Innovations in Public Health Surveillance for Emerging Infections.

Public health surveillance is defined as the ongoing, systematic collection, analysis, and interpretation of health data and is closely integrated with the timely dissemination of information that the public needs to know and upon which the public should act. Public health surveillance is central to modern public health practice by contributing data and information usually through a national notifiable disease reporting system (NNDRS). Although early identification and prediction of future disease trends may be technically feasible, more work is needed to improve accuracy so that policy makers can use these predictions to guide prevention and control efforts. In this article, we review the advantages and limitations of the current NNDRS in most countries, discuss some lessons learned about prevention and control from the first wave of COVID-19, and describe some technological innovations in public health surveillance, including geographic information systems (GIS), spatial modeling, artificial intelligence, information technology, data science, and the digital twin method. We conclude that the technology-driven innovative public health surveillance systems are expected to further improve the timeliness, completeness, and accuracy of case reporting during outbreaks and also enhance feedback and transparency, whereby all stakeholders should receive actionable information on control and be able to limit disease risk earlier than ever before.

An evaluation of the notifiable disease surveillance system in Chegutu District, Zimbabwe, 2020: a cross-sectional study.

Introduction: in 2018-2019 Chegutu District had one notification form Tally 1 (T1) that was completed instead of seven for detected notifiable diseases. Different figures of cholera were reported through weekly rapid disease notification system with 106 patients and Notifiable Diseases Surveillance System (NDSS) with 111 patients, causing data discrepancy. We evaluated the NDSS to determine reasons for underperformance and data discrepancy. Methods: we conducted descriptive cross-sectional study using updated centres for disease control and prevention guidelines for surveillance system evaluation. We recruited forty-six health workers. Interviewer-administered questionnaires and checklists were used to collect data on reasons for underperformance, reasons for data discrepancy, knowledge of NDSS, surveillance system attributes and usefulness. Epi InfoTM7 generated frequencies, proportions, and means. Likert scale was used to assess health worker knowledge. Results: of the forty-six health workers, 34 (78%) had fair knowledge of NDSS. The reason for system underperformance was lack of training in NDSS 42 (91%). Data discrepancy was attributed to typographical mistakes made during data entry on WhatsApp platform 32 (70%). Eighty per cent (37) were willing to complete T1 forms. Six participants who were timed took ten minutes to complete T1 forms. Among 17 health facilities, only three had fifteen T1 forms that were adequate to notify first five cases in an outbreak. Notifiable diseases surveillance system data was used for planning health education 28 (68%). Conclusion: the NDSS was unstable due to health workers' inadequate knowledge and unavailability of T1 forms. Notifiable diseases surveillance system was found to be simple, acceptable, and useful. We recommended NDSS training of health workers.

Notifiable diseases interoperable framework toward improving Iran public health surveillance system: Lessons learned from COVID-19 pandemic.

BACKGROUND: Direct transmission of notifiable disease information in a real-time and reliable way to public health decision-makers is imperative for early identification of epidemiological trends as well as proper response to potential pandemic like ongoing coronavirus disease 2019 crisis. Thus, this research aimed to develop of semantic-sharing and collaborative-modeling to meet the information exchange requirements of Iran's notifiable diseases surveillance system. MATERIALS AND METHODS: First, the Iran's Notifiable diseases Minimum Data Set (INMDS) was determined according to a literature review coupled with agreements of experts. Then the INMDS was mapped to international terminologies and classification systems, and the Health Level seven-Clinical Document Architecture (HL7-CDA) standard was leveraged to define the exchangeable and machine-readable data formats. RESULTS: A core dataset consisting of 15 classes and 96 data fields was defined. Data elements and response values were mapped to Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) reference terminology. Then HL7-CDA standard for interoperable data exchange were defined. CONCLUSION: The notifiable disease surveillance requires an integrative participation of multidisciplinary team. In this field, data interoperability is more essential due to the heterogeneous nature of health information systems. Developing of INMDS based on HL7-CDA along with SNOMED-CT codes offers an inclusive and interoperable dataset that can help make notifiable diseases data more comparable and reportable across studies and organizations. The proposed data model will be further modifications in the future according probable changes in Iran's notifiable diseases list.