Infectious Disease Surveillance

Murray, Jillian; Cohen, Adam L.

doi:10.1016/b978-0-12-803678-5.00517-8

Cited by 86 publications

(58 citation statements)

References 4 publications

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“…Biosurveillance focuses on systematically collecting and combining health information with appropriate analysis and interpretations to achieve early warning, detection, and overall situational awareness of disease activity. Infectious disease surveillance is an important epidemiological tool used to describe the burden of disease, monitor its trends, and detect outbreaks of new and existing pathogens [ 1 ]. Traditionally, biosurveillance systems rely on the reporting of identified diseases formally by field investigators, physicians, veterinarians, laboratories, and other healthcare providers to relevant health agencies.…”

Section: Introductionmentioning

confidence: 99%

Digital Biosurveillance for Zoonotic Disease Detection in Kenya

2021

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Infectious disease surveillance is crucial for early detection and situational awareness of disease outbreaks. Digital biosurveillance monitors large volumes of open-source data to flag potential health threats. This study investigates the potential of digital surveillance in the detection of the top five priority zoonotic diseases in Kenya: Rift Valley fever (RVF), anthrax, rabies, brucellosis, and trypanosomiasis. Open-source disease events reported between August 2016 and October 2020 were collected and key event-specific information was extracted using a newly developed disease event taxonomy. A total of 424 disease reports encompassing 55 unique events belonging to anthrax (43.6%), RVF (34.6%), and rabies (21.8%) were identified. Most events were first reported by news media (78.2%) followed by international health organizations (16.4%). News media reported the events 4.1 (±4.7) days faster than the official reports. There was a positive association between official reporting and RVF events (odds ratio (OR) 195.5, 95% confidence interval (CI); 24.01–4756.43, p < 0.001) and a negative association between official reporting and local media coverage of events (OR 0.03, 95% CI; 0.00–0.17, p = 0.030). This study highlights the usefulness of local news in the detection of potentially neglected zoonotic disease events and the importance of digital biosurveillance in resource-limited settings.

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Section: Introductionmentioning

confidence: 99%

Digital Biosurveillance for Zoonotic Disease Detection in Kenya

2021

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“…In contrast, active surveillance requires public health staff to engage actively in the system and take action in order to receive reports of disease cases. 1 The Pan-American Health Organization (PAHO) recommends that countries should conduct surveillance of pneumonias and meningitis in children aged <5 years to assess disease burden and population profile. 3,4 The Sistema Regional de Vacunas or Regional Vaccine System (SIREVA) is a network of laboratories conducting surveillance of bacterial pneumonia, sepsis/bacteremia and meningitis in Latin America since 1993, organized by PAHO.…”

Section: Introductionmentioning

confidence: 99%

“…Surveillance data are needed to describe the current burden and epidemiology of disease and to monitor trends and patterns in disease activity. 1 They can be used to detect unusual disease patterns and trigger disease control measures, to inform resource allocation decisions in public health, and to evaluate disease control and prevention initiatives. 2 In passive surveillance systems, medical professionals in the community and at health facilities report cases to the public health authorities, which conduct data management and analysis once the data are received.…”

Section: Introductionmentioning

confidence: 99%

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Laboratory-based surveillance in Latin America: attributes and limitations in evaluation of pneumococcal vaccine impact

Guevara¹,

Guzmán-Holst²

2021

Human Vaccines & Immunotherapeutics

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Disease surveillance data are needed to monitor trends in disease activity, inform decision-making in public health and evaluate disease prevention/control measures. The Sistema Regional de Vacunas (SIREVA) supports laboratory-based surveillance of invasive pneumococcal disease (IPD) in Latin American countries, providing information on identification, distribution, and anti-microbial susceptibility of pneumococcal strains. We estimated the proportion of pneumococcal meningitis and sepsis/bacteremia cases captured by SIREVA, by comparing the number of SIREVA-reported isolates in Argentina, Brazil, Chile, Colombia, Ecuador and Mexico with the estimated expected number of cases based on regional estimates of disease incidence. In all six countries, the number of isolates reported by SIREVA was consistently lower than the number of cases expected, across all years with data available. The proportion of SIREVA-reported isolates was highest in Chile (43-83%) and lowest in Mexico (1.4-3.5%). Passive surveillance systems such as SIREVA are important tools for monitoring circulating strains that could be related to pneumococcal disease, but our results show that SIREVA is likely to underestimate pneumococcal disease incidence. This under-reporting will limit the precision of surveillance data in monitoring changes in the incidence of IPD after vaccine introduction, and this should be considered when assessing the impact of vaccination programs.

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“…2 Even though multiple sites for testing for SARS-COV-2 are available in many communities, tracking down and detecting many cases of the virus will require active surveillance, in which public health workers seek out infections rather than waiting for individuals to present for diagnosis. 3…”

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confidence: 99%

Maximizing the Efficiency of Active Case Finding for SARS-CoV-2 Using Bandit Algorithms

Gonsalves

Copple

Paltiel³

et al. 2021

Med Decis Making

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Even as vaccination for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) expands in the United States, cases will linger among unvaccinated individuals for at least the next year, allowing the spread of the coronavirus to continue in communities across the country. Detecting these infections, particularly asymptomatic ones, is critical to stemming further transmission of the virus in the months ahead. This will require active surveillance efforts in which these undetected cases are proactively sought out rather than waiting for individuals to present to testing sites for diagnosis. However, finding these pockets of asymptomatic cases (i.e., hotspots) is akin to searching for needles in a haystack as choosing where and when to test within communities is hampered by a lack of epidemiological information to guide decision makers’ allocation of these resources. Making sequential decisions with partial information is a classic problem in decision science, the explore v. exploit dilemma. Using methods—bandit algorithms—similar to those used to search for other kinds of lost or hidden objects, from downed aircraft or underground oil deposits, we can address the explore v. exploit tradeoff facing active surveillance efforts and optimize the deployment of mobile testing resources to maximize the yield of new SARS-CoV-2 diagnoses. These bandit algorithms can be implemented easily as a guide to active case finding for SARS-CoV-2. A simple Thompson sampling algorithm and an extension of it to integrate spatial correlation in the data are now embedded in a fully functional prototype of a web app to allow policymakers to use either of these algorithms to target SARS-CoV-2 testing. In this instance, potential testing locations were identified by using mobility data from UberMedia to target high-frequency venues in Columbus, Ohio, as part of a planned feasibility study of the algorithms in the field. However, it is easily adaptable to other jurisdictions, requiring only a set of candidate test locations with point-to-point distances between all locations, whether or not mobility data are integrated into decision making in choosing places to test.

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Infectious Disease Surveillance

Cited by 86 publications

References 4 publications

Digital Biosurveillance for Zoonotic Disease Detection in Kenya

Digital Biosurveillance for Zoonotic Disease Detection in Kenya

Laboratory-based surveillance in Latin America: attributes and limitations in evaluation of pneumococcal vaccine impact

Maximizing the Efficiency of Active Case Finding for SARS-CoV-2 Using Bandit Algorithms

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