Epidemiological intelligence community network intervention: a community response for COVID-19 community transmission.

BACKGROUND: Expanding and providing access to early detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through testing community-based strategies among socially vulnerable communities (SVC) are critical to reducing health disparities. The Epidemiological Intelligence Community Network (EpI-Net) community-based intervention sought to increase coronavirus 2019 (COVID-19) testing uptake and prevention practices among SVC in Puerto Rico (PR). We evaluated EpI-Net's community leaders' capacity-building component by assessing pre-post COVID-19 public health workshops' tests' score changes and satisfaction among trained community leaders. METHODS: A total of 24 community leaders from SVC in PR have completed four community workshops. Pre- and post-assessments were completed as part of the health promotors training program to evaluate participants' tests score changes and satisfaction outcomes. RESULTS: Preliminary results showed: (1) high intervention retention levels of community leaders (85.7% acceptance rate); (2) change in post-test scores for community engagement strategies (p = 0.012); (3) change in post-test educational scores in COVID-19 prevention practices (p = 0.014); and (4) a change in scores in public health emergency management strategies (p < 0.001). CONCLUSIONS: The overall workshop satisfaction was 99.6%. Community leaders have shown the importance of community capacity building as a key component for intervention feasibility and impact. TRIAL REGISTRATION: Our study was retrospectively registered under the ClinicalTrial.gov ID NCT04910542.

SARS-CoV-2 Transmission in Alberta, British Columbia, and Ontario, Canada, December 25, 2019, to December 1, 2020.

OBJECTIVE: This study aimed to investigate coronavirus disease (COVID-19) epidemiology in Alberta, British Columbia, and Ontario, Canada. METHODS: Using data through December 1, 2020, we estimated time-varying reproduction number, Rt, using EpiEstim package in R, and calculated incidence rate ratios (IRR) across the 3 provinces. RESULTS: In Ontario, 76% (92 745/121 745) of cases were in Toronto, Peel, York, Ottawa, and Durham; in Alberta, 82% (49 878/61 169) in Calgary and Edmonton; in British Columbia, 90% (31 142/34 699) in Fraser and Vancouver Coastal. Across 3 provinces, Rt dropped to ≤ 1 after April. In Ontario, Rt would remain < 1 in April if congregate-setting-associated cases were excluded. Over summer, Rt maintained < 1 in Ontario, ~1 in British Columbia, and ~1 in Alberta, except early July when Rt was > 1. In all 3 provinces, Rt was > 1, reflecting surges in case count from September through November. Compared with British Columbia (684.2 cases per 100 000), Alberta (IRR = 2.0; 1399.3 cases per 100 000) and Ontario (IRR = 1.2; 835.8 cases per 100 000) had a higher cumulative case count per 100 000 population. CONCLUSIONS: Alberta and Ontario had a higher incidence rate than British Columbia, but Rt trajectories were similar across all 3 provinces.

Stressors and Impact of the COVID-19 Pandemic on Vulnerable Hispanic Caregivers and Children.

Psychological sequelae are important elements of the burden of disease among caregivers. Recognition of the impact of adversity and stress biomarkers is important to prevent mental health problems that affect rearing practices and child well-being. This cross-sectional study explored social determinants of health (SDoH)-mediated stressors during COVID-19 and risks for mental health problems among caregivers of children with prenatal Zika virus exposure. Twenty-five Hispanic caregivers completed surveys assessing SDoH vulnerabilities, COVID-exposures and impact, post-traumatic stress disorder (PTSD) symptomatology, and provided a hair sample for cortisol concentration (HCC). Most caregivers had low education, household income < $15,000/year, and were unemployed. Stressors included disrupted child education and specialized services, and food insecurity. While most reported PTSD symptomatology, multivariate linear regression models adjusted for the caregiver's age, education, and the child's sex, revealed that caregivers with high symptomatology had significantly lower HCC than those with low symptomatology and those with food insecurity had significantly higher HCC than participants without food insecurity. The impact of COVID-19 on daily life was characterized on average between worse and better, suggesting variability in susceptibility and coping mechanisms, with the most resilient identifying community support and spirituality resources. SDoH-mediators provide opportunities to prevent adverse mental health outcomes for caregivers and their children.

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, Rt 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 Rt fluctuations as state and countywide policies are implemented. Policy changes are associated with increases or decreases at different time points. Rt 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 Rt estimates between 1 and 1.4 after March 2020.

Time-varying Reproduction Numbers of COVID-19 in Georgia, USA, March 2, 2020 to November 20, 2020.

BACKGROUND: In 2020, Severe Acute Respiratory Syndrome Coronavirus 2 impacted Georgia, USA. Georgia announced a state-wide shelter-in-place on April 2 and partially lifted restrictions on April 27. We estimated the time-varying reproduction numbers (Rt) of COVID-19 in Georgia, Metro Atlanta, and Dougherty County and environs from March 2, 2020, to November 20, 2020. METHODS: We analyzed the daily incidence of confirmed COVID-19 cases in Georgia, Metro Atlanta, and Dougherty County and its surrounding counties, and estimated Rt using the R package EpiEstim. We used a 9-day correction for the date of report to analyze the data by assumed date of infection. RESULTS: The median Rt estimate in Georgia dropped from between 2 and 4 in mid-March to < 2 in late March to around 1 from mid-April to November. Regarding Metro Atlanta, Rt fluctuated above 1.5 in March and around 1 since April. In Dougherty County, the median Rt declined from around 2 in late March to 0.32 on April 26. Then, Rt fluctuated around 1 in May through November. Counties surrounding Dougherty County registered an increase in Rt estimates days after a superspreading event occurred in the area. CONCLUSIONS: In Spring 2020, Severe Acute Respiratory Syndrome Coronavirus 2 transmission in Georgia declined likely because of social distancing measures. However, because restrictions were relaxed in late April and elections were conducted in November, community transmission continued, with Rt fluctuating around 1 across Georgia, Metro Atlanta, and Dougherty County as of November 2020. The superspreading event in Dougherty County affected surrounding areas, indicating the possibility of local transmission in neighboring counties.

Assessing Early Heterogeneity in Doubling Times of the COVID-19 Epidemic across Prefectures in Mainland China, January-February, 2020.

To describe the geographical heterogeneity of COVID-19 across prefectures in mainland China, we estimated doubling times from daily time series of the cumulative case count between 24 January and 24 February 2020. We analyzed the prefecture-level COVID-19 case burden using linear regression models and used the local Moran's I to test for spatial autocorrelation and clustering. Four hundred prefectures (~98% population) had at least one COVID-19 case and 39 prefectures had zero cases by 24 February 2020. Excluding Wuhan and those prefectures where there was only one case or none, 76 (17.3% of 439) prefectures had an arithmetic mean of the epidemic doubling time <2 d. Low-population prefectures had a higher per capita cumulative incidence than high-population prefectures during the study period. An increase in population size was associated with a very small reduction in the mean doubling time (-0.012, 95% CI, -0.017, -0.006) where the cumulative case count doubled ≥3 times. Spatial analysis revealed high case count clusters in Hubei and Heilongjiang and fast epidemic growth in several metropolitan areas by mid-February 2020. Prefectures in Hubei and neighboring provinces and several metropolitan areas in coastal and northeastern China experienced rapid growth with cumulative case count doubling multiple times with a small mean doubling time.

Doubling Time of the COVID-19 Epidemic by Chinese Province.

COVID-19 epidemic doubling time by Chinese province was increasing from January 20 through February 9, 2020. The harmonic mean of the arithmetic mean doubling time estimates ranged from 1.4 (Hunan, 95% CI, 1.2-2.0) to 3.1 (Xinjiang, 95% CI, 2.1-4.8), with an estimate of 2.5 days (95% CI, 2.4-2.6) for Hubei.

Doubling Time of the COVID-19 Epidemic by Province, China.

In China, the doubling time of the coronavirus disease epidemic by province increased during January 20-February 9, 2020. Doubling time estimates ranged from 1.4 (95% CI 1.2-2.0) days for Hunan Province to 3.1 (95% CI 2.1-4.8) days for Xinjiang Province. The estimate for Hubei Province was 2.5 (95% CI 2.4-2.6) days.

Severe Acute Respiratory Syndrome Coronavirus 2 Transmission Potential, Iran, 2020.

To determine the transmission potential of severe acute respiratory syndrome coronavirus 2 in Iran in 2020, we estimated the reproduction number as 4.4 (95% CI 3.9-4.9) by using a generalized growth model and 3.5 (95% CI 1.3-8.1) by using epidemic doubling time. The reproduction number decreased to 1.55 after social distancing interventions were implemented.