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

The COVID-19 pandemic is the first to unfold in the highly digitalized society of the 21st century and is therefore the first pandemic to benefit from and be threatened by a thriving real-time digital information ecosystem. For this reason, the response to the infodemic required development of a public health social listening taxonomy, a structure that can simplify the chaotic information ecosystem to enable an adaptable monitoring infrastructure that detects signals of fertile ground for misinformation and guides trusted sources of verified information to fill in information voids in a timely manner. A weekly analysis of public online conversations since 23 March 2020 has enabled the quantification of running shifts of public interest in public health-related topics concerning the pandemic and has demonstrated the frequent resumption of information voids relevant for public health interventions and risk communication in an emergency response setting.

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Role of Social Media in vaccination debate about HPV: the VAC Medi+Board study

European Journal of Public Health ◽

10.1093/eurpub/ckz186.084 ◽

2019 ◽

Vol 29 (Supplement_4) ◽

Author(s):

D Artus ◽

H Larson ◽

P Kostkova

Keyword(s):

Public Health ◽

Social Media ◽

Real Time ◽

Hpv Vaccine ◽

Information Diffusion ◽

Health Interventions ◽

Vaccine Uptake ◽

Public Health Interventions ◽

The Government

Abstract Background Whilst it has long been known that anti-vaccination sentiment is widely disseminated through digital networks, 2019 has seen seismic shifts in the landscape. As viral videos originating on Youtube spread across social networks, HPV vaccine uptake tumbled in a number of countries. In Japan, the government came under sufficient pressure that they de-recommended HPV vaccine, seeing a 70% uptake rate in 2013 fall below 1%. However, there have been some reports of successful interventions - a recent campaign run by the HPV Alliance in Ireland has seen a rate back up to a national average of around 75%. A combination of hard-hitting personal testimonials, social media and traditional media looked to promote the HPV vaccine. Methods Social media platforms such as Twitter enable near real-time understandings of vaccine sentiment and information flows at scale. VAC Medi+Board project developed an innovative approach for Twitter data collection, integration, analysis and visualisation to support rapid responses through identifying key influencers and flashpoints in articles about vaccination going viral. Results This pilot study evaluated the debate about HPV on Twitter in a period of several month and developed methods for analysis and visualisation of the content, key influencers, information diffusion throughout the network and size of audience. Through complex network analysis, VAC Medi+Board piloted identification of individuals for targeted public health interventions to combat misinformation. Conclusions In this talk, we will present the VAC Medi+Board HPV study and explore the challenges and opportunities that social media can provide for public health policymakers. Key messages Analysis and graphical visualisation of HPV debate on Twitter to support targeted public health interventions at real-time. Contributing to better understanding the role of social media in the complex picture of vaccines hesitancy.

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

Thorax ◽

10.1136/thoraxjnl-2020-215086 ◽

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.

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

PLoS ONE ◽

10.1371/journal.pone.0246274 ◽

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.

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A Noel Intervention Recurrent autoencoder for real time forecasting and non-pharmaceutical intervention selection to curb the spread of Covid-19 in the world

10.1101/2020.05.05.20091827 ◽

2020 ◽

Author(s):

Qiyang Ge ◽

Zixin Hu ◽

Shudi Li ◽

Wei Lin ◽

Li Jin ◽

...

Keyword(s):

Public Health ◽

Causal Inference ◽

Real Time ◽

Health Planning ◽

Health Interventions ◽

List Type ◽

Public Health Interventions ◽

Peak Number ◽

Additional Intervention ◽

The World

ABSTRACTObjectiveDevelop the AI and casual inference-inspired methods for forecasting and evaluating the effects of public health interventions on curbing the spread of Covid-19.MethodsWe developed recurrent neural network (RNN) for modeling the transmission dynamics of the epidemics and Counterfactual-RNN (CRNN) for evaluating and exploring public health intervention strategies to slow down the spread of Covid-19 worldwide. We applied the developed methods to real-time forecasting the confirmed cases of Covid-19 across the world. The data were collected from January 22 to April 18, 2020 by John Hopkins Coronavirus Resource Center (https://coronavirus.jhu.edu/MAP.HTML).ResultsThe average errors of 1-step to 10-step forecasting were 2.9%. In the absence of a COVID-19 vaccine, we evaluated the potential effects of a number of public health measures. We found that the estimated peak number of new cases and cumulative cases, and the maximum number of cumulative cases worldwide with one week later additional intervention were reduced to 103,872, 2,104,800, and 2,271,648, respectively. The estimated total peak number of new cases and cumulative cases would be the same as the above and the maximum number of cumulative cases would be 3,864,872 in the world with 3 week later additional intervention. Duration time of the Covid-19 spread would be increased from 91 days to 123 days. Our estimation results showed that we were in the eve of stopping the spread of COVID-19 worldwide. However, we observed that transmission would quickly rebound if interventions were relaxed.ConclusionsThe accuracy of the AI-based methods for forecasting the trajectory of Covid-19 was high. The AI and causal inference-inspired methods are a powerful tool for helping public health planning and policymaking. We concluded that the spread of COVID-19 would be stopped very soon.HighlightsAs the Covid-19 pandemic soars around the world, there is urgent need to forecast the number of cases worldwide at its peak, the length of the pandemic before receding and implement public health interventions to significantly stop the spread of Covid-19.Develop artificial intelligence (AI) and causal inference inspired methods for real-time forecasting and evaluation of interventions on the worldwide trajectory of the spread of Covid-19.We estimated the maximum number of cumulative cases under immediate additional intervention to be 2,271,648; under later additional intervention the number increased to 3,864,872 and the case ending time would be May 25, 2020.Without additional intervention, the spread of COVID-19 would be stopped on July 6, 2020.

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COVID-19 epidemic modelling and the effect of public health interventions in India- SEIQHRF model

10.1101/2020.09.16.20182915 ◽

2020 ◽

Author(s):

Pooja Sengupta ◽

Bhaswati Ganguli ◽

Aditya Chatterjee ◽

Sugata SenRoy ◽

Moumita Chatterjee

Keyword(s):

Public Health ◽

Real Time ◽

Intervention Strategies ◽

Health Interventions ◽

Epidemic Modeling ◽

Time Data ◽

Public Health Interventions ◽

Indian States ◽

Epidemic Modelling ◽

Real Time Data

A dynamic epidemic modeling, based on real time data, of COVID19 has been attempted for India and few selected Indian states . Various scenarios of intervention strategies to contain the spread of the disease are explored.

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

10.1101/2020.09.01.20182873 ◽

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.

Download Full-text