Multiple Transmission Chains within COVID-19 Cluster, Connecticut, USA, 20201.
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ABSTRACT:
SUBMITTER: Bart SM
PROVIDER: S-EPMC8462310 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:To support public health policymakers in Connecticut as they begin phased lifting of social distancing restrictions, we developed a county-structured compartmental SEIR-type model of SARS-CoV-2 transmission and COVID-19 disease progression. We calibrated this model to the local dynamics of deaths and hospitalizations and the exact timing of state interventions, including school closures and stay-at-home order. In this technical report, we describe the details of the model design, implementation and calibration, and show projections of epidemic development through the Summer of 2020 under different assumptions about the increase in contact rates following partial state reopening. Our model results are consistent with high effectiveness of state lockdown measures, but changes in human interaction patterns during the coming months are unknown. In addition, a lot of uncertainty remains with respect to several key epidemiological parameters and the effectiveness of increased testing and contact tracing capacity. As more information becomes available, we will update the projections presented in this report. Reports in this series are posted to https://crawford-lab.github.io/covid19_ct/.
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Project description:To support public health policymakers in Connecticut, we developed a flexible county-structured compartmental SEIR-type model of SARS-CoV-2 transmission and COVID-19 disease progression. Our goals were to provide projections of infections, hospitalizations, and deaths, and estimates of important features of disease transmission and clinical progression. In this paper, we outline the model design, implementation and calibration, and describe how projections and estimates were used to meet the changing requirements of policymakers and officials in Connecticut from March 2020 to February 2021. The approach takes advantage of our unique access to Connecticut public health surveillance and hospital data and our direct connection to state officials and policymakers. We calibrated this model to data on deaths and hospitalizations and developed a novel measure of close interpersonal contact frequency to capture changes in transmission risk over time and used multiple local data sources to infer dynamics of time-varying model inputs. Estimated epidemiologic features of the COVID-19 epidemic in Connecticut include the effective reproduction number, cumulative incidence of infection, infection hospitalization and fatality ratios, and the case detection ratio. We conclude with a discussion of the limitations inherent in predicting uncertain epidemic trajectories and lessons learned from one year of providing COVID-19 projections in Connecticut.
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Project description:ObjectivesTo describe recent investigations of potential workplace cancer clusters.MethodsWe identified Health Hazard Evaluations (HHEs) of cancer concerns during 2001-2020. We described information about industry, requestors, cancer characteristics, investigative procedures, and determinations about the presence of a cluster (ie, presence of excess cases, unusual case distribution or exposure).ResultsOf 5754 HHEs, 174 included cancer concerns, comprising 1%-5% of HHEs per year. In 123 HHEs, the cancer cluster concerns involved different cancer primary sites. Investigation procedures varied but included record review (n=63, 36%) and site visits (n=22, 13%). Of 158 HHEs with a cluster determination by investigator(s), 151 (96%) were not considered cancer clusters. In seven HHEs, investigators found evidence of a cluster, but occupational exposure to a carcinogen was not identified.ConclusionsThe proportion of HHEs on workplace cancer cluster concerns remained steady over time; most did not meet the definition of a cluster or uncover an occupational cause. Public health practitioners can use this information to provide updated context when addressing workplace cancer cluster concerns and as motivation to refine investigative approaches. More broadly, this review highlights an opportunity to identify best practices on how to apply community cluster investigation methods to the workplace.
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Project description:We report a dengue outbreak in Key Largo, Florida, USA, from February through August 2020, during the COVID-19 pandemic. Successful community engagement resulted in 61% of case-patients self-reporting. We also describe COVID-19 pandemic effects on the dengue outbreak investigation and the need to increase clinician awareness of dengue testing recommendations.
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Project description:The serial interval and effective reproduction number for coronavirus disease (COVID-19) are heterogenous, varying by demographic characteristics, region, and period. During February 1-July 13, 2020, we identified 4,080 transmission pairs in Georgia, USA, by using contact tracing information from COVID-19 cases reported to the Georgia Department of Public Health. We examined how various transmission characteristics were affected by symptoms, demographics, and period (during shelter-in-place and after subsequent reopening) and estimated the time course of reproduction numbers for all 159 Georgia counties. Transmission varied by time and place but also by persons' sex and race. The mean serial interval decreased from 5.97 days in February-April to 4.40 days in June-July. Younger adults (20-50 years of age) were involved in most transmission events occurring during or after reopening. The shelter-in-place period was not long enough to prevent sustained virus transmission in densely populated urban areas connected by major transportation links.
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