Early-stage spatial disease surveillance of novel SARS-CoV-2 variants of concern in Germany with crowdsourced data

Timo Mitze,Johannes Rode

doi:10.1038/s41598-021-04573-1

Abstract

The emergence and rapid spread of novel variants of concern (VOC) of the coronavirus 2 constitute a major challenge for spatial disease surveillance. We explore the possibility to use close to real-time crowdsourced data on reported VOC cases (mainly the Alpha variant) at the local area level in Germany. The aim is to use these data for early-stage estimates of the statistical association between VOC reporting and the overall COVID-19 epidemiological development. For the first weeks in 2021 after international importation of VOC to Germany, our findings point to significant increases of up to 35–40% in the 7-day incidence rate and the hospitalization rate in regions with confirmed VOC cases compared to those without such cases. This is in line with simultaneously produced international evidence. We evaluate the sensitivity of our estimates to sampling errors associated with the collection of crowdsourced data. Overall, we find no statistical evidence for an over- or underestimation of effects once we account for differences in data representativeness at the regional level. This points to the potential use of crowdsourced data for spatial disease surveillance, local outbreak monitoring and public health decisions if no other data on new virus developments are available.

Highlights

The emergence and rapid spread of novel variants of concern (VOC) of the coronavirus 2 constitute a major challenge for spatial disease surveillance
When novel variants of concern (VOC) of the severe acute respiratory syndrome coronavirus type 2 (SARSCoV-2) were first reported in late 2020, scientists, policy makers and the general public soon asked essential questions: Do these VOC find it easier to transmit from human to human compared to previously circulating strains? Do VOC lead to faster disease progressions, more severe disease cases and higher fatality rates? How can VOC spread be effectively monitored at different spatial levels to respond to local outbreaks and mitigate viral spread?
We focus on two key indicators here: (1) the 7-day incidence rate, i.e., the number of newly reported SARS-CoV-2 infections in the last 7 days per 100,000 population, and (2) the hospitalization rate, i.e., the number of hospitalized COVID-19 patients in intensive care per 100,000 population

Summary

Introduction

The emergence and rapid spread of novel variants of concern (VOC) of the coronavirus 2 constitute a major challenge for spatial disease surveillance. We find no statistical evidence for an over- or underestimation of effects once we account for differences in data representativeness at the regional level This points to the potential use of crowdsourced data for spatial disease surveillance, local outbreak monitoring and public health decisions if no other data on new virus developments are available. Given the significance of VOC for overall SARS-CoV-2 infection dynamics, the goal of this study is twofold: First, it shall document our approach to provide near-time statistical estimates of spatial disease trends in Germany associated with the emergence and rapid spread of VOC during the first weeks in 2021. We combine these data with administrative data on SARS-CoV-2 cases and hospitalized cases (intensive care patients with and without artificial ventilation). The second goal is to assess the quality of crowdsourced

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