ArticleSocio-economic planning sciences2022
Data-driven test strategy for COVID-19 using machine learning: A study in Lahore, Pakistan.
Article in Socio-economic planning sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Identifying and prioritizing resilient health system units to tackle the COVID-19 pandemic.Socio-economic planning sciences · 2023Article
- Impact of epidemic outbreaks (COVID-19) on global supply chains: A case of trade between Turkey and China.Socio-economic planning sciences · 2023Article
- Editorial: Impact of coronavirus disease 2019 (COVID-19) pandemic on nosocomial infection.Frontiers in medicine · 2023Article
- Editorial for the special issue on "Transforming public health through artificial intelligence, machine learning and internet of things".Socio-economic planning sciences · 2022Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsWe aimed at giving a preliminary analysis of the weakness of a current test strategy, and proposing a data-driven strategy that was self-adaptive to the dynamic change of pandemic. The effect of driven-data selection over time and space was also within the deep concern.
methodsA mathematical definition of the test strategy were given. With the real COVID-19 test data from March to July collected in Lahore, a significance analysis of the possible features was conducted. A machine learning method based on logistic regression and priority ranking were proposed for the data-driven test strategy. With performance assessed by the area under the receiver operating characteristic curve (AUC), time series analysis and spatial cross-test were conducted.
resultsThe transition of risk factors accounted for the failure of the current test strategy. The proposed data-driven strategy could enhance the positive detection rate from 2.54% to 28.18%, and the recall rate from 8.05% to 89.35% under strictly limited test capacity. Much more optimal utilization of test resources could be realized where 89.35% of total positive cases could be detected with merely 48.17% of the original test amount. The strategy showed self-adaptability with the development of pandemic, while the strategy driven by local data was proved to be optimal.
conclusionsWe recommended a generalization of such a data-driven test strategy for a better response to the global developing pandemic. Besides, the construction of the COVID-19 data system should be more refined on space for local applications.
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