Ten Epidemiological Parameters of COVID-19: Use of Rapid Literature Review to Inform Predictive Models During the Pandemic

Gallo, Luciana Guerra; Oliveira, Ana Flávia de Morais; Abrahão, Amanda Amaral; Sandoval, Leticia Assad Maia; Martins, Yure Rodrigues Araújo; Almirón, María; Santos, Fabiana Sherine Ganem Dos; Araújo, Wildo Navegantes de; Oliveira, Maria Regina Fernandes de; Peixoto, Henry Maia

doi:10.3389/fpubh.2020.598547

Cited by 20 publications

(22 citation statements)

References 84 publications

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“…In addition to the data and method of choice, estimates of R 0 are sensitive to assumptions on the serial interval, generation time and pre-symptomatic transmission [ 42 , 43 , 58 , 59 ]. The broad range of estimates, from 0.48 to 14.8 [ 60 ], underscores the magnitude of the problem. The fact that peer-reviewed publications discussing these details have only become available towards the end of 2020 [ 61 ] highlights the limited attention this topic has received.…”

Section: Addendummentioning

confidence: 99%

Challenges in control of COVID-19: short doubling time and long delay to effect of interventions

Pellis

Scarabel

Stage

et al. 2021

Phil. Trans. R. Soc. B

106

104

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Early assessments of the growth rate of COVID-19 were subject to significant uncertainty, as expected with limited data and difficulties in case ascertainment, but as cases were recorded in multiple countries, more robust inferences could be made. Using multiple countries, data streams and methods, we estimated that, when unconstrained, European COVID-19 confirmed cases doubled on average every 3 days (range 2.2–4.3 days) and Italian hospital and intensive care unit admissions every 2–3 days; values that are significantly lower than the 5–7 days dominating the early published literature. Furthermore, we showed that the impact of physical distancing interventions was typically not seen until at least 9 days after implementation, during which time confirmed cases could grow eightfold. We argue that such temporal patterns are more critical than precise estimates of the time-insensitive basic reproduction number R 0 for initiating interventions, and that the combination of fast growth and long detection delays explains the struggle in countries' outbreak response better than large values of R 0 alone. One year on from first reporting these results, reproduction numbers continue to dominate the media and public discourse, but robust estimates of unconstrained growth remain essential for planning worst-case scenarios, and detection delays are still key in informing the relaxation and re-implementation of interventions. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

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Section: Addendummentioning

confidence: 99%

Challenges in control of COVID-19: short doubling time and long delay to effect of interventions

Pellis

Scarabel

Stage

et al. 2021

Phil. Trans. R. Soc. B

106

104

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“…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: 89%

“…This fact, coupled with weather variability (seasonality), induces alternating patterns where influenza and RSV infections have a limited temporal overlap [6]. Seasonal influenza has a median basic reproduction number of 1.28 [10]; while for SARS-CoV-2, the median R 0 is 2.79 [11,10]. This difference in transmission potential supports the assumption that while competing for hosts, their common resource, the likelihood of long-term coexistence and co-circulation is high and that this balance may be associated with superinfection.…”

Section: Introductionmentioning

confidence: 99%

Co-circulation of two viral populations under vaccination

Acuña-Zegarra

Núñez-López

Comas‐García

et al. 2021

Preprint

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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.

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“…The Levenberg-Marquardt method via the lmfit 20 library was used for performing the optimization. Ranges of possible parameter values were constrained using information from COVID-19 studies 13,21 .…”

Section: Optimizing Model Parametersmentioning

confidence: 99%

Epidemic Modeling of Multiple Virus Strains: A Case Study of SARS-CoV-2 B.1.1.7 In Moscow

Tseytlin

Makarov

2021

Preprint

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During a long-running pandemic a pathogen can mutate, producing new strains with different epidemiological parameters. Existing approaches to epidemic modeling only consider one virus strain. We have developed a modified Susceptible-Exposed-Infected-Recovered model to simulate multiple virus strains within the same population. As a case study, we investigate the potential effects of SARS-CoV-2 strain B.1.1.7 on the city of Moscow. Our analysis indicates a high risk of a new wave of infections in September-October 2021 with up to 35 000 daily infections at peak. We open-source our code and data.

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Ten Epidemiological Parameters of COVID-19: Use of Rapid Literature Review to Inform Predictive Models During the Pandemic

Cited by 20 publications

References 84 publications

Challenges in control of COVID-19: short doubling time and long delay to effect of interventions

Challenges in control of COVID-19: short doubling time and long delay to effect of interventions

Co-circulation of two viral populations under vaccination

Epidemic Modeling of Multiple Virus Strains: A Case Study of SARS-CoV-2 B.1.1.7 In Moscow

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