The article evaluates the feasibility of controlling COVID-19 outbreaks through isolation of cases and contact tracing using a mathematical model. The study assesses various scenarios, including different initial case numbers, basic reproduction numbers (R₀), delays from symptom onset to isolation, contact tracing probabilities, and the proportion of transmission before symptom onset. Key findings include: 1. **Effectiveness of Isolation and Contact Tracing**: Isolation and contact tracing are effective in controlling outbreaks, especially when R₀ is low (1.5). However, the probability of control decreases with higher R₀ values (2.5, 3.5) and more transmission before symptom onset. 2. **Impact of Initial Case Numbers**: Smaller initial outbreaks (5, 20, or 40 cases) are more likely to be controlled with lower contact tracing probabilities. For larger outbreaks (40 initial cases), higher contact tracing rates (over 70% for R₀ of 2.5, over 90% for R₀ of 3.5) are required. 3. **Delay Between Symptom Onset and Isolation**: The delay between symptom onset and isolation has a significant impact on outbreak control. Shorter delays are more effective, especially for higher R₀ values. 4. **Subclinical Infections**: Subclinical infections significantly reduce the probability of controlling outbreaks within 3 months, even with high contact tracing rates. The study concludes that while isolation and contact tracing can control outbreaks in many scenarios, the effectiveness depends on various factors, including the pathogen's characteristics and the response measures. The model can be updated to reflect new data and specific definitions of outbreak control, aiding in the assessment of local response strategies.The article evaluates the feasibility of controlling COVID-19 outbreaks through isolation of cases and contact tracing using a mathematical model. The study assesses various scenarios, including different initial case numbers, basic reproduction numbers (R₀), delays from symptom onset to isolation, contact tracing probabilities, and the proportion of transmission before symptom onset. Key findings include: 1. **Effectiveness of Isolation and Contact Tracing**: Isolation and contact tracing are effective in controlling outbreaks, especially when R₀ is low (1.5). However, the probability of control decreases with higher R₀ values (2.5, 3.5) and more transmission before symptom onset. 2. **Impact of Initial Case Numbers**: Smaller initial outbreaks (5, 20, or 40 cases) are more likely to be controlled with lower contact tracing probabilities. For larger outbreaks (40 initial cases), higher contact tracing rates (over 70% for R₀ of 2.5, over 90% for R₀ of 3.5) are required. 3. **Delay Between Symptom Onset and Isolation**: The delay between symptom onset and isolation has a significant impact on outbreak control. Shorter delays are more effective, especially for higher R₀ values. 4. **Subclinical Infections**: Subclinical infections significantly reduce the probability of controlling outbreaks within 3 months, even with high contact tracing rates. The study concludes that while isolation and contact tracing can control outbreaks in many scenarios, the effectiveness depends on various factors, including the pathogen's characteristics and the response measures. The model can be updated to reflect new data and specific definitions of outbreak control, aiding in the assessment of local response strategies.