Estimate the incubation period of coronavirus 2019 (COVID-19)

Men, Ke; Li, Yihao; Wang, Xia; Zhang, Guangwei; Hu, Jingjing; Gao, Y.; Han, Ashley; Liu, Wenbin; Han, Henry

doi:10.1016/j.compbiomed.2023.106794

Cited by 11 publications

(2 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The response variable and the five covariates were transformed into seven-day moving averages to correct for large weekly fluctuations. Furthermore, a difference of 14 days was adopted between the response variable number of deaths and the explanatory variables, which can be justified by the incubation time and presentation of symptoms that varied between 2 and 14 days [ 19 , 20 , 21 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru

Galarza,

Sánchez,

Pimentel

et al. 2024

Entropy

View full text Add to dashboard Cite

Amid the COVID-19 pandemic, understanding the spatial and temporal dynamics of the disease is crucial for effective public health interventions. This study aims to analyze COVID-19 data in Peru using a Bayesian spatio-temporal generalized linear model to elucidate mortality patterns and assess the impact of vaccination efforts. Leveraging data from 194 provinces over 651 days, our analysis reveals heterogeneous spatial and temporal patterns in COVID-19 mortality rates. Higher vaccination coverage is associated with reduced mortality rates, emphasizing the importance of vaccination in mitigating the pandemic’s impact. The findings underscore the value of spatio-temporal data analysis in understanding disease dynamics and guiding targeted public health interventions.

show abstract

Section: Materials and Methodsmentioning

confidence: 99%

Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru

Galarza,

Sánchez,

Pimentel

et al. 2024

Entropy

View full text Add to dashboard Cite

show abstract

“…For each simulated individual that contracted COVID-19, was hospitalised, or died, the event date was sampled from the empirical distributions of official government records. In the case of missing data, we used complete cases to confirm published reports that time periods in the progression of COVID-19 were well-approximated by a Weibull distribution [61–63], and then created an objective function using the empirical mean and standard deviation of time delays to numerically optimise the Weibull scale and shape parameters to impute missing event dates. In the i-ENR and i-INF models, the empirical distribution of symptom onset times were used identically, but limited pre-study onset (< 1 month) was possible in the i-INF model provided the notification date was within the study period.…”

Section: Methodsmentioning

confidence: 99%

Published benefits of ivermectin use in Itajaí, Brazil for COVID-19 infection, hospitalisation, and mortality are entirely explained by statistical artefacts

Mills¹,

Antônio

Tucker‐Kellogg

2023

Preprint

View full text Add to dashboard Cite

BackgroundTwo recent publications by Kerr et al. (Cureus 14(1):e21272; Cureus 14(8): e28624) reported dramatic effects of prophylactic ivermectin use for both prevention of COVID-19 and reduction of COVID-19-related hospitalisation and mortality, including a dose-dependent effect of ivermectin prophylaxis. These papers have gained an unusually large public influence: they were incorporated into debates around COVID-19 policies and may have contributed to decreased trust in vaccine efficacy and public health authorities more broadly. Both studies were based on retrospective observational analysis of city-wide registry data from the city of Itajaí, Brazil from July-December 2020.MethodsStarting with initially identified sources of error, we conducted a revised statistical analysis of available data, including data made available with the original papers and public data from the Brazil Ministry of Health. We identified additional uncorrected sources of bias and errors from the original analysis, including incorrect subject exclusion and missing subjects, an enrolment time bias, and multiple sources of immortal time bias. In models assuming no actual effect from ivermectin use, we conducted Monte Carlo simulations to estimate the contribution of these biases to any observed effect.ResultsUntreated statistical artefacts and methodological errors alone lead to dramatic apparent risk reduction associated with ivermectin use in both studies. The magnitude of apparent risk reduction from these artefacts is comparable to the results reported by the studies themselves, including apparent protection from infection, hospitalisation, and death, and including the reported apparent dose-response relationship.ConclusionsThe inference of ivermectin efficacy reported in both papers is unsupported, as the observed effects are entirely explained by untreated statistical artefacts and methodological errors. Our re-analysis calls for caution in interpreting highly publicised observational studies and highlights the importance of common sources of bias in clinical research.

show abstract