Abstract
The case count for coronavirus disease 2019 (COVID-19) is the predominant measure used to track epidemiological dynamics and inform policy decision-making. Case counts, however, are influenced by testing rates and strategies, which have varied over time and space. A method to interpret COVID-19 case counts consistently in the context of other surveillance data is needed, especially for data-limited settings in low- and middle-income countries (LMICs). Statistical analyses were used to detect changes in COVID-19 surveillance data. The pruned exact linear time change detection method was applied for COVID-19 case counts, number of tests, and test positivity rate over time. With this information, change points were categorized as likely driven by epidemiological dynamics or non-epidemiological influences, such as noise. Higher rates of epidemiological change detection are more associated with open testing policies than with higher testing rates. This study quantified alignment of non-pharmaceutical interventions with epidemiological changes. LMICs have the testing capacity to measure prevalence with precision if they use randomized testing. Rwanda stands out as a country with an efficient COVID-19 surveillance system. Subnational data reveal heterogeneity in epidemiological dynamics and surveillance. Relying solely on case counts to interpret pandemic dynamics has important limitations. Normalizing counts by testing rate mitigates some of these limitations, and an open testing policy is key to efficient surveillance. The study findings can be leveraged by public health officials to strengthen COVID-19 surveillance and support programmatic decision-making.
Highlights
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), was first identified in Wuhan, China in December 2019
It is critical to understand the limitations of available data and to identify metrics that are robust to these challenges, for low- and middle-income countries (LMICs)
When the analysis of change types are applied to evaluate the efficiency of national surveillance systems, Rwanda stands out as a country with a strong surveillance system, which is consistent with qualitative evaluation (WHO Regional Office for Africa, 2020)
Summary
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), was first identified in Wuhan, China in December 2019. There has not been a robust evaluation of case counts as a metric for epidemiological dynamics, nor the varied surveillance approaches used to track disease trajectories. It is critical to understand the limitations of available data and to identify metrics that are robust to these challenges, for low- and middle-income countries (LMICs). The case count for coronavirus disease 2019 (COVID-19) is the predominant measure used to track epidemiological dynamics and inform policy decision-making. The pruned exact linear time change detection method was applied for COVID-19 case counts, number of tests, and test positivity rate over time. With this information, change points were categorized as likely driven by epidemiological dynamics or non-epidemiological influences, such as noise. The study findings can be leveraged by public health officials to strengthen COVID-19 surveillance and support programmatic decision-making
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