Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Search Strategy
2.2. Inclusion Criteria
2.3. Data Extraction
2.4. Definition of the Interventions
3. Results
4. Discussion
5. Limitations of the Study
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Title | Author | Setting | Outcome | Model Development | Model Characteristic | Typology of Data |
---|---|---|---|---|---|---|
On the Spread of Coronavirus Infection. A Mechanistic Model to Rate Strategies for Disease Management. | Shiyan Wang | United States | Control of the epidemic spread, reduce spike. | New | Mechanistic | Empirical |
No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic. | Dursun Delen | 26 countries | Control of the epidemic spread, reduce spike. | Existing | Susceptible–infected–recovered (SIR) | Empirical |
Predicting the COVID-19 positive cases in India with concern to Lockdown by using Mathematical and Machine Learning based models. | Ajit Kumar Pasayat | India | Control of the epidemic spread, reduce spike. | Existing | Exponential Growth, Linear Regression | Simulation |
Preparedness and Mitigation by projecting the risk against COVID-19 transmission using Machine Learning Techniques. | Akshay Kumar | India | Risk of hotspot formation. | New | Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) | Simulation |
Quantifying the effect of quarantine control in COVID-19 infectious spread using machine learning. | Raj Dandekar | Wuhan, Italy, South Korea, USA | Control of the epidemic spread. | New | Neural network augmented | Empirical |
COVID-19 Epidemic in Switzerland: Growth Prediction and Containment Strategy Using Artificial Intelligence and Big Data. | Marcello Marini | Switzerland | Outbreak prediction evolution of spread, rate of recovery. | Existing | Agent-based simulation framework, EnerPol | Simulation |
Impacts of Social and Economic Factors on the Transmission of Coronavirus Disease 2019 (COVID-19) in China. | Yun Qiuy | China | Reduce the transmission rate. | Existing | Empirical | Empirical |
Beware of asymptomatic transmission: Study on 2019-nCoV prevention and control measures based on extended SEIR model. | Peng Shao | China | Control of the epidemic spread. | Existing | Susceptible–Exposed–Infectious–Recovered (SEIR) | Simulation |
Author | Outbreak Phase | Intervention Type | Description of Intervention | Results |
---|---|---|---|---|
Shiyan Wang | All the stages of the epidemic | Multiple | (i.) Stay at home order. (ii.) Easing social distancing measures. (iii.) Mandatory quarantine for travelers. (iv.) Non-essential business closure. (v.) Gathering ban. (vi.) School closure. (vii.) Restaurant limits. | The study suggested that non-essential business closure, a gathering ban and school closure could have a strong impact on eventual infection fraction—if the interventions were implemented before the peak infection rate. |
Dursun Delen | All the stages of the epidemic | Single | Social Distancing. | Social distancing policies could help in slowing the spread of COVID-19 (approximately 47% of the variation in the disease transmission rates) as well as in flattening the epidemic curve. |
Ajit Kumar Pasayat | All the stages of the epidemic | Single | (i.) Lockdown is not continuing strictly after May 18th, 2020. (ii.) Lockdown continues. | Partial lockdown could play a positive role in preventing the spread of the disease. |
Akshay Kumar | Beginning of the epidemic | Single | Adaption of lockdown measures according to the risk (low, moderate, and high) of new hot spots. | The study suggested to: (i) Release all constraints except mass gatherings and travel out of district in low-risk areas. (ii) Release partial constraints, i.e., (i) + markets with essential commodities in moderate-risk areas. (iii) Seal the districts with essential commodities at doorsteps in high-risk areas. |
Raj Dandekar | All the stages of the epidemic | Single | Quarantine and isolation. | Strong correlation between strengthening of the quarantine, actions taken by governments, and a decrease in effective reproductive number (Rt). |
Marcello Marini | Beginning of the epidemic | Multiple | (i.) Closure of schools. (ii.) Closure of activities. (iii.) Limitation of public transport. (iv.) Social distancing. | The study estimated that, in the absence of interventions, 42.7% of the Swiss population would have been infected. |
Yun Qiuy | Beginning of the epidemic | Multiple | (i.) Stringent quarantine. (ii.) Massive lockdown. (iii.) Other public health measures. | The interventions significantly reduced the transmission rate of COVID-19. The study also demonstrated that the actual population flow from the outbreak source poses a higher risk to the destination than geographic proximity and similarity in economic conditions. |
Peng Shao | Beginning of the epidemic | Multiple | (i.) Quarantine of infected people. (ii.) Reduction in movement of people. | The measures could help in controlling the spread of the epidemic. |
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Payedimarri, A.B.; Concina, D.; Portinale, L.; Canonico, M.; Seys, D.; Vanhaecht, K.; Panella, M. Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review. Int. J. Environ. Res. Public Health 2021, 18, 4499. https://doi.org/10.3390/ijerph18094499
Payedimarri AB, Concina D, Portinale L, Canonico M, Seys D, Vanhaecht K, Panella M. Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review. International Journal of Environmental Research and Public Health. 2021; 18(9):4499. https://doi.org/10.3390/ijerph18094499
Chicago/Turabian StylePayedimarri, Anil Babu, Diego Concina, Luigi Portinale, Massimo Canonico, Deborah Seys, Kris Vanhaecht, and Massimiliano Panella. 2021. "Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review" International Journal of Environmental Research and Public Health 18, no. 9: 4499. https://doi.org/10.3390/ijerph18094499
APA StylePayedimarri, A. B., Concina, D., Portinale, L., Canonico, M., Seys, D., Vanhaecht, K., & Panella, M. (2021). Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review. International Journal of Environmental Research and Public Health, 18(9), 4499. https://doi.org/10.3390/ijerph18094499