scholarly journals Assessing outcomes of large-scale public health interventions in the absence of baseline data using a mixture of Cox and binomial regressions

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
Thierry Duchesne ◽  
Belkacem Abdous ◽  
Catherine M Lowndes ◽  
Michel Alary
Keyword(s):  
Public Health ◽  
Large Scale ◽  
Baseline Data ◽  
Health Interventions ◽  
Public Health Interventions

scholarly journals COVID-19 Control: Can Germany Learn From China?

2020 ◽  
Author(s):  
Olaf Müller ◽  
Guangyu Lu ◽  
Albrecht Jahn ◽  
Oliver Razum
Keyword(s):  
Public Health ◽  
Health Systems ◽  
Large Scale ◽  
Early Stage ◽  
Incidence Rates ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Low Incidence ◽  
Psychosocial Costs ◽  
Similar Stage

The coronavirus disease 2019 (COVID-19) outbreak started in China in December 2019 and has developed into a pandemic. Using mandatory large-scale public health interventions including a lockdown with locally varying intensity and duration, China has been successful in controlling the epidemic at an early stage. The epicentre of the pandemic has since shifted to Europe and The Americas. In certain cities and regions, health systems became overwhelmed by high numbers of cases and deaths, whereas other regions continue to experience low incidence rates. Still, lockdowns were usually implemented country-wide, albeit with differing intensities between countries. Compared to its neighbours, Germany has managed to keep the epidemic relatively well under control, in spite of a lockdown that was only partial. In analogy to many countries at a similar stage, Germany is now under increasing pressure to further relax lockdown measures to limit economic and psychosocial costs. However, if this is done too rapidly, Germany risks facing tens of thousands more severe cases of COVID-19 and deaths in the coming months. Hence, it could again follow China’s example and relax measures according to local incidence, based on intensive testing.

scholarly journals Tensions between research and public health: modelling the risks and benefits of SARS-CoV-2 vaccine field trials versus human infection challenge studies.

2020 ◽  
Author(s):  
George S Heriot ◽  
Euzebiusz Jamrozik ◽  
Michael J Selgelid
Keyword(s):  
Public Health ◽  
Vaccine Efficacy ◽  
Large Scale ◽  
Vaccine Development ◽  
Local Population ◽  
Field Trials ◽  
Human Infection ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Post Trial

Background: Human infection challenge studies (HICS) with SARS-CoV-2 are under consideration as a way of accelerating vaccine development. We evaluate potential vaccine research strategies under a range of epidemic conditions determined, in part, by the intensity of public health interventions. Methods: We constructed a compartmental epidemiological model incorporating public health interventions, vaccine efficacy trials and a post-trial population vaccination campaign. The model was used to estimate the duration and benefits of large-scale field trials in comparison with HICS accompanied by an expanded safety trial, and to assess the marginal risk faced by HICS participants. Results: Field trials may demonstrate vaccine efficacy more rapidly than a HICS strategy under epidemic conditions consistent with moderate mitigation policies. A HICS strategy is the only feasible option for testing vaccine efficacy under epidemic suppression, and maximises the benefits of post-trial vaccination. Less successful or absent mitigation results in minimal or no benefit from post-trial vaccination, irrespective of trial design. Conclusions: SARS-CoV-2 HICS are the optimal method of vaccine testing for populations maintained under epidemic suppression, where vaccination offers the greatest benefits to the local population.

scholarly journals Contacts in context: large-scale setting-specific social mixing matrices from the BBC Pandemic project

2020 ◽  
Author(s):  
Petra Klepac ◽  
Adam J Kucharski ◽  
Andrew JK Conlan ◽  
Stephen Kissler ◽  
Maria L Tang ◽  
...  
Keyword(s):  
Public Health ◽  
Large Scale ◽  
Health Interventions ◽  
Multiple Sources ◽  
Public Health Interventions ◽  
School Closures ◽  
Contact Patterns ◽  
Social Mixing ◽  
Contact Data ◽  
And Control

AbstractSocial mixing patterns are crucial in driving transmission of infectious diseases and informing public health interventions to contain their spread. Age-specific social mixing is often inferred from surveys of self-recorded contacts which by design often have a very limited number of participants. In addition, such surveys are rare, so public health interventions are often evaluated by considering only one such study. Here we report detailed population contact patterns for United Kingdom based self-reported contact data from over 36,000 volunteers that participated in the massive citizen science project BBC Pandemic. The amount of data collected allows us generate fine-scale age-specific population contact matrices by context (home, work, school, other) and type (conversational or physical) of contact that took place. These matrices are highly relevant for informing prevention and control of new outbreaks, and evaluating strategies that reduce the amount of mixing in the population (such as school closures, social distancing, or working from home). In addition, they finally provide the possibility to use multiple sources of social mixing data to evaluate the uncertainty that stems from social mixing when designing public health interventions.

scholarly journals An analysis of COVID-19 clusters in India

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pooja Sengupta ◽  
Bhaswati Ganguli ◽  
Sugata SenRoy ◽  
Aditya Chatterjee
Keyword(s):  
Public Health ◽  
Cluster Analysis ◽  
Population Density ◽  
Case Studies ◽  
Compartment Model ◽  
Health Interventions ◽  
Disease Spread ◽  
Second Phase ◽  
Public Health Interventions ◽  
Homogeneous Groups

Abstract Background In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread. Methods A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments. Results The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control. Conclusions The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

scholarly journals Impact of reduction of susceptibility to SARS-CoV-2 on epidemic dynamics in four early-seeded metropolitan regions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Thomas J. Barrett ◽  
Karen C. Patterson ◽  
Timothy M. James ◽  
Peter Krüger
Keyword(s):  
Public Health ◽  
Growth Phase ◽  
Chronic Phase ◽  
Health Interventions ◽  
Resource Utilisation ◽  
Policy Planning ◽  
Infection Rates ◽  
Public Health Interventions ◽  
Metropolitan Regions ◽  
Epidemic Dynamics

AbstractAs we enter a chronic phase of the SARS-CoV-2 pandemic, with uncontrolled infection rates in many places, relative regional susceptibilities are a critical unknown for policy planning. Tests for SARS-CoV-2 infection or antibodies are indicative but unreliable measures of exposure. Here instead, for four highly-affected countries, we determine population susceptibilities by directly comparing country-wide observed epidemic dynamics data with that of their main metropolitan regions. We find significant susceptibility reductions in the metropolitan regions as a result of earlier seeding, with a relatively longer phase of exponential growth before the introduction of public health interventions. During the post-growth phase, the lower susceptibility of these regions contributed to the decline in cases, independent of intervention effects. Forward projections indicate that non-metropolitan regions will be more affected during recurrent epidemic waves compared with the initially heavier-hit metropolitan regions. Our findings have consequences for disease forecasts and resource utilisation.

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