Spatio-Temporal Small Area Surveillance Of The COVID-19 Pandemics

This article proposes a new spatio-temporal spline model suited for COVID-19 surveillance that allows estimating and monitoring for small areas, as demonstrated in two Spanish regions. It develops new epidemiological tools to be used by regional public health services for small area surveillance.

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Disease Tracking and Surveillance

Disease surveillance helps us detect disease cases, understand burden of disease and risk factors, provide the basis for timely and informed decision-making, guide control measures, and monitor impacts. Since the onset of COVID-19, surveillance efforts have worked to provide real-time tracking and forecast data, despite challenges with diagnostic capacity, case reporting, insufficient contact tracing, and fragmented data systems. COVID-19 has highlighted the need to invest in modern data systems, expand and skill up the workforce, and ensure data reporting and interpretation retain high ethical and epidemiological standards.