Sample sizes to achieve multiple surveillance objectives in primary care sentinel systems monitoring respiratory pathogens: a simulation approach
Presanis, A. M.; Nyberg, T.; Rolfes, M. A.; Quinot, C.; Goudie, R.; Whitaker, H. J.; Elson, W. H.; Byford, R.; Mikdashi, T.; Wong, J. Y.; Andrews, N.; Villar, S. S.; Cowling, B. J.; Charlett, A.; Dabrera, G.; Pebody, R.; Lopez Bernal, J.; de Lusignan, S.; De Angelis, D.
Show abstract
Influenza surveillance has typically been carried out using influenza-like illness (ILI) rates and proportions of laboratory tests positive for influenza as metrics to monitor, with sample sizes for the number of tests to carry out based on the precision of the resulting estimate of proportions positive. The transition out of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic period has encouraged the establishment of integrated surveillance of respiratory pathogens, in the context of multiple surveillance objectives, as set out by WHO in its revised integrated surveillance guidance and Mosaic Respiratory Surveillance Framework. These objectives include outbreak detection, situational awareness and intensity evaluation, among others. We illustrate how to design respiratory surveillance in primary care, by considering multiple surveillance objectives for different metrics of different types of respiratory pathogen circulation seasons in England, the USA and Hong Kong. We focus on a proxy of influenza activity as a metric to compare between these countries/regions. Taking advantage of England's integrated sentinel primary care surveillance system, we propose further metrics to monitor: a proxy of respiratory activity, novelly defined as the product of an acute respiratory infection (ARI) consultation rate and the proportion of tests positive for \emph{at least one pathogen}; pathogen-specific ARI-based activity proxies for more detailed monitoring of influenza and SARS-CoV-2; and integrated monitoring of proportions positive for all pathogens tested. We use a simulation approach to determine sample sizes by optimising either the probability of, or time to, detection of different events in monitored metrics, according to the different surveillance objectives. We find that sample sizes to maximise detection probabilities or minimise detection times vary by metric, objective, event and country/region. At a national level, the current sample sizes used are sufficient to detect most events in most weeks for both the USA and Hong Kong, but for England the numbers of swabs taken for ILI consultations may not be sufficient in all weeks, particularly at the start of the season when outbreak detection is important. However, broadening the criteria for swabbing to acute respiratory symptoms does allow for sufficient sample sizes.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- A flexible method for optimising sharing of healthcare resources and demand in the context of the COVID-19 pandemic 92%
- Sickness Absence Rates in NHS England Staff during the COVID-19 Pandemic: insights from multivariate regression and time series modelling 92%
- A Bayesian Susceptible-Infectious-Hospitalized-Ventilated-Recovered Model to Predict Demand for COVID-19 Inpatient Care in a Large Healthcare System 92%
Similar papers in this journal
- TRACKing Excess Deaths (TRACKED): an interactive online tool to monitor excess deaths associated with COVID-19 pandemic in the United Kingdom 91%
- Forecasting influenza incidence as an ordinal variable using machine learning 90%
- Baseline nowcasting methods for handling delays in epidemiological data 89%
Similar papers in this journal
- Individual and population level uncertainty interact to determine the performance of outbreak surveillance systems 93%
- Ensemble forecasts of COVID-19 activity to support Australia's pandemic response: 2020-22 92%
- The multi-dimensional challenges of controlling respiratory virus transmission in indoor spaces: Insights from the linkage of a microscopic pedestrian simulation and SARS-CoV-2 transmission model 92%
Similar papers in this journal
- Border quarantine, vaccination and public health measures to mitigate the impact of COVID-19 importations: a modelling study 94%
- Modelling the impact of household size distribution on the transmission dynamics of COVID-19 92%
- Time-aggregated mobile phone mobility data are sufficient for modelling influenza spread: the case of Bangladesh 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.