Test positivity - Evaluation of a new metric to assess epidemic dispersal mediated by non-symptomatic cases

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.

Keywords: COVID-19; Geo-epidemiology; Infection; Resource-limited countries; Test positivity.

Fig. 1

Epidemic curve and early metrics for COVID-19 infections, Uruguay, March to May, 2020. (A). Daily test positivity against epidemic days; (B). Daily number of new cases versus daily test positivity; (C). Cumulative cases and deaths over the period, March to May 2020; (D). Cases and tests per million inhabitants; (E). Fatalities and tests per million inhabitants; (F). Deaths per million inhabitants and tests per million inhabitants. There was a positive correlation between the daily number of new cases versus daily test positivity (r= 0.75 [CI= 0.61, 0.85]), Fig. 1B.

Fig. 2

Inferential statistics on COVID-19, Uruguay, March to May, 2020. (A). Daily test positivity against epidemic days and tests per million inhabitants; (B). Daily test positivity versus cumulative tests per million inhabitants reveal an L-shaped curve; (C). Temporal evaluation of daily test positivity reveals three distinct stage over the period, March to May 2020; (D). Cumulative cases and cumulative active cases plotted against a temporal timeline. There was a negative correlation between the daily test positivity and tests/million inhabitants (r= - 0.66 [CI= - 0.79, - 0.48]), Fig. 2A.

Fig. 3

External validity and comparison of observed patterns for COVID-19 in six countries. (A). Deaths per million inhabitants against test positivity; (B). Tests per million inhabitants against test positivity; (C). Tests per million inhabitants against test positivity, excluding Chile; (D). Deaths per million inhabitants against tests per million inhabitants, excluding Chile; (E). Three-dimensional plot of deaths per million inhabitants against temporal scale and test positivity; (F). Three-dimensional plot of tests per million inhabitants against temporal scale and test positivity. There was a positive correlation for five countries except Chile, between the test positivity and deaths per million inhabitants (r= 0.65 [CI= 0.44, 0.80]), Fig. 3A. However, there was a negative correlation between test positivity and tests per million inhabitants after excluding Chile (r= - 0.74 [CI= - 0.86, - 0.55]). This demonstrates that TP may be a reproducible metric, provided that the context and other variables are also considered.

Fig. 4

Country-level comparison of cases, mortality and test positivity per million inhabitants for COVID-19. Plot of deaths per million inhabitants against tests per million inhabitants in (A). April 24, 2020; (B). April 27, 2020; (C). April 28, 2020; (D). May 4, 2020; (E). May 6, 2020; (F). May 9, 2020. Fig. 4 (A – F) show a synopsis of a geo-temporal analysis; it includes (low-resolution or aggregate) geographical data and a temporal description of epidemics that started less than 6 weeks apart. For visual comparisons, each plot is divided into 4 quadrants ('low & low', 'high & low', 'low & high', and 'high & high') It is shown that high TP is associated with a faster growth of deaths/mill inh. The opposite pattern (low test positivity & low deaths/mil inh) shows one exception.

Fig. 5

Selected country classification by cases, mortality and test positivity for COVID-19. Plot for (A). Uruguay; (B). Chile; (C). Cuba; (D). Bolivia; (E). Argentina; (F). Mexico. Fig. 5 (A – F) show the same data depicted in fig 4, now at individual countries. The left column (A, C, E) displays countries with one digit of test positivity percentages. The right column (B, D, F) shows two-digit test positivity percentages.

Fig. 6

Temporal-related metrics in the analysis of COVID-19 cases in three countries. (A) Argentina; (B) Cuba; (C). Uruguay.

Fig. 7

Plots of temporal pattern of the ratio between recovered and active cases in selected countries. (A) Argentina; (B) Cuba; (C). Uruguay.