scholarly journals Association between COVID ‐19 public health interventions and major trauma presentation in the northern region of New Zealand

2021 ◽  
Author(s):  
Matthew J. McGuinness ◽  
Christopher Harmston ◽  
Keyword(s):  
Public Health ◽  
New Zealand ◽  
Major Trauma ◽  
Northern Region ◽  
Health Interventions ◽  
Public Health Interventions

scholarly journals PCV97 Cost-Effectiveness Analysis of Public Health Interventions to Prevent Obesity in New Zealand

2011 ◽  
Vol 14 (7) ◽  
pp. A382
Author(s):  
P. Mernagh ◽  
K. Coleman ◽  
J. Cumming ◽  
T. Green ◽  
J. Harris ◽  
...  
Keyword(s):  
Public Health ◽  
New Zealand ◽  
Cost Effectiveness ◽  
Cost Effectiveness Analysis ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Effectiveness Analysis

‘Safer communities … together’? Plural policing and COVID-19 public health interventions in Aotearoa New Zealand

2021 ◽  
pp. 1-17
Author(s):  
Antje Deckert ◽  
Nicholas J. Long ◽  
Pounamu Jade Aikman ◽  
Nayantara Sheoran Appleton ◽  
Sharyn Graham Davies ◽  
...  
Keyword(s):  
Public Health ◽  
New Zealand ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Aotearoa New Zealand

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.

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