Productive disruption: opportunities and challenges for innovation in infectious disease surveillance

Buckee, Caroline O.; Cardenas, Maria Isabel Estupiñan; Corpuz, June; Ghosh, Arpita; Haque, Farhana; Karim, Jahirul; Mahmud, Ayesha S.; Maude, Richard J.; Mensah, Keitly; Motaze, Nkengafac Villyen; Nabaggala, Maria Sarah; Metcalf, C. Jessica E.; Mioramalala, Sedera Aurélien; Mubiru, Frank; Peak, Corey M.; Pramanik, Santanu; Rakotondramanga, Jean Marius; Remera, Eric; Sinha, Ipsita; Sovannaroth, Siv; Tatem, Andrew J.; Zaw, Win

doi:10.1136/bmjgh-2017-000538

Cited by 16 publications

(18 citation statements)

References 20 publications

(19 reference statements)

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“…There are still many inequities in accessibility to resources globally that make collection and availability of data difficult, stratifying health status and potential interventions by socio-economic means and access to urban areas [ 115 ]. Limited resources to collect and organize data to develop effective surveillance systems is further impacted with difficulties in combining datasets recorded for varying formats, quality standards, and reporting requirements [ 116 ].…”

Section: Discussionmentioning

confidence: 99%

A systematic review of spatial decision support systems in public health informatics supporting the identification of high risk areas for zoonotic disease outbreaks

2018

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BackgroundZoonotic diseases account for a substantial portion of infectious disease outbreaks and burden on public health programs to maintain surveillance and preventative measures. Taking advantage of new modeling approaches and data sources have become necessary in an interconnected global community. To facilitate data collection, analysis, and decision-making, the number of spatial decision support systems reported in the last 10 years has increased. This systematic review aims to describe characteristics of spatial decision support systems developed to assist public health officials in the management of zoonotic disease outbreaks.MethodsA systematic search of the Google Scholar database was undertaken for published articles written between 2008 and 2018, with no language restriction. A manual search of titles and abstracts using Boolean logic and keyword search terms was undertaken using predefined inclusion and exclusion criteria. Data extraction included items such as spatial database management, visualizations, and report generation.ResultsFor this review we screened 34 full text articles. Design and reporting quality were assessed, resulting in a final set of 12 articles which were evaluated on proposed interventions and identifying characteristics were described. Multisource data integration, and user centered design were inconsistently applied, though indicated diverse utilization of modeling techniques.ConclusionsThe characteristics, data sources, development and modeling techniques implemented in the design of recent SDSS that target zoonotic disease outbreak were described. There are still many challenges to address during the design process to effectively utilize the value of emerging data sources and modeling methods. In the future, development should adhere to comparable standards for functionality and system development such as user input for system requirements, and flexible interfaces to visualize data that exist on different scales.PROSPERO registration number: CRD42018110466.Electronic supplementary materialThe online version of this article (10.1186/s12942-018-0157-5) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

A systematic review of spatial decision support systems in public health informatics supporting the identification of high risk areas for zoonotic disease outbreaks

2018

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“…Data such as emergency department visits, medication sales, online search queries, and social media postings have also been suggested as real-time indicators of outbreak activity, although their integration into public health responses remains a subject of debate (54)(55)(56). The need to overcome reporting biases is a central challenge, because observations may be too nonspecific to distinguish between meaningful and spurious signals in settings with high technological capacity while also being insensitive to even high-risk events in resource-poor settings (57,58). Although nontraditional data sources have, in some applications, supported inferences about epidemic dynamics (59), limited information about cases from such sources remains a barrier.…”

Section: Emerging Data and Analyticsmentioning

confidence: 99%

Emerging Challenges and Opportunities in Infectious Disease Epidemiology

Lewnard

Reingold

2019

American Journal of Epidemiology

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Much of the intellectual tradition of modern epidemiology stems from efforts to understand and combat chronic diseases persisting through the 20th century epidemiologic transition of countries such as the United States and United Kingdom. After decades of relative obscurity, infectious disease epidemiology has undergone an intellectual rebirth in recent years amid increasing recognition of the threat posed by both new and familiar pathogens. Here, we review the emerging coalescence of infectious disease epidemiology around a core set of study designs and statistical methods bearing little resemblance to the chronic disease epidemiology toolkit. We offer our outlook on challenges and opportunities facing the field, including the integration of novel molecular and digital information sources into disease surveillance, the assimilation of such data into models of pathogen spread, and the increasing contribution of models to public health practice. We next consider emerging paradigms in causal inference for infectious diseases, ranging from approaches to evaluating vaccines and antimicrobial therapies to the task of ascribing clinical syndromes to etiologic microorganisms, an age-old problem transformed by our increasing ability to characterize human-associated microbiota. These areas represent an increasingly important component of epidemiology training programs for future generations of researchers and practitioners.

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“…Incentive schemes and directed feedback to health-care workers have been successful in encouraging reporting of lateral flow test results for purposes of disease surveillance. 7 The use of now widely-available technology such as mobile phone cameras to interpret and transmit images of malarial lateral flow tests has resulted in increased test sensitivity and reporting. 8…”

Section: Connectivity Of Rapid-testing Diagnostics and Surveillance Omentioning

confidence: 99%

Corrigendum

2019

Bull. World Health Organ.

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Productive disruption: opportunities and challenges for innovation in infectious disease surveillance

Cited by 16 publications

References 20 publications

A systematic review of spatial decision support systems in public health informatics supporting the identification of high risk areas for zoonotic disease outbreaks

A systematic review of spatial decision support systems in public health informatics supporting the identification of high risk areas for zoonotic disease outbreaks

Emerging Challenges and Opportunities in Infectious Disease Epidemiology

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