Forecasting SARS-CoV-2 transmission and clinical risk at small spatial scales by the application of machine learning architectures to syndromic surveillance data

• Published in: Nature machine intelligence Vol. 4; no. 10; pp. 814 - 827
• Main Authors: Ward, Thomas, Johnsen, Alexander, Ng, Stanley, Chollet, François
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01-10-2022; Nature Publishing Group
• Subjects: 631/326/596/4130; 639/705/117; 692/308/174; Algorithms; Asymptomatic; Clinical outcomes; COVID-19; Disease transmission; Engineering; Epidemics; Geography; Health care; Health services; Health surveillance; Hospitalization; Infections; Internet; Machine learning; Neural networks; Pandemics; Public health; Risk; Search engines; Severe acute respiratory syndrome coronavirus 2; Spatial data; Surveillance; Trends; Viral diseases; United Kingdom > UK
• ISSN: 2522-5839; 2522-5839
• DOI: 10.1038/s42256-022-00538-9

Timely and well-informed syndromic surveillance is essential for effective public health policy. The monitoring of traditional epidemiological indicators can be lagged and misleading, which hampers efforts to identify hotspot locations. The increasing predominance of digitalized healthcare-seeking behaviour necessitates that it is fully exploited for the public benefit of effective pandemic management. Using the highest-resolution spatial data for Google Trends relative search volumes, Google mobility, telecoms mobility, National Health Service Pathways calls and website testing journeys, we have developed a machine learning early indicator modelling approach of SARS-CoV-2 transmission and clinical risk at small geographic scales. We trained shallow learning algorithms as the baseline against a geospatial neural network architecture that we termed the spatio-integrated long short-term memory (SI-LSTM) algorithm. The SI-LSTM algorithm was able to—for the assessed temporal periods—accurately identify hotspot locations over time horizons of a month or more with an accuracy in excess of 99%, and an improved performance of up to 15% against the shallow learning algorithms. Furthermore, in public health operational use, this model highlighted the localized exponential growth of the Alpha variant in late 2020, the Delta variant in April 2021 and the Omicron variant in November 2021 within the United Kingdom prior to their spatial dispersion and growth being confirmed by clinical data.
Identifying epidemic hotspots in a timely way with syndromic surveillance can provide highly valuable information for public health policy. A machine learning early indicator model that uses highly granular data from digitalized healthcare-seeking behaviour, including from Google Trends and National Health Service Pathways calls, can identify SARS-CoV-2 risk at small geographic scales. The model can retrospectively identify hotspots in the United Kingdom for various variants in 2020 and 2021 before the wider spread and growth of these variants being confirmed by clinical data.