scholarly journals COVID-19 Pandemic in Rajasthan: Mathematical Modelling and Social Distancing

2020 ◽  
Vol 22 (2) ◽  
pp. 129-137
Author(s):  
Shiv Dutt Gupta ◽  
Rohit Jain ◽  
Sunil Bhatnagar
Keyword(s):  
Mathematical Modelling ◽  
Prediction Models ◽  
The State ◽  
Health Interventions ◽  
Contact Tracing ◽  
Public Health Interventions ◽  
Social Distancing ◽  
Extended Application ◽  
Prolonged Duration ◽  
Sd Model

Background: Mathematical modelling of epidemics and pandemics serves as an input to policymakers and health planners for preparedness and planning for the containment of infectious diseases and their progression in the population. The susceptible–exposed–infectious/asymptomatic–recovered social distancing (SEIAR-SD) model, an extended application of the original Kermack–McKendrick and Fred Brauer models, was developed to predict the incidence of the COVID-19 pandemic and its progression and duration in the state of Rajasthan, India. Objective: The study aimed at developing a mathematical model, the SEIAR-SD model, of the COVID-19 pandemic in the state of Rajasthan, for predicting the number of cases, progression of the pandemic and its duration. Materials and methods: The SEIAR-SD model was applied for different values of population proportion, symptomatic and asymptomatic cases and social distancing parameters to evaluate the effect of variations in the number of infected persons, size of the pandemic and its duration, with value of other parameters fixed in the model. Actual reported cases were plotted and juxtaposed on the prediction models for comparison. Results: Social distancing was the crucial determinant of the magnitude of COVID-19 cases, the progression of the pandemic and its duration. In the absence of any proven treatment or vaccine, effective social distancing would reduce the number of infections and shorten the peak and duration of the pandemic. Loosening social distancing will increase the number of cases and lead to a heightened peak and prolonged duration of the pandemic. Conclusions: In the absence of an effective treatment or a vaccine against COVID-19, social distancing (lockdown) and public health interventions—case detection with testing and isolation, contact tracing and quarantining—will be crucial for the prevention of the spread of the pandemic and for saving lives.

scholarly journals Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

2021 ◽  
Vol 18 (9) ◽  
pp. 4499
Author(s):  
Anil Babu Payedimarri ◽  
Diego Concina ◽  
Luigi Portinale ◽  
Massimo Canonico ◽  
Deborah Seys ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Public Transportation ◽  
Prediction Models ◽  
Positive Impact ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Commercial Activities

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

scholarly journals A window of opportunity for intensifying testing and tracing efforts to prevent new COVID-19 outbreaks due to more transmissible variants

2021 ◽  
Vol 47 (7/8) ◽  
pp. 329-338
Author(s):  
Jianhong Wu ◽  
Francesca Scarabel ◽  
Zachary McCarthy ◽  
Yanyu Xiao ◽  
Nicholas H Ogden
Keyword(s):  
Public Health ◽  
Reproduction Number ◽  
Health Interventions ◽  
Contact Tracing ◽  
Window Of Opportunity ◽  
New Wave ◽  
Public Health Interventions ◽  
Contact Rates ◽  
Large Outbreak ◽  
Restrictive Measures

Background: When public health interventions are being loosened after several days of decline in the number of coronavirus disease 2019 (COVID-19) cases, it is of critical importance to identify potential strategies to ease restrictions while mitigating a new wave of more transmissible variants of concern (VOCs). We estimated the necessary enhancements to public health interventions for a partial reopening of the economy while avoiding the worst consequences of a new outbreak, associated with more transmissible VOCs. Methods: We used a transmission dynamics model to quantify conditions that combined public health interventions must meet to reopen the economy without a large outbreak. These conditions are those that maintain the control reproduction number below unity, while accounting for an increase in transmissibility due to VOC. Results: We identified combinations of the proportion of individuals exposed to the virus who are traced and quarantined before becoming infectious, the proportion of symptomatic individuals confirmed and isolated, and individual daily contact rates needed to ensure the control reproduction number remains below unity. Conclusion: Our analysis indicates that the success of restrictive measures including lockdown and stay-at-home orders, as reflected by a reduction in number of cases, provides a narrow window of opportunity to intensify case detection and contact tracing efforts to prevent a new wave associated with circulation of more transmissible VOCs.

scholarly journals State heterogeneity of human mobility and COVID-19 epidemics in the European Union

2020 ◽  
Author(s):  
Xiaoling Yuan ◽  
Kun Hu ◽  
Jie Xu ◽  
Xuchen Zhang ◽  
Wei Bao ◽  
...  
Keyword(s):  
Public Health ◽  
European Union ◽  
Human Mobility ◽  
Turning Points ◽  
Temporal Trends ◽  
The State ◽  
Health Interventions ◽  
The European Union ◽  
Public Health Interventions ◽  
The Eu

SummaryHuman mobility was associated with epidemic changes of coronavirus disease 2019 (COVID-19) in China, where strict public health interventions reduced human mobility and COVID-19 epidemics. But its association with COVID-19 epidemics in the European Union (EU) is unclear. In this quasi-experimental study, we modelled the temporal trends in human mobility and epidemics of COVID-19 in the 27 EU states between January 15 and May 9, 2020. COVID-19 and human mobility had 3 trend-segments, including an upward trend in COVID-19 daily incidence and a downward trend in most human mobilities in the middle segment. Compared with the EU states farther from Italy, the state-wide lockdown dates were more likely linked to turning points of human mobilities in the EU states closer to Italy, which were also more likely linked to second turning points of COVID-19 epidemics. Among the examined human mobilities, the second turning points in driving mobility and the first turning points in parks mobility were the best factors that connected lockdown dates and COVID-19 epidemics in the EU states closer to Italy. Our findings highlight the state- and mobility-heterogeneity in the associations of public health interventions and human mobility with changes of COVID-19 epidemics in the EU states.

scholarly journals Public Health Interventions’ Effect on Hospital Use in Patients With COVID-19: Comparative Study (Preprint)

2020 ◽  
Author(s):  
Xiaofeng Wang ◽  
Rui Ren ◽  
Michael W Kattan ◽  
Lara Jehi ◽  
Zhenshun Cheng ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
New York ◽  
New York State ◽  
The United States ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Social Distancing ◽  
Transmission Rates ◽  
Hospital Use

BACKGROUND Different states in the United States had different nonpharmaceutical public health interventions during the COVID-19 pandemic. The effects of those interventions on hospital use have not been systematically evaluated. The investigation could provide data-driven evidence to potentially improve the implementation of public health interventions in the future. OBJECTIVE We aim to study two representative areas in the United States and one area in China (New York State, Ohio State, and Hubei Province), and investigate the effects of their public health interventions by time periods according to key interventions. METHODS This observational study evaluated the numbers of infected, hospitalized, and death cases in New York and Ohio from March 16 through September 14, 2020, and Hubei from January 26 to March 31, 2020. We developed novel Bayesian generalized compartmental models. The clinical stages of COVID-19 were stratified in the models, and the effects of public health interventions were modeled through piecewise exponential functions. Time-dependent transmission rates and effective reproduction numbers were estimated. The associations of interventions and the numbers of required hospital and intensive care unit beds were studied. RESULTS The interventions of social distancing, home confinement, and wearing masks significantly decreased (in a Bayesian sense) the case incidence and reduced the demand for beds in all areas. Ohio’s transmission rates declined before the state’s “stay at home” order, which provided evidence that early intervention is important. Wearing masks was significantly associated with reducing the transmission rates after reopening, when comparing New York and Ohio. The centralized quarantine intervention in Hubei played a significant role in further preventing and controlling the disease in that area. The estimated rates that cured patients become susceptible in all areas were small (<0.0001), which indicates that they have little chance to get the infection again. CONCLUSIONS The series of public health interventions in three areas were temporally associated with the burden of COVID-19–attributed hospital use. Social distancing and the use of face masks should continue to prevent the next peak of the pandemic.

scholarly journals Early evidence that social distancing and public health interventions flatten the COVID-19 curve in Italy

2020 ◽  
Author(s):  
Valentina Rotondi ◽  
Liliana Andriano ◽  
Jennifer Beam Dowd ◽  
Melinda C. Mills
Keyword(s):  
Health Care Systems ◽  
Scientific Evidence ◽  
Health Interventions ◽  
Infection Rates ◽  
Public Health Interventions ◽  
Social Distancing ◽  
Early Evidence ◽  
The World ◽  
Care Systems ◽  
Policy Measures

With the world experiencing one of the largest pandemics in one-hundred years, governments and policymakers are looking for scientific evidence to introduce rapid and effective policies. Here we provide evidence from two provinces in Italy with comparable early infection rates but different timing of mitigating policy measures. Lodi prohibited movement on February 23, 2020 and Bergamo 2 weeks later on March 8, before the entire lockdown of Italy on March 11. This comparison provides early evidence that rapid restriction of movement and social distancing measures may slow the transmission of the virus and “flatten the curve”, ultimately reducing pressure on health care systems

scholarly journals Spread of COVID-19 through Georgia, USA. Near-term projections and impacts of social distancing via a metapopulation model

2020 ◽  
Author(s):  
Stephen J Beckett ◽  
Marian Dominguez-Mirazo ◽  
Seolha Lee ◽  
Clio Andris ◽  
Joshua S Weitz
Keyword(s):  
Public Health ◽  
Disease Risk ◽  
State Level ◽  
Health Interventions ◽  
County Level ◽  
Public Health Interventions ◽  
Social Distancing ◽  
Viral Spread ◽  
Near Term ◽  
Premature Removal

Epidemiological forecasts of COVID-19 spread at the country and/or state level have helped shape public health interventions. However, such models leave a scale-gap between the spatial resolution of actionable information (i.e. the county or city level) and that of modeled viral spread. States and nations are not spatially homogeneous and different areas may vary in disease risk and severity. For example, COVID-19 has age-stratified risk. Similarly, ICU units, PPE and other vital equipment are not equally distributed within states. Here, we implement a county-level epidemiological framework to assess and forecast COVID-19 spread through Georgia, where 1,933 people have died from COVID-19 and 44,638 cases have been documented as of May 27, 2020. We find that county-level forecasts trained on heterogeneity due to clustered events can continue to predict epidemic spread over multi-week periods, potentially serving efforts to prepare medical resources, manage supply chains, and develop targeted public health interventions. We find that the premature removal of physical (social) distancing could lead to rapid increases in cases or the emergence of sustained plateaus of elevated fatalities.

scholarly journals Mandating the Use of Proximity Tracking Apps During Coronavirus Disease 2019: Ethical Justifications

2020 ◽  
Vol 7 ◽  
Author(s):  
Riya Dave ◽  
Rashmi Gupta
Keyword(s):  
Public Health ◽  
Large Degree ◽  
Health Interventions ◽  
World Health ◽  
Contact Tracing ◽  
Public Health Interventions ◽  
Societal Factors ◽  
Digital World ◽  
Health Organization ◽  
Mandatory Policy

The rise of the coronavirus disease 2019 (COVID-19) in a digital world has expectedly called upon technologies, such as wearables and mobile devices, to work in conjunction with public health interventions to tackle the pandemic. One significant example of this integration is the deployment of proximity tracking apps on smartphones to enhance traditional contact tracing methods. Many countries have adopted proximity tracking apps; however, there is a large degree of global differentiation in the voluntariness of the apps. Further, the concept of a mandatory policy—forcing individuals to use the apps—has been met with ethical concerns (e.g., privacy and liberty). While ethical considerations surrounding deployment have been put forth, such as by the World Health Organization, ethical justifications for a mandatory policy are lacking. Here, we use the Faden–Shebaya framework, which was formed to justify public health interventions, to determine if the compulsory use of proximity tracking apps is ethically appropriate. We show that while theoretically justified, due to the current state of proximity tracking applications and societal factors, it is difficult to defend a mandatory policy in practice.

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