Association among sentinel surveillance, meteorological factors, and infectious disease in Gwangju, Korea

Abstract Introduction: The outbreak of new infectious diseases is threatening human survival. Transmission of such diseases is determined by several factors, with climate being a very important factor. This study was conducted to assess the correlation between the occurrence of infectious diseases and climatic factors using data from the Sentinel Surveillance System and meteorological data from Gwangju, Jeollanam-do, Republic of Korea. Result The climate of Gwangju from June to September is humid, with this city having the highest average temperature, whereas that from December to February is cold and dry. Infection rates of Salmonella (Temperature: r = 0.710**; Relative humidity: r = 0.669**), E. coli (r = 0.617**; r = 0.626**), Rotavirus (r=-0.408**; r=-0.618**), Norovirus (r=-0.463**; r=-0.316**), Influenza virus (r=-0.726**; r=-0.672**), Coronavirus (r=-0.684**; r=-0.408**), and Coxsackievirus (r = 0.654**; r = 0.548**) have been shown to have a high correlation with seasonal changes, specifically in these meteorological factors. Discussion & Conclusions: Pathogens showing distinct seasonality in the occurrence of infection were observed, and there was a high correlation with the climate characteristics of Gwangju. In particular, viral diseases show strong seasonality, and further research on this matter is needed. Due to the current COVID-19 pandemic, quarantine and prevention have become important to block the spread of infectious diseases. For this purpose, studies that predicts infectivity through various types of data related to infection are important.

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Heterogeneous treatment effects of safe water on infectious disease: Do meteorological factors matter?

Cliometrica ◽

10.1007/s11698-017-0169-6 ◽

2017 ◽

Vol 13 (1) ◽

pp. 55-82 ◽

Cited By ~ 2

Author(s):

Kota Ogasawara ◽

Yukitoshi Matsushita

Keyword(s):

Infectious Disease ◽

Treatment Effects ◽

Meteorological Factors ◽

Heterogeneous Treatment Effects ◽

Safe Water

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Sentinel versus passive surveillance for measuring changes in dengue incidence: Evidence from three concurrent surveillance systems in Iquitos, Peru

10.1101/040220 ◽

2016 ◽

Cited By ~ 2

Author(s):

Sandra Olkowski ◽

Steven T. Stoddard ◽

Eric S. Halsey ◽

Amy C. Morrisson ◽

Christopher M. Barker ◽

...

Keyword(s):

Infectious Disease ◽

Disease Burden ◽

Disease Incidence ◽

Sentinel Surveillance ◽

Surveillance Data ◽

Data Consistency ◽

Outbreak Detection ◽

Surveillance Systems ◽

Passive Surveillance ◽

Outcome Monitoring

Monitoring changes in infectious disease incidence is fundamental to outbreak detection and response, intervention outcome monitoring, and identifying environmental correlates of transmission. In the case of dengue, little is known about how consistently surveillance data track disease burden in a population over time. Here we use four years of monthly dengue incidence data from three sources: population-based ('passive') surveillance including suspected cases, 'sentinel' surveillance with 100% laboratory confirmation and complete reporting, and door-to-door ('cohort') surveillance conducted three times per week in Iquitos, Peru, to quantify their relative consistency and timeliness. Data consistency was evaluated using annual and monthly expansion factors (EFs) as cohort incidence divided by incidence in each surveillance system, to assess their reliability for estimating disease burden (annual) and monitoring disease trends (monthly). Annually, passive surveillance data more closely estimated cohort incidence (average annual EF=5) than did data from sentinel surveillance (average annual EF=19). Monthly passive surveillance data generally were more consistent (ratio of sentinel/passive EF standard deviations=2.2) but overestimated incidence in 26% (11/43) of months, most often during the second half of the annual high season as dengue incidence typically wanes from its annual peak. Increases in sentinel surveillance incidence were correlated temporally (correlation coefficient = 0.86) with increases in the cohort, while passive surveillance data were significantly correlated at both zero-lag and a one-month lag (0.63 and 0.44, respectively). Together these results suggest that, rather than relying on a single data stream, a clearer picture of changes in infectious disease incidence might be achieved by combining the timeliness of sentinel surveillance with the representativeness of passive surveillance.

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P2.31: Development of a framework for sentinel surveillance projects in infectious disease epidemiology

Biometrical Journal ◽

10.1002/bimj.200490043 ◽

2004 ◽

Vol 46 (S1) ◽

pp. 137-137

Author(s):

R. Bornemann ◽

A. Ammon ◽

J. Dreesman ◽

T. Eckmanns ◽

A. Hauri ◽

...

Keyword(s):

Infectious Disease ◽

Sentinel Surveillance ◽

Disease Epidemiology ◽

Infectious Disease Epidemiology

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Tissue section lifting for electron microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100081292 ◽

1978 ◽

Vol 36 (2) ◽

pp. 86-87

Author(s):

Adrian F. van Dellen

Keyword(s):

Infectious Disease ◽

Electron Microscopy ◽

Tissue Culture ◽

Organ System ◽

Surrounding Tissue ◽

Paraffin Sections ◽

Surface Areas ◽

Specific Determination ◽

High Degree ◽

Accuracy And Repeatability

The morphologic pathologist may require information on the ultrastructure of a non-specific lesion seen under the light microscope before he can make a specific determination. Such lesions, when caused by infectious disease agents, may be sparsely distributed in any organ system. Tissue culture systems, too, may only have widely dispersed foci suitable for ultrastructural study. In these situations, when only a few, small foci in large tissue areas are useful for electron microscopy, it is advantageous to employ a methodology which rapidly selects a single tissue focus that is expected to yield beneficial ultrastructural data from amongst the surrounding tissue. This is in essence what "LIFTING" accomplishes. We have developed LIFTING to a high degree of accuracy and repeatability utilizing the Microlift (Fig 1), and have successfully applied it to tissue culture monolayers, histologic paraffin sections, and tissue blocks with large surface areas that had been initially fixed for either light or electron microscopy.

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