scholarly journals Forecasting COVID-19-Associated Hospitalizations under Different Levels of Social Distancing in Lombardy and Emilia-Romagna, Northern Italy: Results from an Extended SEIR Compartmental Model

2020 ◽  
Vol 9 (5) ◽  
pp. 1492 ◽  
Author(s):  
Chiara Reno ◽  
Jacopo Lenzi ◽  
Antonio Navarra ◽  
Eleonora Barelli ◽  
Davide Gori ◽  
...  
Keyword(s):  
Hospital Care ◽  
Compartmental Model ◽  
Deterministic Model ◽  
Northern Italy ◽  
Health Interventions ◽  
Contact Rate ◽  
Public Health Interventions ◽  
Social Distancing ◽  
Containment Measures ◽  
Different Levels

The outbreak of coronavirus disease 2019 (COVID-19) was identified in Wuhan, China, in December 2019. As of 17 April 2020, more than 2 million cases of COVID-19 have been reported worldwide. Northern Italy is one of the world’s centers of active coronavirus cases. In this study, we predicted the spread of COVID-19 and its burden on hospital care under different conditions of social distancing in Lombardy and Emilia-Romagna, the two regions of Italy most affected by the epidemic. To do this, we used a Susceptible-Exposed-Infectious-Recovered (SEIR) deterministic model, which encompasses compartments relevant to public health interventions such as quarantine. A new compartment L was added to the model for isolated infected population, i.e., individuals tested positives that do not need hospital care. We found that in Lombardy restrictive containment measures should be prolonged at least until early July to avoid a resurgence of hospitalizations; on the other hand, in Emilia-Romagna the number of hospitalized cases could be kept under a reasonable amount with a higher contact rate. Our results suggest that territory-specific forecasts under different scenarios are crucial to enhance or take new containment measures during the epidemic.

scholarly journals Forecasting COVID-19-Associated Hospitalizations under Different Levels of Social Distancing in Lombardy and Emilia-Romagna, Northern Italy: Results from an Extended SEIR Compartmental Model

2020 ◽  
Author(s):  
Chiara Reno ◽  
Jacopo Lenzi ◽  
Antonio Navarra ◽  
Eleonora Barelli ◽  
Davide Gori ◽  
...  
Keyword(s):  
Hospital Care ◽  
Compartmental Model ◽  
Deterministic Model ◽  
Northern Italy ◽  
Health Interventions ◽  
Contact Rate ◽  
Public Health Interventions ◽  
Social Distancing ◽  
Containment Measures ◽  
Different Levels

The outbreak of coronavirus disease 2019 (COVID-19) was identified in Wuhan, China, in December 2019. As of April 17, 2020, more than 2 million cases of COVID-19 have been reported worldwide. Northern Italy is one of the world’s centers of active coronavirus cases. In this study, we predicted the spread of COVID-19 and its burden on hospital care under different conditions of social distancing in Lombardy and Emilia-Romagna, the two regions of Italy most affected by the epidemic. To do this, we used a Susceptible-Exposed-Infectious-Recovered (SEIR) deterministic model, which encompasses compartments relevant to public health interventions such as quarantine. A new compartment L was added to the model for isolated infected population, i.e., individuals tested positives that do not need hospital care. We found that in Lombardy restrictive containment measures should be prolonged at least until early July to avoid a resurgence of hospitalizations; on the other hand, in Emilia-Romagna the number of hospitalized cases could be kept under a reasonable amount with a higher contact rate. Our results suggest that territory-specific forecasts under different scenarios are crucial to enhance or take new containment measures during the epidemic.

scholarly journals Multinational modeling of SARS-CoV-2 spreading dynamics: Insights on the heterogeneity of COVID-19 transmission and its potential healthcare burden

2020 ◽  
Author(s):  
Erick R. Martinez-Loran ◽  
J. Jesús Naveja ◽  
Omar Y. Bello-Chavolla ◽  
Flavio F. Contreras-Torres
Keyword(s):  
Public Health ◽  
Health Interventions ◽  
Contact Rate ◽  
Public Health Interventions ◽  
Local Responses ◽  
Transmission Parameters ◽  
Exposed Population ◽  
Containment Measures ◽  
Severe Infections ◽  
Different Levels

SummaryBackgroundModelling and projections of COVID-19 using a single set of transmission parameters can be an elaborated because the application of different levels of containment measures at different stages of the worldwide COVID-19 outbreak.MethodsWe developed a piecewise fitting SEIR methodology to fit the progress of the COVID-19 that can be applied on any of the 185 countries listed in John Hopkins Coronavirus Resource Center. The contagious contact rate, the rate of removal and the initially exposed population were obtained at three different stages of the pandemic for a set of 18 countries, and globally for the total number of cases worldwide. The active number of infections and the removed populations were fitted simultaneously to validate the SEIR model against the available time series reports on the number of confirmed infections, recoveries and deaths. We evaluate the effect of a reduction of contagious contact rate on the level of burden put on local healthcare infrastructure considering different levels of intervention. As a guideline for future public health interventions, we also estimated the maximum number of future cases and its potential peak date.FindingsWe project that the peak in the number of infections worldwide will take place after the third quarter of 2020 with a decline rate that might extend beyond 2020. For 12 out of the 18 countries analyzed, we observe that, following the trend at the date of this study, the number of severe infections will surpass their healthcare capacity. For a 90% reduction scenario of the contagious contact rate, four out of the 18 countries analyzed will undergo a significant delay in the peak of infection, extending the course of the epidemic further than our simulation window (365 days).InterpretationWe identify three stages for the COVID-19 transmission dynamics, which suggest that it is highly heterogeneous between countries and its contagious contact rate, is currently affected by both local responses of the public health interventions and to the population’s adherence to the measures.FundingNo funding received.

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 Evolution of disease transmission rate during the course of SARS-COV-2: Patterns and determinants

2020 ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Region Of Interest ◽  
Health Interventions ◽  
Transmission Model ◽  
Public Health Interventions ◽  
Containment Measures ◽  
The Impact

Abstract To date, many studies have argued the potential impact of public health interventions on flattening the epidemic curve of SARS-CoV-2. Most of them have focused on simulating the impact of interventions in a region of interest by manipulating contact patterns and key transmission parameters to reflect different scenarios. Our study looks into the evolution of the daily effective reproduction number during the epidemic via a stochastic transmission model. We found this measure (although model-dependent) provides an early signal of the efficacy of containment measures. This epidemiological parameter when updated in real-time can also provide better predictions of future outbreaks. Our results found a substantial variation in the effect of public health interventions on the dynamic of SARS-CoV-2 transmission over time and across countries, that could not be explained solely by the timing and number of the adopted interventions. This suggests that further knowledge about the idiosyncrasy of their implementation and effectiveness is required. Although sustained containment measures have successfully lowered growth in disease transmission, more than half of the 101 studied countries failed to maintain the effective reproduction number close to or below 1. This resulted in continued growth in reported cases. Finally, we were able to predict with reasonable accuracy which countries would experience outbreaks in the next 30 days.

scholarly journals Early evidence that social distancing and public health interventions flatten the COVID-19 curve in Italy

2020 ◽  
Author(s):  
Valentina Rotondi ◽  
Liliana Andriano ◽  
Jennifer Beam Dowd ◽  
Melinda C. Mills
Keyword(s):  
Health Care Systems ◽  
Scientific Evidence ◽  
Health Interventions ◽  
Infection Rates ◽  
Public Health Interventions ◽  
Social Distancing ◽  
Early Evidence ◽  
The World ◽  
Care Systems ◽  
Policy Measures

With the world experiencing one of the largest pandemics in one-hundred years, governments and policymakers are looking for scientific evidence to introduce rapid and effective policies. Here we provide evidence from two provinces in Italy with comparable early infection rates but different timing of mitigating policy measures. Lodi prohibited movement on February 23, 2020 and Bergamo 2 weeks later on March 8, before the entire lockdown of Italy on March 11. This comparison provides early evidence that rapid restriction of movement and social distancing measures may slow the transmission of the virus and “flatten the curve”, ultimately reducing pressure on health care systems

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 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 Perspectives on public health interventions in the management of the COVID-19 pandemic in Thailand

2021 ◽  
Vol 5 ◽  
pp. 245
Author(s):  
Wirichada Pan-ngum ◽  
Tassawan Poomchaichote ◽  
Pimnara Peerawaranun ◽  
Natinee Kulpijit ◽  
Anne Osterrieder ◽  
...  
Keyword(s):  
Online Survey ◽  
Public Perceptions ◽  
Health Interventions ◽  
Protective Equipment ◽  
Main Concern ◽  
Public Health Interventions ◽  
Traditional Media ◽  
Social Distancing ◽  
Healthcare Interventions ◽  
High Level

Background: Any government needs to react quickly to a pandemic and make decisions on healthcare interventions locally and internationally with little information regarding the perceptions of people and the reactions they may receive during the implementation of restrictions. Methods: We report an anonymous online survey in Thailand conducted in May 2020 to assess public perceptions of three interventions in the Thai context: isolation, quarantine and social distancing. A total of 1,020 participants, of whom 52% were women, responded to the survey. Results: Loss of income was the main concern among respondents (>80% for all provinces in Thailand). Traditional media and social media were important channels for communication during the pandemic. A total of 92% of respondents reported that they changed their social behaviour even before the implementation of government policy with 94% reporting they performed social distancing, 97% reported using personal protective equipment such as masks and 95% reported using sanitizer products. Conclusions: This study showed a high level of compliance from individuals with government enforced or voluntarily controls such as quarantine, isolation and social distancing in Thailand. The findings from this study can be used to inform future government measures to control the pandemic and to shape communication strategies.

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