Geographically weighted generalized Farrington algorithm for rapid outbreak detection over short data accumulation periods

AbstractWhole-genome sequencing is likely to become increasingly used by local clinical microbiology laboratories, where sequencing volume is low compared with national reference laboratories. Here, we describe a universal protocol for simultaneous DNA extraction and sequencing of numerous different bacterial species, allowing mixed species sequence runs to meet variable laboratory demand. We assembled test panels representing 20 clinically relevant bacterial species. The DNA extraction process used the QIAamp mini DNA kit, to which different combinations of reagents were added. Thereafter, a common protocol was used for library preparation and sequencing. The addition of lysostaphin, lysozyme or buffer ATL (a tissue lysis buffer) alone did not produce sufficient DNA for library preparation across the species tested. By contrast, lysozyme plus lysostaphin produced sufficient DNA across all 20 species. DNA from 15 of 20 species could be extracted from a 24-h culture plate, while the remainder required 48–72 h. The process demonstrated 100% reproducibility. Sequencing of the resulting DNA was used to recapitulate previous findings for species, outbreak detection, antimicrobial resistance gene detection and capsular type. This single protocol for simultaneous processing and sequencing of multiple bacterial species supports low volume and rapid turnaround time by local clinical microbiology laboratories.

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Field Evaluation of the Performance of Two Rapid Diagnostic Tests for Meningitis in Niger and Burkina Faso

Microorganisms ◽

10.3390/microorganisms9040832 ◽

2021 ◽

Vol 9 (4) ◽

pp. 832 ◽

Cited By ~ 1

Author(s):

Marc Rondy ◽

Mamadou Tamboura ◽

Fati Sidikou ◽

Issaka Yameogo ◽

Kambire Dinanibe ◽

...

Keyword(s):

Burkina Faso ◽

Field Evaluation ◽

Health Centers ◽

Outbreak Detection ◽

Health Staff ◽

Chain Reaction ◽

Predictive Values ◽

Independent Evaluation ◽

Polymerase Chain ◽

Lateral Flow Tests

New lateral flow tests for the diagnosis of Neisseria meningitidis (Nm) (serogroups A, C, W, X, and Y), MeningoSpeed, and Streptococcus pneumoniae (Sp), PneumoSpeed, developed to support rapid outbreak detection in Africa, have shown good performance under laboratory conditions. We conducted an independent evaluation of both tests under field conditions in Burkina Faso and Niger, in 2018–2019. The tests were performed in the cerebrospinal fluid of suspected meningitis cases from health centers in alert districts and compared to reverse transcription polymerase chain reaction tests performed at national reference laboratories (NRLs). Health staff were interviewed about feasibility. A total of 327 cases were tested at the NRLs, with 26% confirmed Nm (NmC 63% and NmX 37%) and 8% Sp. Sensitivity and specificity were, respectively, 95% (95% CI: 89–99) and 90% (95% CI: 86–94) for Nm and 92% (95% CI: 75–99) and 99% (95% CI: 97–100) for Sp. Positive and negative predictive values were, respectively, 77% (95% CI: 68–85) and 98% (95% CI: 95–100) for Nm and 86% (95% CI: 67–96) and 99% (95% CI: 98–100) for Sp. Concordance showed 82% agreement for Nm and 97% for Sp. Interviewed staff evaluated the tests as easy to use and to interpret and were confident in their readings. Results suggest overall good performance of both tests and potential usefulness in meningitis outbreak detection.

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877. Use of Statistical Process Control Charts for Early Detection of Healthcare Facility-Associated Nontuberculous Mycobacterial Outbreaks

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.1065 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S475-S476

Author(s):

Arthur W Baker ◽

Ahmed Maged ◽

Salah Haridy ◽

Jason E Stout ◽

Jessica L Seidelman ◽

...

Keyword(s):

Early Detection ◽

Process Control ◽

Statistical Process Control ◽

Moving Average ◽

Healthcare Facility ◽

Mycobacterium Abscessus ◽

Control Limit ◽

Outbreak Detection ◽

Statistical Process ◽

Traditional Surveillance

Abstract Background Nontuberculous mycobacteria (NTM) are increasingly implicated in healthcare facility-associated (HCFA) outbreaks. However, systematic methods for NTM surveillance and outbreak detection are lacking and represent an emerging need. We examined how statistical process control (SPC) might perform in NTM outbreak detection. Methods SPC charts were optimized for surgical site infection surveillance and adapted to analyze 3 NTM outbreaks that occurred from 2013-2016 at a single hospital. The first 2 outbreaks occurred contemporaneously and consisted of pulmonary Mycobacterium abscessus complex (MABC) acquisition and cardiac surgery-associated extrapulmonary MABC infection, respectively. The third outbreak was a pseudo-outbreak of Mycobacterium avium complex (MAC) at a bronchoscopy suite. We retrospectively analyzed monthly rates of unique patients who had: 1) positive respiratory cultures for MABC obtained on hospital day 3 or later; 2) positive non-respiratory cultures for MABC; and 3) positive bronchoalveolar lavage (BAL) cultures for MAC collected at the bronchoscopy suite. For each outbreak, we used these rates to construct a standardized moving average (MA) SPC chart with MA span of 3 months. Rolling baselines were estimated from average rates for months 7 through 12 prior to each monthly data point. SPC detection was indicated by the first data point above the upper control limit (UCL) of 3 standard deviations. Traditional surveillance detection was defined as the time of outbreak detection by routine infection control procedures. Results SPC detection occurred 5, 4, and 9 months prior to traditional surveillance outbreak detection for the three outbreaks, respectively (Figures 1 and 2). Prospective NTM surveillance using the MA chart potentially would have prevented an estimated 19 cases of pulmonary MABC, 9 cases of extrapulmonary MABC, and 80 cases of BAL MAC isolation (Table). No data points exceeded the UCL during 95 cumulative months of post-outbreak surveillance, suggesting low burden of false positive SPC signals. Figure 1. Use of a moving average statistical process control (SPC) chart for early detection of hospital-associated outbreaks of pulmonary Mycobacterium abscessus complex (MABC) and cardiac surgery-associated extrapulmonary MABC infection. The pulmonary chart analyzes cases of hospital-onset respiratory isolation of MABC. The extrapulmonary chart analyzes cases of positive non-respiratory cultures for MABC. CL, center line; LCL, lower control limit; UCL, upper control limit. Figure 2. Use of a moving average statistical process control (SPC) chart for early detection of a pseudo-outbreak of Mycobacterium avium complex (MAC) that occurred at a bronchoscopy suite. The chart analyzes cases of MAC isolated from bronchoalveolar lavage cultures. CL, center line; LCL, lower control limit; UCL, upper control limit. Table. Estimated cases of hospital-associated nontuberculous mycobacteria that would have been prevented by prospective surveillance with a moving average statistical process control (SPC) chart. Conclusion A single MA SPC chart detected 3 HCFA NTM outbreaks an average of 6 months earlier than traditional surveillance. SPC has potential to improve NTM surveillance, promoting early cluster detection and prevention of HCFA NTM. Disclosures All Authors: No reported disclosures

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Assessment of the core and support functions of the integrated disease surveillance and response system in Zanzibar, Tanzania

BMC Public Health ◽

10.1186/s12889-021-10758-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Fatma Saleh ◽

Jovin Kitau ◽

Flemming Konradsen ◽

Leonard E. G. Mboera ◽

Karin L. Schiøler

Keyword(s):

Infectious Disease ◽

Early Detection ◽

Disease Surveillance ◽

Health Facilities ◽

Outbreak Detection ◽

World Health ◽

Support Functions ◽

The Core ◽

Surveillance And Response ◽

Facility Level

Abstract Background Disease surveillance is a cornerstone of outbreak detection and control. Evaluation of a disease surveillance system is important to ensure its performance over time. The aim of this study was to assess the performance of the core and support functions of the Zanzibar integrated disease surveillance and response (IDSR) system to determine its capacity for early detection of and response to infectious disease outbreaks. Methods This cross-sectional descriptive study involved 10 districts of Zanzibar and 45 public and private health facilities. A mixed-methods approach was used to collect data. This included document review, observations and interviews with surveillance personnel using a modified World Health Organization generic questionnaire for assessing national disease surveillance systems. Results The performance of the IDSR system in Zanzibar was suboptimal particularly with respect to early detection of epidemics. Weak laboratory capacity at all levels greatly hampered detection and confirmation of cases and outbreaks. None of the health facilities or laboratories could confirm all priority infectious diseases outlined in the Zanzibar IDSR guidelines. Data reporting was weakest at facility level, while data analysis was inadequate at all levels (facility, district and national). The performance of epidemic preparedness and response was generally unsatisfactory despite availability of rapid response teams and budget lines for epidemics in each district. The support functions (supervision, training, laboratory, communication and coordination, human resources, logistic support) were inadequate particularly at the facility level. Conclusions The IDSR system in Zanzibar is weak and inadequate for early detection and response to infectious disease epidemics. The performance of both core and support functions are hampered by several factors including inadequate human and material resources as well as lack of motivation for IDSR implementation within the healthcare delivery system. In the face of emerging epidemics, strengthening of the IDSR system, including allocation of adequate resources, should be a priority in order to safeguard human health and economic stability across the archipelago of Zanzibar.

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Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

npj Digital Medicine ◽

10.1038/s41746-021-00459-8 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Ania Syrowatka ◽

Masha Kuznetsova ◽

Ava Alsubai ◽

Adam L. Beckman ◽

Paul A. Bain ◽

...

Keyword(s):

Public Health ◽

Artificial Intelligence ◽

Real Time ◽

Scoping Review ◽

Grey Literature ◽

Use Cases ◽

Outbreak Detection ◽

Response To Treatment ◽

Pandemic Preparedness ◽

Modeling Approaches

AbstractArtificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

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Transformation of microbiology data into a standardised data representation using OpenEHR

Scientific Reports ◽

10.1038/s41598-021-89796-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Antje Wulff ◽

◽

Claas Baier ◽

Sarah Ballout ◽

Erik Tute ◽

...

Keyword(s):

Real Life ◽

Primary Source ◽

Multidrug Resistant ◽

Data Representation ◽

Data Models ◽

Outbreak Detection ◽

Standard Data ◽

Quality Checks ◽

Computer Based ◽

Control Application

AbstractThe spread of multidrug resistant organisms (MDRO) is a global healthcare challenge. Nosocomial outbreaks caused by MDRO are an important contributor to this threat. Computer-based applications facilitating outbreak detection can be essential to address this issue. To allow application reusability across institutions, the various heterogeneous microbiology data representations needs to be transformed into standardised, unambiguous data models. In this work, we present a multi-centric standardisation approach by using openEHR as modelling standard. Data models have been consented in a multicentre and international approach. Participating sites integrated microbiology reports from primary source systems into an openEHR-based data platform. For evaluation, we implemented a prototypical application, compared the transformed data with original reports and conducted automated data quality checks. We were able to develop standardised and interoperable microbiology data models. The publicly available data models can be used across institutions to transform real-life microbiology reports into standardised representations. The implementation of a proof-of-principle and quality control application demonstrated that the new formats as well as the integration processes are feasible. Holistic transformation of microbiological data into standardised openEHR based formats is feasible in a real-life multicentre setting and lays the foundation for developing cross-institutional, automated outbreak detection systems.

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