scholarly journals Flexible, Freely Available Stochastic Individual Contact Model for Exploring COVID-19 Intervention and Control Strategies: Development and Simulation (Preprint)

2020 ◽  
Author(s):  
Timothy Churches ◽  
Louisa Jorm
Keyword(s):  
Public Health ◽  
Open Source ◽  
Health Interventions ◽  
Social Distancing ◽  
The Public ◽  
Epidemic Curve ◽  
Health Measures ◽  
Local Areas ◽  
Individual Contact ◽  
Sydney Australia

BACKGROUND Throughout March 2020, leaders in countries across the world were making crucial decisions about how and when to implement public health interventions to combat the coronavirus disease (COVID-19). They urgently needed tools to help them to explore what will work best in their specific circumstances of epidemic size and spread, and feasible intervention scenarios. OBJECTIVE We sought to rapidly develop a flexible, freely available simulation model for use by modelers and researchers to allow investigation of how various public health interventions implemented at various time points might change the shape of the COVID-19 epidemic curve. METHODS “COVOID” (COVID-19 Open-Source Infection Dynamics) is a stochastic individual contact model (ICM), which extends the ICMs provided by the open-source EpiModel package for the R statistical computing environment. To demonstrate its use and inform urgent decisions on March 30, 2020, we modeled similar intervention scenarios to those reported by other investigators using various model types, as well as novel scenarios. The scenarios involved isolation of cases, moderate social distancing, and stricter population “lockdowns” enacted over varying time periods in a hypothetical population of 100,000 people. On April 30, 2020, we simulated the epidemic curve for the three contiguous local areas (population 287,344) in eastern Sydney, Australia that recorded 5.3% of Australian cases of COVID-19 through to April 30, 2020, under five different intervention scenarios and compared the modeled predictions with the observed epidemic curve for these areas. RESULTS COVOID allocates each member of a population to one of seven compartments. The number of times individuals in the various compartments interact with each other and their probability of transmitting infection at each interaction can be varied to simulate the effects of interventions. Using COVOID on March 30, 2020, we were able to replicate the epidemic response patterns to specific social distancing intervention scenarios reported by others. The simulated curve for three local areas of Sydney from March 1 to April 30, 2020, was similar to the observed epidemic curve in terms of peak numbers of cases, total numbers of cases, and duration under a scenario representing the public health measures that were actually enacted, including case isolation and ramp-up of testing and social distancing measures. CONCLUSIONS COVOID allows rapid modeling of many potential intervention scenarios, can be tailored to diverse settings, and requires only standard computing infrastructure. It replicates the epidemic curves produced by other models that require highly detailed population-level data, and its predicted epidemic curve, using parameters simulating the public health measures that were enacted, was similar in form to that actually observed in Sydney, Australia. Our team and collaborators are currently developing an extended open-source COVOID package comprising of a suite of tools to explore intervention scenarios using several categories of models.

scholarly journals Flexible, Freely Available Stochastic Individual Contact Model for Exploring COVID-19 Intervention and Control Strategies: Development and Simulation

10.2196/18965 ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e18965 ◽  
Author(s):  
Timothy Churches ◽  
Louisa Jorm
Keyword(s):  
Public Health ◽  
Open Source ◽  
Health Interventions ◽  
Social Distancing ◽  
The Public ◽  
Epidemic Curve ◽  
Health Measures ◽  
Local Areas ◽  
Individual Contact ◽  
Sydney Australia

Background Throughout March 2020, leaders in countries across the world were making crucial decisions about how and when to implement public health interventions to combat the coronavirus disease (COVID-19). They urgently needed tools to help them to explore what will work best in their specific circumstances of epidemic size and spread, and feasible intervention scenarios. Objective We sought to rapidly develop a flexible, freely available simulation model for use by modelers and researchers to allow investigation of how various public health interventions implemented at various time points might change the shape of the COVID-19 epidemic curve. Methods “COVOID” (COVID-19 Open-Source Infection Dynamics) is a stochastic individual contact model (ICM), which extends the ICMs provided by the open-source EpiModel package for the R statistical computing environment. To demonstrate its use and inform urgent decisions on March 30, 2020, we modeled similar intervention scenarios to those reported by other investigators using various model types, as well as novel scenarios. The scenarios involved isolation of cases, moderate social distancing, and stricter population “lockdowns” enacted over varying time periods in a hypothetical population of 100,000 people. On April 30, 2020, we simulated the epidemic curve for the three contiguous local areas (population 287,344) in eastern Sydney, Australia that recorded 5.3% of Australian cases of COVID-19 through to April 30, 2020, under five different intervention scenarios and compared the modeled predictions with the observed epidemic curve for these areas. Results COVOID allocates each member of a population to one of seven compartments. The number of times individuals in the various compartments interact with each other and their probability of transmitting infection at each interaction can be varied to simulate the effects of interventions. Using COVOID on March 30, 2020, we were able to replicate the epidemic response patterns to specific social distancing intervention scenarios reported by others. The simulated curve for three local areas of Sydney from March 1 to April 30, 2020, was similar to the observed epidemic curve in terms of peak numbers of cases, total numbers of cases, and duration under a scenario representing the public health measures that were actually enacted, including case isolation and ramp-up of testing and social distancing measures. Conclusions COVOID allows rapid modeling of many potential intervention scenarios, can be tailored to diverse settings, and requires only standard computing infrastructure. It replicates the epidemic curves produced by other models that require highly detailed population-level data, and its predicted epidemic curve, using parameters simulating the public health measures that were enacted, was similar in form to that actually observed in Sydney, Australia. Our team and collaborators are currently developing an extended open-source COVOID package comprising of a suite of tools to explore intervention scenarios using several categories of models.

scholarly journals Public Health Policies and Global COVID-19 Outbreak

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
M Komaroff ◽  
A A Belhouchet
Keyword(s):  
Public Health ◽  
South Korea ◽  
Health Interventions ◽  
Health Policies ◽  
P Value ◽  
Public Health Interventions ◽  
Public Health Policies ◽  
The Public ◽  
Health Measures ◽  
The Usa

Abstract Background Was the world prepared to face the pandemic with a standard strategy? Objectives To evaluate the association between public health interventions against the COVID-19 outbreak and the outcome. Methods The observational study included data on incidence of confirmed COVID-19 cases (outcome) and public health non-pharmaceutical interventions (exposure) from five countries: France, Italy, Japan, South Korea, and the USA, December 31, 2019 through April 12, 2020. The public health measures were grouped into five categories: lockdown, movement restrictions, public health measures, social (including social distancing) and economic measures, and use of facial mask. The multiple linear regressions were utilized to test the hypothesis that implementation of some public health measures was associated with the change in the incident number of COVID-19 cases, 2-sided, α = 0.05. Results The incidence of COVID-19 would be significantly greater without lockdown (1.89 times, p-value <.0001), public health and economic measures (25.17, p-value <.0001), and using masks (11.93, p-value=0.002), assuming that all other public health policies are the same. The effectiveness increases with earlier time of implementation. Among considered countries, South Korea was the most efficacious, where all measures were statistically significantly efficacious (p-value <0.05). Conclusions The findings demonstrate an association between public health measures and the outcome. The experience from South Korea should be studied further as the most effective non-pharmacological approach to fight the disease. This paper is the first step to develop the standardized approach utilizing the public health interventions to be applied effectively to the globe population. Key messages the most effective measures to control the COVID-19, and future outbreaks. The effect of particular measure varied by country and time of implementation.

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 Effective mitigation strategy in early stage of COVID-19 pandemic in China

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Xiao-Yue Yu ◽  
Chen Xu ◽  
Hu-Wen Wang ◽  
Rui-Jie Chang ◽  
Yin-Qiao Dong ◽  
...  
Keyword(s):  
Public Health ◽  
Early Stage ◽  
Mitigation Strategy ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Epidemic Control ◽  
The Public ◽  
The Past ◽  
Health Measures ◽  
Global Pandemic

Abstract In the past five months, success in control the national epidemic of coronavirus disease 2019 (COVID-19) has been witnessed in China. The implementation of public health measures accounts for the success which include different interventions in the early or later stages of the outbreak. It is clear that although not all measures were universally effective worldwide, their achievements have been significant. More solidarity is needed to deal with this global pandemic with more learning and understanding. Understanding which of the public health interventions implemented in China were effective may provide ideas for international epidemic control.

scholarly journals Intervention strategies against COVID-19 and their estimated impact on Swedish healthcare capacity

2020 ◽  
Author(s):  
Jasmine M Gardner ◽  
Lander Willem ◽  
Wouter Van Der Wijngaart ◽  
Shina Caroline Lynn Kamerlin ◽  
Nele Brusselaers ◽  
...  
Keyword(s):  
Public Health ◽  
Intensive Care ◽  
Healthcare System ◽  
Healthcare Workers ◽  
Health Interventions ◽  
Parameter Estimates ◽  
List Type ◽  
Public Health Interventions ◽  
Social Distancing ◽  
Health Measures

AbstractObjectivesDuring March 2020, the COVID-19 pandemic has rapidly spread globally, and non-pharmaceutical interventions are being used to reduce both the load on the healthcare system as well as overall mortality.DesignIndividual-based transmission modelling using Swedish demographic and Geographical Information System data and conservative COVID-19 epidemiological parameters.SettingSwedenParticipantsA model to simulate all 10.09 million Swedish residents.Interventions5 different non-pharmaceutical public-health interventions including the mitigation strategy of the Swedish government as of 10 April; isolation of the entire household of confirmed cases; closure of schools and non-essential businesses with or without strict social distancing; and strict social distancing with closure of schools and non-essential businesses.Main outcome measuresEstimated acute care and intensive care hospitalisations, COVID-19 attributable deaths, and infections among healthcare workers from 10 April until 29 June.FindingsOur model for Sweden shows that, under conservative epidemiological parameter estimates, the current Swedish public-health strategy will result in a peak intensive-care load in May that exceeds pre-pandemic capacity by over 40-fold, with a median mortality of 96,000 (95% CI 52,000 to 183,000). The most stringent public-health measures examined are predicted to reduce mortality by approximately three-fold. Intensive-care load at the peak could be reduced by over two-fold with a shorter period at peak pandemic capacity.ConclusionsOur results predict that, under conservative epidemiological parameter estimates, current measures in Sweden will result in at least 40-fold over-subscription of pre-pandemic Swedish intensive care capacity, with 15.8 percent of Swedish healthcare workers unable to work at the pandemic peak. Modifications to ICU admission criteria from international norms would further increase mortality.What is already known?-The COVID-19 pandemic has spread rapidly in Europe and globally since March 2020.-Mitigation and suppression methods have been suggested to slow down or halt the spread of the COVID-19 pandemic. Most European countries have enacted strict suppression measures including lockdown, school closures, enforced social distancing; while Sweden has chosen a different strategy of milder mitigation as of today (10 April 2020).-Different national policy decisions have been justified by socio-geographic differences among countries. Such differences as well as the tempo and stringency of public-health interventions are likely to affect the impact on each country’s mortality and healthcare system.What this study adds?-Individual-based modelling of COVID-19 spread using Swedish demographics and conservative epidemiological assumptions indicates that the peak of the number of hospitalised patients with COVID-19 can be expected in early May under the current strategy, shifted earlier and attenuated with more stringent public health measures.-Healthcare needs are expected to substantially exceed pre-pandemic capacity even if the most aggressive interventions considered were implemented in the coming weeks. In particular the need for intensive care unit beds will be at least 40-fold greater than the pre-pandemic capacity if the current strategy is maintained, and at least 10-fold greater if strategies approximating the most stringent in Europe are introduced by 10 April.-Our model predicts that, using median infection-fatality-rate estimates, at least 96,000 deaths would occur by 1 July without mitigation. Current policies reduce this number by approximately 15%, while even more aggressive social distancing measures, such as adding household isolation or mandated social distancing can reduce this number by more than 50%.

scholarly journals Reconceptualizing Social Distancing: Teletherapy and Social Inequality During the COVID-19 and Loneliness Pandemics

2020 ◽  
Vol 60 (5) ◽  
pp. 626-638 ◽  
Author(s):  
José G. Luiggi-Hernández ◽  
Andrés I. Rivera-Amador
Keyword(s):  
Public Health ◽  
Psychological Impact ◽  
Psychological Effects ◽  
Health Interventions ◽  
Negative Consequences ◽  
Cautionary Tale ◽  
Public Health Interventions ◽  
Social Distancing ◽  
Health Measures ◽  
The Impact

The purpose of this article is to highlight the impact of the COVID-19 pandemic amid a preexisting loneliness epidemic, as well as argue in favor of the reconceptualization of social distancing as physical distancing. As public health measures require us to take up possibly isolating practices in order to reduce and eliminate the spread of the virus, it is important to develop or take up new forms of prosocial yet physically distant dynamics in order to address the negative psychological impact of these measures. The negative consequences of public health interventions might increase feelings loneliness and isolation experienced within Western industrialized societies. For this reason, teletherapy serves as temporary (and limited) intervention that could ameliorate the psychological effects of isolation. It could also serve as a space for the development of critical consciousness, as people reflect on the sociopolitical and economic impacts these measures have on them, and how they wish to address them. Nevertheless, we also offer an ethical cautionary tale to the application of teletherapy beyond the current emergency pandemic of the COVID-19.

scholarly journals Don’t abandon your mask (and other public health measures) quite yet.

2021 ◽  
Vol 1 (S5) ◽  
pp. 4-6
Author(s):  
R. Amour Forse ◽  
Anthony O. Obinna ◽  
Manish Singh
Keyword(s):  
Public Health ◽  
Healthcare Professionals ◽  
Hand Washing ◽  
Social Distancing ◽  
The Public ◽  
Health Measures ◽  
Long Time

With the implementation of Covid-19 vaccines there has been a sence to be less vigulent with the public health measures, such as masks, social distancing, quarantee, and hand washing. Historically these measures have been around for a long time. During that time and recntly they have been very effective. Based on these observations, healthcare professionals should not abdondon them but make them an important part of their daily regime.

scholarly journals Understanding the impact of the COVID-19 pandemic on well-being and virtual care for people living with dementia and care partners living in the community

2020 ◽  
Author(s):  
P Roach ◽  
A Zwiers ◽  
E Cox ◽  
K Fischer ◽  
A Charlton ◽  
...  
Keyword(s):  
Public Health ◽  
Lived Experience ◽  
Neuropsychiatric Symptoms ◽  
Well Being ◽  
Social Distancing ◽  
The Public ◽  
Health Measures ◽  
Care Partners ◽  
Informal Supports ◽  
The Impact

AbstractThe COVID-19 pandemic has necessitated public health measures that have impacted the provision of care for people living with dementia and their families. Additionally, the isolation that results from social distancing may be harming well-being for families, as formal and informal supports become less accessible. For those with living with dementia and experiencing agitation, social distancing may be even harder to maintain, or social distancing could potentially aggravate dementia-related neuropsychiatric symptoms. To understand the lived experience of social and physical distancing during the COVID-19 pandemic in Canada we remotely interviewed 21 participants who normally attend a dementia specialty clinic in Calgary, Alberta, during a period where essential businesses were closed and healthcare had abruptly transitioned to telemedicine. The impacts of the public health measures in response to the pandemic emerged in three main categories of experience: 1) personal; 2) health services; and 3) health status (of both person living with dementia and care partner). This in-depth understanding of the needs and experiences of the pandemic for people living with dementia suggests that innovative means are urgently needed to facilitate provision of remote medicine and also social interaction and integration.

scholarly journals Kenya’s Response to COVID 19, a Descriptive Review

2020 ◽  
Vol 12 (9) ◽  
pp. 69
Author(s):  
Peter S. Ongwae ◽  
Kennedy M. Ongwae
Keyword(s):  
Public Health ◽  
Health Care Systems ◽  
World Health ◽  
The Public ◽  
Epidemic Curve ◽  
Health Measures ◽  
First Case ◽  
The World ◽  
Care Systems ◽  
Health Organization

Coronavirus Disease 2019 (COVID-19) is a respiratory viral infection caused by Severe Acute Respiratory Syndrome Corona Virus 2. The first case of the infection was confirmed in Wuhan China in 2019, by early March 2020 the infection had spread to all the continents of the World attaining a pandemic status as declared by the World Health Organization on 11th March 2020. Kenya reported its first confirmed COVID-19 case on 13th March 2020, increasing to 5206 cases as reported on 24th June 2020. COVID-19 is a novel infection with no known cure, currently, the mainstay to the infection is through public health measures. These measures are hand hygiene, cough etiquette, face masking and social distancing among others. This review aims to examine the literature on the public health measures which have been used to control outbreaks caused by respiratory viruses. The review will also identify the public health measures which Kenya is using to control the pandemic. A descriptive survey on the confirmed COVID-19 cases in Kenya shows that infection is on the rise and the epidemic curve is on the ascending trajectory. The review informs that the country requires a high level of preparedness to handle COVID-19. The areas to consider include, having robust health care systems with an adequate number of; hospital beds, healthcare workers and personal protective equipment.

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