmental conditions and which reflect the impact of the on-going disease control measures. We conduct a detailed analysis of this model, and demonstrate its application using publicly reported data. Among other findings, our analytical and numerical results indicate that the coronavirus infection would remain endemic, which necessitates long-term disease prevention and intervention programs.

show abstract

Section: Numerical Resultsmentioning

confidence: 99%

Section: Numerical Resultsmentioning

confidence: 99%

A mathematical model for the novel coronavirus epidemic in Wuhan, China

Yang¹,

Wang²

2020

Mathematical Biosciences and Engineering

410

402

View full text Add to dashboard Cite

show abstract

“…In the epidemic period, our simulations assumed that only a relatively “small” number of people have traveled to SP state; thus, the inflow rate (Δ) of the model is based only on the number of newborns in the state. Spencer et al 15 reported an average recovery period of approximately 15 days; hence, we defined the recovery rate from COVID-19 as γ = 1/15 per day. The incubation period of the infection varied between 2 and 14 days, with an average of 5-7; therefore, σ = 1/15 .…”

Section: Resultsmentioning

confidence: 99%

Modeling transmission dynamics of severe acute respiratory syndrome coronavirus 2 in São Paulo, Brazil

Cruz

Crema-Cruz

Campos

2021

Rev. Soc. Bras. Med. Trop.

View full text Add to dashboard Cite

INTRODUCTION Severe acute respiratory syndrome coronavirus 2 has been transmitted to more than 200 countries, with 92.5 million cases and 1,981,678 deaths. METHODS This study applied a mathematical model to estimate the increase in the number of cases in São Paulo state, Brazil during four epidemic periods and the subsequent 300 days. We used different types of dynamic transmission models to measure the effects of social distancing interventions, based on local contact patterns. Specifically, we used a model that incorporated multiple transmission pathways and an environmental class that represented the pathogen concentration in the environmental reservoir and also considered the time that an individual may sustain a latent infection before becoming actively infectious. Thus, this model allowed us to show how the individual quarantine and active monitoring of contacts can influence the model parameters and change the rate of exposure of susceptible individuals to those who are infected. RESULTS The estimated basic reproductive number, R o , was 3.59 (95% confidence interval [CI]: 3.48 - 3.72). The mathematical model data prediction coincided with the real data mainly when the social distancing measures were respected. However, a lack of social distancing measures caused a significant increase in the number of infected individuals. Thus, if social distancing measures are not respected, we estimated a difference of at least 100,000 cases over the next 300 days. CONCLUSIONS: Although the predictive capacity of this model was limited by the accuracy of the available data, our results showed that social distancing is currently the best non-pharmacological measure.

show abstract

“…The hierarchy facilitates coexistence and prevents competitive exclusion of the weaker species in a process that is mainly driven by the relative magnitude of the reproduction numbers of both species [10] that reflect the abilities to use a contested common resource. Seasonal influenza has a median basic reproduction number of 1.28 [11]; while for SARS-CoV-2, the median R 0 is 2.79 [12, 11]. This difference in transmission potential supports the assumption that while competing for hosts, their common resource, the likelihood of long-term coexistence and cocirculation is high.…”

Section: Introductionmentioning

confidence: 92%

“…According to [22], for seasonal influenza, the median reproduction number for influenza is 1.28 and that for SARS-CoV-2 is 2.79, but other sources report large variability [32]. Likewise, [11] citing several sources, reports an R 0 for influenza in the range 1.06–3.4 with a mean of 1.68. The same source gives an incubation period in the range of 1–6.3 days with a mean of 2.61 and an infectious period ( η i ) range of 1–9 days with a mean of 4.58 days.…”

Section: Model Set-upmentioning

confidence: 99%

Co-circulation of two viral populations under vaccination

Acuña-Zegarra

Núñez-López

Comas‐García

et al. 2021

Preprint

View full text Add to dashboard Cite

The interaction and possibly interference between viruses infecting a common host population is the problem addressed in this work. We model two viral diseases both of the SIRS type that have similar mechanism of transmission and for which a vaccine exists. The vaccine is characterized by its coverage, induced temporal immunity and efficacy. The population dynamics of both diseases considers infected individuals of each disease and hosts that are susceptible to one but have recovered from the other. We do not incorporate coinfection. We postulate two main transmission factors affecting the effective contact rates: i) that the virus with higher reproduction number can superinfect the one with lower reproduction number and ii) that there is some protection induced by vaccination against the weaker virus that reduces the probability of infection by the stronger virus. Our results indicate that coexistence of the viruses is possible in the long term. The time-dependent effective contact rate may induce either alternating outbreaks of each disease or synchronous outbreaks. We also found the existence of bi-stability triggered by a backward bifurcation, conducive to scenarios where, at the population level, vaccine application may promote persistence of both diseases provided the effective coverage and vaccine efficacy are low.

show abstract

Distinguishing Viruses Responsible for Influenza-Like Illness

Cited by 28 publications

References 141 publications

A mathematical model for the novel coronavirus epidemic in Wuhan, China

A mathematical model for the novel coronavirus epidemic in Wuhan, China

Modeling transmission dynamics of severe acute respiratory syndrome coronavirus 2 in São Paulo, Brazil

Co-circulation of two viral populations under vaccination

Contact Info

Product

Resources

About