WHO South-East Asia Journal of Public Health
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Year : 2020  |  Volume : 9  |  Issue : 2  |  Page : 141-146

Monitoring COVID-19 where capacity for testing is limited: use of a three-step analysis based on test positivity ratio

World Health Organization Health Emergencies Programme, World Health Organization Regional Office for South-East Asia, New Delhi, India

Correspondence Address:
Dr Sirenda Vong
World Health Organization Health Emergencies Programme, World Health Organization Regional Office for South-East Asia, New Delhi
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DOI: 10.4103/2224-3151.294308

PMID: 32978347

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In an effort to monitor coronavirus disease 2019 (COVID-19), many countries have been calculating the ratio of cases confirmed to tests performed (test positivity ratio – TPR). While inferior to sentinel surveillance, TPR has the benefit of being easily calculated using readily available data; however, interpreting TPR and its trends can be complex because both the numerator and the denominator are constantly changing. We describe a three-step process where the ratio of relative increase in cases to relative increase in tests is accounted for in an adjusted TPR. This adjusted value more appropriately reflects the case number and factors out the effect of changes in the number of tests done. Unadjusted and adjusted TPRs are then assessed step-wise with reference to the epidemic curve and the cumulative numbers of cases and tests. Use of this three-step analysis and its potential use in guiding public health interventions are demonstrated for selected countries and subnational areas of the World Health Organization South-East Asia Region, together with the Republic of Korea as a reference. To date, application of the three-step analysis to data from countries of the region has signalled potential inadequacies of testing strategies. Further work is needed on approaches to support countries where testing capacity is likely to remain constrained. One example would be enumeration of the average number of tests needed to detect one COVID-19 case, which could be stratified by factors such as location and population. Such data would allow evidence-informed strategies that best balance the highest detection rate with the prevailing testing capacity.

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