Detection of Infectious Disease Outbreaks From Laboratory Data With Reporting Delays

Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease

The rapid detection of outbreaks is a key step in the effective control and containment of infectious diseases. In particular, the identification of cases which might be epidemiologically linked is crucial in directing outbreak-containment efforts and shaping the intervention of public health authorities. Often this requires the detection of clusters of cases whose numbers exceed those expected by a background of sporadic cases. Quantifying exceedances rapidly is particularly challenging when only few cases are typically reported in a precise location and time. To address such important public health concerns, we present a general method which can detect spatio-temporal deviations from a Poisson point process and estimate the odds of an isolate being part of a cluster. This method can be applied to diseases where detailed geographical information is available. In addition, we propose an approach to explicitly take account of delays in microbial typing. As a case study, we considered invasive group A Streptococcus infection events as recorded and typed by Public Health England from 2015 to 2020.

Bayesian spatiotemporal modeling with sliding windows to correct reporting delays for real-time dengue surveillance in Thailand

Background

The ability to produce timely and accurate estimation of dengue cases can significantly impact disease control programs. A key challenge for dengue control in Thailand is the systematic delay in reporting at different levels in the surveillance system. Efficient and reliable surveillance and notification systems are vital to monitor health outcome trends and early detection of disease outbreaks which vary in space and time.

Methods

Predicting the trend in dengue cases in real-time is a challenging task in Thailand due to a combination of factors including reporting delays. We present decision support using a spatiotemporal nowcasting model which accounts for reporting delays in a Bayesian framework with sliding windows. A case study is presented to demonstrate the proposed nowcasting method using weekly dengue surveillance data in Bangkok at district level in 2010.

Results

The overall real-time estimation accuracy was 70.69% with 59.05% and 79.59% accuracy during low and high seasons averaged across all weeks and districts. The results suggest the model was able to give a reasonable estimate of the true numbers of cases in the presence of delayed reports in the surveillance system. With sliding windows, models could also produce similar accuracy to estimation with the whole data.

Conclusions

A persistent challenge for the statistical and epidemiological communities is to transform data into evidence-based knowledge that facilitates policy making about health improvements and disease control at the individual and population levels. Improving real-time estimation of infectious disease incidence is an important technical development. The effort in this work provides a template for nowcasting in practice to inform decision making for dengue control.

The Transformation of Reference Microbiology Methods and Surveillance for Salmonella With the Use of Whole Genome Sequencing in England and Wales

The use of whole genome sequencing (WGS) as a method for supporting outbreak investigations, studying Salmonella microbial populations and improving understanding of pathogenicity has been well-described (1–3). However, performing WGS on a discrete dataset does not pose the same challenges as implementing WGS as a routine, reference microbiology service for public health surveillance. Challenges include translating WGS data into a useable format for laboratory reporting, clinical case management, Salmonella surveillance, and outbreak investigation as well as meeting the requirement to communicate that information in an understandable and universal language for clinical and public health action. Public Health England have been routinely sequencing all referred presumptive Salmonella isolates since 2014 which has transformed our approach to reference microbiology and surveillance. Here we describe an overview of the integrated methods for cross-disciplinary working, describe the challenges and provide a perspective on how WGS has impacted the laboratory and surveillance processes in England and Wales.

A modelling approach for correcting reporting delays in disease surveillance data

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.