scholarly journals Spread of COVID-19 through Georgia, USA. Near-term projections and impacts of social distancing via a metapopulation model

2020 ◽  
Author(s):  
Stephen J Beckett ◽  
Marian Dominguez-Mirazo ◽  
Seolha Lee ◽  
Clio Andris ◽  
Joshua S Weitz
Keyword(s):  
Public Health ◽  
Disease Risk ◽  
State Level ◽  
Health Interventions ◽  
County Level ◽  
Public Health Interventions ◽  
Social Distancing ◽  
Viral Spread ◽  
Near Term ◽  
Premature Removal

Epidemiological forecasts of COVID-19 spread at the country and/or state level have helped shape public health interventions. However, such models leave a scale-gap between the spatial resolution of actionable information (i.e. the county or city level) and that of modeled viral spread. States and nations are not spatially homogeneous and different areas may vary in disease risk and severity. For example, COVID-19 has age-stratified risk. Similarly, ICU units, PPE and other vital equipment are not equally distributed within states. Here, we implement a county-level epidemiological framework to assess and forecast COVID-19 spread through Georgia, where 1,933 people have died from COVID-19 and 44,638 cases have been documented as of May 27, 2020. We find that county-level forecasts trained on heterogeneity due to clustered events can continue to predict epidemic spread over multi-week periods, potentially serving efforts to prepare medical resources, manage supply chains, and develop targeted public health interventions. We find that the premature removal of physical (social) distancing could lead to rapid increases in cases or the emergence of sustained plateaus of elevated fatalities.

scholarly journals SARS-CoV-2 shifting transmission dynamics and hidden reservoirs potentially limit efficacy of public health interventions in Italy

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Marta Giovanetti ◽  
Eleonora Cella ◽  
Francesca Benedetti ◽  
Brittany Rife Magalis ◽  
Vagner Fonseca ◽  
...  
Keyword(s):  
Public Health ◽  
Epidemiological Data ◽  
Health Interventions ◽  
Transmission Dynamics ◽  
Public Health Interventions ◽  
Viral Spread ◽  
Three Phase ◽  
Phylodynamic Analysis ◽  
Epidemic Wave ◽  
Over Time

AbstractWe investigated SARS-CoV-2 transmission dynamics in Italy, one of the countries hit hardest by the pandemic, using phylodynamic analysis of viral genetic and epidemiological data. We observed the co-circulation of multiple SARS-CoV-2 lineages over time, which were linked to multiple importations and characterized by large transmission clusters concomitant with a high number of infections. Subsequent implementation of a three-phase nationwide lockdown strategy greatly reduced infection numbers and hospitalizations. Yet we present evidence of sustained viral spread among sporadic clusters acting as “hidden reservoirs” during summer 2020. Mathematical modelling shows that increased mobility among residents eventually catalyzed the coalescence of such clusters, thus driving up the number of infections and initiating a new epidemic wave. Our results suggest that the efficacy of public health interventions is, ultimately, limited by the size and structure of epidemic reservoirs, which may warrant prioritization during vaccine deployment.

scholarly journals Public Health Interventions’ Effect on Hospital Use in Patients With COVID-19: Comparative Study (Preprint)

2020 ◽  
Author(s):  
Xiaofeng Wang ◽  
Rui Ren ◽  
Michael W Kattan ◽  
Lara Jehi ◽  
Zhenshun Cheng ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
New York ◽  
New York State ◽  
The United States ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Social Distancing ◽  
Transmission Rates ◽  
Hospital Use

BACKGROUND Different states in the United States had different nonpharmaceutical public health interventions during the COVID-19 pandemic. The effects of those interventions on hospital use have not been systematically evaluated. The investigation could provide data-driven evidence to potentially improve the implementation of public health interventions in the future. OBJECTIVE We aim to study two representative areas in the United States and one area in China (New York State, Ohio State, and Hubei Province), and investigate the effects of their public health interventions by time periods according to key interventions. METHODS This observational study evaluated the numbers of infected, hospitalized, and death cases in New York and Ohio from March 16 through September 14, 2020, and Hubei from January 26 to March 31, 2020. We developed novel Bayesian generalized compartmental models. The clinical stages of COVID-19 were stratified in the models, and the effects of public health interventions were modeled through piecewise exponential functions. Time-dependent transmission rates and effective reproduction numbers were estimated. The associations of interventions and the numbers of required hospital and intensive care unit beds were studied. RESULTS The interventions of social distancing, home confinement, and wearing masks significantly decreased (in a Bayesian sense) the case incidence and reduced the demand for beds in all areas. Ohio’s transmission rates declined before the state’s “stay at home” order, which provided evidence that early intervention is important. Wearing masks was significantly associated with reducing the transmission rates after reopening, when comparing New York and Ohio. The centralized quarantine intervention in Hubei played a significant role in further preventing and controlling the disease in that area. The estimated rates that cured patients become susceptible in all areas were small (<0.0001), which indicates that they have little chance to get the infection again. CONCLUSIONS The series of public health interventions in three areas were temporally associated with the burden of COVID-19–attributed hospital use. Social distancing and the use of face masks should continue to prevent the next peak of the pandemic.

scholarly journals Revealing fine-scale spatiotemporal differences in SARS-CoV-2 introduction and spread

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Gage K. Moreno ◽  
Katarina M. Braun ◽  
Kasen K. Riemersma ◽  
Michael A. Martin ◽  
Peter J. Halfmann ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
The United States ◽  
Health Interventions ◽  
Evidence Based ◽  
Full Genome ◽  
Public Health Interventions ◽  
Viral Spread ◽  
Milwaukee County ◽  
Local Patterns

Abstract Evidence-based public health approaches that minimize the introduction and spread of new SARS-CoV-2 transmission clusters are urgently needed in the United States and other countries struggling with expanding epidemics. Here we analyze 247 full-genome SARS-CoV-2 sequences from two nearby communities in Wisconsin, USA, and find surprisingly distinct patterns of viral spread. Dane County had the 12th known introduction of SARS-CoV-2 in the United States, but this did not lead to descendant community spread. Instead, the Dane County outbreak was seeded by multiple later introductions, followed by limited community spread. In contrast, relatively few introductions in Milwaukee County led to extensive community spread. We present evidence for reduced viral spread in both counties following the statewide “Safer at Home” order, which went into effect 25 March 2020. Our results suggest patterns of SARS-CoV-2 transmission may vary substantially even in nearby communities. Understanding these local patterns will enable better targeting of public health interventions.

scholarly journals Mathematical Parameters of the COVID-19 Epidemic in Brazil and Evaluation of the Impact of Different Public Health Measures

Biology ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 220 ◽  
Author(s):  
Renato M. Cotta ◽  
Carolina P. Naveira-Cotta ◽  
Pierre Magal
Keyword(s):  
Public Health ◽  
Initial Conditions ◽  
Transmission Rate ◽  
Time Variable ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Problem Model ◽  
Social Distancing ◽  
Health Measures ◽  
The Impact

A SIRU-type epidemic model is employed for the prediction of the COVID-19 epidemy evolution in Brazil, and analyze the influence of public health measures on simulating the control of this infectious disease. The proposed model allows for a time variable functional form of both the transmission rate and the fraction of asymptomatic infectious individuals that become reported symptomatic individuals, to reflect public health interventions, towards the epidemy control. An exponential analytical behavior for the accumulated reported cases evolution is assumed at the onset of the epidemy, for explicitly estimating initial conditions, while a Bayesian inference approach is adopted for the estimation of parameters by employing the direct problem model with the data from the first phase of the epidemy evolution, represented by the time series for the reported cases of infected individuals. The evolution of the COVID-19 epidemy in China is considered for validation purposes, by taking the first part of the dataset of accumulated reported infectious individuals to estimate the related parameters, and retaining the rest of the evolution data for direct comparison with the predicted results. Then, the available data on reported cases in Brazil from 15 February until 29 March, is used for estimating parameters and then predicting the first phase of the epidemy evolution from these initial conditions. The data for the reported cases in Brazil from 30 March until 23 April are reserved for validation of the model. Then, public health interventions are simulated, aimed at evaluating the effects on the disease spreading, by acting on both the transmission rate and the fraction of the total number of the symptomatic infectious individuals, considering time variable exponential behaviors for these two parameters. This first constructed model provides fairly accurate predictions up to day 65 below 5% relative deviation, when the data starts detaching from the theoretical curve. From the simulated public health intervention measures through five different scenarios, it was observed that a combination of careful control of the social distancing relaxation and improved sanitary habits, together with more intensive testing for isolation of symptomatic cases, is essential to achieve the overall control of the disease and avoid a second more strict social distancing intervention. Finally, the full dataset available by the completion of the present work is employed in redefining the model to yield updated epidemy evolution estimates.

scholarly journals A Net Benefit Approach for the Optimal Allocation of a COVID-19 Vaccine

2020 ◽  
Author(s):  
Erin Kirwin ◽  
Ellen Rafferty ◽  
Kate Harback ◽  
Jeff Round ◽  
Christopher McCabe
Keyword(s):  
Public Health ◽  
High Risk ◽  
Health Interventions ◽  
Policy Implications ◽  
Public Health Interventions ◽  
School Closures ◽  
Viral Spread ◽  
Monetary Benefit ◽  
Net Monetary Benefit ◽  
The Impact

AbstractCoronavirus disease 2019 (COVID-19) is a severe, novel virus that has spread globally. The implementation of a combination of public health interventions is required to reduce viral spread and avoid overwhelming acute care systems. Once available, an effective vaccination will further mitigate the impact of the COVID-19 pandemic. However, decision makers will initially need to prioritise access to limited vaccine stockpiles as these will be insufficient to vaccine the whole population.The aim of this study is to identify optimal vaccine allocation strategies defined by age and risk target groups, coverage, effectiveness, and cost of vaccine, within a dynamic context where other public health responses and population behaviour change. In this study we use an epidemiological model of COVID-19 that has been enhanced to produce expected costs and Quality Adjusted Life Year results as well as total cases, hospitalisations, deaths, and net monetary benefit. We use the model to simulate hypothetical scenarios where vaccine is allocated beginning on October 15, 2020 with vaccine assumptions ranging from moderately optimistic to ‘worst-case scenario’. Net monetary benefit is used as the objective for optimisation.In a scenario with a sterilizing vaccine that is 80% effective, a stockpile sufficient for 40% population coverage, and prioritisation of those over the age of 60 at high risk of poor outcomes, active cases are reduced by 29.2% and net monetary benefit increased by $297 million dollars, relative to an identical scenario with no vaccine. The relative impact of prioritisation strategies varies greatly depending on concurrent public health interventions, for example, polices such as school closures and senior contact reductions have similar impacts on incremental net monetary benefit when there is no prioritisation given to any age or risk group (147 vs. 120 million, respectively), but when older and high risk groups are given priority, the benefit of school closures is much larger than reducing contacts for seniors (iNB 122 vs. 79 million, respectively). Results demonstrated that rank ordering of different prioritisation options varied greatly by prioritisation criteria, with different vaccine effectiveness and coverage, and by concurrently implemented policies.The results of this paper have three key policy implications: (i) that optimal vaccine allocation will depend on the public health policies, and human behaviours in place at the time of allocation; (ii) the outcomes of vaccine allocation policies can be greatly supported with interventions targeting contact reduction in critical sub-populations; and (iii) the identification of the optimal strategy depends on which outcomes are prioritised.

scholarly journals Knowledge and Determinants of Behavioral Responses to the Pandemic of COVID-19

2021 ◽  
Vol 8 ◽  
Author(s):  
Gang Lv ◽  
Jing Yuan ◽  
Stephanie Hsieh ◽  
Rongjie Shao ◽  
Minghui Li
Keyword(s):  
Public Health ◽  
Health Behaviors ◽  
Self Efficacy ◽  
Behavioral Responses ◽  
Face Mask ◽  
Preventive Health ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Social Distancing ◽  
Preventive Health Behaviors

Background: Understanding knowledge and behavioral responses to the pandemic of coronavirus disease 2019 (COVID-19) is important for appropriate public health interventions.Objectives: To assess knowledge of COVID-19 and to examine determinants associated with the adoption of preventive health behaviors among future health care providers.Methods: An anonymous online survey was sent out to pharmacy students in high and low-endemic areas of COVID-19 in China. Based on recommendations from the Chinese Center for Disease Control and Prevention, preventive health behaviors examined in this study included washing hands, wearing a face mask, and maintaining social distancing. The Health Belief Model (HBM) was used and measured by a seven-point Likert scale (one as extremely unlikely; seven as extremely likely). Multivariate linear regression models were used to examine predictors of preventive health behaviors.Results: Among 203 respondents who finished the survey, a medium level of knowledge (4.41 ± 0.95) of COVID-19 was reported. Respondents were extremely likely to wear a face mask (6.85 ± 0.60), but only moderately likely to engage in washing hands (5.95 ± 1.38) and maintaining social distancing (6.19 ± 1.60). Determinants of washing hands were cue to action, self-efficacy, knowledge, and gender; wearing a face mask were cue to action, self-efficacy, knowledge, and ethnicity; and maintaining social distancing were cue to action and self-efficacy.Conclusions: Public health interventions should consider incorporating cue to action, self-efficacy, and knowledge as factors to potentially improve the adoption of face mask-wearing, hand washing, and social distancing as appropriate individual preventive measures, especially if local and regional authorities are considering reopening schools sometime in future.

scholarly journals SARS-CoV-2 shifting transmission dynamics and hidden reservoirs limited the efficacy of public health interventions in Italy

2020 ◽  
Author(s):  
Marta Giovanetti ◽  
Eleonora Cella ◽  
Francesca Benedetti ◽  
Brittany Rife Magalis ◽  
Vagner Fonseca ◽  
...  
Keyword(s):  
Public Health ◽  
Epidemiological Data ◽  
Health Interventions ◽  
Transmission Dynamics ◽  
Public Health Interventions ◽  
Viral Spread ◽  
Three Phase ◽  
Phylodynamic Analysis ◽  
Epidemic Wave ◽  
Over Time

AbstractWe investigated SARS-CoV-2 transmission dynamics in Italy, one of the countries hit hardest by the pandemic, using phylodynamic analysis of viral genetic and epidemiological data. We observed the co-circulation of at least 13 different SARS-CoV-2 lineages over time, which were linked to multiple importations and characterized by large transmission clusters concomitant with a high number of infections. Subsequent implementation of a three-phase nationwide lockdown strategy greatly reduced infection numbers and hospitalizations. Yet we present evidence of sustained viral spread among sporadic clusters acting as “hidden reservoirs” during summer 2020. Mathematical modelling shows that increased mobility among residents eventually catalyzed the coalescence of such clusters, thus driving up the number of infections and initiating a new epidemic wave. Our results suggest that the efficacy of public health interventions is, ultimately, limited by the size and structure of epidemic reservoirs, which may warrant prioritization during vaccine deployment.

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