Abstract
Rationale, aims and objectives In the US, the reluctance of the federal
government to impose a national stay-at-home policy in wake of COVID19
pandemic has left the decision of how to achieve social distancing to
individual state governors. We hypothesized that in the absence of
formal guidelines, the decision to close a state reflects the classic
Weber-Fechner law of psychophysics- the amount by which a stimulus (such
as number of cases or deaths) must increase in order to be noticed as a
fraction of the intensity of that stimulus. Methods On April 12, 2020 we
downloaded data from the New York Times database from all 50 states and
the District of Columbia; by that time all but 7 states had issued the
stay-at-home orders. We fitted the Weber-Fechner logarithmic function by
regressing the log2 of cases and deaths respectively against the daily
counts. We also conducted Cox regression analysis to determine if the
probability of issuing the stay-at-home order increases proportionally
as the number of cases or deaths increases. Results We found that the
decision to issue the state-at-home order reflects the Weber-Fechner
law. Both the number of infections (p=<0.0001; R2=0.79) and
deaths (p<0.0001; R2=0.63) were significantly associated with
the decision to issue the stay-at-home orders. The results indicate that
for each doubling of infections or deaths, an additional 4 to 6 states
will issue stay-at-home orders. Cox regression showed that when the
number of deaths reached 256 and the number of infected people were over
16,000 the probability of issuing “stay-at-home” order was close to
100%. We found no difference in decision-making according to the
political affiliation; the results remain unchanged on July 16,2020.
Conclusions when there are not clearly articulated rules to follow,
decision-makers resort to simple heuristics, in this case one consistent
with the Weber-Fechner law.