Abstract
Introduction
Starting in the late 2019, the COVID-19 pandemic has now affected the entire world. Many countries have
experienced more than one pandemic wave, each characterized by different magnitude and duration. However, marked geographical differences are being observed even within small regions. The example of South Tyrol might be instructing: during the first pandemic phase, areas with a very high incidence were observed [1] amidst areas with a very low incidence [2]. However, between late 2020 and early 2021, areas with low baseline incidence became more informative for studying the epidemiological dynamics of the pandemic. In July 2020, we initiated the Cooperative Health Research In South Tyrol (CHRIS) COVID-19 study, a longitudinal project to monitor SARS-CoV-2 infections in Val Venosta (South Tyrol, Italy). The newly launched project was based on the CHRIS study [3], an ongoing, longitudinal, general population study with 13,393 adults recruited at baseline, in 2018. We randomly drawn a population-representative sample of 1450 CHRIS participants, reflecting the age group-sex distribution of the adult population of the study area to estimate the cumulative incidence of SARS-CoV-2 infection during the first pandemic wave. Regular online questionnaires and laboratory test follow-ups were implemented to monitor the progress of the pandemic to date.
Aims
This first analysis of the CHRIS COVID-19 study aimed to characterize the spread and course of the COVID-19 pandemic in middle and upper Val Venosta (South Tyrol, Italy) between February 2020 and May 2021.
Methods
Participants were 845, who responded to an online screening questionnaire including questions on COVID-19 related anamnesis, symptoms and lifestyle. Contextually, they were invited to undergo a nasopharyngeal swab for PCR test and a serum antibody test. Follow-up of all participants who tested negative to this baseline assessment was conducted by administering a shorter online questionnaire every 4 weeks for updating the anamnesis or symptomatology.
Incidence was estimated with the Clopper-Pearson method for extreme proportions [4]. Association between
baseline incidence and anamnesis and symptoms was assessed using (significance level: 0.001). The temporal trend of the total number of reported symptoms for each individual was assessed by fitting
zero-inflated negative binomial linear models with random intercept, using the month on which symptoms were reported as a predictor. We used longitudinal cluster analysis based on the k-means method to model the dynamic over time [5], with the optimal number of clusters defined according to the Calinski & Harabatz criterion [6].
Results
Until August 2020, the estimated cumulative incidence of SARS-CoV-2 infection in the study area was 0.95%
(95% confidence interval, CI, 0.41-1.86%), calibrated to the age and sex distribution of the population. Positivity was associated with having undergone a nasopharyngeal swab, having had a previous positive serological test, or having been isolated because of suspected or confirmed SARS-CoV-2 infection. The risk of infection was associated with the number of reported symptoms, loss of taste, loss of smell, and dry cough. Between September 2020 and May 2021, of the 836 participants testing negative at the baseline, 699 completed at least one and up to 10 follow-up questionnaires. Of them, 60.5% (95%CI 57.1-63.8%) underwent 1+ nasopharyngeal swabs and 20.5% (95%CI 17.1-24.3%) reported to have tested positive at least once. The temporal trend of the cumulative number of reported symptoms for each participant showed that infections peaked in October 2020 (2nd pandemic wave) and in February 2021 (3rd pandemic wave), closely resembling the shifted trends of hospitalizations and intensive care admissions reported by the local healthcare system in the same period. The zero-inflated mixed-model, showing the pattern of the pandemic waves both by the presence of symptoms and by the cumulative number of reported symptoms, confirmed these findings. Similar patterns emerged from the dynamic of single symptoms, where cluster analysis showed that the main peaks were associated with clusters containing the most common COVID-19-related symptoms [7]. Lower intensity clusters reflected flat patterns mainly comprising generic symptoms. Predictive analyses based on individual symptoms confirmed the symptomatic profile associated with COVID-19 reported previously [8].
Conclusions
During the first pandemic phase, the middle and upper Val Venosta had lower incidence of SARS-CoV-2
infections than nearby regions. Subsequently, the trend became more similar to that observed in South Tyrol,
with a heavy second wave in autumn 2020 and a further third wave in early 2021. The observed proportion of
PCR-positive persons suggests that no herd immunity could have been achieved spontaneously. This monitoring presented major methodological challenges from an epidemiological perspective. Data collection
was conditioned by public health interventions aimed at countering the pandemic itself, which may have also
altered individual behavior. These circumstances may translate into selection bias. Furthermore, testing capacity and screening guidelines have been following different patterns through the period, altering the probability of having undergone a PCR test. For example, by late November 2020, a mass screening through a rapid antigen test via nasopharyngeal swab involved ~70% of the population, resulting in a peak of tests and positive cases observed within a narrow time window. Analyzing the reported symptoms in addition to measures of disease occurrence can add value to monitoring the temporal trend of infections, since not all individuals have equivalent propensity to receive a test.
References
1. Melotti R., Scaggiante F., Falciani M., et al., Prevalence and determinants of serum antibodies to SARSCoV-2 in the general population of the Gardena Valley. medRxiv. Cold Spring Harbor Laboratory Press, 2021.
2. Gögele M., Melotti R., Foco L., et al., Caratterizzazione epidemiologica, molecolare e genetica della malattia da nuovo coronavirus in Val Venosta: lo studio CHRIS Covid-19. 44th Congress of the Italian Epidemiological Association (AIE), 2020.
3. Pattaro C., Gögele M., Mascalzoni D., et al., The Cooperative Health Research in South Tyrol (CHRIS)
study: rationale, objectives, and preliminary results. Journal of Translational Medicine, 2015; 13:348.
4. Newcombe RG., Two-sided confidence intervals for the single proportion: comparison of seven methods.
Stat Med., 1998; 17:857 72.
5. Genolini C., Falissard B., Kml: A package to cluster longitudinal data. Computer Methods and Programs in
Biomedicine, 2011; 104:e112 21.
6. Calinski T., Harabasz J., A dendrite method for cluster analysis. Communications in Statistics. Taylor &
Francis, 1974; 3:1 27.
7. Sudre C.H., Lee K.A., Lochlainn M.N., et al., Symptom clusters in COVID-19: A potential clinical prediction
tool from the COVID Symptom Study app. Sci Adv., 2021; 7.
8. Osuchowski M.F., Winkler M.S., Skirecki T., et al., The COVID-19 puzzle: deciphering pathophysiology and
phenotypes of a new disease entity. Lancet Respir Med., 2021; 9:622 42.