tetano
Editor, Senior Moderator
Proc Natl Acad Sci U S A
. 2022 Sep 20;119(38):e2209234119.
doi: 10.1073/pnas.2209234119. Epub 2022 Sep 12.
COVID-19 cases and deaths in the United States follow Taylor's law for heavy-tailed distributions with infinite variance
Joel E Cohen[SUP] 1 2 3 4 [/SUP], Richard A Davis[SUP] 3 [/SUP], Gennady Samorodnitsky[SUP] 5 [/SUP]
Affiliations
Abstract
The spatial and temporal patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases and COVID-19 deaths in the United States are poorly understood. We show that variations in the cumulative reported cases and deaths by county, state, and date exemplify Taylor's law of fluctuation scaling. Specifically, on day 1 of each month from April 2020 through June 2021, each state's variance (across its counties) of cases is nearly proportional to its squared mean of cases. COVID-19 deaths behave similarly. The lower 99% of counts of cases and deaths across all counties are approximately lognormally distributed. Unexpectedly, the largest 1% of counts are approximately Pareto distributed, with a tail index that implies a finite mean and an infinite variance. We explain why the counts across the entire distribution conform to Taylor's law with exponent two using models and mathematics. The finding of infinite variance has practical consequences. Local jurisdictions (counties, states, and countries) that are planning for prevention and care of largely unvaccinated populations should anticipate the rare but extremely high counts of cases and deaths that occur in distributions with infinite variance. Jurisdictions should prepare collaborative responses across boundaries, because extremely high local counts of cases and deaths may vary beyond the resources of any local jurisdiction.
Keywords: COVID-19; Taylor’s law; fluctuation scaling; lognormal–Pareto distribution; variance function.
. 2022 Sep 20;119(38):e2209234119.
doi: 10.1073/pnas.2209234119. Epub 2022 Sep 12.
COVID-19 cases and deaths in the United States follow Taylor's law for heavy-tailed distributions with infinite variance
Joel E Cohen[SUP] 1 2 3 4 [/SUP], Richard A Davis[SUP] 3 [/SUP], Gennady Samorodnitsky[SUP] 5 [/SUP]
Affiliations
- PMID: 36095214
- DOI: 10.1073/pnas.2209234119
Abstract
The spatial and temporal patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases and COVID-19 deaths in the United States are poorly understood. We show that variations in the cumulative reported cases and deaths by county, state, and date exemplify Taylor's law of fluctuation scaling. Specifically, on day 1 of each month from April 2020 through June 2021, each state's variance (across its counties) of cases is nearly proportional to its squared mean of cases. COVID-19 deaths behave similarly. The lower 99% of counts of cases and deaths across all counties are approximately lognormally distributed. Unexpectedly, the largest 1% of counts are approximately Pareto distributed, with a tail index that implies a finite mean and an infinite variance. We explain why the counts across the entire distribution conform to Taylor's law with exponent two using models and mathematics. The finding of infinite variance has practical consequences. Local jurisdictions (counties, states, and countries) that are planning for prevention and care of largely unvaccinated populations should anticipate the rare but extremely high counts of cases and deaths that occur in distributions with infinite variance. Jurisdictions should prepare collaborative responses across boundaries, because extremely high local counts of cases and deaths may vary beyond the resources of any local jurisdiction.
Keywords: COVID-19; Taylor’s law; fluctuation scaling; lognormal–Pareto distribution; variance function.