Will we see protection or reinfection in COVID-19?

Ota, Miyo

doi:10.1038/s41577-020-0316-3

Cited by 89 publications

(79 citation statements)

References 1 publication

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“…Therefore, the Quarantined Recovered (Q-R) state is introduced with the same connections to R as the connections between S and Q-U. Though the reinfection rate of SARS-CoV-2 has been a point of recent debate, it is assumed that the number of re-infected individuals is small (30)(31)(32)(33)(34). Therefore, individuals cannot transition from R to S, hence the separately categorized quarantined populations.…”

Section: An Enhanced Epidemiological Sidhre-q Modelmentioning

confidence: 99%

Validating and modeling the impact of high-frequency rapid antigen screening on COVID-19 spread and outcomes

Nash

Badea

Reddy

et al. 2020

Preprint

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High frequency screening of populations has been proposed as a strategy in facilitating control of the COVID-19 pandemic. Here we use computational modeling, coupled with clinical data from a rapid antigen test, to predict the impact of frequent rapid testing on COVID-19 spread and outcomes. Using patient nasopharyngeal swab specimens, we demonstrate that the sensitivity and specificity of the rapid antigen test compared to quantitative real-time polymerase chain reaction (qRT-PCR) are 84.7% and 85.7%, respectively; moreover, sensitivity correlates directly with viral load. Based on COVID-19 data from three regions in the United States and Sao Jose do Rio Preto, Brazil, we show that high frequency, strategic population-wide rapid testing, even at varied accuracy levels, diminishes COVID-19 infections, hospitalizations, and deaths at a fraction of the cost of nucleic acid detection via qRT-PCR. We propose large-scale antigen-based surveillance as a viable strategy to control SARS-CoV-2 spread and to enable societal re-opening.

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Section: An Enhanced Epidemiological Sidhre-q Modelmentioning

confidence: 99%

Validating and modeling the impact of high-frequency rapid antigen screening on COVID-19 spread and outcomes

Nash

Badea

Reddy

et al. 2020

Preprint

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“…Furthermore, this study is a single-center, retrospective study with limited participants, therefore, more prospective clinical researches are needed. Some researchers hypothesized that the re-positive cases could be a virus re-infection [13]. Immunologically speaking, after the acute infection of the SARS-CoV-2, the human body could generate specific neutralizing antibodies against this virus for at least 7 days [14]; furthermore a recent animal experiment in rhesus macaque indicated re-infection phenomenon would not happen [15].…”

Section: Discussionmentioning

confidence: 99%

Clinical Characteristics Analysis of the “Re-positive” Discharged COVID-19 Pneumonia Patients in Wuhan, China

Sun

Zhou

et al. 2020

Preprint

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Background: To analyze the clinical characteristics of the re-positive discharged COVID-19 patients and find markers to distinguish them.Methods：The demographic features, clinical symptoms, laboratory results, comorbidities, co-infections, treatments, illness severities and chest CT scan results of 267 patients were collected during 1st January and 15th February 2020. COVID-19 was diagnosed by RT-PCR. The subsequent clinical symptoms and nucleic acid test results was obtained during the 14 days post-hospitalization quarantine.Results: 30 out of 267 COVID-19 patients were detected re-positive during the post-hospitalization quarantine. Re-positive patients couldn’t be distinguished by demographic features, clinical symptoms, laboratory results, comorbidities, co-infections, treatments, chest CT scan results or subsequent clinical symptoms. However, the re-positive rate were found illness severity correlated, along with APACHE II and CURB-65.Conclusion: Common clinical characteristics arn’t able to distinguish re-positive patients. However, severe and critical cases with high APACHE II and CURB-65 scores are more likely to turn re-positive after discharge.Authors Shengyang He, Wenwu Sun, Kefu Zhou contributed equally to this work.

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“…For instance, it has been reported that the coronavirus can still be detected in throat swabs from recovered patients up to 13 days after they present no symptoms of the disease (as evaluated by clinical assessments), indicating that even people considered to be recovered from Covid-19 may still potentially infect others [ 19 ]. Furthermore, it has been speculated the possibility of reinfection for patients who recovered from Covid-19 (in this regard, preliminary data from primates support the hypothesis of immunization and protection against reinfection [ 20 ]). In fact, we are aware that the model does not cover extensively the possible complexities related to disease propagation, but it focuses on the main course of propagation instead.…”

Section: Discussionmentioning

confidence: 99%

A discrete-time-evolution model to forecast progress of Covid-19 outbreak

Curado

2020

PLoS ONE

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Here we present a discrete-time-evolution model with one day interval to forecast the propagation of Covid-19. The proposed model can be easily implemented with daily updated data sets of the pandemic publicly available by distinct online sources. It has only two adjustable parameters and it predicts the evolution of the total number of infected people in a country for the next 14 days if parameters do not change during the analyzed period. The model incorporates the main aspects of the disease such as the fact that there are asymptomatic and symptomatic phases (both capable of propagating the virus), and that these phases take almost two weeks before the infected person status evolves to the next (asymptomatic becomes symptomatic or symptomatic becomes either recovered or dead). A striking advantage of the model for its implementation by the health sector is that it gives directly the number of total infected people in each day (in thousands, tens of thousands or hundred of thousands). Here, the model is tested with data from Brazil, UK and South Korea, presenting low error rates on the prediction of the evolution of the disease in all analyzed countries. We hope this model may be a useful tool to estimate the propagation of the disease.

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Will we see protection or reinfection in COVID-19?

Cited by 89 publications

References 1 publication

Validating and modeling the impact of high-frequency rapid antigen screening on COVID-19 spread and outcomes

Validating and modeling the impact of high-frequency rapid antigen screening on COVID-19 spread and outcomes

Clinical Characteristics Analysis of the “Re-positive” Discharged COVID-19 Pneumonia Patients in Wuhan, China

A discrete-time-evolution model to forecast progress of Covid-19 outbreak

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