key: cord-0875312-ruwc6sjm
authors: Politis, Maria D.; Hua, Xinyi; Ogwara, Chigozie A.; Davies, Margaret R.; Adebile, Temitayo M.; Sherman, Maya P.; Zhou, Xiaolu; Chowell, Gerardo; Spaulding, Anne C.; Fung, Isaac Chun-Hai
title: nSpatially refined time-varying reproduction numbers of SARS-CoV-2 in Arkansas and Kentucky and their relationship to population size and public health policy, March – November, 2020
date: 2022-01-11
journal: Ann Epidemiol
DOI: 10.1016/j.annepidem.2021.12.012
sha: 7a4dbbeb81baf9efadf4dd0a80a614890fb2be29
doc_id: 875312
cord_uid: ruwc6sjm

PURPOSE: To examine the time-varying reproduction number, R(t), for COVID-19 in Arkansas and Kentucky and investigate the impact of policies and preventative measures on the variability in R(t). METHODS: Arkansas and Kentucky county-level COVID-19 cumulative case count data (March 6-November 7, 2020) were obtained. R(t) was estimated using the R package ‘EpiEstim’, by county, region (Delta, non-Delta, Appalachian, non-Appalachian), and policy measures. RESULTS: The R(t) was initially high, falling below 1 in May or June depending on the region, before stabilizing around 1 in the later months. The median R(t) for Arkansas and Kentucky at the end of the study were 1.15 (95% credible interval [CrI], 1.13, 1.18) and 1.10 (95% CrI, 1.08, 1.12), respectively, and remained above 1 for the non-Appalachian region. R(t) decreased when facial coverings were mandated, changing by -10.64% (95% CrI, -10.60%, -10.70%) in Arkansas and -5.93% (95% CrI, -4.31%, -7.65%) in Kentucky. The trends in R(t) estimates were mostly associated with the implementation and relaxation of social distancing measures. CONCLUSIONS: Arkansas and Kentucky maintained a median R(t) above 1 during the entire study period. Changes in R(t) estimates allows quantitative estimates of potential impact of policies such as facemask mandate.

Coronavirus disease 2019 , caused by severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), was first reported in humans in Wuhan in December 2019. From the early stages of the pandemic to November 2020, there has been a rise in both cases and deaths among states that contain large rural areas in the United States (US). 1 Arkansas, one of eight states that did not implement a stay-at-home order, and Kentucky, a state that has been more proactive from the beginning of the pandemic, are two southern states that have very similar COVID-19 morbidity and mortality rates, yet differed in their approach in addressing this pandemic. Both states have regions that are classified as rural (the Delta in Arkansas and Appalachia in Kentucky), which face higher percentages of health disparities and socioeconomic stress compared to their respective state counterparts.

In both states, disparities in rurality, poverty, health conditions, and healthcare access have a significant role. In Arkansas, 41% of Arkansans live in rural counties, 2 compared to only 14% of the US population who live in nonmetropolitan counties. In Kentucky, 25 .3% of individuals in Appalachia live in poverty compared to 15.3% in non-Appalachia. 3 These rural communities face challenges with the pandemic and may be unsuited to handle large surges within their healthcare systems. 4, 5 Fifty percent of rural residents are at a higher risk of hospitalization and serious illness if they became infected with COVID-19 compared to 40% of metropolitan residents because of pre-existing health conditions. 6 Rural residents are more likely to be older, poorer, and have more comorbidities including obesity, diabetes, hypertension, heart disease, and chronic lower respiratory disease than urbanites. [6] [7] [8] [9] [10] The states of Arkansas and Kentucky were chosen for this study's time period due to the increasing incidence of COVID-19 in the southern US. We also wanted to highlight two southern states that share similar cultural heritages, but are of different political climates in 2020 (a Republican governor in Arkansas and a Democratic governor in Kentucky). The time-varying reproduction number, R t , represents a pathogen's changing transmission potential over time. As the average number of secondary cases per case at a certain time t, R t >1 indicates sustained transmission and <1 epidemic decline. [11] [12] [13] Examining the R t among these two states will provide a better indication of COVID-19 transmission, especially among vulnerable rural areas. Our study aimed to estimate the R t for COVID-19 within Arkansas and Kentucky and to compare the R t among the two states, as well to determine if it differs among the urban and rural areas of each state, and to investigate the impact of policies, and preventative and relaxation measures on the R t .

Using data from the New York Times GitHub data repository, 14 we downloaded the cumulative confirmed case count from March 6 -November 7, 2020, for Arkansas and Kentucky, including the counties located in each state. We used the Delta Regional Authority 15 and the Appalachian Regional Commission 16 to classify the counties in Arkansas as Delta and non-Delta, and Appalachian and non-Appalachian in Kentucky. A detailed list of all 75 and 120 counties of Arkansas and Kentucky are provided in Supplementary Tables 1 and 2. The first case in Arkansas was reported on March 11, 2020, and the first case in Kentucky was reported on March 6, 2020. The study cutoff point was November 7, 2020. The management of negative incident case counts is described in Appendix A. We merged the county-level data to obtain the regional-level data (Delta, non-Delta, Appalachian, and non-Appalachian). To generate R t , from the reported cumulative case count numbers, we utilized the daily number of new confirmed COVID-19 cases. We accessed 2019 county-level population data for Arkansas and Kentucky from the U.S. Census Bureau. 17 For sensitivity analysis, statewide hospitalization data for Arkansas and Kentucky, were downloaded from the COVID Tracking Project. 18 The first date of report was April 1, 2020 for Arkansas and April 10, 2020 for Kentucky. Due to an observed weekend effect, the 3-day moving average was applied to both hospitalization datasets before they were further analyzed.

We downloaded the executive orders from the governors' offices of each state and identified the date of the implementation and relaxation of public health interventions in each state respectively ( Table 1) .

R t was estimated using the instantaneous reproduction number method as implemented in the R package 'EpiEstim' version 2.2-3. This measure was defined by Cori et al. 11 as the ratio between I t , the number of incident cases at the time t, and the total infectiousness of all infected individuals at the time t. This method has been implemented worldwide in multiple studies to estimate the R t of SARS-CoV-2 and is briefly described in Appendix B. [19] [20] [21] [22] [23] [24] [25] [26] We shifted the time series by nine days backward (assuming a mean incubation period of 6 days and a median delay to testing of 3 days) 27 for generating R t by the assumed date of infection, 13 and we specified the serial interval (mean = 4.60 days; standard deviation = 5.55 days). 28 Besides using the 7-day sliding window, we also analyze R t by the different non-overlapping time periods when different combinations of non-pharmaceutical interventions have been implemented, known as policy change R t (PCR t ) thereafter. We estimated the 1-week sliding window R t and PCR t for both states at the state and regional levels. We calculated the median R t difference percentage changes and the 95% credible interval (CrI), comparing with the previous policy interval, by bootstrapping (1000 random samples for each R t distribution) for each state-level PCR t , each respective state region, and the hot-spot analyses for each state (Supplementary Tables 3-6) .

We also performed the similar analysis at the county-level in which we identified as hot spots based on the reported data and local news (Appendix C). For Arkansas, we analyzed data from Washington, Benton, Lincoln, and Yell Counties, respectively, and combined data from Washington County and adjacent Benton County for analysis as they are one metropolitan area (Supplementary Figure 1) . For Kentucky, we analyzed Jefferson, Shelby, Elliott, and Warren Counties, respectively, and combined data from Jefferson County and adjacent Shelby County for analysis as they are one metropolitan area (Supplementary Figure 2) .

A sensitivity analysis was performed to estimate 1-week sliding window R t utilizing statewide hospitalization data (Appendix D).

We conducted linear regression between the log 10 -transformed per capita cumulative case count and the log 10 -transformed population size, 29, 30 at four different dates: May 7 th , July 7 th , September 7 th , and November 7 th . See Appendix E for details and results.

Statistical analysis was performed using R 4.0.3 (R Core Team, R Foundation for Statistical Computing, Vienna, Austria). Maps were created using ArcGIS Pro Version 2.4.0 (Esri, Redlands, CA, USA), with color codes arranged according to quintiles of the values.

The Georgia Southern University Institutional Review Board made a non-human subjects determination for this project (H20364) under the G8 exemption category.

As of November 7, 2020, there were 119,057 cumulative confirmed COVID-19 cases in Arkansas (57,836 for Delta and 61,221 for non-Delta) and 122,024 cases in Kentucky (27, 480 for Appalachian and 94,544 for non-Appalachian). Supplementary Figures 3 and 4 present the spatial variation of cumulative case count and cumulative incidence per 100,000 population by county in Arkansas and Kentucky at four different dates: May 7 th , July 7 th , September 7 th , and November 7 th , 2020, respectively.

Overall, the median R t for Arkansas and Kentucky at the end of the study were 1.15 (95% CrI, 1.13, 1.18) and 1.10 (95% CrI, 1.08, 1.12), respectively. Between both states, the R t estimates followed similar patterns. However, they were different when examining certain policy changes.

From March 11 to November 7, 2020, Arkansas revealed two major surges of new cases in July and October (Figure 1 ). The 7-day sliding window R t estimates in Arkansas was high at the beginning, nearing an R t estimate of 3, dropping below 1 in mid-April, and having peaks above 1 for a few months before steadily staying around 1. At the end of the study, the median 7day sliding window R t estimate was 1.15 (95% CrI, 1.13, 1.18). In the Delta region, the 7-day sliding window R t estimates had more pronounced decreased peaks in mid-May and mid-June, whereas the non-Delta region had two peaks below 1 in the early stages, an increased peak in mid-May that was above 1, and then stabilized around 1. At the end of the study, the Delta and non-Delta median 7-day sliding window R t estimates were 1.14 (95% CrI, 1.10, 1.17) and 1.17 (95% CrI, 1.13, 1.20), respectively, with both regions demonstrating extensive community transmission of SARS-CoV-2, with a median R t >1.

At the beginning, the PCR t estimates were high in Arkansas and both the Delta and non-Delta regions. The PCR t estimates declined statewide (median R t difference percentage: -53.56%, 95% CrI, -53.1%, -54.1%) and both Delta (-44.56%, 95% CI, -43.4%, -45.8%) and non-Delta regions (-62.67%, 95%, -62.4%, -63.0%) after schools closed on March 17 th . The PCR t estimate remained stable statewide and in the Delta region when gatherings were restricted to 10 individuals or fewer on March 23 rd , but declined by -10.81% (95% CrI, -26.9%, +8.35%) to below 1 in the non-Delta region. The PCR t estimates increased statewide (+6.68%; 95% CrI, +5.58%, +7.75%) and the non-Delta region (+14.29%; 95% CrI, -5.14%, +23.68%) after May 11 th , when restaurant dine-in operations could resume. Both regions (Delta region: -12.08%; 95%

CrI, -11.9%, -12.3%; Non-Delta region: -10.97%; 95% CrI, -10.6%, -11.3%), as well as Arkansas as a whole (-10.64%; 95% CrI, -10.60%, -10.70%), saw a decrease in the PCR t estimate when face masks were required in public beginning on July 20 th . There was an increase in the PCR t estimates statewide (+11.56%; 95% CrI, +9.88%, +13.27%) and both regions (Delta region: +9.07%; 95% CrI, +6.85%, +11.18%; Non-Delta region: +14.51%; 95% CrI, +12.3%, +16.7%) after August 24 th , when schools reopened with in-person instruction.

From March 6 to November 7, 2020, Kentucky's daily incidence data showed a steady increase (Figure 2 ). In Kentucky, the 7-day sliding window R t estimate was high in March and decreased in April. The R t estimate had peaks that stayed around 1 and by the end of the study its median was 1.10 (95% CrI, 1.08, 1.12). Both regions (Appalachian and non-Appalachian) demonstrated an extensive community transmission of SARS-CoV-2, with a median 7-day sliding window R t larger than 1. The Appalachian and non-Appalachian regions' median 7-day sliding window R t estimates were 1.07 (95% CrI, 1.04, 1.11) and 1.11 (95% CrI, 1.09, 1.14), respectively, at the end of the study.

The PCR t estimates were high among Kentucky and both regions, as the pandemic began spreading through the states. Out-of-state travel restrictions were issued on March 30 th , decreasing the PCR t estimate statewide and in the non-Appalachian region, yet PCR t increased in the Appalachian region (+32.85%; 95% CrI, +30.3%, +35.8%). The PCR t estimate decreased to below 1 in the Appalachian region (-53.51%; 95% CrI, -45.16%, -61.2%) and remained stable in the entire state, after April 4 th , when the state adopted on a voluntary basis guidance from the Centers for Disease Control and Prevention (CDC) recommending that individuals wear cloth masks in some situations. The PCR t estimates statewide (+5.19%; 95% CrI, +4.47%, +5.91%) and both regions (Appalachian region: +33.46%; 95% CrI, +20.7%, +46.8%; Non-Appalachian region: +1.93%; 95% CrI, +1.3%, +2.51%) increased after gatherings of 10 or less were allowed on May 14 th . The PCR t estimates decreased to near 1 statewide (-5.93%; 95% CrI, -4.31%, -7.65%) and both regions (Appalachian region: -13.34%; 95% CrI, -11.5%, -15.2%; Non-Appalachian region: -4.39%; 95% CrI, -2.56%, -6.33%) beginning on July 9 th , with the executive order requiring face coverings in public. There was an increase in the PCR t estimates statewide (+8.97%; 95% CrI, +8.86%, +9.08%) and both regions after September 28 th , when schools reopened with in-person instruction (Appalachian region: +7.49%; 95% CrI, +7.48%, +7.51%;

Non-Appalachian region: +9.39%; 95% CrI, +9.23%, +9.56%).

The purpose of this paper was to estimate and compare state and county-level R t trajectories of COVID-19 epidemics in Arkansas and Kentucky, focusing on differences between urban and rural areas. The implementation of preventative and relaxation measures impacted case burden and the direction of the R t trajectories. We observed decreased R t estimates when facial coverings were mandated, changing by -10.64% in Arkansas and -5.93% in Kentucky from the previous policy interval.

This paper uses R t to examine the COVID-19 transmission over several months, as well as examine how it varied by public health interventions and policy changes. The R t estimates provided public health policy makers near-real time indicators of the trajectory of the epidemic and whether their public health interventions were able to put the epidemic under control.

Several studies have examined the R t estimates with respect to policy and interventions and used R t estimates as predictive models and quantitative measures of epidemic growth or decline. [31] [32] [33] Here, the R t trajectories of Arkansas and Kentucky differed among rural and urban areas, increasing or decreasing, depending on the implementation of preventative and relaxation measures. The R t will be useful as the pandemic progresses to inform policymakers and public health professions of the direction of potential outbreaks, assisting in preventing health care surges and implementing more preventative measures and policies. For example, both Kentucky and Arkansas implemented mandated facial coverings or masks in July, 2020, which was reflected by a decrease in COVID-19 transmission.

Our study sought to further examine if differences in COVID-19 transmission occurred among location, specifically urban versus rural, since we observed that the role of population size in counties has had a less significant effect on the spread of COVID-19. One study examined trends in the distribution of COVID-19 hotspot counties and found that more hotspot counties were occurring in the southern states of the US during summer months in 2020. 34 This followed the trend and wave progression that occurred in the US, hitting the large metropolitan areas first, followed by spread in the Southern region and then in the Mid-West region. Another study found that that many of the less vulnerable counties that had a low Social Vulnerability Index had slightly higher average incidence and death rates early in the pandemic, and as the pandemic progressed, the trends crossed, with many of the most vulnerable counties facing higher rates. 35 Many of the urban metropolitan areas and cities were impacted first, before spreading to the rural areas. This may be due to the linkage of metropolitan areas, through social, economic, and commuting relationships.

Arkansas, one of eight states in the US that did not implement a stay at home or lockdown order, lacked the immediate response, as seen by other states, could explain the higher R t estimate, as it was at 2 or higher at the beginning of the pandemic. 36 Arkansas had 22 cases before the first preventative measure, the closing of schools on March 17 th , was implemented.

Additionally, the only time the PCR t estimate was below 1 was when face coverings were implemented in July, demonstrating a decrease in COVID-19 transmission. One of the biggest drivers in COVID-19 transmission in Arkansas was the poultry plant outbreaks that occurred among employees and spread through community transmission. 37 In Lincoln County, Arkansas, many COVID-19 cases were attributable to the correctional facility outbreak, rather than community transmission. 38 Additionally, there was an increase in mass testing at the correctional facility in Lincoln County, which could explain the large peaks in R t estimates that we observed. 39 One study conducted among a correctional facility in Arkansas observed that if testing for COVID-19 was only among symptomatic individuals, then fewer cases would have been detected, allowing for a greater transmission of disease to occur. 40 At the beginning of the pandemic, many states in the South and Midwest of the US observed increased COVID-19 infection rates, yet Kentucky's rate was notably low. 41 Kentucky took a very conservative method in their approach, as was observed by the policies and measures implemented, to slow the transmission of COVID-19. A decrease in COVID-19 transmission was seen in the Appalachian region, when the state adopted the guidance from the CDC recommending that people wear cloth masks in some situations and when Kentucky passed an executive order requiring face coverings in public. The Kentucky Appalachian region has high rates of comorbidities, especially respiratory diseases due to the coal industry, but saw an increase in mask wearing when required. 42 In Jefferson, Shelby, and Warren Counties in Kentucky, a decrease in PCR t was observed in transmission towards the beginning of the pandemic, when an order was issued to restrict out-of-state travel. This decrease in transmission may have been due to less travel that occurred across state lines, as Warren County is near the Tennessee border and Nashville, the Tennessee capital, and Jefferson and Shelby Counties border Indiana, and is near Cincinnati in Ohio. 43 During the study period, both Arkansas and Kentucky maintained a median R t above 1.

These two states had different political parties in charge of the governor's office in 2020, yet share similar cultural and heritage histories. Additionally, within both of these states, we found that mandated face coverings were associated with a decreased R t estimate and reopening of schools were associated with an increased R t estimate. There are a few similarities of R t estimates and PCR t estimates among these two states on a statewide level, which may suggest underlying factors, such as COVID-19 variants and pathology, rather than social determinants of health.

However, once we examine regional level, and even county level, we find both similarities (decreased COVID-19 transmission with mandated face coverings) and differences (increased COVID-19 transmission with gatherings of 10 or less allowed). The findings of this study among two similar southern states also relates to many other regions. Among different regions in the US, face coverings mandates and reopening of schools also showed a decreased and increased, respectively, of COVID-19 transmission. In the Western states (North Dakota, Montana, and Wyoming), it was found that the R t estimate decreased following a face covering mandate. 44 An increase in COVID-19 transmission was observed in South Carolina following the reopening of schools (15.3%). 45 While the R t differed among rural and urban areas at the beginning of the pandemic, as the pandemic progressed, the R t was similar across the urban and rural counties in both states.

Although population size has been found to have a less significant effect on COVID-19 spread than hypothesized at the early pandemic, it is still important to discuss the disparities that occur between rural and urban locations and the implications the pandemic has on rural locations.

Rural areas have had lower testing rates, as well as poorer health care infrastructures to handle cases. 46 Rural health care and public health systems are more vulnerable and have struggled to respond to the COVID-19 crisis. 47 Additionally, most healthcare systems do not have the capacity to handle surges in cases, and only one percent of the nation's intensive care unit beds are located in rural areas. 48 Many care and patient populations are different in rural communities and it is an important aspect to understanding the spread of COVID-19. Although policy and preventative measures are statewide, it does show differences among rural and urban communities. One study found that rural Americans were less likely than urban Americans to follow most recommended COVID-19 prevention behaviors. 49 There were several limitations in this study. One limitation was the lack of data on superspreading events that occurred in each state (for example, within prisons 50 and nursing homes, 51 as well as in religious settings, schools and sport camps, and social events 52 ). The lack of testing data, as well as hospitalization data, by county level may lead to testing bias. This would have provided further insight into the rural and urban disparities that may be present.

Many of the counties located in both Arkansas and Kentucky contained large prison populations.

The counties of Lincoln, Arkansas and Elliot, Kentucky, both contain county correctional facilities and prisons. 38, 53 The reason for the unstable R t in these counties may stem from disease amplification in prison outbreaks rather than community spread. However, it is difficult to pinpoint certain related outbreaks, and there is limited county-level data specific to correctional facilities. Additionally, there were 1,755 unknown county-level cumulative cases in Arkansas.

These cases were included in our state-level data analysis, but they were excluded from the Delta, non-Delta, and county-level hot spots analyses. Kentucky had all county-level data and all reported cases were used in all analyses.

This study observed that both Arkansas and Kentucky, as well as the respective regions, had an extensive spread of COVID-19, since both states maintained a median R t above 1. The direction of the trend of the R t estimates were reflected by the implementation of preventative measures and their subsequent relaxation as the pandemic progressed. This study was able to examine the changing transmission potential of COVID-19 over time in rural and urban areas in two socio-demographically similar Southern states. Further research is needed to examine the rural and urban differences in the spread of the COVID-19 pandemic in the US.

No conflicts of interest to disclose.

We have nothing to disclose.

We did not receive any funding support for this study. Governor Beshear issued new executive order that continued to ban anyone with a positive or presumptively positive case of COVID-19 from entering Kentucky, except as ordered for medical treatment. It also kept in place requirements of social distancing on public transportation. Those traveling from out of state into Kentucky and staying were being asked to selfquarantine for 14 days.

Everybody working for an essential business that was reopening should be wearing a mask. D May 14 Groups of 10 people or fewer could gather. E July 9 Required use of face coverings/masks in public. July 20

Cabinet for Health and Family Services issued new order that pulled back on guidance covering social, non-commercial mass gatherings. The Kentucky Department of Public Health issued a new travel advisory that recommended a 14-day self-quarantine for travelers who went to any of eight states -Alabama, Arizona, Florida, Georgia, Idaho, Nevada, South Carolina and Texasthat were reporting a positive coronavirus testing rate equal to or greater than 15%. The advisory also included Mississippi, which was quickly approaching a positive testing rate of 15%, and the U.S. Commonwealth of Puerto Rico. July 27

Announced the closing of bars for two weeks, effective, Tuesday, July 28. Announced that restaurants would be limited to 25% of prepandemic capacity indoors; outdoor accommodations remain limited only by the ability to provide proper social distancing.

Recommended that public and private schools avoided offering in-person instruction until the third week of August. August 6

Extended the state's mandate requiring face coverings in some situations for another 30 days. August 10 Governor Beshear recommended that schools waited to begin in-person classes until Sept. 28.

Issued an executive order allowing bars and restaurants to operate at 50% of capacity, as long as people could remain six feet from anyone who was not in their household or group.

Bars and restaurants would be required to halt food and beverage service by 10 p.m. and close at 11 p.m. local time.

Governor Beshear offered an update on his administration's travel advisory, which recommended a 14-day self-quarantine for Kentuckians who traveled to states and territories that were reporting a positive coronavirus testing rate equal to or greater than 15%. The current areas meeting this threshold included Florida, Nevada, Mississippi, Idaho, South Carolina, Texas, Alabama and Arizona. September 4

Extended the state's mandate requiring face coverings in some situations for another 30 days. F September 28 Schools reopened with in-person instruction. October 6

Extended the state's mandate requiring face coverings in some situations for another 30 days.

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262 COVID-19 cases reported in Elliott County prison

time-varying reproduction number (R t ) (middle panel), and R t per policy change

Restricted gatherings to 10 people or fewer

C = Required businesses, manufacturers, construction companies, and places of worship to implement social distancing protocols

One reopening of restaurants, dinein operations may continue

Governor announced that Phase 2 of reopening would begin on Jun 15, 2020, allowing restaurants and businesses to operate at two-thirds capacity

Required use of face coverings/masks in public

G = Schools reopened for in-person instruction

The daily number of incidence (left panel), time-varying reproduction number (R t ) (middle panel), and R t per policy change

A = School closure and restaurants cease in-person dining

B = Order issued to restrict out-ofstate travel

Adopted on a voluntary basis the new guidance from the U.S. Centers for Disease Control and Prevention (CDC) recommending that people wear cloth masks in some situations

D = Groups of 10 people or fewer may gather

E = Required use of face coverings/masks in public

F = Schools reopened with in-person instruction

The preliminary version of this project has its origin from an MPH class group project (EPID 7135 Epidemiology of Infectious Diseases). The authors would like to thank Ar'reil Smithson, MPH, Diane Martinez-Piedrahita, MPH, Holly Richmond-Woods, DVM, MPH, and Terrance D. Jacobs, MPH for their participation in the class group project. The authors would like to thank Aubrey Dayton-Kehoe, MPH, for sharing her R code with us.