Trends and symptoms of SARS-CoV-2 infection: a longitudinal study on an Alpine population representative sample

Abstract

Objectives: The continuous monitoring of SARS-CoV-2 infection waves and the emergence of novel pathogens pose a challenge for effective public health surveillance strategies based on diagnostics. Longitudinal population representative studies on incident events and symptoms of SARS-CoV-2 infection are scarce. We aimed at describing the evolution of the COVID-19 pandemic during 2020 and 2021 through regular monitoring of self-reported symptoms in an Alpine community sample.

Design: To this purpose, we designed a longitudinal population representative study, the Cooperative Health Research in South Tyrol COVID-19 study.

Participants and outcome measures: A sample of 845 participants was retrospectively investigated for active and past infections with swab and blood tests, by August 2020, allowing adjusted cumulative incidence estimation. Of them, 700 participants without previous infection or vaccination were followed up monthly until July 2021 for first-time infection and symptom self-reporting: COVID-19 anamnesis, social contacts, lifestyle and sociodemographic data were assessed remotely through digital questionnaires. Temporal symptom trajectories and infection rates were modelled through longitudinal clustering and dynamic correlation analysis. Negative binomial regression and random forest analysis assessed the relative importance of symptoms.

Results: At baseline, the cumulative incidence of SARS-CoV-2 infection was 1.10% (95% CI 0.51%, 2.10%). Symptom trajectories mimicked both self-reported and confirmed cases of incident infections. Cluster analysis identified two groups of high-frequency and low-frequency symptoms. Symptoms like fever and loss of smell fell in the low-frequency cluster. Symptoms most discriminative of test positivity (loss of smell, fatigue and joint-muscle aches) confirmed prior evidence.

Conclusions: Regular symptom tracking from population representative samples is an effective screening tool auxiliary to laboratory diagnostics for novel pathogens at critical times, as manifested in this study of COVID-19 patterns. Integrated surveillance systems might benefit from more direct involvement of citizens' active symptom tracking.

Keywords: COVID-19; SARS-CoV-2; epidemiology; health policy.

Figure 1

Graphical representation of the study design and methods. (A) Study design. (B) Methods: each aim (either pale blue or pale pink foreground) was addressed with both aggregate (pale yellow background) and individual-level (light grey background) outcome and methods. ¹Performed at the study centre. ²Self-reported contacts with positive or symptomatic individuals OR any positive swab test. SAb, serum antibody.

Figure 2

Results of the zero-inflated negative binomial model. ^AModelling the probability of a symptomatic episode with any number of symptoms (reversed log odds from the original model). ^BModelling the expected number of symptoms conditional on a symptomatic episode.

Figure 3

Discriminatory capacity of symptoms derived from random forest analysis. The relative change represents the decrease in accuracy in the discriminatory ability of the model if the symptom is not included.

Figure 4

Results of the cluster analysis on the incidence of symptoms over time. (A) Trajectories of symptoms (coloured plain lines) and self-reported T⁺ (black dotted line). The dynamic correlation between the trajectory of each symptom and self-reported T⁺ is included within brackets in the legend keys. (A, B) Two clusters of symptoms are distinguished by warm (high frequency) and cold (low frequency) colours, respectively. (B) The heatmap in grey scale represents incidence of each symptom across time and reflects y-axis incidence in panel A.