ArticleMethods (San Diego, Calif.)2021
Test positivity - Evaluation of a new metric to assess epidemic dispersal mediated by non-symptomatic cases.
Article in Methods (San Diego, Calif.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Combinatorial (bio-geo-temporal) and non-combinatorial analysis of the COVID-19 dissemination that affected Georgia (the country) in 2021.Frontiers in public health · 2026Article
- A Low-Cost Early Warning Method for Infectious Diseases with Asymptomatic Carriers.Healthcare (Basel, Switzerland) · 2024Article
- Heterogeneity in COVID-19 infection among older persons in South Africa: Evidence from national surveillance data.Frontiers in public health · 2023Article
- Modelling the persistence of Covid-19 positivity rate in Italy.Socio-economic planning sciences · 2022Article
- Toward interdisciplinary methods appropriate for optimal epidemic control.Methods (San Diego, Calif.) · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epidemic control may be hampered when the percentage of asymptomatic cases is high. Seeking remedies for this problem, test positivity was explored between the first 60 to 90 epidemic days in six countries that reported their first COVID-19 case between February and March 2020: Argentina, Bolivia, Chile, Cuba, Mexico, and Uruguay. Test positivity (TP) is the percentage of test-positive individuals reported on a given day out of all individuals tested the same day. To generate both country-specific and multi-country information, this study was implemented in two stages. First, the epidemiologic data of the country infected last (Uruguay) were analyzed. If at least one TP-related analysis yielded a statistically significant relationship, later assessments would investigate the six countries. The Uruguayan data indicated (i) a positive correlation between daily TP and daily new cases (r = 0.75); (ii) a negative correlation between TP and the number of tests conducted per million inhabitants (TPMI, r = -0.66); and (iii) three temporal stages, which differed from one another in both TP and TPMI medians (p < 0.01) and, together, revealed a negative relationship between TPMI and TP. No significant relationship was found between TP and the number of active or recovered patients. The six countries showed a positive correlation between TP and the number of deaths/million inhabitants (DMI, r = 0.65, p < 0.01). With one exception -a country where isolation was not pursued-, all countries showed a negative correlation between TP and TPMI (r = 0.74). The temporal analysis of country-specific policies revealed four patterns, characterized by: (1) low TPMI and high DMI, (2) high TPMI and low DMI; (3) an intermediate pattern, and (4) high TPMI and high DMI. Findings support the hypothesis that test positivity may guide epidemiologic policy-making, provided that policy-related factors are considered and high-resolution geographical data are utilized.
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