scholarly journals Effects of Public Health Interventions on the Epidemiological Spread During the First Wave of the COVID-19 Outbreak in Thailand

2020 ◽  
Author(s):  
Sipat Dr. Triukose ◽  
Sirin Dr. Nitinawarat ◽  
Ponlapat Satian ◽  
Anupap Dr. Somboonsavatdee ◽  
Ponlachart Dr. Chotikarn ◽  
...  
Keyword(s):  
Public Health ◽  
Respiratory Disease ◽  
Broad Spectrum ◽  
Hubei Province ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Level Of Risk

A novel infectious respiratory disease was recognized in Wuhan (Hubei Province, China) in December 2019. In February 2020, the disease was named "coronavirus disease 2019" (COVID-19). COVID-19 became a pandemic in March 2020, and, since then, different countries have implemented a broad spectrum of policies. Thailand is considered to be among the top countries in handling its first wave of the outbreak -- 12 January to 31 July 2020. Here, we illustrate how Thailand tackled the COVID-19 outbreak, particularly the effects of public health interventions on the epidemiological spread. This study shows how the available data from the outbreak can be analyzed and visualized to quantify the severity of the outbreak, the effectiveness of the interventions, and the level of risk of allowed activities during an easing of a "lockdown." This study shows how a well-organized governmental apparatus can overcome the havoc caused by a pandemic.

scholarly journals Effects of public health interventions on the epidemiological spread during the first wave of the COVID-19 outbreak in Thailand

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246274
Author(s):  
Sipat Triukose ◽  
Sirin Nitinawarat ◽  
Ponlapat Satian ◽  
Anupap Somboonsavatdee ◽  
Ponlachart Chotikarn ◽  
...  
Keyword(s):  
Public Health ◽  
Respiratory Disease ◽  
Broad Spectrum ◽  
Hubei Province ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Level Of Risk

A novel infectious respiratory disease was recognized in Wuhan (Hubei Province, China) in December 2019. In February 2020, the disease was named “coronavirus disease 2019” (COVID-19). COVID-19 became a pandemic in March 2020, and, since then, different countries have implemented a broad spectrum of policies. Thailand is considered to be among the top countries in handling its first wave of the outbreak—12 January to 31 July 2020. Here, we illustrate how Thailand tackled the COVID-19 outbreak, particularly the effects of public health interventions on the epidemiological spread. This study shows how the available data from the outbreak can be analyzed and visualized to quantify the severity of the outbreak, the effectiveness of the interventions, and the level of risk of allowed activities during an easing of a “lockdown.” This study shows how a well-organized governmental apparatus can overcome the havoc caused by a pandemic.

scholarly journals Effect of public health interventions on COVID-19 cases: an observational study

Thorax ◽  
2021 ◽  
pp. thoraxjnl-2020-215086
Author(s):  
Weihong Qiu ◽  
Heng He ◽  
Peng Zhang ◽  
Wenwen Yang ◽  
Tingming Shi ◽  
...  
Keyword(s):  
Public Health ◽  
Attack Rate ◽  
Hubei Province ◽  
Health Interventions ◽  
Public Health Interventions ◽  
The Public ◽  
Daily Average ◽  
Harmonic Means ◽  
Epidemiological Characteristics ◽  
Doubling Times

BackgroundAs the epidemic of COVID-19 is gradually controlled in China, a summary of epidemiological characteristics and interventions may help control its global spread.MethodsData for COVID-19 cases in Hubei Province (capital, Wuhan) was extracted until 7 March 2020. The spatiotemporal distribution of the epidemic in four periods (before 10 January, 10–22 January, 23 January–6 February and 7 February–7 March) was evaluated, and the impacts of interventions were observed.ResultsAmong 67 706 COVID-19 cases, 52 111 (76.97%) were aged 30–69 years old, and 34 680 (51.22%) were women. The average daily attack rates (95% CI) were 0.5 (0.3 to 0.7), 14.2 (13.2 to 15.1), 45.7 (44.0 to 47.5) and 8.6 (7.8 to 9.3) cases per 106 people in four periods, and the harmonic means (95% CI) of doubling times were 4.28 (4.01 to 4.55), 3.87 (3.78 to 3.98), 5.40 (4.83 to 6.05) and 45.56 (39.70 to 52.80) days. Compared with the first period, daily attack rates rose rapidly in the second period. In the third period, 14 days after 23 January, the daily average attack rate in and outside Wuhan declined by 33.8% and 48.0%; the doubling times increased by 95.0% and 133.2%. In the four periods, 14 days after 7 February, the daily average attack rate in and outside Wuhan decreased by 79.1% and 95.2%; the doubling times increased by 79.2% and 152.0%.ConclusionsThe public health interventions were associated with a reduction in COVID-19 cases in Hubei Province, especially in districts outside of Wuhan.

scholarly journals The Real-time Effect of Public Health Interventions on the COVID-19 Epidemic in Hubei Province

2021 ◽  
Vol 18 (5) ◽  
pp. 907-921
Author(s):  
Jiamin Liu ◽  
Ze Chen ◽  
Yanyan Ouyang ◽  
Xu Guo ◽  
Wangli Xu
Keyword(s):  
Public Health ◽  
Real Time ◽  
Hubei Province ◽  
Time Effect ◽  
Health Interventions ◽  
Public Health Interventions ◽  
The Real

The Real-time Effect of Public Health Interventions on the COVID-19 Epidemic in Hubei Province

2021 ◽  
Vol 18 (5) ◽  
pp. 61-75
Author(s):  
Jiamin Liu ◽  
Ze Chen ◽  
Yanyan Ouyang ◽  
Xu Guo ◽  
Wangli Xu
Keyword(s):  
Public Health ◽  
Real Time ◽  
Hubei Province ◽  
Time Effect ◽  
Health Interventions ◽  
Public Health Interventions ◽  
The Real

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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