Why COVID-19 Felt Like a Different Disease Around the World
The same virus, a thousand different faces.
When the COVID-19 pandemic began, a strange pattern emerged. News reports from different corners of the globe seemed to describe different illnesses. While one country reported waves of patients with high fever and cough, another documented unusual losses of smell and taste. This wasn't just media hype; it was one of the pandemic's earliest scientific puzzles 1 . Why would the same virus manifest so differently depending on where you lived, when you got sick, or what other health conditions you had? The answer, as researchers would soon discover, lies in a complex interplay of geography, time, and individual patient characteristics. This is the story of how science began to unravel these patterns during the pandemic's critical first chapter.
Early in the pandemic, before vaccines and widespread variants, researchers conducted a remarkable meta-analysis of over 40,000 patients across multiple countries 1 . By systematically surveying 48 research articles, they identified 18 commonly occurring symptoms and mapped them using the Human Phenotype Ontology—a standardized vocabulary for human diseases. What they found was striking: the most common symptoms weren't the same everywhere.
| Region | Most Common Symptom | Other Frequent Symptoms |
|---|---|---|
| China | Fever | Cough, muscle pain, fatigue |
| United States | Cough | Fever, digestive issues, fatigue |
| Italy | Loss of smell/taste | Fever, cough, nervous system symptoms |
| Europe & US (vs. China) | More frequent nervous system and abdominal symptoms (e.g., diarrhea) | |
Source: Meta-analysis of 48 research articles covering 40,000+ patients 1
Symptom patterns differed significantly between continents, suggesting environmental or genetic factors at play.
Loss of smell and taste emerged as distinctive symptoms in European populations during the early pandemic.
While geography told one part of the story, individual health conditions told another. Comorbidities—the simultaneous presence of other diseases in a patient—proved to be powerful determinants of COVID-19 severity 3 .
Research consistently showed that patients with pre-existing conditions faced dramatically different outcomes. A Swiss study of 1,124 hospitalized patients found that those without any comorbidities had the lowest rates of critical condition (5.3%) and complications (10.2%) 3 . However, the presence of certain conditions significantly increased risks.
Patients with no comorbidities had the lowest rates of critical condition (5.3%) and complications (10.2%) 3 .
| Comorbidity | Increased Risk of Critical Condition |
|---|---|
| Chronic Obstructive Pulmonary Disease (COPD) | 2.72x |
| Obesity | 2.01x |
| Arrhythmia | 1.87x |
| Diabetes Mellitus | 1.67x |
| Arterial Hypertension | 1.65x |
Source: Data from a Swiss cohort study of hospitalized COVID-19 patients 3
These conditions didn't just increase the risk of severe disease; they also shaped the complications patients experienced. For instance, acute kidney failure emerged as a frequent complication, affecting 17.1% of the study population, while patients with pre-existing arrhythmia showed the highest overall complication rate at 42% 3 .
The U.S. Centers for Disease Control and Prevention (CDC) has since compiled an extensive list of conditions that increase risk. Age remains the strongest risk factor, with the risk of death being 140 times higher in ages 75-84 compared to those 18-29 years 6 .
So how did scientists manage to compare and standardize symptoms across thousands of patients from different countries and medical systems? The answer lies in an innovative bioinformatics approach using ontology—a standardized, computer-interpretable method for knowledge modeling 1 .
In the featured study, researchers manually identified COVID-19-related symptoms and comorbidities from dozens of articles, then mapped them to terms in the Human Phenotype Ontology (HPO) 1 . This process ensured that synonyms used in different studies (like "fever" and "pyrexia") were standardized to the same HPO code, allowing for accurate comparison and integration of data across the global research landscape 1 . The knowledge learned was then modeled and represented in the Coronavirus Infectious Disease Ontology (CIDO), which supported further semantic queries and analysis 1 .
Standardizes vocabulary for symptoms and clinical features, enabling consistent data integration across international studies 1 .
Models and represents knowledge about coronavirus diseases, linking symptoms, etiology, transmission, and treatments 1 .
Searches and maps phenotype and symptom terms to their standardized HPO identifiers 1 .
Statistically analyzes the association between specific comorbidities and critical COVID-19 outcomes, while adjusting for factors like age and gender 3 .
The symptom patterns observed in the early pandemic were not necessarily permanent. Researchers proposed a "spiral model" hypothesis to address how specific symptoms changed during different stages of the pandemic 1 . As the virus evolved, so did its presentation.
Original strain with prominent fever, cough, and respiratory symptoms. Geographic variations in neurological and digestive symptoms noted.
Increased transmissibility with similar symptom profile to original strain but potentially more severe outcomes.
More severe disease with increased risk of hospitalization. Greater reports of gastrointestinal symptoms.
Higher transmissibility but potentially less severe disease. More upper respiratory symptoms, sore throat, and fatigue reported.
Different variants of concern—such as Alpha, Delta, and Omicron—each carried distinct mutations that potentially affected both infectivity and disease severity 5 .
Later research would also reveal that immune responses to SARS-CoV-2 differed across populations. A 2025 study comparing antibody responses in mild COVID-19 cases from Uganda and the United Kingdom found distinct immune response patterns, suggesting that geographic and demographic factors shaped the quality and durability of immunity 7 .
These findings highlight why tailored vaccination strategies and clinical approaches remain essential for optimizing COVID-19 management across diverse global populations.
The story of differential COVID-19 symptoms reveals a fundamental truth about infectious diseases: the path of an illness is never determined by the pathogen alone. It emerges from a complex negotiation between the virus, our bodies, our environments, and our societies. The same SARS-CoV-2 virus could present as a fever in Wuhan, a cough in New York, or a loss of smell in Milan based on a tapestry of factors still being unraveled.
Regional variations in symptom presentation
Evolving symptoms with new variants
Individual health conditions shaping outcomes
What began as anecdotal observations between countries solidified into evidence-based understanding through systematic science. This knowledge does more than satisfy curiosity—it informs clinical practice, guides public health responses, and ultimately helps healthcare systems prepare for the diverse ways a global pathogen can manifest in their specific population. As we continue to live with COVID-19, this understanding of its many faces remains one of our most powerful tools in adapting to an ever-changing viral landscape.