Dataset Description

This dataset contains a mixture of categorical clinical parameters (clinical symptoms, disease severity) and numerical immunological parameters (immunoglobin, T cells, memory B cells, antibodies) taken over a period of 6 months, denoted by timepoint and days post onset of symptoms, along with basic demographics (age, sex) for each donor.

For more details about each variable, see the variable legend for this dataset.

Figure 1. Clinical study overview (Tomic, A et al. NatComm, 2022)

Questions to answer

The goal of this workflow is to use this COVID Pitch dataset to answer two important questions:

• Are there certain immune parameters that can explain the disease severity experienced by individuals and that are dependent on time post SARS-CoV-2 infection?

• Can we utilize certain immune parameters measured early after infection to predict whether an individual builds a durable immune response to SARS-CoV-2?

As this study was conducted at the beginning of the COVID-19 pandemic, these questions were critical for determining correlates of protection for this disease, understanding immune profiles from various degrees of infection and innovating measures for future cases. Below, we explain the broad groups of variables in this dataset that allowed to find answers to these questions.

Clinical Parameters

These variables consist of clinical symptoms most commonly associated with SARS-CoV-2 infection, along with the severity of disease experienced by the donors

📋Clinical Symptoms

Common symptoms for SARS-CoV-2 infection:

• Fever

• Cough

• Change or loss of taste

• Anosmia : Complete loss of sense of smell

• Fatigue

• Shortness of breath

• Nasal congestion

• Sore throat

• Myalgia : Muscle pain or soreness

• Arthralgia : Joint pain

• Headache

• Diarrhoea

• Vomiting

• Nausea

• Chest pain

• Anorexia : Excessive weight loss

• Asthma

😷Disease Severity

• Asymptomatic: Donor presents on symptoms while infected

• Mild: Donor presents with moderate symptoms upon infection

• Severe: Donor presents with more extreme symptoms upon infection

Immunological Parameters

These variables comprise of various immunological assays that quantify immune parameters such as T cells, memory B cells, antibodies and associated processes to obtain a comprehensive view of the cellular and humoral adaptive immune responses.

🛡️Antibodies

• Pseudo-Neutralisating Antibodies (pseudoNA Abs): Concentration of neutralizing antibodies to inhibit infection by a SARS-CoV-2 pseudovirus determines efficacy of the donor's antibodies

• Antibody-Dependent Effector Functions: Effector functions such as monocyte phagocytosis (ADMP) and NK cell activation (ADNKA) give insight to how antibodies are neutralizing the virus

• Immunoglobin Assays: Provides measurements of antibodies specific to SARS-CoV-2 proteins both in the mucous membranes (S-IgA) and circulating in the blood (N-IgG, S-IgG )

• Meso Scale Discovery (MSD) Assays: Gives insight to whether the donor's antibodies provide protection against various coronavirus strains (e.g. 229e, NL63) and other respiratory viruses (e.g. MERS)

🦠T cells

• SARS-CoV-2 protein-specific T cell responses: Conveys proteins of the SARS-CoV-2 virus the T cells of the donor reacts to most by measuring concentration of T cells specifically responding to each protein (e.g. ORF3, nsp3b, S1)

• T cell proliferative responses: Provides insight into which SARS-CoV-2 proteins are most targeted by the CD4 and CD8 cells by measuring their proliferation for each protein

🧪B cells

• Spike-specific IgG+ Memory B cells: Concentration of memory B cells specific to seasonal coronavirus stains gives insight to the durable immunity the body has to the various viruses

Responder Status

The seropositivity of the donors at 6 months post symptoms onset determined whether the donor was a low or high responder. This seropositivity was calculated by the titer of the anti-nucleocapsid-specific antibodies. A titer of greater than or equal to 1.4 indicated seropositivity. We use the corresponding variable, Responder, as the feature that machine learning algorithms predict using early timepoint immune parameters.

Demographic and Time variables

👥Demographics

• Age

• Sex

⏳Time

• Timepoint: Day after positive infection diagnosis when sample was taken

• Days pso: Days after onset of symptoms