Background COVID-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).
Methods We 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.
Findings Higher rates of epidemiological change detection are more associated with open testing policies than with higher testing rates. The non-pharmaceutical intervention most correlated with epidemiological change is workplace closing. 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.
Interpretation Relying 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.
Funding This publication is based on models and data analysis performed by the Institute for Disease Modeling at the Bill & Melinda Gates Foundation.
Research in Context
EVIDENCE BEFORE THIS STUDY
Evidence before this study We searched for articles on the current practices, challenges, and proposals for COVID-19 surveillance in LMICs. We used Google Scholar with search terms including “COVID surveillance.” Existing studies were found to be qualitative, anecdotal, or highly location-specific.
ADDED VALUE OF THIS STUDY
Added value of this study We developed a quantitative method that makes use of limited information available from LMICs. Our approach improves interpretation of epidemiological data and enables evaluation of COVID-19 surveillance dynamics across countries.
IMPLICATIONS OF ALL THE AVAILABLE EVIDENCE
Implications of all the available evidence Our results demonstrate the importance of open testing for strong surveillance systems, bolstering existing anecdotal evidence. We show strong alignment across LMICs between workplace restrictions and epidemiological changes. We demonstrate the importance of considering sub-national heterogeneity of epidemiological dynamics and surveillance.