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Frequently Asked Questions

What does Atypical Illness mean?

Atypical Illness refers to an unusual incidence of elevated flu-like illness levels, similar to what CDC refers to as influenza-like illness (or ILI)1. Kinsa quantifies Atypical Illness by comparing real-time illness incidence2 and our illness forecast generated before widespread presentation of COVID-19. This allows us to estimate the proportion of the population in individual communities who are displaying unusually high levels of flu-like symptoms.

Key Takeaway: Atypical illness means levels of illness in a region are higher than what is expected.

 

Is this a map of COVID-19 infections?

We are not directly measuring COVID-19 infections. This is a map of unusually elevated prevalence of flu-like symptoms. There are a number of possible causes, all of which may be contributing to elevations in reported illness levels:

  1. Increased healthcare seeking behavior in light of the COVID-19 pandemic
  2. Seasonally abnormal cold/flu viruses circulating in particular communities. Per CDC, as of March 16, “flu activity as reported by clinical laboratories remains high.”3
  3. COVID-19 Infections

That said, since March 1 we’ve seen a very strong correlation between cumulative atypical illness incidence and positive COVID-19 tests (at the state level) in terms of geographies affected and timing within affected geographies, which suggests that our data provides a useful indication of where COVID-19 may likely be occurring.

We recommend interpreting our data as complementary to other available data sources including explicit COVID-19 testing4, ER Admissions data5, etc. when determining where to allocate scarce resources.

Key Takeaway: This is not a map of COVID-19 infections. This map shows regions where Kinsa has detected unusually high levels of illness.

 

Why are many neighboring counties so similar?

Many cities span multiple counties, and residents of the two counties regularly mix and share illness exposures. We also, particularly in sparse rural counties with limited device penetration, include information from neighboring counties in our estimate of illness levels. It is most accurate to think of illness levels as representing a geographic area centered around a particular county rather than the illness levels of a single county exclusively.

Key Takeaway: Similar illness levels across counties are expected due to patterns of human travel and interaction, which are not confined to counties. 

 

Why are Atypical illness levels not available for some counties?

While both the Observed Illness and Atypical Illness map modes are based on information aggregated from Kinsa thermometers, we take a more conservative approach to our atypical illness levels. In some parts of the country where there aren’t as many of our thermometers, we aren’t as confident in our typical flu season forecast. As our distribution of thermometers improves, we expect to be able to reach more of these areas, which are currently marked in gray in the atypical map mode.

Key Takeaway: In areas with fewer thermometers, there isn’t enough data to determine if the illness levels are atypical.

 

Are you seeing increased activity and does this affect your illness signal?

As of March 2020, we are seeing 2-3x the number of users taking temperatures than we've tracked in previous flu seasons. This does not impact our illness signal, as our modeling already accounts for rapid changes in our user base. We also benchmark our signal against the Centers for Disease Control (CDC) at the end of every flu season when the CDC has finalized their illness reporting. We regularly see an in-season correlation of R2 of >= 0.95.

Key Takeaway: A significant and rapid change in temperature readings does not affect our illness signal.

 

How often is the map updated?

Illness incidence levels are updated nightly.

 

Where can I learn more about your scientific methods?

See Miller et al (2018 Clin Infect Dis.)6 for additional detail about how Kinsa measures illness prevalence.

Kinsa’s forecast extends the findings outlined in Dalziel et. al. (2018 Science)7 by applying similar modeling methods to Kinsa’s county-level illness incidence data.

For additional information, see our technical appendix here.

 

How can I get in touch with you?

Do you work in public health, media, or at a business that wants to explore a partnership with Kinsa? You can contact us here.

 

Information about our data privacy. 

Kinsa takes individual privacy extremely seriously and never shares individual health information. See Kinsa’s privacy principle for more information.

 


1https://www.cdc.gov/flu/weekly/overview.htm
2Miller AC, Singh I, Koehler E, Polgreen PM. A Smartphone-Driven Thermometer Application for Real-time Population- and Individual-Level Influenza Surveillance. Clin Infect Dis. 2018;67:388–97.
3https://www.cdc.gov/flu/weekly/#ILINet
4https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html
5https://a816-health.nyc.gov/hdi/epiquery/visualizations?PageType=ps&PopulationSource=Syndromic
6https://www.ncbi.nlm.nih.gov/pubmed/29432526
7https://www.ncbi.nlm.nih.gov/pubmed/30287659