LAUSR

BackgroundCOVID-19 case counts are 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 consistently interpret COVID-19 case counts in the context of other surveillance data is needed, especially for data-limited settings in low- and middle-income countries (LMICs). MethodsWe leverage statistical analyses to detect changes in COVID-19 surveillance data. We apply the pruned exact linear time change detection method for COVID-19 case counts, number of tests, and test positivity rate over time. With this information, we categorize change points as likely driven by epidemiological dynamics or non-epidemiological influences such as noise. FindingsHigher rates of epidemiological change detection are more associated with open testing policies than with higher testing rates. We quantify 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. Sub-national data reveal heterogeneity in epidemiological dynamics and surveillance. InterpretationRelying solely on case counts to interpret pandemic dynamics has important limitations. Normalizing counts by testing rate mitigates some of these limitations, and open testing policy is key to efficient surveillance. Our findings can be leveraged by public health officials to strengthen COVID-19 surveillance and support programmatic decision-making. FundingThis publication is based on models and data analysis performed by the Institute for Disease Modeling at the Bill & Melinda Gates Foundation.