1

Clare Brown, Sean G. Young, Mick Tilford, Jenil Patel, Suman

Maity, Jaimi Allen, Jyotishka Datta, Benjamin C. Amick III,

Mark L. Williams*

*corresponding author

November 6, 2020

2

COVID-19 Forecasts, Projections, and Impact Assessments

The University of Arkansas for Medical Sciences (UAMS) Fay W. Boozman College of

Public Health (COPH) faculty conducted three types of assessments for this bi-weekly report: 1)

short-term forecasts of confirmed and probable cases, hospitalizations, and deaths, 2) long-term

projections of infections and hospitalizations; and 3) findings from the Arkansas Pandemic Poll.

All forecasts and projections were developed using COVID-19 data from the Arkansas

Department of Health through Nov. 1. All findings related to the Arkansas Pandemic Poll come

from data collected by the COPH from Oct. 3 through Oct. 17.

Summary points are:

15-day models continue to predict increasing numbers of daily cases, hospitalizations, and deaths due to COVID-19. The 15-day model forecasts 112,101 cumulative

confirmed COVID-19 cases in Arkansas by Nov. 16. Including confirmed and

probable cases, the 15-day model forecasts 121,627 cases by Nov. 16.

Fifteen-day models continue to show Arkansans between 35 and 59 will have the highest number of COVID-19 cases. Young adults 18 to 34 will have the second

highest number of cases. These two age groups will make up around 68% of the

COVID-19 caseload.

All counties in Arkansas reported new COVID-19 cases in the past two weeks. Two counties had two-week rates of change greater than 100%, and 12 counties had rates

of change greater than 50%.

The 15-day models are forecasting 7,893 cumulative hospitalizations and 2,627 cumulative intensive care patients by Nov. 16.

The trend for greatest number of hospitalizations continues to be in adults 60 to 74 years, who surpass the previously highest group of adults 35 to 59. Children younger

than 17 continue to have the fewest number of hospitalizations.

The mid-term model is forecasting hospitalizations by Dec. 30 will, if the forecast holds true, increase by 2,443 over hospitalizations on Nov. 1.

The 15-day model is forecasting 2,202 cumulative deaths by Nov. 16.

The long-term eSIR model suggests the pandemic will peak in March or April 2021 with between 20,000 and 63,000 active infections.

There are strong differences in willingness to accept a COVID-19 vaccine by race/ethnicity. Blacks have lower COVID-19 vaccine acceptance than Hispanics and

Whites. Differences by race/ethnicity appear to be centered on perceived vaccine

safety.

There are clear differences in willingness to accept a COVD-19 vaccine by acceptance of infection mitigation behaviors. Stronger beliefs about the necessity and

effectiveness of COVID-19 mitigation behaviors are strongly correlated with greater

willingness to accept a COVID-19 vaccine. This suggests addressing acceptance of

mitigation behaviors is likely to positively impact vaccine acceptance.

Despite differences by race/ethnicity and beliefs about mitigation behaviors, Arkansans in general appear to be tepid toward accepting a COVID-19 vaccine.

.

3

COVID-19 Cases and Infection

15-day forecast of confirmed COVID-19 cases in Arkansas. Figure 1 shows actual and

forecast COVID-19 cases in Arkansas. The model forecasts Arkansas will reach a cumulative

112,101 confirmed

COVID-19 cases by

Nov. 16, an increase of

7,738 confirmed cases.

As shown in Figure

1, confirmed COVID-19

cases continue to

increase, with little

change in the rate of the

growth curve since early

July. The model is

forecasting a steady

increase in cases over the

next 15-days.

The 15-day forecast

in the last report was

101,780 cumulative

confirmed cases by Nov.

1, around 2.4% less than the 104,239 confirmed cases reported on that date by the Arkansas

Health Department.

In addition to the 15-

day forecast of

confirmed cases, we

forecast future cases

using both confirmed

and probable cases. The

15-day forecast of

cumulative confirmed

and probable cases is

121,627 by Nov. 16, as

seen in Figure 2. As this

is the first time we are

providing this forecast,

we cannot assess its

accuracy. We will

include this assessment

in our next report.

Figure 1 Forecast confirmed COVID-19 cases through Nov. 16

Figure 2 Forecast confirmed plus probable COVID-19 cases through Nov. 16

121,627

4

Confirmed cases are those identified using the PCR test. The Department of Health

distinguishes between confirmed and probable cases. Probable cases are diagnosed using an

antigen test, which is considered less reliable than the more commonly used PCR test. The

Department of Health

provides the number of total

cases in Arkansas by adding

the number of confirmed

and probable tests together.

Because the Department of

Health was not including

positive antigen tests until

Sept. 2, we included only

positive antigen tests on or

after Sept. 2.

Forecast confirmed

cases by age and race. The

greatest growth in cases will

continue to be in adults 18

to 59, as shown in Figure 3.

The 15-day model is forecasting 39,846 confirmed cases in adults 35 to 59 by Nov. 16. The

second highest growth will be in young adults 18 to 34. The model is forecasting 35,960

cumulative confirmed cases in young adults 18 to 34 by Nov. 16. Together, these two age groups

will account for 68% of the COVID-19 caseload in the state. The least growth will be in adults

older than 75, with a projected 8,265 confirmed cases by Nov. 16.

As shown in Figure 4,

the 15-day model is

forecasting increasing

cases in all racial/ethnic

groups in Arkansas.

However, the model

forecasts the increase will

be greater in Whites.

Indeed, if we compare the

slopes for Blacks and

Hispanics to that of

Whites, the slopes are

increasing far more

modestly in Blacks and

Hispanics. The 15-day

model is forecasting

60,826 cumulative

confirmed COVID-19 cases among Whites by Nov. 16, an increase of approximately 10,000

cases over the number reported through Nov. 1st.

As shown in Table 1, we assessed the relative change in case rates by age and race/ethnicity

from Oct. 19 to Nov. 1. The greatest relative change was among adults older than 75 and those

60 to 74. The relative rate of increase was 16% and 13% respectfully. Those 17 or younger and

Figure 3 Forecast COVID-19 cases through Nov. 16 by age

Figure 4 Forecast COVID-19 cases through Nov. 16 by race

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adults 35 to 59 both had relative rates of increase of 10% and 8%. Arkansans 18 to 34 had the

lowest rate of change in the past two weeks.

Table 1: Relative change in confirmed cases by race/ethnicity and age

Increase Relative change

Race/ethnicity

White 4,871 11%

Black 1,576 8%

Hispanic 658 4%

Age

< 17

18 to 34

35 to 59

60 to 74

> 75

1,162

2,515

3,272

1,470

981

10%

8%

9%

12%

16%

Map 1 Relative change in reported cases by County

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The relative change in cases from Oct. 19 to Nov. 1 was greatest among Whites, 11%. Blacks

had a relative change of 8%, and Hispanics 4%.

Relative change in community COVID-19 cases. Map 1, on the previous page, shows the

relative change in each county’s case rate in the last two weeks. The relative change is

determined by calculating the percent change between case rates from the most recent two-week

period, Oct. 19 to Nov. 1, with the case rates from the prior two-week period, Oct. 5 to Oct 18.

Counties in red had the greatest relative change.

Fewer counties are showing large change rates in this report than in the previous one.

Counties with positive change rates greater than 50%, shown in red, continue to be concentrated

in rural counties in Arkansas. In the current report, we identified 12 counties with a positive rate

of change over 50%, compared to 17 counties in the previous report. Additionally, two counties

had change rates greater than 100%. Cleveland County had a change rate of 483% and Sevier

County 130%. Thirty-eight of the 75 counties in Arkansas had no change or negative change

rates.

It is important to note that the rate of change, if viewed without knowing underlying rates,

may not tell the full story of a county’s COVID-19 burden. A steady rate of 250 cases per 10,000

from one reporting period to the next would have a rate of change of 0%, even though the

disease rate is

high. There are

a number of

factors to

consider

beyond the

underlying

rate. Changes

in rates can be

affected by

recent events.

For example,

ADH recently

hosted two

free mass

testing events

in Cleveland

and Scott

counties,

which likely

contributed to

the high

relative

increases in

those counties.

While what can be said about a county’s COVID-19 burden is limited when assessing change

rates alone, change rates are useful when combined with other data. Map 2 shows the number of

COVID-19 cases per capita for each county. Per capita rates can be used along with data shown

on Map 1 to draw conclusions about how COVID-19 is spreading throughout the state. Per capita

Map 2 COVID-19 cases per 10,000 population, Oct. 19 to Nov. 1

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rates are concerning when high. No county had a per capita rate in the last two weeks that

exceeded 100 per 10,000. The three counties with the highest per capita rates in the last two

weeks were Poinsett, 81, Craighead, 63, and Greene, 59.

When data are combined to form an overall picture, what we can conclude, for example, is

that Poinsett County has a high and slowly increasing caseload, compared to Cleveland County,

which has a more modest, but rapidly increasing caseload. If we look at data from the two maps

together, we can conclude that the pandemic has now firmly established itself in rural areas of

the state, primarily in the northeastern and western counties of the state, with comparatively high

case and relative change rates. We can also conclude the state would benefit from more

widespread testing to better describe the scope and magnitude of the pandemic.

COVID-19 positivity rates. Broadly defined, the COVID-19 positivity rate is the number of

people who test positive for COVID-19 as a proportion of the number of people who have been

tested. The positivity rate is an indicator of COVID-19 transmission in the state. A lower

positivity rate is indicative of less transmission, and a higher rate is indicative of greater COVID-

19 transmission. The positivity rate is dependent on the number of tests conducted. The positivity

rate has also taken on greater significance as part of CDC guidelines for local schools having in-

person classes. According to guidelines, the ideal positivity rate is less than 5%, but for practical

purposes less than 10% is acceptable.

Testing for

COVID-19 in

the state is on

par with the

national average

(2.7/1,000

versus

3.9/1,000).

Figure 5 shows

the seven-day

moving average

of the positivity

rates for

Arkansas and

the United

States.

Following the

second week in

May, the

positivity rate in

Arkansas increased and remained above the national average, except for two drops below the

national average for short periods in August and September. The October positivity rate declined

compared to rates in August and September. As of Oct. 31, the positivity rate in Arkansas was

11%, higher than the national rate of 6.8%.

Mid-term forecast of COVID-19 cases. The mid-term forecast provides a look at what

might happen between the end of November to the beginning of 2021. We used a SEIR model to

predict a seven-day rolling average.

Figure 5 COVID-19 positivity rate for Arkansas and the U.S.

8

As shown in

Figure 6, the

model forecasts

150,777

cumulative

cases on Dec.

31. The growth

rate will be

2.6% per week.

If the forecast

holds true,

Arkansas will

add 50,000 new

cases over the

number

reported to the

Department of Health on Oct. 31. If we include estimated active cases not reported, largely

because they are asymptomatic, we can expect an additional 20,000 active cases.

Long-term projection of active cases. As shown above in Figure 7, the eSIR model is

projecting the peak of the epidemic in Arkansas will be in late March or early April, with a mean

prediction of 35,718 active infections. The light-pink shaded region in Figure 7 shows the

uncertainty in the model (90% confidence interval), while the red line shows the mean estimate.

Summary. The short-term forecast describes significant continued growth in COVID-19

cases over the next 15 days. A plausible reason for this outcome is that portions of the

community do not see themselves at high risk of infection and are behaving accordingly. A

second plausible reason is pandemic fatigue. While not measured, it is described by many as

people and households simply tired of following CDC and state guidelines. The greatest number

Figure 7 Projected active COVID-19 infections

Figure 6 Projected COVID-19 cases through Dec. 31

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of cases are in adults between the ages of 18 and 59. Adults younger than 60 may have

developed the impression that COVID-19 is not a significant risk for them or if infected they will

not develop serious disease.

For the long-term projections the timing and number of cases at the peak of the pandemic has

not changed substantially from the previous reports, although the lower bound of the confidence

interval has expanded downward. What this suggests is a greater amount of uncertainty in the

model.

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COVID-19 Hospitalizations and ICU Admissions

Short-term forecasts of hospitalizations. Figure 8 shows the 15-day forecast for COVID-19

hospitalizations in the state on Nov. 16. The estimated trend in hospitalizations is consistent with

the increasing trend in confirmed cases. The 15-day model forecasts there will be 7,893

cumulative hospitalizations in Arkansas by Nov. 16, an increase of 801 or 11% in

hospitalizations over Nov. 1.

Figure 9 below shows a similar growth pattern for patients needing intensive care. The 15-

day model is forecasting 2,627 COVID-19 cumulative intensive care patients by Nov. 16, an

increase of 197 or 8% over Nov. 1.

In our last report, forecast

hospitalizations and patients needing

intensive care were very close to actual

numbers, within 1% and 2%

respectively. The model forecast 7,109

cumulative hospitalizations by Nov. 1

while the actual number was 7,092, a

difference of 17 hospitalizations.

Cumulative intensive care patients were

forecasted to be 2,401, less than 30

fewer than the actual number of

intensive care patients on Nov. 1 of

2,430.

Similar to the previous report, the

forecast of hospitalizations by age,

shown in Figure 10 on the next page,

presents a similar growth pattern

compared to the growth pattern for cases

shown in Figure 3, and emphasizes the

reasons why mitigation of COVID-19 is

important, especially for older adults.

The current report forecast the greatest

number of hospitalizations will be in

adults 60 to 74. Adults 60 to 74 are

forecast to have 2,545 cumulative

hospitalizations by Nov. 16, increasing

by 290 hospitalization. This compares

to 2,454 hospitalizations in adults 35 to

59, the second highest number.

The hospitalization rate of adults 60

to 74 diagnosed with COVID-19 is 18%,

almost three times higher than the

hospitalization rate of 6% among adults

Figure 8 Forecast hospitalizations

Figure 9. Forecast intensive care

11

35 to 59.

Hospitalizations in

adults 35 to 59 are

forecast to increase

by 138. The group

with the third highest

number of

hospitalizations are

adults over 75.

Almost one quarter of

adults over 75

diagnosed with

COVID-19 will be

hospitalized.

The groups with

the fewest

hospitalizations are young adults between 18 and 34 and children 17 or younger. Young adults

have a relatively low rate of growth in hospitalizations compared to older adults. Nonetheless,

the number of actual hospitalizations is not trivial. Young adults 18 to 34 are forecast to have

719 hospitalizations by Nov. 16, an increase of 42. The relative change in hospitalizations by

race/ethnicity and age are shown in Table 2.

Children younger than 17 are forecast to have approximately 163 cumulative hospitalizations

by Nov. 16. While the number of hospitalizations is small compared to other age groups, this

group continues to have growing hospitalization numbers. The number of actual hospitalizations

in children under 17 was 147 on Nov. 1. An increase of 16 hospitalizations represents an increase

of 11% in just two weeks.

Table 2: Relative change in hospitalizations by race/ethnicity and age

Increase Relative change

Race/ethnicity

White 283 8%

Black 65 4%

Hispanic 15 2%

Age

< 17*

18 to 24

35 to 59

60 to 74

> 75

*

30

94

143

135

*

5%

4%

7%

9%

*number of cases too small to report

Figure 10 Forecast COVID-19 hospitalizations by age

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Our forecasts of hospitalizations by age groups for Nov. 1 were fairly accurate and close to

actual hospitalizations observed for all groups, with differences less than 11% between actual

and forecasted hospitalizations across all age groups.

We also forecasted

hospitalizations by race as

shown in Figure 11. All races

are forecast to show steady

increases in hospitalizations.

As expected, the majority of

hospitalizations were noted in

Whites. By Nov. 16, we

expect hospitalizations to rise

from 3,822 to 4,545 among

Whites, 1,919 to 2,331 among

Blacks, and 744 to 925 among

Hispanics.

Figure 11

Forecast COVID-19 hospitalizations by race

13

Hospitalizations by county. Evaluating the distribution of hospitalizations across the state

can help understand the impact COVID-19 may have on regional and state health system

resources. We created two graphics related to county-level hospitalization. For privacy reasons,

three counties with fewer than 10 hospitalizations were excluded from the analyses.

Map 3 provides the hospitalization rates per 100,000 residents. Make note that these are per

100,000, rather than per 10,000 like the maps related to positive cases. Sixty-seven of the 75

counties in Arkansas have hospitalization rates per 100,000 that are greater than 100. This means

that one out of every 1,000 people has been hospitalized for COVID-19 in nearly every Arkansas

county. The counties with the highest per capita hospitalization rates are Lee, 614.0, Chicot,

584.7, and Hempstead, 557.0. For these three counties, one of every 200 residents have been

hospitalized for COVID-19.

Map 3 Hospitalization Rates per 100,000 Residents by County of Residence

14

Similarly, understanding the percent of COVID-19 positive patients who have been

hospitalized can be an important measure of disease spread and an indicator of future

hospitalizations when combined with the number of new local cases. Map 4 provides the percent

of confirmed COVID-19 cases hospitalized. For example, a value of 5% means that 5 out of 100

of COVID-19 cases from that county were hospitalized. Fourteen counties have rates above 10,

which means that one of every 10 COVID-19 cases in those 14 counties were hospitalized. There

are nine counties with less than 5% hospitalization rates. The counties with the highest rates are

Cleburne, 12.4%, Lawrence, 12.3%, and Sharp, 12.3%. These are rural counties.

Mid-term hospitalization projections through Dec. 31. We introduce, for the first time,

mid-term projections for hospitalizations. Based on the SEIR prediction of the total cases, we

factored in the hospitalization rate, estimated over 15-day average to adjust for higher variability,

to predict the total number of cases requiring hospitalization at any given time. As shown in

Figure 12, the number of hospitalizations will continue to increase through the end of December.

Map 4 Percent of Positive Cases that were Hospitalized by County of Residence

15

By Dec. 31, hospitalizations are forecast to reach 9,537 cumulative hospitalizations, an increase

of 2,443 over Nov. 1. These results are based on limited data and as we receive more data we

will have greater confidence in our projection.

Long-Term Projections. Table 3, below, shows the long-term projections for

hospitalizations. By April 7, it is expected there will be 857 individuals hospitalized for COVID-

19 disease based on over 35,000 active infections. Of these hospitalizations, 299 will require

intensive care. We also consider a worst-case scenario. The worst-case scenario projects 1,426

hospitalizations based on almost 60,000 active infections on March 30. If the projected number

of hospitalizations holds true, the number of patients requiring intensive care would be 499.

Summary. The 15-day models are forecasting the greatest number of hospitalizations due to

COVID-19 will be in adults 60 to 74. COVID-19 disease is more severe in older people. The

rapid increase in the growth trend, with a 17% hospitalization rate in this group, highlights the

growing impact COVID-19 will have on the state’s hospitals. The forecast cases and

hospitalizations continue to be worrisome for older Arkansans and emphasizes the need for

continuing mitigation practices by all age groups. Almost a fourth of adults 60 and over who test

Table 3: Long-term projections of active infections, hospitalizations, intensive care, and

ventilations in Arkansas

Mean-Case Estimates Worst-Case Estimates

Peak Date April 7 March 30

Active Infections 35,718 59,421

Hospitalizations 857 1,426

Intensive Care 299 499

Ventilations 104 174

Figure 12 Projected hospitalizations by Dec. 31

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positive for COVID-19 are hospitalized. Currently, growth in COVID-19 cases among these age

groups is relatively slow. But, with family holidays approaching and anticipated mixing of

family members and friends of all ages, infection rates in older adults could markedly increase. If

this were to happen, hospitalizations will also dramatically increase.

The second highest hospitalizations were noted in adults 35 to 59. The high growth in

COVID-19 cases and hospitalization among adults 18 to 65 is important. The vast majority of the

workforce is between the ages of 18 and 65. Even if not hospitalized with COVID-19 disease,

these adults will likely be out of the workforce for extended periods of time in isolation. Isolating

persons with COVID-19 will have a ripple effect, as persons in close contact with infected

persons are quarantined. As the pandemic in Arkansas continues to increase, isolating infected

persons and quarantining their contacts will result in significant numbers of people unable to

work.

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COVID-19 Deaths

15-day forecast of COVID-19 deaths. The 15-day model is forecasting 2,202 deaths by

Nov. 16, as shown in Figure 13. The forecast is an increase of 357 or 16% over deaths reported

on Nov. 1. Our previous forecast of COVID-19 deaths was within 5% of actual numbers. The

model forecast 1,925 deaths by Nov. 1. The actual number was 1,845, a difference of 80.

Mid-term projections

of COVID-19 deaths. Mid-

term projections provide a

look at what might happen

between the end of

November and late-

December. As has been

stressed previously, the

farther out in time a model

projects, the less confidence

we have in model outcomes.

We use a SEIR model to

predict a seven-day rolling

average.

As shown in Figure 14,

the seven-day rolling

average forecast of

cumulative deaths in the

state on Dec. 31 is 2,887.

This is an increase of 1,042

deaths compared to actual

deaths on Nov. 1. The

weekly growth rate of deaths

through the end of the year

is forecast to be 2.4%.

To assess changes in the

number of deaths since the

last report, we measured the

relative change in the

number of cumulative deaths

by age and by race/ethnicity

from Oct. 19 to Nov. 1. The

growth rates in deaths align

with the growth rates in

hospitalizations, with the

exception of the younger 3

age categories. The younger

age categories show lower growth rates in deaths. This is consistent with the expectation that

COVID-19 will cause milder disease in children and young adults compared to older adults. The

relative change in deaths by race/ethnicity and age are shown in Table 4.

Figure 13 Projected COVID-19 deaths through Nov. 16

Figure 14 Projected COVID-19 deaths through Dec. 31

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Summary. Consistent with increasing cases and hospitalizations over the last two weeks,

deaths rates continued to increase and are expected to do so for the foreseeable future. The recent

spikes in cases and hospitalizations may be reflected in an increasing death rate in the upcoming

forecasts. As the slope of the 15-day forecast suggests, the number of deaths is likely to increase

at a high rate in the next two weeks. If we compare the 15-day and mid-term forecasts,

November and December are likely to see 1,000 deaths, which is half again as many COVID-19

deaths in Arkansas as between March and October. Unfortunately, given current data, we cannot

take the Thanksgiving holiday into account. But, with the gathering of families over the holidays,

we are likely to see a sharp increase in cases, hospitalizations, and deaths similar to previous

holidays.

Table 4: Relative change in deaths by race/ethnicity and age

Increase Relative change

Race/ethnicity

White 96 9%

Black 65 4%

Hispanic* - 1%

Age

< 17*

18 to 24*

35 to 59*

60 to 74

> 75

-

-

-

33

79

-

5%

3%

7%

8%

*number of cases too small to report

19

Arkansas Pandemic Poll

The Fay W. Boozman College of Public Health at the University of Arkansas for Medical

Sciences (COPH) instituted a random digit dial (RDD) telephone poll to assess Arkansan’s views

on the COVID-19 pandemic. The pulse poll captures a random sample of adults in Arkansas

using random digit dialing. However, to ensure the results reflect the adult population of

Arkansas as a whole, we weighted survey results based on age and gender. To date, almost 9,000

Arkansans have completed the survey.

In this report, we examine vaccine acceptance among Arkansans. Vaccine acceptance is the

willingness to take a vaccine. We used a recently validated measure for vaccine acceptance

designed for the general population. We asked the respondent to focus on COVID-19 when

answering questions (see Methodological Notes). Data on vaccine acceptance was collected

between Oct. 3 and Oct. 17. The sample used for this assessment was 1,100.

Vaccine Acceptance in Arkansas

Figure 14 shows the mean levels of vaccine acceptance using a 10-item scale. In addition to

the overall measure, the scale has five subscales. The total scale score is the average score on all

10 items. Responses on a single item ranged from 1 to 7, with 4 signifying neither agreement nor

disagreement. The higher the

overall score, the more a person

is willing to accept a vaccine.

The overall vaccine acceptance

score of the 1,100 Arkansas who

participated in the poll is 5.0.

High vaccine acceptance would

be 6.0 or greater, so 5.0 is

slightly better than neither agree

nor disagree.

As shown in Figure 14,

‘safety’ refers to the subscale

measuring perceived vaccine

safety, and had a mean value of 4.7. ‘Effect’ refers to perceived vaccine effectiveness and the

belief that vaccines are necessary. This subscale had a mean value of 5. ‘Accept’ refers to

subscale measuring acceptance of vaccine selection and scheduling. This subscale had a mean of

5.1. “Value’ measures perceived value and effect of a vaccine. This subscale had a mean of 5.3.

Perceived legitimacy of authorities to require vaccinations is measured by the subscale ‘Legit.” It

had a sample mean of 4.9. Overall, no scale or subscale mean was significantly greater than 4.

This suggests Arkansans are very tepid when it comes to accepting a coronavirus vaccine.

3

3.5

4

4.5

5

5.5

6

Total Score Safety Effect Accept Value Legit

Figure 14 Mean Values for Vaccince Accpetance and the

Subscales in Arkansas

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Variation in vaccine acceptance by

race/ethnicity. There were no differences in

vaccine acceptance by sex or age. However,

racial/ethnic differences were observed, as

shown in Figure 15. Acceptance was highest

among Hispanics (5.3) and lowest among

Blacks (4.5). The mean acceptance score for

Whites was 5.1. The differences across

race/ethnicity were relatively large and

statistically significant.

There were also differences across all

acceptance subscales by race/ethnicity, as

shown in Figure 16. Blacks score lowest on

all subscales, but especially on perceived

vaccine safety. Blacks reported their highest

scores on believing vaccines have positive value, 4.7 compared to 5.6 for Hispanics, and 5.2 for

Whites. On the subscale, selection of vaccines and their scheduling, the mean for Blacks is 4.8

compared to 5.6 for Hispanics and 5.2 for

Whites.

Overall, Blacks were least willing to

accept vaccines. This finding suggests the

state should make special efforts to

understand how Blacks perceive the

acceptability of vaccines differently than

Whites and Hispanics, and develop

communication and distribution programs

accordingly. However, the results also

argue for a primary need to move the

whole Arkansas population higher in

terms of vaccine acceptance.

Vaccine acceptance by pandemic

characteristics. We considered vaccine acceptance by beliefs about other pandemic mitigation

behaviors: wearing a mask regularly in public, agree wearing a mask helps stop the spread of

COVID-19, perceived chance of getting COVID, agree with the decision to allow large groups to

gather, and past two weeks attended church, temple, or other religious gathering. Other questions

were explored but the pattern is the same.

Chances of getting COVID-19. We asked respondents what they thought the chances are of

getting the coronavirus. There were differences across all vaccine acceptance subscales. As

shown in Figure 17 on the next page, respondents who said there was no chance they would get

infected had a mean vaccine acceptance score of 4.6 compared to a mean of 5.1 for those who

felt there was some chance they would get infected. There were similar differences across

vaccine effectiveness (5.1 vs. 4.6) and legitimacy of government to require vaccines (5.0 vs. 4.5).

Vaccine safety had the lowest values for both any chance (4.9) and no chance (4.2) of getting

3

3.5

4

4.5

5

5.5

6

Safety Effect Accept Value Legit

Figure 16 Vaccine Acceptance Subscales by

Race/Ethnicity

White Black Hispanic

3

3.5

4

4.5

5

5.5

6

White Black Hispanic

Figure 15 Overall Vaccine Acceptance Scale by

Race/Ethnicity

21

COVID-19. The acceptance of scheduling of vaccines (5.2 vs. 4.8) and vaccines have a positive

value (5.4 vs. 4.9) had the highest mean scores.

Mask Wearing.

We asked three

questions about

mask wearing. First,

we asked if a

respondent had

regularly worn a

mask in the past

two weeks. Second,

we asked if a

respondent thought

the mask helped

protect him/her

from the getting the

coronavirus. Third,

we asked if a

respondent believed

a state order requiring citizens to wear a face mask in public is needed.

Figure 18 shows the results for persons who said they regularly wear masks in the past two

weeks. For this group, the overall vaccine acceptance score was 5.1 compared to 4.1 for those

who did not

regularly wear a

mask. Differences

across the

subscales are most

striking for vaccine

safety (4.7 vs. 3.7)

and legitimacy of

the government to

require vaccines

(5.0 vs. 3.6).

Differences

between regular

mask wearers and

non-regular mask

wearers in vaccine

acceptance was less stark across the subscales of vaccines have a positive value (5.4 vs. 4.6),

vaccines are effective (5.1 vs. 4.2), and acceptance of selection and scheduling of vaccines (5.1

vs. 4.3).

3

3.5

4

4.5

5

5.5

6

Total Safety Effect Accept Value Legit

Figure 18 Vaccine Accpetance by Regularly Wears Mask

Yes No

3

3.5

4

4.5

5

5.5

6

Total Safety Effect Accept Value Legit

Figure 17 Vaccine Acceptance by Chance of Getting Coronavirus

No chance Any chance

22

Next, we examined if

respondents believe masks

helped stop the spread of

the coronavirus. Overall, if

the respondent believes

wearing masks helps stop

the spread of the virus, the

total vaccine acceptance

measure was higher, 5.2,

than if the respondent does

not believe masks help

stop viral transmission,

4.5. Large differences were

found for perceived safety

(4.9 vs. 4.0) and legitimacy

of government to require

vaccines (5.2 vs. 4.3). Positive value of the vaccine had the highest values (5.4 vs. 5.0) with the

smallest difference. Both effectiveness of the vaccine (5.2 vs.4.5) and acceptance of selection

and scheduling the vaccine (5.2 vs. 4.7) showed large mean differences.

Finally, we assessed if respondents believed the state order to wear a mask was needed was

related to vaccine acceptance. As Figure 20 shows, the difference in mean scores was not large.

Those who felt a state order was needed had a mean score of 5.1 compared to those who felt it

was not needed, 4.8.

The mean difference

across the vaccine

acceptance subscale

was largest for

legitimacy of

government to

require vaccines (5.1

vs. 4.6) and smallest

for the belief that

vaccines have a

positive value (5.4 vs.

5.2). Perceived safety

of the vaccine was

low for those who felt

the order was

warranted (4.8) and those who felt it was not warranted (4.4). For vaccine effectiveness (5.1 vs.

4.8) and acceptance of the scheduling of vaccines (5.2 vs. 4.9) the results were similar to the

overall vaccine acceptance scale scores.

Social Gathering. Several questions in the Pandemic Poll ask about social gatherings. One

asks about whether a respondent agrees or disagrees with the decision to allow large social

gatherings. A second question asks the respondent if he/she attended church, temple or other

religious event in person in the past two weeks.

3

3.5

4

4.5

5

5.5

6

Total Safety Effect Accept Value Legit

Figure 19 Vaccine Acceptance by Masks Helps Stop the Spread of

the Coronavirus

Yes No

3

3.5

4

4.5

5

5.5

6

Total Safety Effect Accept Value Legit

Figure 20 Vaccince Acceptance by Statewide Mask order Needed

Yes No

23

As shown in Figure 21, the mean difference on the vaccine acceptance scale and subscales

between those who believe large gatherings should be allowed and those who do not are small

(5.1 vs. 4.8). Furthermore, there was no real mean difference between the groups on the subscale

measuring the value

of vaccines.

Generally, if a

person agreed with

the decision to

allow large group

gatherings, they

were less likely to

accept vaccines

than those who

disagree with the

decision to allow

large gatherings.

As shown in

Figure 22, there

were small

differences in the

overall measure of

vaccine acceptance

by the measure of

church attendance,

4.8 vs. 5.1. And,

differences were

small across all the

sub-scales. Overall,

greater church

attendance was

associated with

lower vaccine

acceptance, but the

difference was not

large.

Summary. As Arkansas works towards developing a COVID-19 vaccination program, it is

important to understand variation in vaccine acceptance across the state. We used a valid and

reliable tool designed to assess vaccine acceptance in the general population. The tool was

designed to assess multiple components of vaccine acceptance. Essentially, the measure assumes

a person’s decision to accept a vaccine is the product of an evaluation of vaccine safety,

effectiveness, scheduling, beliefs about the positive value the vaccine has for self and society,

and legitimacy of the government to require vaccines. We found important differences for

vaccine acceptance and its subscale components by race/ethnicity and beliefs about pandemic

mitigation efforts. Asking a simple question about taking a COVID-19 vaccine misses important

3

3.5

4

4.5

5

5.5

6

Total Safety Effect Accept Value Legit

Figure 21 Vaccince Acceptance and Agree with Decision to Allow Large

Social Gatherings

Agree Disagree

3

3.5

4

4.5

5

5.5

6

Total Safety Effect Accept Value Legit

Figure 22 Vaccine Accpetance by Whether Attended Church, Temple of

Other Religious Gathering in person in Past Two Weeks

Yes No

24

opportunities to target different approaches to different groups to improve vaccine acceptance.

What is clear is: Blacks have lower vaccine acceptance than Hispanics and Whites and

differences may center on perceived vaccine safety. It is also clear that the more a person agrees

with and practices COVID-19 mitigation practices, the more likely he/she is to accept a vaccine.

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Methodological Notes

Short-term forecasts. Time series forecasting is a method that uses observed data to predict

future values. The purpose of the models is to fit the best curve to data and extend the curve into

the future. To forecast aspects of the pandemic in Arkansans, the models used COVID-19 cases,

hospitalizations, ICU admissions, and death data reported to the Arkansas Department of Health.

It should be noted the report defines a “case” as a COVID-19 test result reported and posted by

the Department of Health. As indicated by recent research, the number of undiagnosed COVID-

19 infections in the community may be higher by 40 to 50%. We cannot provide a precise

number of undiagnosed infections in the community, as an antibody seroprevalence study has not

yet been completed in the state.

Mid-term Projections. The SEIR model projects COVID-19 cases and deaths using the

same basic parameters — susceptible (S), exposed (E), infected (I), and recovered (R), that have

been widely used to model epidemics since the 1920s. In addition, SEIR models account for the

changing social conditions, such as the face mask order and opening schools, changing infection

probabilities, and symptomatic and asymptomatic spread of cases. To arrive at the best model fit

for mid-term projections of COVID-19, we first used a SEIR model (Exposed (E)) to model

existing cases. The resulting fit was very good, but required a second step to project cases out to

predicted date. The difficulty with SEIR-like models is that actual COVID-19 cases may not

accurately represent viral spread. This can occur for a number of reasons, including variation in

rates of testing and limited knowledge of the contribution of asymptomatic infections to viral

spread. To extend our SEIR model projections, we calculated a seven-day rolling average model

using the number of cases to date. Results between the SEIR and seven-day rolling average

estimates were consistent, with a fit coefficient above 75%.

Long-term projections. The eSIR model is based on the extended state-space SIR (eSIR)

model. A standard SIR model has three components: susceptible (S), infected (I), and removed

(R), including both recoveries and deaths. The proportion of the population falling into each

mutually exclusive category is assumed to vary over time, creating the standard epidemic curve.

The model creates projections of active infections, including mild and asymptomatic infections,

over time. Active infections are not cumulative infections from the beginning of the pandemic,

nor are they restricted to new cases on a given day. Rather, the model estimates the proportion of

the population with an unresolved infection at a given point in time.

Changing model assumptions and their impact on projections. Since the last report, the

model’s assumption regarding the likelihood of transmission has been adjusted slightly upward

to better match Arkansas data. The model was also extended an additional six months into the

future to better observe the predicted post-peak dynamics. The eSIR model was originally

developed using assumptions based on data from China, such as the R0 estimate. R0 (pronounced

R-naught) is a measure of how many people one infected person can infect. The model learns

and improves over time by adjusting internal assumptions as more Arkansas-specific data

become available. For example, the R0 changed in the model over time from 3.15 to 1.39. Earlier

versions of the model, working with less Arkansas data, relied more heavily on the assumptions

derived from Chinese studies. Consequently, in the beginning, the model predicted a more

aggressive epidemic than we have observed in Arkansas. As more Arkansas data have become

available, the model has adjusted itself to better reflect the more extended epidemic curve we

now observe.

26

Comparison to other models. Curve fitting models, like the widely cited University of

Washington IHME model, tend to make strong assumptions, which are unlikely to hold as more

data become available. In addition, curve fitting models cannot account for epidemic dynamics.

This often results in severe reductions in predictive strength beyond short-term windows.

SIR/eSIR models, like we use in this report, have a stronger theoretical basis for long-term

projections. Regarding the eSIR model’s relatively late date for a peak, this is in line with other

long-term projection models, such as the CIDRAP Viewpoint, which predicts the COVID-19

pandemic will last 18 to 24 months. Furthermore, reports from week to week cannot be

compared to each other. As more data are added to a model, differences reflect new Arkansas-

specific data. Therefore, the results reported above should not be compared to the previous

reports. However, the eSIR model may be suggesting the COVID-19 growth curve may be

leveling off.

Arkansas Pandemic Poll. One the challenges of looking at responses by race/ethnicity is

that some racial/ethnic groups in the poll have too few numbers to be included in the analyses.

For example, we do not have enough respondents who are Marshallese to include them in the

analyses. However, the Marshallese are an important racial/ethnic group in the state with respect

to the COVID-19 pandemic.

The percentage of individuals who felt that they have a low chance of getting infected in the

last two weeks. Response categories vary from 1 (no chance) to 5 (high chance). Responses 1, 2,

and 3 were recoded as low chance and 4 and 5 are recoded as high chance of contracting

COVID-19.

Vaccine Acceptance Measure

Original source: A survey instrument for measuring vaccine acceptance. Sarathchandra, D,

Navin. C, Largent, M, McCright, A. Preventive Medicine 109:1-7, 2020.

Instructions for the interviewer to read to the respondent. The following questions ask your

views on vaccines particularly potential COVID-19 vaccines. Please indicate how much you

agree or disagree with the following. Interviewer, read response categories following each

question. (Note the underlined text in the instructions is the only change from the original

instrument.) The response categories are: strongly disagree, moderately disagree, slightly

disagree, I am not sure, slightly agree, moderately agree, strongly agree. The questions are :

vaccines are safe; vaccines contain dangerous ingredients; some vaccines are unnecessary since

they target relatively harmless diseases; vaccines are effective in preventing diseases; we give

children the right number of vaccines; we give children too many vaccines, vaccines conflict

with my beliefs that children should use natural products and avoid toxins; vaccines are a major

advance for humanity; the government should not force children to get vaccinated to attend

school; to protect public health, we should follow government guidelines about vaccines.

The overall vaccine acceptance scale is the sum of 10 items. Subscales are the following

sums: 1&2 perceived safety of vaccines; 3&4 perceived effectiveness and necessity of vaccines;

5&6 acceptance of vaccine selection and scheduling; 7&8 perceives the value of vaccines; 9&10

perceived legitimacy of authorities to require vaccinations.

The Cronbach’s alpha for overall scale is 0.83.

27

Glossary of Terms

Active infection = a positive infection, with or without a COVID-19 test, that has not yet

recovered or died

Case = a positive COVID-19 test result reported to the Arkansas Department of Health

Community = population not in a prison or population not in a prison or nursing home

Cumulative = total number of a given outcome (e.g., cases) up to date

Extended state-space SIR (eSIR) model = a model based on three components: susceptible

(S), infected (I), and removed (R, including both recoveries and deaths)

Susceptible-Exposed-Infected-Recovered model (SEIR) = another variant of standard

epidemiological model considering exposure as another factor controlling for disease dynamics

Hospitalization = a positive infection or case that was admitted to the hospital

ICU = intensive care unit admission

Infection = a COVID-19 infection, with or without a test and regardless of having recovered

or died

Non-incarcerated (NI) = representative of an individual who is not in a jail or in a

correctional facility

Positivity Rate = The number of people who test positive for covid-19 as a proportion of

people have been tested

Projections = long-term predictions

Recovered = a positive infection that is no longer symptomatic or shedding virus

Susceptible = an individual who can be infected with the disease of interest

Time series forecast = short-term forecast of events through a sequence of time