Abstract
One difficulty for real‐time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistical problems, infrastructure difficulties, and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty. Implementation of the model is fast due to the use of the integrated nested Laplace approximation. The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and severe acute respiratory infection data in the state of Paraná, Brazil.
Highlights
Surveillance systems play a crucial role in managing infectious disease risk
Timeliness is affected by conflicting factors due to the disease incidence: Delays may decrease during the high-transmission season because of awareness among doctors and patients; delays may increase during high-transmission seasons because of the saturation of the health care system
The particular formulation of the model that we propose readily allows for dependence along both the columns and rows of Figure 1 to capture the temporal variability of the disease occurrence and the temporal structure of the delay mechanism
Summary
Surveillance systems play a crucial role in managing infectious disease risk. The main requirements for a good surveillance system are timeliness, sensitivity, and specificity, together with readily interpretable outputs.[1]. Disease surveillance in most countries is passive, relying on the cases reported by health care providers from patients seeking care. Surveillance and warning systems relying on reported incidence to assess risk can be misinformed, if this delay is not somehow corrected. Assuming for simplicity that T is “today,” the values nt,d in the grey boxes of Figure 1 are missing and so are the corresponding the totals Nt. Assuming for simplicity that T is “today,” the values nt,d in the grey boxes of Figure 1 are missing and so are the corresponding the totals Nt These occurred-but-not-yet-reported events are called the run-off triangle,[4] all values of which potentially need to be estimated for accurate risk assessment (eg, for detecting a sharp increase in occurrences). The following section discusses some recent approaches to this problem, along with the motivation for the one proposed in this paper
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