Here’s a method to try to estimate the number of cases in a region which I haven’t seen calculations of:
1. Identify the places which have the best testing regimes
2. Try to estimate what fraction of cases are identified in those places, potentially along with other variables like how long from infection until the case is identified
3. Use those numbers to extrapolate to other places, based on other similarities between those places besides # of confirmed cases (e.g., number of deaths, or rate of infection in travelers coming from that place, or hospital utilization rate)
I have made some initially attempts to do this, which I’ll try to post later today. I’m wondering if anyone has thoughts or sources on any of these 3 points (e.g., which places have the best testing regimes?), or on the method as a whole.
This paper looks at cases which were confirmed in Shenzhen (Guangdong, China) Jan 14 - Feb 12, which is while coronavirus was being brought under control there (by the end of the study the cases had fallen to less than 1⁄3 of their peak). I suspect that they qualify for point 1, a place with an unusually good testing regime.
The paper reports that “Cases detected through symptom-based surveillance were confirmed on average 5.5 days (95% CI 5.0, 5.9) after symptom onset (Figure 3, Table S2); compared to 3.2 days (95% CI 2.6,3.7) in those detected by contact-based surveillance”, and also that the median incubation period was 4.8 days from infection to symptom onset (in the smaller sample where both of those dates were known).
Adding 5.5+4.8, that implies that an average of 10.3 days passed between when a person became infected and when they tested positive for cases detected based on symptoms, and 8.0 days for those detected by contact tracing. Since the paper reports that 77% of cases were detected through symptom-based surveillance, that gives an overall average of 9.8 days. (And this is only for the cases that were detected; it’s not adjusting at all for people who were infected by never got a positive test.)
That means that in places where testing is as good as it was in Shenzhen, then the number of positive tests is telling us about the number of infections 9.8 days ago. If the number of cases in that region is doubling every 4 days, then that’s 2.4 doublings, so the number of confirmed cases would only be 18% of the actual number of cases due to the delay in testing (again, without factoring in people who never got tested). (With a 3 day doubling period it would be 10%, with a 5 day doubling period 26%.)
So in places that don’t have a good testing regime it would be significantly less than that.
Here’s a method to try to estimate the number of cases in a region which I haven’t seen calculations of:
1. Identify the places which have the best testing regimes
2. Try to estimate what fraction of cases are identified in those places, potentially along with other variables like how long from infection until the case is identified
3. Use those numbers to extrapolate to other places, based on other similarities between those places besides # of confirmed cases (e.g., number of deaths, or rate of infection in travelers coming from that place, or hospital utilization rate)
I have made some initially attempts to do this, which I’ll try to post later today. I’m wondering if anyone has thoughts or sources on any of these 3 points (e.g., which places have the best testing regimes?), or on the method as a whole.
This paper looks at cases which were confirmed in Shenzhen (Guangdong, China) Jan 14 - Feb 12, which is while coronavirus was being brought under control there (by the end of the study the cases had fallen to less than 1⁄3 of their peak). I suspect that they qualify for point 1, a place with an unusually good testing regime.
The paper reports that “Cases detected through symptom-based surveillance were confirmed on average 5.5 days (95% CI 5.0, 5.9) after symptom onset (Figure 3, Table S2); compared to 3.2 days (95% CI 2.6,3.7) in those detected by contact-based surveillance”, and also that the median incubation period was 4.8 days from infection to symptom onset (in the smaller sample where both of those dates were known).
Adding 5.5+4.8, that implies that an average of 10.3 days passed between when a person became infected and when they tested positive for cases detected based on symptoms, and 8.0 days for those detected by contact tracing. Since the paper reports that 77% of cases were detected through symptom-based surveillance, that gives an overall average of 9.8 days. (And this is only for the cases that were detected; it’s not adjusting at all for people who were infected by never got a positive test.)
That means that in places where testing is as good as it was in Shenzhen, then the number of positive tests is telling us about the number of infections 9.8 days ago. If the number of cases in that region is doubling every 4 days, then that’s 2.4 doublings, so the number of confirmed cases would only be 18% of the actual number of cases due to the delay in testing (again, without factoring in people who never got tested). (With a 3 day doubling period it would be 10%, with a 5 day doubling period 26%.)
So in places that don’t have a good testing regime it would be significantly less than that.