scholarly journals Maximum incubation period for COVID-19 infection: do we need to rethink the 14-day quarantine policy?

2020 ◽  
Author(s):  
Boris Bikbov ◽  
Alexander Bikbov
Keyword(s):  
Public Health ◽  
Incubation Period ◽  
Published Data ◽  
Global Policy ◽  
The Public ◽  
Epidemic Curve ◽  
Recent Policy ◽  
Isolation Period ◽  
Infection Spread

Manuscript presents the shortcomings of the widely accepted 14 days maximal incubation period for COVID-19 infection. We listed the accumulating published data which indicate substantially longer incubation period up to 32 days (in some studies more than 14 days incubation period was registered in more than 5% of patients with traced contacts), and recent policy requirements in some Chinese provinces to increase the mandatory isolation period over the 14 days for travelers coming from countries with rising epidemic curve. The data summarized in our comment could lead to substantial changes in global policy to minimize the risks of further infection spread and have important implications to the public health.

scholarly journals The Health-Security Nexus and the European Union: Toward a Research Agenda

2017 ◽  
Vol 8 (4) ◽  
pp. 613-625 ◽  
Author(s):  
Hylke DIJKSTRA ◽  
Anniek DE RUIJTER
Keyword(s):  
Public Health ◽  
European Union ◽  
Research Agenda ◽  
Security Policy ◽  
Integrated Approach ◽  
The European Union ◽  
Health Security ◽  
The Public ◽  
Integrated Policy ◽  
Recent Policy

AbstractThe European Union is increasingly moving toward an integrated policy approach, which also acknowledges linkages between public health and (external) security policy. This introduction to the Special Issue sets out a research agenda on the emerging health-security nexus. It analyses recent policy developments with respect to the public health and security, and discusses interactions along the health-security nexus in the context of the European Union. It suggests drivers behind the integrated approach and it critically examines the health-security nexus from the perspective of effectiveness and legitimacy.

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.

How to augment Manual Contact Tracing with Digital Contact Advice (Preprint)

2020 ◽  
Author(s):  
Ramesh Raskar ◽  
Suraj Kapa ◽  
Deepti Pahwa ◽  
Renaud Falgas ◽  
Lagnojita Sinha ◽  
...  
Keyword(s):  
Public Health ◽  
Incubation Period ◽  
Infected Individual ◽  
The Other ◽  
Contact Tracing ◽  
Top Down ◽  
Bottom Up ◽  
The Public ◽  
Hybrid Solution ◽  
Hybrid Solutions

UNSTRUCTURED Manual contact tracing is a top-down solution that starts with contact tracers at the public health level, who identify the contacts of infected individuals, interview them to get additional context about the exposure, and also monitor their symptoms and support them until the incubation period is past. On the other hand, digital contact tracing is a bottom-up solution that starts with citizens who on obtaining a notification about possible exposure to an infected individual may choose to ignore the notification, get tested to determine if they were actually exposed or self-isolate and monitor their symptoms over the next two weeks. Most experts recommend a combination of manual and digital contact tracing, though they are limited published studies to guide implementation. One possible hybrid solution digital and manual contact tracing could involve a smartphone based alert that requests the possible contact of an infected individual to call Public Health (PH) for next steps, or that suggests ways to self-assess in order to reduce the burden on PH by only advising the most critical cases to call. In this paper, we aim to compare manual and digital approaches to contact tracing and provide suggestions for potential hybrid solutions.

scholarly journals Optimum Testing Strategy for COVID-19 and Its Effect on Health Expenditure in India: An Experience of the Initial 155 Days of the Pandemic

2021 ◽  
pp. 001946622098182
Author(s):  
Saswata Ghosh ◽  
Arup Kumar Das ◽  
Akhilesh Yadav
Keyword(s):  
Public Health ◽  
Health System ◽  
Population Size ◽  
Health Expenditure ◽  
Health Spending ◽  
Published Data ◽  
Middle Income ◽  
Public Health Expenditure ◽  
The Public ◽  
Testing Rate

India has gradually increased its testing capacity of COVID-19 by mid-September 2020. However, the level of testing is substantially low in comparison with many high- and middle-income countries. Evidently, the pandemic in India is likely to be prolonged and affect millions in comparison to other countries, due to its huge population size. The possibility of a sudden upsurge of infections may turn overwhelming, jeopardising the health system, if an appropriate testing policy is not immediately adopted, given that the public health expenditure capacity of India has remained at a suboptimal level. Against this backdrop, a descriptive analysis has been carried out using the published data of the number of infections, tests and daily COVID-19 cases and public health expenditure data published by different sources and available in the public domain. The analysis suggests that a differential strategy is required to deal with the situation, which varies across states and depends upon the health spending capacity of individual states and their population size, among other factors. The specific strategy recommendations would be as follows. First, the testing rate should not be too high or too low, and this can be assessed using a marker: marginal return on testing. Second, India should follow the upper-middle-income-country standard in assessing the testing rate. Third, as a long-term strategy, there is a need to strengthen the public health system to avert a future catastrophe in the form of such pandemic.

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 How should we present the epidemic curve for COVID-19?

2021 ◽  
Author(s):  
Jean-Paul R. Soucy ◽  
Sarah A. Buchan ◽  
Kevin A. Brown
Keyword(s):  
Public Health ◽  
Symptom Onset ◽  
Public Reporting ◽  
Downward Trend ◽  
Public Health Authority ◽  
Onset Date ◽  
Current Response ◽  
Sample Collection ◽  
The Public ◽  
Epidemic Curve

Epidemic curves are used by decision makers and the public to infer the trajectory of the COVID-19 pandemic and to understand the appropriateness of current response measures. Symptom onset date is commonly used to date cases on the epidemic curve in public health reports and dashboards. However, third-party trackers often plot cases on the epidemic curve by the date they were publicly reported by the public health authority. These two curves create very different impressions of epidemic progression. On April 1, the epidemic curve for Ontario, Canada based on public reporting date showed an accelerating epidemic, whereas the curve based on a proxy variable for symptom onset date showed a rapidly declining epidemic. This illusory downward trend (the "ghost trend") is a feature of epidemic curves anchored using date variables earlier in time than the date a case was publicly reported, such as symptom onset date or sample collection date. This is because newly discovered cases are backdated, creating a perpetual downward trend in incidence due to incomplete data in the most recent days. Public reporting date is not subject to backdating bias and can be used to visualize real-time epidemic curves meant to inform the public and policy makers.

scholarly journals How should we present the epidemic curve for COVID-19?

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jean-Paul R. Soucy ◽  
Sarah A. Buchan ◽  
Kevin A. Brown
Keyword(s):  
Public Health ◽  
Symptom Onset ◽  
Health Authority ◽  
Public Reporting ◽  
Decision Makers ◽  
Public Health Authority ◽  
Onset Date ◽  
The Public ◽  
Epidemic Curve ◽  
Incident Cases

Epidemic curves are used by decision makers and the public to infer the trajectory of the COVID-19 pandemic and to understand the appropriateness of response measures. Symptom onset date is commonly used to date incident cases on the epidemic curve in public health reports and dashboards; however, third-party trackers date cases by the date they were publicly reported by the public health authority. These two curves create very different impressions of epidemic progression. On April 1, 2020, the epidemic curve based on public reporting date for Ontario, Canada showed an accelerating epidemic, whereas the curve based on a proxy variable for symptom onset date showed a rapidly declining epidemic. This illusory downward trend is a feature of epidemic curves anchored using date variables earlier in time than the date a case was publicly reported, such as the symptom onset date. Delays between the onset of symptoms and the detection of a case by the public health authority mean that recent days will always have incomplete case data, creating a downward bias. Public reporting date is not subject to this bias and can be used to visualize real-time epidemic curves meant to inform the public and decision makers.

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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