Determining the communicable period of SARS-CoV-2: A rapid review of the literature, March to September 2020

Park, Mina; Pawliuk, Colleen; Nguyen, Tribesty; Griffitt, Amanda; Dix-Cooper, Linda; Fourik, Nadia; Dawes, Martin

doi:10.2807/1560-7917.es.2021.26.14.2001506

Cited by 15 publications

(14 citation statements)

References 163 publications

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“…During the first wave in spring 2020, hospitalization rates were high, reaching up to 70% in France, 55% in Spain, 50% in the United Kingdom, and 20% in Germany until the end of April. 12,13…”

Section: Introductionmentioning

confidence: 99%

Predictors for adult COVID-19 hospitalized inpatient mortality rate in North West Ethiopia

Kebede

Gizaw

2022

SAGE Open Medicine

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Objectives: The spread of severe acute respiratory syndrome coronavirus 2 in Ethiopia is below par understood and to date has been poorly characterized by a lower number of confirmed cases and deaths to other regions of the sub-Sahara African including Ethiopia. Timely and effective predictors for inpatient mortality rate were crucial for improving the management of hospitalized cases. This study aimed to assessed predictors for inpatient mortality of COVID-19 hospitalized adult patients in two diagnosed and treatment centers, North West Ethiopia. Methods: A facility-based retrospective cohort study was conducted among COVID-19 adult admitted cases in two treatment centers, Northwest Ethiopia, from 1 October 2020 to 30 December 2020. Data from the records of children were extracted using a standardized checklist. Epi-Data version 3.2 was used for data entry, and Stata version 14 was used for analysis. Bi-variable and multivariable Cox regression analyses were conducted to identify predictors of mortality. Finally, variables with P < 0.05 were a significant predictor of inpatient mortality. Result: The mean (±standard deviation) age of participant cases was 48.6 (±18.8) years. The median (±interquartile range) time for death reported after was 13 (±6) days. The overall incidence rate inpatient mortality rate was determined as 1.8 (95% confidence interval: 1.72, 2.15) per100 person per days of observation. Cases at baseline age ⩾ 61 years (adjusted hazard ratio = 1.56; 95% confidence interval: 1.3, 2.4), being male gender (adjusted hazard ratio = 1.9; 95% CI: 2.1, 8.6), admission with comorbidity (adjusted hazard ratio: 4.4, 95% confidence interval: 2.3, 8.4), and decreased neutrophil count ⩽ 65 103/uL at (P < 0.03) were independent predictors for inpatient mortality. Conclusion: In general, 72.4% of COVID-19 inpatient deaths were occurred within 2 weeks after admission. The mortality risk factors for severe patients identified in this study using a multivariate Cox regression model included elderly age (⩾60 years), being male, baseline comorbidity, and neutrophil count ⩽65 103/uL were associated with inpatient mortality.

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Section: Introductionmentioning

confidence: 99%

Predictors for adult COVID-19 hospitalized inpatient mortality rate in North West Ethiopia

Kebede

Gizaw

2022

SAGE Open Medicine

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“…For all variants, we are unaware of any studies which directly estimate the post-infectiousness onset recovery day distribution f r ( x ). However, viral culturing studies have consistently found live viral isolation to cease between 9 and 10 days post-symptom onset (30, 31, 41). We therefore assume a gamma distribution for f r ( x ) with mean ⟨ f r ⟩ = ⟨ f I ⟩ + 9.5 days for each variant, and we assume a scale parameter of 0.25 days to give tight distributions with relatively small interquartile ranges as indicted in (41).…”

Section: Methodsmentioning

confidence: 99%

“…Meanings of quantities are as described in Table 2. (30,31,38,41). Meanings of quantities are as described in Table 2…”

mentioning

confidence: 99%

The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19

Demers

Fagan

Potluri

et al. 2022

Preprint

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The severe shortfall in testing supplies during the initial phases of the COVID-19 pandemic and ensuing struggle to control disease spread have affirmed the need to plan rigorous optimized supply-constrained resource allocation strategies for the next inevitable novel disease epidemic. To address the challenge of optimizing limited resource usage in the face of complicated disease dynamics, we develop an integro partial differential equation disease model which incorporates realistic latent, incubation, and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals, and we analyze the influence of these elements on controllability and optimal resource allocation between two testing strategies, `clinical' targeting symptomatic individuals and `non-clinical' targeting non-symptomatic individuals, for reducing total infection sizes. We apply our model to not only the original, delta, and omicron COVID-19 variants, but also to generic diseases which have different offsets between latent and incubation period distributions which allow for or prevent varying degrees of presymptomatic transmission or preinfectiousness symptom onset. We find that factors which reduce controllability generally call for reduced levels of non-clinical testing, while the relationship between symptom onset, controllability, and optimal strategies is complicated. Although greater degrees of presymptomatic transmission reduce disease controllability, they may enhance or reduce the role of non-clinical testing in optimal strategies depending on other disease factors like overall transmissibility and latent period length. Our model allows a spectrum of diseases to be compared under the same lens such that the lessons learned from COVID-19 can be adapted to resource constraints in the next emerging epidemic and analyzed for optimal strategies under a consistent mathematical framework.

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“…For instance, if we investigate on the municipality-level, then D contains an index for each municipality. Furthermore, the values for the average latent and infectious periods ν and ω are chosen based on the literature as 3 days [25,26] and 9 days [27][28][29], respectively.…”

Section: Parameter Initialization and Data Sourcesmentioning

confidence: 99%

The role of inter-regional mobility in forecasting SARS-CoV-2 transmission

Uiterkamp

Gösgens

Heesterbeek

et al. 2022

J. R. Soc. Interface.

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In this paper, we present a method to forecast the spread of SARS-CoV-2 across regions with a focus on the role of mobility. Mobility has previously been shown to play a significant role in the spread of the virus, particularly between regions. Here, we investigate under which epidemiological circumstances incorporating mobility into transmission models yields improvements in the accuracy of forecasting, where we take the situation in The Netherlands during and after the first wave of transmission in 2020 as a case study. We assess the quality of forecasting on the detailed level of municipalities, instead of on a nationwide level. To model transmissions, we use a simple mobility-enhanced SEIR compartmental model with subpopulations corresponding to the Dutch municipalities. We use commuter information to quantify mobility, and develop a method based on maximum likelihood estimation to determine the other relevant parameters. We show that taking inter-regional mobility into account generally leads to an improvement in forecast quality. However, at times when policies are in place that aim to reduce contacts or travel, this improvement is very small. By contrast, the improvement becomes larger when municipalities have a relatively large amount of incoming mobility compared with the number of inhabitants.

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Determining the communicable period of SARS-CoV-2: A rapid review of the literature, March to September 2020

Cited by 15 publications

References 163 publications

Predictors for adult COVID-19 hospitalized inpatient mortality rate in North West Ethiopia

Predictors for adult COVID-19 hospitalized inpatient mortality rate in North West Ethiopia

The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19

The role of inter-regional mobility in forecasting SARS-CoV-2 transmission

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