Abstract
Background
During the COVID-19 pandemic, cases were tracked using multiple surveillance systems. Some systems were completely novel and others incorporated multiple data streams to estimate case incidence and/or prevalence. How well these different surveillance systems worked as epidemic indicators is unclear. This has implications for future disease surveillance and outbreak management.
Methods
Data from twelve surveillance systems used to monitor the COVID-19 in England were extracted (Jan20-Nov21). These were integrated as daily time-series and comparisons undertaken using Spearman correlation between candidate alternatives and the most timely (updated daily, clinical case register) and the least-biased (from comprehensive household sampling) COVID-19 epidemic indicators, with comparisons focused on the period Sep20- Nov21.
Findings
Spearman statistic correlations during the full focus period between least-biased indicator (from household surveys) and other epidemic indicator time series were 0.94 (clinical cases, the most timely indicator), 0.92 (self-report case status on a digital App), 0.67 (emergency department attendances), 0.64 (NHS111 website visits), 0.63 (wastewater concentrations), 0.60 (admissions to hospital with +COVID-19 status), 0.45 (NHS111 calls), 0.08 (Google search rank for ‘covid’), -0.04 (consultations with general practitioners) and -0.37 (Google search rank for ‘coronavirus’). Time lags (-14 to +14 days) did not markedly improve these rho statistics. Clinical cases (the most timely indicator) captured a more consistent proportion of cases than the self-report digital App did.
Interpretation
A suite of monitoring systems is useful. The household-survey system was a most comprehensive and least-biased epidemic monitor but not very timely. Data from laboratory testing, self-reporting digital App and attendances to emergency departments were comparatively useful, fairly accurate and timely epidemic trackers.
During the COVID-19 pandemic, cases were tracked using multiple surveillance systems. Some systems were completely novel and others incorporated multiple data streams to estimate case incidence and/or prevalence. How well these different surveillance systems worked as epidemic indicators is unclear. This has implications for future disease surveillance and outbreak management.
Methods
Data from twelve surveillance systems used to monitor the COVID-19 in England were extracted (Jan20-Nov21). These were integrated as daily time-series and comparisons undertaken using Spearman correlation between candidate alternatives and the most timely (updated daily, clinical case register) and the least-biased (from comprehensive household sampling) COVID-19 epidemic indicators, with comparisons focused on the period Sep20- Nov21.
Findings
Spearman statistic correlations during the full focus period between least-biased indicator (from household surveys) and other epidemic indicator time series were 0.94 (clinical cases, the most timely indicator), 0.92 (self-report case status on a digital App), 0.67 (emergency department attendances), 0.64 (NHS111 website visits), 0.63 (wastewater concentrations), 0.60 (admissions to hospital with +COVID-19 status), 0.45 (NHS111 calls), 0.08 (Google search rank for ‘covid’), -0.04 (consultations with general practitioners) and -0.37 (Google search rank for ‘coronavirus’). Time lags (-14 to +14 days) did not markedly improve these rho statistics. Clinical cases (the most timely indicator) captured a more consistent proportion of cases than the self-report digital App did.
Interpretation
A suite of monitoring systems is useful. The household-survey system was a most comprehensive and least-biased epidemic monitor but not very timely. Data from laboratory testing, self-reporting digital App and attendances to emergency departments were comparatively useful, fairly accurate and timely epidemic trackers.
| Original language | English |
|---|---|
| Journal | The Lancet Public Health |
| Publication status | Accepted/In press - 15 Sep 2023 |
Keywords
- surveillance
- epidemic
- COVID-19
- symptoms
- health care seeking