COVID-19 heterogeneity in islands chain environment

Abstract

Background: It is critical to capture data and modeling from the COVID-19 pandemic to understand as much as possible and prepare for future epidemics and possible pandemics. The Hawaiian Islands provide a unique opportunity to study heterogeneity and demographics in a controlled environment due to the geographically closed borders and mostly uniform pandemic-induced governmental controls and restrictions.

Objective: The goal of the paper is to quantify the differences and similarities in the spread of COVID-19 among different Hawaiian islands as well as several other archipelago and islands, which could potentially help us better understand the effect of differences in social behavior and various mitigation measures. The approach should be robust with respect to the unavoidable differences in time, as the arrival of the virus and promptness of mitigation measures may vary significantly among the chosen locations. At the same time, the comparison should be able to capture differences in the overall pandemic experience.

Methods: We examine available data on the daily cases, positivity rates, mobility, and employ a compartmentalized model fitted to the daily cases to develop appropriate comparison approaches. In particular, we focus on merge trees for the daily cases, normalized positivity rates, and baseline transmission rates of the models.

Results: We observe noticeable differences among different Hawaiian counties and interesting similarities between some Hawaiian counties and other geographic locations. The results suggest that mitigation measures should be more localized, that is, targeting the county level rather than the state level if the counties are reasonably insulated from one another. We also notice that the spread of the disease is very sensitive to unexpected events and certain changes in mitigation measures.

Conclusions: Despite being a part of the same archipelago and having similar protocols for mitigation measures, different Hawaiian counties exhibit quantifiably different dynamics of the spread of the disease. One potential explanation is that not sufficiently targeted mitigation measures are incapable of handling unexpected, localized outbreak events. At a larger-scale view of the general spread of the disease on the Hawaiian island counties, we find very interesting similarities between individual Hawaiian islands and other archipelago and islands.

Fig 1. The state of Hawai‘i and its counties.

Kaua‘i county encompasses the islands of Kaua‘i and Ni’ihau. Honolulu county contains only the island of Oahu. Maui county comprises the islands of Maui, Moloka’i, Lana’i, and Kaho’olawe. Hawai‘i county contains only the island of Hawai‘i. Taken from Maps of World, Countries & Cities—Mapsof.Net.

Fig 2. Hawai‘i Covid-19 mitigation timeline.

Timeline of events related to the pandemic in the State of Hawai‘i from March 6, 2020 to September 24, 2020.

Fig 3. Safe travel protocols per counties.

Kaua‘i county has the most restricted travel regulations since Dec. 2, 2020 following a significant initial surge in cases with the introduction of the Safe Travels Program on October 15, 2020.

Fig 4. Diagram of our basic compartmental model.

Illustration of the compartments and their interactions.

Fig 5. Honolulu, Maui and Hawai‘i counties with a normalized model fit, Kaua‘i with normalized daily cases.

It is clearly observed that counties started to differ in response to the spread of COVID-19 after the Safe Travels Program opened.

Fig 6. Estimated distribution of permuted difference, Δ, between the mean differences in L₂-norms for the intervals before Oct. 15 and the one until Jan 15 for the three pairs of counties.

The observed values, shown by black vertical lines, clearly suggest that the hypotheses of equality of the mean differences for the three pairs of counties should be rejected.

Fig 7. Testing, positivity and mobility plots.

A: Honolulu. A sharp increase in the test positivity rate (along with the daily cases) in July indicates an outbreak of the disease. The later decrease in the positivity rate with the increased number of tests indicates a substantial slowdown of the spread of the disease. B: Hawaii. A sharp increase in the test positivity rate around August indicates an outbreak the disease. The later decrease in the positivity rate with the number of tests hovering around the same value indicates a welcome slowdown of the spread of the disease. Maui: A series of ups and downs in the test positivity rate and the number of daily cases indicate the occurrences of outbreaks of the disease. The significant increase in these numbers at the beginning of this year suggests a serious spread of the virus. A noticeable jump in the daily case number that does not correlate with the positivity rate can be explained by a jump in the number of tests, since the latter are performed for people with higher chances of having the virus. C: Kauai. The number of daily cases and test positivity rate are still well correlated, even though the raw numbers are small. Similar to Hawai‘i county, we can see a jump in the daily case numbers that correlates with the increased number of tests rather than the test positivity rate, which is likely due to the biased nature of the population sample on which the tests are performed. Overall mobility suggest a modest correlation with the number of daily cases. It shows a major dip in mobility triggered by the first stay-at-home order back in March 2020. The mobility data clearly suggests why the second lockdown was not as efficient as the first one.

Fig 8. Honolulu county cumulative daily counts distributed per zip code from March 2020 to January 18, 2021.

A: Map produced using Excel Map charts). B: Honolulu county cumulative daily counts distributed per zip code.

Fig 9. Hawai‘i and Maui cumulative daily counts distributed per zip code from October 2020 to January 18, 2021.

A. Hawaii County. Cases restricted to the two major towns Hilo and Kona. B. Maui county. There were a few clusters on Maui which is an explanation for some of the higher spikes, in particular in early January in Kahului which is zip code 96732.

Fig 10. Merged trees.

Merge trees elucidate the qualitative structure of the daily case numbers over time.

Fig 11. Comparison between the daily cases between Hawai‘i counties and other geo-regions.

A: Honolulu County and Iceland. B. Hawai‘i County and Puerto-Rico. C: Maui county and Japan. Dots are daily cases and the curves are the computational fit using our compartmentalized model. The blue fit for the Hawai‘i counties starting October 15, 2020 corresponds to not taking into account the travelers.