Space-time cluster detection techniques for infectious diseases: A systematic review.
Spat Spatiotemporal Epidemiol
; 44: 100563, 2023 02.
Статья
в английский
| MEDLINE | ID: covidwho-2232258
ABSTRACT
BACKGROUND:
Public health organizations have increasingly harnessed geospatial technologies for disease surveillance, health services allocation, and targeting place-based health promotion initiatives.METHODS:
We conducted a systematic review around the theme of space-time clustering detection techniques for infectious diseases using PubMed, Web of Science, and Scopus. Two reviewers independently determined inclusion and exclusion.RESULTS:
Of 2,887 articles identified, 354 studies met inclusion criteria, the majority of which were application papers. Studies of airborne diseases were dominant, followed by vector-borne diseases. Most research used aggregated data instead of point data, and a significant proportion of articles used a repetition of a spatial clustering method, instead of using a "true" space-time detection approach, potentially leading to the detection of false positives. Noticeably, most articles did not make their data available, limiting replicability.CONCLUSION:
This review underlines recent trends in the application of space-time clustering methods to the field of infectious disease, with a rapid increase during the COVID-19 pandemic.ключевые слова
Полный текст:
Имеется в наличии
Коллекция:
Международные базы данных
база данных:
MEDLINE
Основная тема:
Communicable Diseases
/
COVID-19
Тип исследования:
Диагностическое исследование
/
Наблюдательное исследование
/
Отзывы
/
Систематический обзор/метаанализ
Пределы темы:
Люди
Язык:
английский
Журнал:
Spat Spatiotemporal Epidemiol
Год:
2023
Тип:
Статья
Аффилированная страна:
J.sste.2022.100563
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