key: cord-1008689-t617yac2
authors: Harris, J. E.
title: Data From the COVID-19 Epidemic in Florida Suggest That Younger Cohorts Have Been Transmitting Their Infections to Less Socially Mobile Older Adults
date: 2020-07-02
journal: nan
DOI: 10.1101/2020.06.30.20143842
sha: 670048a848ed23d3e81d68ea84b689f817ce94a7
doc_id: 1008689
cord_uid: t617yac2

We analyzed the daily incidence of newly reported COVID-19 cases among adults aged 20-39 years, 40-59 years, and 60 or more years in the sixteen most populous counties of the state of Florida from March 1 through June 27, 2020. In all 16 counties, an increase in reported COVID-19 case incidence was observed in all three age groups soon after the governor-ordered Full Phase 1 reopening went into effect. Trends in testing, hospitalization and mortality do not support the hypothesis that the observed increase in case incidence was merely the result of liberalization of testing criteria. Parameter estimates from a parsimonious two-group heterogeneous SIR model strongly support the hypothesis that younger persons, having first acquired their infections through increasing social contact with their peers, then transmitted their infections to older, less socially mobile individuals.

States has shifted toward younger adults (Malmgren, Guo, and Kaplan 2020) . One possible explanation is that younger adults have tended to adhere less strictly to recommended social distancing measures, especially as many states, counties and municipalities have begun to reopen. A particular concern is that the higher prevalence of active infection among younger individuals will ultimately result in a higher rate of cross-infection in older persons.

In this article, we use publicly available data on confirmed individual COVID-19 cases compiled by the state of Florida to test whether the incidence of new coronavirus infections has in fact been rising more rapidly among younger cohorts. We then explore whether the available data can be used to assess whether cross-infection of older cohorts is already occurring.

Analytic Sample. We downloaded a data file of confirmed individual COVID-19 cases on June 28, 2020 from the website of the Florida Department of Public Health (Florida Department of Public Health 2020b). The database covered 141,040 cases diagnosed through June 27, 2020, showing the age of the individual, the date of diagnosis, county of residence, whether hospitalized, and known vital status. We excluded 225 cases listed as diagnosed before March 1, 2020, as well as 222 cases of individuals with unknown age. Focusing on adults, we further excluded 12,572 cases with recorded age less than 20 years, leaving 128,021 cases.

We classified the remaining cases into three age groups: 20-39 years old ("younger"), 40-59 years old ("middle aged"), and 60 years or more ("older"). We further focused on cases among residents of the 16 counties with the largest projected population aged 20 years or more (Population Studies Program 2019). The breakdown by county and age group was as follows: 

To analyze COVID-19 testing patterns, we further relied on a series of county reports, issued daily by the Florida Department of Public Health from May 13, 2020 through our closing date (Florida Department of Public Health 2020a). We also downloaded data on the daily numbers of positive and negative tests for the entire state of Florida up to the closing date from the COVID Tracking Project website (COVID Tracking Project 2020).

Descriptive Analyses. We used data on confirmed COVID-19 cases to analyze trends in daily incidence by age group in each of the 16 most populous counties. We related these trends

to key statewide regulatory events. We then analyzed trends in testing at both the state and county level, trends in hospitalization rates of older people, and trends in 28-day mortality by age group to assess whether the liberalization of testing criteria was a factor in determining COVID-19 case incidence.

To quantitatively test the hypothesis that younger cohorts of infected individuals have been cross-infecting older persons, we relied on a parsimonious twogroup, heterogeneous SIR model (Ellison 2020) . To that end, we collapsed the daily COVID-19 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted July 2, 2020. . https: //doi.org/10.1101 //doi.org/10. /2020 incidence data for the younger and middle-aged groups into a single group, ages 20-59 years, retaining the incidence data for the older group, ages 60 years or more. with younger persons, as well as the probability of transmission when a susceptible older person comes into contact with an infective younger person. The main objective of our modeling effort is to assess the relative magnitude of the latter intergroup transmission parameter . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted July 2, 2020. . https: //doi.org/10.1101 //doi.org/10. /2020 The parameters capture the rates at which infective individuals transition to their respective resistant states through recovery or death. While older persons are known to have a significantly higher case mortality rate, we take both parameters to be well approximated by the inverse of the serial interval between successive infections. A recent review gives a range of serial intervals from 3.1 and 7.5 days (Griffin et al. 2020 

y 2 jt = α 21 X 1 j,t−1 + α 22 X 2 j,t−1 + ε 2 jt j . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 2, 2020. . https://doi.org/10. 1101 

We begin with estimates of the daily incidence of confirmed COVID-19 cases for the 16 populous counties combined. For each of the three adult age groups, Figure 2 shows the daily incidence rate per 100,000 population for each day from March 1 through our closing date. The vertical axis is measured on a logarithmic scale so that an exponential rise in incidence would correspond to a straight line on the plot (Harris 2020a) . The sky-blue datapoints correspond to the younger age group (20-39 years), the lime datapoints correspond to the middle-aged group (40-59 years), and the mango datapoints represent the older group (60+ years). Figure 2 shows an initial exponential rise in incidence in all three adult age groups during March, followed by a flattening and decline in the incidence curve beginning around Sunday, March 22 and extending to around Sunday, May 17. Since then, the incidence appears to be increasing in all three adult age groups, most markedly in the younger age group (20-39 years). 

Ages 40-59

Ages 20-39

. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 2, 2020. . https://doi.org/10.1101/2020.06.30.20143842 doi: medRxiv preprint Figure 3 interprets the data displayed in Figure 2 . For each calendar week and each age group, we have superimposed the geometric mean incidence, * represented as larger data points connected by line segments. The youngest group corresponds to the larger blue points, the middle-aged group corresponds to the larger green points, and the older group is represented by the larger orange points. The superimposition of the larger connected points helps us see that the incidence has recently been increasing in all three age groups. From the week beginning Sunday May 17 to the week beginning Sunday June 24, the average daily incidence of new COVID-19 cases has increased by 11.83-fold among the younger group, 5.98-fold among the middle-aged group, and 3.96-fold among the older group. { } . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 2, 2020. . https://doi.org/10. 1101 Further superimposed on the incidence trends in Figure 3 are black arrows marking the dates of key orders issued by Florida Gov. Ron DeSantis. Specifically, Order Number 20-68, effective March 17, 2020, imposed restrictions on pubs, bars, nightclubs, restaurants and beaches (Desantis 2020a) . Subsequent Order Number 20-91, effective April 3, 2020, limited movement outside the home to essential activities and confined business activities to essential services (Desantis 2020b) . Order Number 20-112, effective May 4, 2020, began Phase 1 of the state's reopening, permitting restaurants and retail stores to operate at 25 percent capacity and liberalizing prior prohibitions on elective medical procedures (Desantis 2020c ). Order Number 20-123, effective May 18, 2020, put Full Phase 1 into effect, allowing restaurants, retail establishments, and gyms to operate at 50 percent capacity, opening professional sports events and training camps, and permitting amusement parks and vacation rentals to operate subject to prior approval (Desantis 2020d).

Causal inferences relating trends in COVID-19 incidence to specific regulatory actions must be approached with care (Harris 2020b, c) . Still, it is noteworthy that the deceleration of initial exponential surge in early March began soon after Order Number 20-68, while the backward bending of the incidence curve began soon after Order Number 20-91. Moreover, the downward trend in incidence halted soon after Executive Order 20-112, while the resumption of the upward epidemic curve began soon after Executive Order 20-123.

We next inquire whether the increases in COVID-19 incidence observed from mid-May onward in Figures 2 and 3 could have been at least partly due to more liberalized testing. At the start of the epidemic in the United States, many jurisdictions initially restricted COVID-19 testing to those individuals with more severe symptoms (Harris 2020c) . These restrictions were likely motivated by the scarcity of testing materials and required protective personal equipment.

There is growing evidence that incidence rates based upon voluntary, supply-constrained testing may have significantly understated the true prevalence of SARS-CoV-2 infection (Havers et al. 2020 ). As testing criteria were liberalized -that is, as supply constraints were relaxed -more people with less severe symptoms would thus be expected to test positive.

To address this potential explanation, Figure 4 shows the trends in the total number of test results and the number of positive tests reported in Florida on a daily basis from March 29 through the June 27 closing date. The data for the figure, which is based upon testing for the . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 2, 2020. . https://doi.org/10.1101/2020.06.30.20143842 doi: medRxiv preprint entire state, were derived from the COVID Tracking Project (COVID Tracking Project 2020).

The dark blue datapoints represent the numbers of test results -whether positive or negativereported each day, while the yellow datapoints represent only the numbers of positive tests reported on the same day. Thus, each test was assigned to the date it was read as positive or negative, and not necessarily to the date it was performed. The left-hand axis is shown on a logarithmic scale in order to compare proportional changes in the two data series. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 2, 2020. . https://doi.org/10.1101/2020.06.30.20143842 doi: medRxiv preprint median number of tests was 26,380 per day, with an interquartile range of 20,710 to 37,000.

Statewide testing reached its highest peaked at 60,640 on June 27.

By contrast, the trend in the numbers of positive tests did not parallel the temporal pattern of total tests. Up to the middle of May, total positive tests were falling as total testing was increasing. By the week of May 10, approximately 5.2 percent of tests were read as positive (median 5.19%, interquartile range 2.81-6.19%). By the final week of our sample, the positive test rate had increased to 15.8 percent (median 15.80%, interquartile range 9.57-18.54%). 

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The copyright holder for this preprint this version posted July 2, 2020. . https://doi.org/10. 1101 As in Figure 4 above, Figure 6 shows data on total tests and positive tests for Broward

County. The data were derived from the daily county reports of the Florida Department of Public Health (Florida Department of Public Health 2020a). While there is significantly more sampling variation, the data nonetheless show an increase in total testing soon after the effective date of Full Phase 1. Positive tests, however, only gradually increased during the week of May 24.

Thereafter, the increase in positive tests has substantially outstripped the change in total tests. 

We next inquire whether the large numbers of more recently diagnosed cases during the month of June have been less severe than those previously diagnosed. To that end, it would seem appropriate to examine hospitalization rates, which should be a more reliable indicator of disease burden (Harris 2020b) . The methodological problem here is that the data from the Florida Department of Health are apparently derived from tracking positively tested individuals, and not from querying hospital admission departments. As a result, there have been substantial delays in ascertaining recent hospitalization rates. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 2, 2020. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 2, 2020. . https://doi.org/10.1101/2020.06.30.20143842 doi: medRxiv preprint At the same time, as shown in Figure 5 , the incidence rate of new COVID-19 diagnoses among older persons in Broward County had increased by three-fold. 

There is a substantial time delay -an average of 16 days -from the onset of symptoms to death from COVID-19 (Harris 2020b, Muzimoto and Chowell 2020). As a result, mortality rates from COVID-19 are at best a lagging indicator of the effects of public policies. What's more, in the analysis of the impacts of such policies, the event of death should be pegged to the date of initial diagnosis, and not the death of death itself (Harris 2020b) . In an analysis of trends in COVID-19 case mortality in Los Angeles County and the state of New Jersey, we had a sufficiently long case follow-up to assess whether death rates in those jurisdictions were indeed falling. In general, however, data on case fatality will be subject to right truncation. We could falsely conclude that death rates are falling when we simply haven't waited long enough to see who has died. 

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The copyright holder for this preprint this version posted July 2, 2020. . https://doi.org/10. 1101 In attempt to overcome these methodological difficulties, Table 1 shows the results of a 28-day mortality follow-up of individuals diagnosed with COVID-19 in Florida during two 2week intervals: April 4-17 and May 15-28. The latter 2-week interval was the latest time period for which we could observe and follow newly diagnosed COVID-19 cases for 28 days. The cases have been further broken down into the 10-year age classification internal to the Florida database as well as the hospitalization status of each individual. 

In what follows here, we combine the two youngest age groups into a single age group of individuals 20-59 years of age, retaining the older group aged 60 years or more. Figure 9 plots . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 2, 2020. . https: //doi.org/10.1101 //doi.org/10. /2020 the daily incidence of new COVID-19 diagnoses for the two broader age groups in Hillsborough County, a jurisdiction containing the city of Tampa. Once again, as in Figures 2, 3 and 5 , we see the rise in COVID-19 diagnoses in both broad age groups, beginning soon after Full Phase 1 reopening went into effect. Like Broward and other counties, Hillsborough has also issued its own emergency administrative orders (Hillsborough County 2020). On June 27, the closing date for this study, Mayor Jane Castor of Tampa issued an executive order requiring face coverings in any indoor location open to the public (Castor 2020). 

For each of the two combined age groups (20-59 years and 60+ years) and for each of the 16 populous counties, we used Poisson regression to estimate the daily percentage rate of 

Ages 20-59

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The copyright holder for this preprint this version posted July 2, 2020. . https: //doi.org/10.1101 //doi.org/10. /2020 increase of COVID-19 cases during Full Phase 1 from May 18 through our closing date June 17. Figure 10 plots the daily rate of increase among persons 60 years or more versus the corresponding daily rate of increase among persons 20-59 years. The size of each point is proportional to the total number of adult COVID-19 cases in each county during the Full Phase 1 period.

The plot in Figure 10 shows a consistent monotonic relationship across counties between the COVID-19 growth rates of younger and older adults during the Full Phase 1 reopening period. The slope of the best-fitting weighted least squares regression line, where the weights were the number of COVID-19 cases in each county, was +0.677 (standard error 0.141), while the unrestricted constant term was -0.0003 (standard error 0.012). That is, COVID-19 incidence among older adults aged 60 or more was on average growing two-thirds as rapidly as COVID-19

incidence among younger adults aged 20-60 years. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 2, 2020. . https://doi.org/10.1101/2020.06.30.20143842 doi: medRxiv preprint Table 2 shows the estimated county-specific regression coefficients for the daily incidence of new infections in older persons, that is , where the regression model was run separately for each county. Estimated coefficients significant at the 5percent level (two-sided t-test) are shown in boldface, while coefficients significant only at the 10-percent level are shown in italics. Each county-specific regression had 41 observations.

Nearly all the county-specific regression showed a significant estimate for the intergroup transmission parameter , reflecting the cross-infection of older persons by younger persons.

At the same time, the intragroup transmission parameters were in general not statistically significant. The notable exceptions to the overall pattern were Duval and Palm Beach Counties. Table 3 shows the results of pooling the regressions for the 16 counties. In this specification, we allowed for county-specific interactions with the intergroup transmission variable , but constrained the coefficient of the intragroup transmission variable to

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The copyright holder for this preprint this version posted July 2, 2020. . https: //doi.org/10.1101 //doi.org/10. /2020 be uniform across counties. The omitted county in the list of county-specific interactions was Brevard County.

Again, nearly all the intergroup transmission parameter estimates were statistically significant. Pooling the data from all counties improved the precision of the intergroup parameter. Nearly all the estimated county-specific intergroup transmission parameters that were estimated with precision exceeded the pooled estimate of the intragroup transmission parameter . While the constant term was estimated with precision, its estimated value of 0.725 per 100,000 population was much smaller than baseline value of 4.2 per 100,000 for all 16 counties at the start of Full Phase 1, as shown in Figure 2 . 

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The copyright holder for this preprint this version posted July 2, 2020. . https: //doi.org/10.1101 //doi.org/10. /2020 Figure 11 shows the fit of the latter model to the data on the incidence of COVID-19 infections among older persons in Hillsborough County from the Full Phase 1 reopening onward.

The peach-colored datapoints are the original observations, taken from Figure 9 . The connected line segments correspond to the predictions of the model. Table 3 .

We also ran models of the incidence of COVID-19 infections among the younger age group. In a model analogous to that of 

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The copyright holder for this preprint this version posted July 2, 2020. . https://doi.org/10. 1101 is difficult to draw definitive conclusions about trends in the incidence of new SARS-CoV-2 infections. Recent estimates from serologic surveys suggest that, at least in the period before the Full Phase 1 reopening, the actual incidence of infection was significantly higher (Havers et al. 2020 ) Nonetheless, the available evidence from this detailed study in the 16 most populous counties in Florida points to a substantial rise in case incidence in both younger and older adults after Full Phase 1 reopening went into effect on May 18.

We lack detailed data on the symptomatology and case severity of individuals voluntarily undergoing testing. Without such data, it is difficult to evaluate definitively the hypothesis that the observed rise in COVID-19 case incidence, as seen in all adult age groups in Figures 2, 3 and 5, was due in part to liberalization of testing criteria, thus resulting in expanded testing of milder cases. Still, the patterns of total tests and positive tests seen in Figures 4 for the entire state and in Figure 6 for a specific county are inconsistent with supply constraints on testing as an important explanation for the overall rise in COVID-19 incidence. Our analysis indicates that the time path of positive tests was largely independent of the number of total tests, with positive tests rising substantially as a fraction of total tests in recent weeks.

We lack complete data on the hospitalization status of all persons with confirmed SARS-CoV-2 infections. The recent rapid rise in the COVID-19 caseload creates an even greater resource burden on case tracking, and thus exacerbates this problem. Without more complete data on hospitalization status, it is difficult to determine definitively whether older persons more than 60 years of age are now coming down with more or less severe cases of COVID-19. While we found evidence of a declining hospitalization rate among older persons during the earlier phases of the epidemic in Florida, we found no evidence of further changes in hospitalization rates since the recent post Full Phase 1 reopening. Hospital-based data with detailed patient information may be the best solution to this problem.

COVID-19 case mortality is a lagging indicator of the impact of public policies. With a mean delay of 16 days from symptom onset until death (Harris 2020b, Muzimoto and Chowell 2020), we will have to wait a month to assess the full effects of a given intervention on fatal outcomes. Given these data limitations, we were able to ascertain only that in-hospital mortality had significantly improved for the COVID-19 patients aged 65-84 during the earlier phases of the epidemic, but could draw no conclusions about patients coming down with the disease during the more recent upsurge in cases in the state. Improvements in clinical care, including the more . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 2, 2020. . https://doi.org/10.1101/2020.06.30.20143842 doi: medRxiv preprint judicious use of high-flow oxygenation rather than mechanical ventilation, the turning of patients onto a prone or semi-prone position, the use of prophylactic anticoagulants, high-dose corticosteroids and other treatments may have had a significant favorable effect on mortality.

It may be difficult to determine definitively whether younger persons, having become infected as a result of increased interpersonal contact after Full Phase 1 reopening, then crossinfected older people, who remained largely at home. While data on social mobility may be helpful, a more compelling approach will require large-scale case tracking that identifies infector-infected pairs. Still, the evidence accumulated here is consistent with the cross-infection hypothesis. As shown in Figure 11 , those counties with higher rates of increase of COVID-19 infection among young persons also had higher rates of increase among older persons. As shown in Tables 2 and   3 , parameter estimates based upon a parsimonious, two-group heterogeneous SIR model indicate that the estimated cross-infection effects of young persons on older persons were at least as large as the within-older group transmission effect. The only salient exceptions among the 16 most populous counties were the unconstrained estimates for Palm Beach County and Duval County in Table 2 , where the estimated intra-group transmission among older persons was significant.

These two exceptions require further study. Census data do not show these two counties to be outliers in terms of the elderly living arrangements or the proportions of elderly persons driving or employed (Florida Department of Health 2020).

There is the alternative possibility that older adults on their own frequented these establishments and, at least in principle, cross-infected their younger counterparts. Social contact matrices for the United State suggest that elderly persons have about one-third as many social contacts as younger persons (Prem, Cook, and Jit 2017) . However, contact matrices capturing social interactions under normal non-epidemic conditions are unlikely to accurately represent contacts under the pressure of a persistent, life-threatening pandemic. Older persons, effectively quarantined by government order, would be more dependent on younger persons for a wide array of social needs.

An exogenous event -namely, the reopening under Executive Order Number 20-123 establishing Full Phase 1 -appears to have resulted in less strict adherence to social distancing measures by younger adults, who increasingly frequented pubs, bars, nightclubs, restaurants, beaches, retail stores, gyms, and amusement parks. These younger adults, once infected, appear . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted July 2, 2020. . https://doi.org/10.1101/2020.06.30.20143842 doi: medRxiv preprint .1 1 10 100 3/1 3/8 3/15 3/22 3/29 4/5 4/12 4/19 4/26 5/3 5/10 5/17 5/24 5/31 6/7 6/14 6/21 6/28

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Ages 60+

COVID−19 Cases per 100,000 Population (Logarithmic Scale)

.1 1 10 100 3/1 3/8 3/15 3/22 3/29 4/5 4/12 4/19 4/26 5/3 5/10 5/17 5/24 5/31 6/7 6/14 6/21 6/28

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The copyright holder for this preprint this version posted July 2, 2020. . https://doi.org/10. 1101 

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