Connection

Co-Authors

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This is a "connection" page, showing publications co-authored by Christel Faes and Niel Hens.
Christel Faes
Connection Strength
3.060
Niel Hens
  1. Quantifying superspreading for COVID-19 using Poisson mixture distributions. Sci Rep. 2021 07 08; 11(1):14107.
    View in: PubMed
    Score: 0.239
  2. The risk of underestimating the contribution of children to SARS-CoV-2 pandemic. Clin Infect Dis. 2021 Jun 30.
    View in: PubMed
    Score: 0.238
  3. A data-driven metapopulation model for the Belgian COVID-19 epidemic: assessing the impact of lockdown and exit strategies. BMC Infect Dis. 2021 May 30; 21(1):503.
    View in: PubMed
    Score: 0.237
  4. On realized serial and generation intervals given control measures: The COVID-19 pandemic case. PLoS Comput Biol. 2021 03; 17(3):e1008892.
    View in: PubMed
    Score: 0.234
  5. Modelling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories. Epidemics. 2021 06; 35:100449.
    View in: PubMed
    Score: 0.234
  6. The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19. Nat Commun. 2021 03 09; 12(1):1524.
    View in: PubMed
    Score: 0.233
  7. Time between Symptom Onset, Hospitalisation and Recovery or Death: Statistical Analysis of Belgian COVID-19 Patients. Int J Environ Res Public Health. 2020 10 17; 17(20).
    View in: PubMed
    Score: 0.227
  8. Authors' response: Estimating the generation interval for COVID-19 based on symptom onset data. Euro Surveill. 2020 07; 25(29).
    View in: PubMed
    Score: 0.223
  9. Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020. Euro Surveill. 2020 04; 25(17).
    View in: PubMed
    Score: 0.219
  10. Inference of the generalized-growth model via maximum likelihood estimation: A reflection on the impact of overdispersion. J Theor Biol. 2020 01 07; 484:110029.
    View in: PubMed
    Score: 0.211
  11. Assessing inference of the basic reproduction number in an SIR model incorporating a growth-scaling parameter. Stat Med. 2018 12 20; 37(29):4490-4506.
    View in: PubMed
    Score: 0.195
  12. Using additive and coupled spatiotemporal SPDE models: a flexible illustration for predicting occurrence of Culicoides species. Spat Spatiotemporal Epidemiol. 2017 Nov; 23:11-34.
    View in: PubMed
    Score: 0.182
  13. Structural differences in mixing behavior informing the role of asymptomatic infection and testing symptom heritability. Math Biosci. 2017 03; 285:43-54.
    View in: PubMed
    Score: 0.174
  14. Infectious diseases epidemiology, quantitative methodology, and clinical research in the midst of the COVID-19 pandemic: Perspective from a European country. Contemp Clin Trials. 2020 12; 99:106189.
    View in: PubMed
    Score: 0.057
  15. Can COVID-19 symptoms as reported in a large-scale online survey be used to optimise spatial predictions of COVID-19 incidence risk in Belgium? Spat Spatiotemporal Epidemiol. 2020 11; 35:100379.
    View in: PubMed
    Score: 0.057
  16. Mapping maternal mortality rate via spatial zero-inflated models for count data: A case study of facility-based maternal deaths from Mozambique. PLoS One. 2018; 13(11):e0202186.
    View in: PubMed
    Score: 0.050
  17. Assessing the relationship between epidemic growth scaling and epidemic size: The 2014-16 Ebola epidemic in West Africa. Epidemiol Infect. 2018 Oct 15; 147:e27.
    View in: PubMed
    Score: 0.049
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.