Epidemiology

Unequal impact of the COVID-19 pandemic in 2020 on life expectancy across urban areas in Chile: a cross-sectional demographic study

1. Gonzalo Mena
2. José Manuel Aburto

Reviewed by ScreenIT

Deviations in Predicted COVID-19 cases in the US during early months of 2021 relate to rise in B.1.526 and its family of variants

1. P. Nandakishore
2. M. Liu
3. Prakash R.
4. S. Gourneni
5. R. Sukumaran
6. J.M. Berman
7. S. Iyer
8. C. Glorioso

Reviewed by ScreenIT

Mortality trends and length of stays among hospitalized patients with COVID-19 in Ontario and Québec (Canada): a population-based cohort study of the first three epidemic waves

1. Yiqing Xia
2. Huiting Ma
3. David L Buckeridge
4. Marc Brisson
5. Beate Sander
6. Adrienne Chan
7. Aman Verma
8. Iris Ganser
9. Nadine Kronfli
10. Sharmistha Mishra
11. Mathieu Maheu-Giroux

Reviewed by ScreenIT

Stochastic simulation of successive waves of COVID-19 in the province of Barcelona

1. M. Bosman
2. A. Esteve
3. L. Gabbanelli
4. X. Jordan
5. A. López-Gay
6. M. Manera
7. M. Martínez
8. P. Masjuan
9. Ll.M. Mir
10. J. Paradells
11. A. Pignatelli
12. I. Riu
13. V. Vitagliano

Reviewed by ScreenIT

International risk of SARS-CoV-2 Omicron variant importations originating in South Africa

1. Yuan Bai
2. Zhanwei Du
3. Mingda Xu
4. Lin Wang
5. Peng Wu
6. Eric H Y Lau
7. Benjamin J Cowling
8. Lauren Ancel Meyers

Reviewed by ScreenIT

Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada

1. Eliseos J. Mucaki
2. Ben C. Shirley
3. Peter K. Rogan

Reviewed by ScreenIT

Risk factors relate to the variability of health outcomes as well as the mean: A GAMLSS tutorial

1. David Bann
2. Liam Wright
3. Tim J Cole

• Curated by eLife

  Evaluation Summary:

  Using data from the 1970 British Birth Cohort study, the authors demonstrated the utility of Generalized Additive Models for Location, Scale and Shape (GAMLSS) to investigate the association of three risk factors (sex, socioeconomic circumstances, and physical inactivity) with body mass index and mental wellbeing. This work provides empirical evidence for why we should consider how risk factors influence the variability and not just the mean of outcomes. From the perspective of developing personalized medicine, it is important to know whether interventions have response heterogeneity as the first step. If such heterogeneity is identified, the next step will be to identify the factors associated with the heterogeneity (or those who will be benefitted from the intervention). Therefore, this study contributes to the first step by investigating the possibility of response heterogeneity.

  (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #3 agreed to share their name with the authors.)

Reviewed by eLife

Might first-hand experience of ill-health and economic hardship during the COVID-19 pandemic strengthen public support for vaccination and the reallocation of health sector funding towards health emergency preparedness in South Africa?

1. R Mattes
2. K Dalal
3. H Rhoma
4. S Lambert
5. T De Wet
6. GTH Ellison

Reviewed by ScreenIT

Covid-19 social distancing: when less is more

1. C. Neuwirth
2. C. Gruber

Reviewed by ScreenIT

A Scaling Law for PCR Positivity in the COVID Second Wave

1. Dr Keith Johnson

Reviewed by ScreenIT