Reassessing the First Year of COVID-19: Estimating Infections and Tracking Pandemic Trends with Probabilistic Bias Analysis

Background: The novel SARS-CoV-2 virus, first identified in China in December 2019, rapidly spread worldwide, resulting in 114 million confirmed cases and 2.5 million reported deaths by February 28, 2021. Yet considerable uncertainty persists regarding the true scale of infections, as limited testing capacity and inconsistent surveillance substantially hindered accurate case detection. Robust estimates of infection burden are essential for understanding the pandemic's full impact and guiding effective public health responses. Methods: This study aimed to quantify the health burden of COVID-19 in India, Mexico, the United Kingdom, and the United States. A probabilistic bias analysis model is used to estimate the true number of SARS-CoV-2 infections, accounting for factors such as repeated testing, infection waves, and viral mutations to provide a more accurate assessment of the pandemic's true scale in each country. Results: By February 28, 2021, the estimated total COVID-19 infections across India, Mexico, the United Kingdom, and the United States reached 286.7 million - more than six times the reported cases. India had the highest estimated infections (129.3 million), followed by the United States (98.6 million), Mexico (44.9 million), and the United Kingdom (14 million). Detection rates varied significantly, with Mexico underreporting infections by a factor of 22, while the United Kingdom and the United States had the highest detection rates. Testing capacity played a key role, with high-income countries conducting over four times more tests per 1,000 people than lower-income nations. Conclusion: This study demonstrating substantial underreporting of SARS-CoV-2 during the pre-vaccination period. These retrospective estimates provide a more accurate historical baseline for interpreting pandemic dynamics and remain valuable for assessing long-term health impacts and improving preparedness for future epidemics.