Models

Imagination and remembrance: what role should historical epidemiology play in a world bewitched by mathematical modelling of COVID‑19 and other epidemics?

April, 2021

Abstract

Although every emerging infectious disease occurs in a unique context, the behaviour of previous pandemics ofers an insight into the medium- and long-term outcomes of the current threat. Where an informative historical analogue exists, epidemiologists and policymakers should consider how the insights of the past can inform current forecasts and responses.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

January, 2021

See also: https://www.covprecise.org/

Abstract

Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.

Avoiding Ambiguity

By: Sunetra Gupta

Limits of mathematical reasoning.

Danger of using models to make decisions.

Models may or may not help us understand complexity yet can create an illusion of reality.

Examination of Statistical Accuracy of COVID-19 Daily Death Count Predictions

May, 2020

Abstract

OBJECTIVE: This paper provides a formal evaluation of the predictive performance of a model (and updates) developed by the Institute for Health Metrics and Evaluation (IHME) for predicting daily deaths attributed to COVID-19 for the United States.

STUDY DESIGN: To assess the accuracy of the IHME models, we examine both forecast accuracy, as well as the predictive performance of the 95% prediction intervals (PI).