Statistical modelling of COVID-19 data: Putting generalized additive models to work

@article{Fritz2022StatisticalMO,
  title={Statistical modelling of COVID-19 data: Putting generalized additive models to work},
  author={Cornelius Fritz and Giacomo De Nicola and Martje Rave and Maximilian Weigert and Yegane Khazaei and Ursula Berger and Helmut Kuchenhoff and G{\"o}ran Kauermann},
  journal={Statistical Modelling},
  year={2022}
}
Over the course of the COVID-19 pandemic, Generalized Additive Models (GAMs) have been successfully employed on numerous occasions to obtain vital data-driven insights. In this article we further substantiate the success story of GAMs, demonstrating their flexibility by focusing on three relevant pandemic-related issues. First, we examine the interdepency among infections in different age groups, concentrating on school children. In this context, we derive the setting under which parameter… 

References

SHOWING 1-10 OF 55 REFERENCES

A statistical model for the dynamics of COVID‐19 infections and their case detection ratio in 2020

A modeling approach can be used to quantify the effect of different testing strategies, visualize the dynamics in the case detection ratio over time, and obtain information about the underlying true infection numbers, thus enabling a clearer picture of the course of the COVID-19 pandemic in 2020.

Nowcasting fatal COVID‐19 infections on a regional level in Germany

The proposed approach and the presented results provide reliable insight into the state and the dynamics of the pandemic during the early phases of the infection wave in spring 2020 in Germany, when little was known about the disease and limited data were available.

Inferring UK COVID‐19 fatal infection trajectories from daily mortality data: Were infections already in decline before the UK lockdowns?

A Bayesian inverse problem approach applied to UK data on first‐wave Covid‐19 deaths and the disease duration distribution suggests that fatal infections were in decline before full UK lockdown (24 March 2020), and that fatal Infections in Sweden started to decline only a day or two later.

Nowcasting the COVID‐19 pandemic in Bavaria

A novel application of nowcasting to data on the current COVID‐19 pandemic in Bavaria is presented, based on a hierarchical Bayesian model that considers changes in the reporting delay distribution over time and associated with the weekday of reporting.

Explaining regional differences in mortality during the first wave of Covid-19 in Italy

It is found that Covid-19 mortality at regional level is negatively associated with the degree of intergenerational co-residence, number of intensive care unit beds per capita, and delay in the outbreak of the epidemic.

Projections for COVID-19 pandemic in India and effect of temperature and humidity

Probabilistic forecasting in infectious disease epidemiology: the 13th Armitage lecture

A multivariate time series model for weekly surveillance counts on norovirus gastroenteritis from the 12 city districts of Berlin, in six age groups, from week 2011/27 to week 2015/26 is described and the following year is used to assess the quality of the predictions.

Regional now- and forecasting for data reported with delay: toward surveillance of COVID-19 infections

The model is applied to German data and provides a stable tool for monitoring current infection levels as well as predicting infection numbers in the immediate future at the regional level through nowcasting of cases that have not yet been reported and through predictions of future infections.

A statistical framework for the analysis of multivariate infectious disease surveillance counts

A framework for the statistical analysis of counts from infectious disease surveillance databases is proposed and a multivariate formulation is proposed, which is well suited to capture space-time dependence caused by the spatial spread of a disease over time.

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

  • N. Perra
  • Computer Science
    Physics Reports
  • 2021
...