COVID19

A TALE OF TWO SCIENTIFIC PARADIGMS: CONFLICTING SCIENTIFIC OPINIONS ON WHAT “FOLLOWING THE SCIENCE” MEANS FOR SARS-COV-2 AND THE COVID-19 PANDEMIC

August, 2021

DSA ADS Course - 2021

COVID19, Public Policy, Health Policy, Modeling, Model-driven Science, Empirically-driven Science, Lockdowns, Non-pharmaceutical Interventions, NPIs

Discuss 2 paradigms: Model-driven Science vs. Empirically-driven Science

Forecasting for COVID19 has Failed

June, 2020

DSA ADS Course - 2021

Forecasting, COVID19, John Ioannidis, Public Policy, Health Policy, Causal Inference, Forensic Medicine, Causality, Intuitive Causation, Probabilistic Causation

Forecasting is usually impossible in high causal density environments. Scenario planning with applied probability and adaptation to near real-time data is optimal strategy. Epidemic forecasting is usually a fools errand yet appropriate analysis of experience and historical precedent is helpful.

Longitudinal analysis shows durable and broad immune memory after SARS-CoV-2 infection with persisting antibody responses and memory B and T cells

July, 2021

Highlights

  • Most recovered COVID-19 patients mount broad, durable immunity after infection
  • Neutralizing antibodies show a bi-phasic decay with half-lives >200 days
  • Spike IgG+ memory B cells increase and persist post-infection
  • Durable polyfunctional CD4 and CD8 T cells recognize distinct viral epitope regions

Summary

Reconciling estimates of global spread and infection fatality rates of COVID- 19: An overview of systematic evaluations

DSA ADS Course - 2021

Reconciling estimates of global spread and infection fatality rates of COVID- 19: An overview of systematic evaluations

Seroprevalence, COVID19, Public Policy, Health Policy, Infection Fatality Rates

It is critical to estimate as soon as possible the infection fatality rate of next pandemic pathogen from seroprevalence data.

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