EFFECT OF THE DELAY IN THE REPORTS OF COVID-19 CASES ON NEAR REAL-TIME CLUSTERS DETECTION

Abstract

The COVID-19 pandemic has strongly impacted the vast majority of countries in the world. As of today (April 12th, 2023), more than 762 million confirmed cases and nearly 6.9 million deaths are considered widely underestimated. During a pandemic, detecting clusters of patients is crucial to allocate resources and aiding decision-making better as emergent outbreaks continue to grow. However, delays in reporting suspected or confirmed cases can affect the detection of clusters in near real-time. This study aimed to assess whether the delays in reporting COVID-19 in Mexico presented specific Spatiotemporal patterns and whether they significantly affected the detection of clusters. To do this, we used the daily records of the Mexican Ministry of Health for three dates at the beginning and during the increase in cases of the fourth wave (January 2022). We compared the clusters obtained using the data available on the same date and during the following days, including delayed data. We carried out cluster detection using the flexible spatial scan statistic (FlexScan) on the R platform. The results indicate that the spatial distribution of delays was heterogeneous and that delays affect cluster detection.