Compound Risks of Hurricane Evacuation Amid the COVID‐19 Pandemic in the United States

Abstract The 2020 Atlantic hurricane season was extremely active and included, as of early November, six hurricanes that made landfall in the United States during the global coronavirus disease 2019 (COVID‐19) pandemic. Such an event would necessitate a large‐scale evacuation, with implications for the trajectory of the pandemic. Here we model how a hypothetical hurricane evacuation from four counties in southeast Florida would affect COVID‐19 case levels. We find that hurricane evacuation increases the total number of COVID‐19 cases in both origin and destination locations; however, if transmission rates in destination counties can be kept from rising during evacuation, excess evacuation‐induced case numbers can be minimized by directing evacuees to counties experiencing lower COVID‐19 transmission rates. Ultimately, the number of excess COVID‐19 cases produced by the evacuation depends on the ability of destination counties to meet evacuee needs while minimizing virus exposure through public health directives. These results are relevant to disease transmission during evacuations stemming from additional climate‐related hazards such as wildfires and floods.


Introduction
The information here supports the manuscript listed above and includes text, figures, and tables. The data were generated in July and August, 2020.
Text S1. Transmission model for 3,142 US counties We formulate COVID-19 transmission as a discrete Markov process during both day and night. Daytime transmission lasts for ! days and the nighttime transmission " days ( ! + " = 1). Here, we assume daytime transmission lasts for 8 hours and nighttime transmission lasts for 16 hours, i.e., ! = 1/3 day and " = 2/3 day. The transmission dynamics are depicted by the following equations.
Daytime transmission: Nighttime transmission: Here, #$ , #$ , #$ & , #$ ( , #$ and #$ are the susceptible, exposed, reported infected, unreported infected, recovered and total populations in the subpopulation commuting from county to is the transmission rate of reported infections; is the relative transmissibility of unreported infections; is the average latency period (from infection to contagiousness); is the average duration of contagiousness; is the fraction of documented infections; is a multiplicative factor adjusting random movement; 6 #$ = ( #$ + $# )/2 is the average number of commuters between counties and ; and # ' and # + are the daytime and nighttime populations of county .
Text S2. The pseudo-code for the greedy optimization algorithm Input: Origin = 1, 2, … , Destination = 1, 2, … , , where , (1) ≥ , (2) ≥ ⋯ ≥ , ( ) Evacuation matrix = { $# }, $# is the number of evacuees from origin to destination in the baseline scenario Capacity of evacuees that can be accommodated by each destination: $ The fraction of evacuees that can't be reallocated for each origin-destination pair: Variables: # :: the current number of evacuees in origin that could be reallocated to different counties : the currently available destination county with lowest , .
: the current evacuation matrix, -# is the number of evacuees assigned from origin to destination .
Run projection using # # : total infection in all origin and destination counties End Output as the optimized evacuation matrix.  . Comparison of total cases in the origin and destination counties combined (left column), the origin counties only (middle column) and destination counties only (right column) for the no-evacuation, baseline, low and high evacuation scenarios. Simulations were performed for three settings: no increase (top row), 10% increase (middle row) and 20% increase (bottom row) of transmission rates in destination counties. Box plots show the median and interquartile and whiskers show the 95% CIs. Asterisks indicate that excess cases are significantly higher than the no-evacuation scenario (Wilcoxon signed rank test, < 1 ).

Figure S3.
Time series for confirmed cases in origin and destinations counties. Simulations were performed for the no-evacuation scenario (blue line) and the high evacuation scenario with 20% increase of transmission rate in destination counties (red line). Blue and red dashed lines indicate 95% CIs of simulation results from 100 runs. Figure S4. Evolution of the total cases (blue lines) and the number of assigned evacuees (red lines) in the greedy algorithm. Results are shown for the settings with no increase, 10% increase and 20% increase of transmission rates in destination counties. The optimization starts from an evacuation matrix 0.1 × , where represents the evacuation matrix in the baseline scenario. Figure S5. The change in the number of evacuees to destination counties in the optimized evacuation plan compared with the baseline evacuation scenario. Evacuation was optimized for the setting in which transmission rates in destination counties increase by 10%. Figure S6. Sensitivity analysis of optimization assuming 20% of evacuees cannot be relocated. Excess cases for the baseline and optimized evacuation scenarios are compared for the origin and destination counties combined (left column), only origin counties (middle column) and only destination counties (right column). Simulations were performed with no increase of transmission rates in destination counties. Boxes and whiskers show the median, interquartile and 95% CIs. Asterisks indicate that excess cases are significantly lower than the baseline scenario (Wilcoxon signed rank test, < 10 13 ). Results are obtained from 100 model simulations.  Table S2. The median number of total cases in origin and destination counties for different evacuation scenarios (no evacuation, baseline, low and high) and levels of elevated transmission rates ( , ) in destination counties (no change, 10% increase, 20% increase). In the no evacuation scenario ( , ) in destination counties is not increased. Note that the median excess cases shown in Table 1 is not necessarily the difference of the median total cases between the evacuation and non-evacuation scenarios shown in Table S1. That is, where ,-5 ( ) and 78,-5 ( ) are the total numbers of cases for evacuation and nonevacuation scenarios in the th simulation.