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

Risk perceptions and behaviors concerning rural tourism and economic-political drivers of COVID-19 policy in 2020

When COVID-19 was first introduced to the United States, state and local governments enacted a variety of policies intended to mitigate the virulence of the epidemic. At the time, the most effective measures to prevent the spread of COVID-19 included stay-at-home orders, closing of nonessential businesses, and mask mandates. Although it was well known that regions with high population density and cold climates were at the highest risk for disease spread, rural counties that are economically reliant on tourism were incentivized to enact fewer precautions against COVID-19. The uncertainty of the COVID-19 pandemic, the multiple policies to reduce transmission, and the changes in outdoor recreation behavior had a significant impact on rural tourism destinations and management of protected spaces. We utilize fine-scale incidence and demographic data to study the relationship between local economic and political concerns, COVID-19 mitigation measures, and the subsequent severity of outbreaks throughout the continental United States. We also present results from an online survey that measured travel behavior, health risk perceptions, knowledge and experience with COVID-19, and evaluation of destination attributes by 407 out-of-state visitors who traveled to Maine from 2020 to 2021. We synthesize this research to present a narrative on how perceptions of COVID-19 risk and public perceptions of rural tourism put certain communities at greater risk of illness throughout 2020. This research could inform future rural destination management and public health policies to help reduce negative socioeconomic, health and environmental impacts of pandemic-derived changes in travel and outdoor recreation behavior.

Impact of public health policy and mobility change on transmission potential of severe acute respiratory syndrome coronavirus 2 in Rhode Island, March 2020 - November 2021

To study the SARS-CoV-2 transmission potential in Rhode Island (RI) and its association with policy changes and mobility changes, the time-varying reproduction number, Rt, was estimated. The daily incident case counts (16 March 2020, through 30 November 2021) were bootstrapped within a 15-day sliding window and multiplied by Poisson-distributed multipliers (λ = 4, sensitivity analysis: 11) to generate 1000 estimated infection counts, to which EpiEstim was applied to generate Rt time series. The median Rt percentage change when policies changed was estimated. The time lag correlations were assessed between the 7-day moving average of the relative changes in Google mobility data in the first 90 days, and Rt and estimated infection count, respectively. There were three major pandemic waves in RI in 2020-2021: spring 2020, winter 2020-2021 and fall-winter 2021. The median Rt fluctuated within the range of 0.5-2 from April 2020 to November 2021. Mask mandate (18 April 2020) was associated with a decrease in Rt (-25.99%, 95% CrI: -37.42%, -14.30%). Termination of mask mandates on 6 July 2021 was associated with an increase in Rt (36.74%, 95% CrI: 27.20%, 49.13%). Positive correlations were found between changes in grocery and pharmacy, Rt retail and recreation, transit, and workplace visits, for both Rt and estimated infection count, respectively. Negative correlations were found between changes in residential area visits for both Rt and estimated infection count, respectively. Public health policies enacted in RI were associated with changes in the pandemic trajectory. This ecological study provides further evidence of how non-pharmaceutical interventions and vaccination slowed COVID-19 transmission in RI.

COVID-19 Pandemic: Did Strict Mobility Restrictions Save Lives and Healthcare Costs in Maharashtra, India?

INTRODUCTION

Maharashtra, India, remained a hotspot during the COVID-19 pandemic. After the initial complete lockdown, the state slowly relaxed restrictions. We aim to estimate the lockdown's impact on COVID-19 cases and associated healthcare costs.

METHODS

Using daily case data for 84 days (9 March-31 May 2020), we modeled the epidemic's trajectory and predicted new cases for different phases of lockdown. We fitted log-linear models to estimate the growth rate, basic (R₀), daily reproduction number (R_e), and case doubling time. Based on pre-restriction and Phase 1 R₀, we predicted new cases for the rest of the restriction phases, and we compared them with the actual number of cases during each phase. Furthermore, using the published and gray literature, we estimated the costs and savings of implementing these restrictions for the projected period, and we performed a sensitivity analysis.

RESULTS

The estimated median R₀ during the different phases was 1.14 (95% CI: 0.85, 1.45) for pre-lockdown, 1.67 (95% CI: 1.50, 1.82) for phase 1 (strict mobility restrictions), 1.24 (95% CI: 1.12, 1.35) for phase 2 (extension of phase 1 with no restrictions on agricultural and essential services), 1.12 (95% CI: 1.01, 1.23) for phase 3 (extension of phase 2 with mobility relaxations in areas with few infections), and 1.05 (95% CI: 0.99, 1.123) for phase 4 (implementation of localized lockdowns in high-case-load areas with fewer restrictions on other areas), respectively. The corresponding doubling time rate for cases (in days) was 17.78 (95% CI: 5.61, -15.19), 3.87 (95% CI: 3.15, 5.00), 10.37 (95% CI: 7.10, 19.30), 20.31 (95% CI: 10.70, 212.50), and 45.56 (95% CI: 20.50, -204.52). For the projected period, the cases could have reached 631,819 without the lockdown, as the actual reported number of cases was 64,975. From a healthcare perspective, the estimated total value of averted cases was INR 194.73 billion (USD 2.60 billion), resulting in net cost savings of 84.05%. The Incremental Cost-Effectiveness Ratio (ICER) per Quality Adjusted Life Year (QALY) for implementing the lockdown, rather than observing the natural course of the pandemic, was INR 33,812.15 (USD 450.83).

CONCLUSION

Maharashtra's early public health response delayed the pandemic and averted new cases and deaths during the first wave of the pandemic. However, we recommend that such restrictions be carefully used while considering the local socio-economic realities in countries like India.

Late Surges in COVID-19 Cases and Varying Transmission Potential Partially Due to Public Health Policy Changes in 5 Western States, March 10, 2020, to January 10, 2021

OBJECTIVE

This study investigates the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission potential in North Dakota, South Dakota, Montana, Wyoming, and Idaho from March 2020 through January 2021.

METHODS

Time-varying reproduction numbers, R _t , of a 7-d-sliding-window and of non-overlapping-windows between policy changes were estimated using the instantaneous reproduction number method. Linear regression was performed to evaluate if per-capita cumulative case-count varied across counties with different population size or density.

RESULTS

The median 7-d-sliding-window R _t estimates across the studied region varied between 1 and 1.25 during September through November 2020. Between November 13 and 18, R _t was reduced by 14.71% (95% credible interval, CrI, [14.41%, 14.99%]) in North Dakota following a mask mandate; Idaho saw a 1.93% (95% CrI [1.87%, 1.99%]) reduction and Montana saw a 9.63% (95% CrI [9.26%, 9.98%]) reduction following the tightening of restrictions. High-population and high-density counties had higher per-capita cumulative case-count in North Dakota on June 30, August 31, October 31, and December 31, 2020. In Idaho, North Dakota, South Dakota, and Wyoming, there were positive correlations between population size and per-capita weekly incident case-count, adjusted for calendar time and social vulnerability index variables.

CONCLUSIONS

R _t decreased after mask mandate during the region's case-count spike suggested reduction in SARS-CoV-2 transmission.

SARS-CoV-2 Transmission Potential and Policy Changes in South Carolina, February 2020 - January 2021

INTRODUCTION

We aimed to examine how public health policies influenced the dynamics of coronavirus disease 2019 (COVID-19) time-varying reproductive number (R _t ) in South Carolina from February 26, 2020, to January 1, 2021.

METHODS

COVID-19 case series (March 6, 2020, to January 10, 2021) were shifted by 9 d to approximate the infection date. We analyzed the effects of state and county policies on R _t using EpiEstim. We performed linear regression to evaluate if per-capita cumulative case count varies across counties with different population size.

RESULTS

R _t shifted from 2-3 in March to <1 during April and May. R _t rose over the summer and stayed between 1.4 and 0.7. The introduction of statewide mask mandates was associated with a decline in R _t (-15.3%; 95% CrI, -13.6%, -16.8%), and school re-opening, an increase by 12.3% (95% CrI, 10.1%, 14.4%). Less densely populated counties had higher attack rates (P < 0.0001).

CONCLUSIONS

The R _t dynamics over time indicated that public health interventions substantially slowed COVID-19 transmission in South Carolina, while their relaxation may have promoted further transmission. Policies encouraging people to stay home, such as closing nonessential businesses, were associated with R _t reduction, while policies that encouraged more movement, such as re-opening schools, were associated with R _t increase.

SARS-CoV-2 transmission potential and rural-urban disease burden disparities across Alabama, Louisiana, and Mississippi, March 2020 - May 2021

PURPOSE

To quantify and compare SARS-CoV-2 transmission potential across Alabama, Louisiana, and Mississippi and selected counties.

METHODS

To determine the time-varying reproduction number R_t of SARS-CoV-2, we applied the R package EpiEstim to the time series of daily incidence of confirmed cases (mid-March 2020 - May 17, 2021) shifted backward by 9 days. Median R_t percentage change when policies changed was determined. Linear regression was performed between log₁₀-transformed cumulative incidence and log₁₀-transformed population size at four time points.

RESULTS

Stay-at-home orders, face mask mandates, and vaccinations were associated with the most significant reductions in SARS-CoV-2 transmission in the three southern states. R_t across the three states decreased significantly by ≥20% following stay-at-home orders. We observed varying degrees of reductions in R_t across states following other policies. Rural Alabama counties experienced higher per capita cumulative cases relative to urban ones as of June 17 and October 17, 2020. Meanwhile, Louisiana and Mississippi saw the disproportionate impact of SARS-CoV-2 in rural counties compared to urban ones throughout the study period.

CONCLUSION

State and county policies had an impact on local pandemic trajectories. The rural-urban disparities in case burden call for evidence-based approaches in tailoring health promotion interventions and vaccination campaigns to rural residents.

Spatially Refined Time-Varying Reproduction Numbers of COVID-19 by Health District in Georgia, USA, March-December 2020

This study quantifies the transmission potential of SARS-CoV-2 across public health districts in Georgia, USA, and tests if per capita cumulative case count varies across counties. To estimate the time-varying reproduction number, R_t of SARS-CoV-2 in Georgia and its 18 public health districts, we apply the R package 'EpiEstim' to the time series of historical daily incidence of confirmed cases, 2 March-15 December 2020. The epidemic curve is shifted backward by nine days to account for the incubation period and delay to testing. Linear regression is performed between log₁₀-transformed per capita cumulative case count and log₁₀-transformed population size. We observe R_t fluctuations as state and countywide policies are implemented. Policy changes are associated with increases or decreases at different time points. R_t increases, following the reopening of schools for in-person instruction in August. Evidence suggests that counties with lower population size had a higher per capita cumulative case count on June 15 (slope = -0.10, p = 0.04) and October 15 (slope = -0.05, p = 0.03), but not on August 15 (slope = -0.04, p = 0.09), nor December 15 (slope = -0.02, p = 0.41). We found extensive community transmission of SARS-CoV-2 across all 18 health districts in Georgia with median 7-day-sliding window R_t estimates between 1 and 1.4 after March 2020.