# A Hybrid Model for COVID-19 Monitoring and Prediction

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## Abstract

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## 1. Introduction

## 2. Predicting the Pandemic with Artificial Intelligence Models

## 3. Proposed Model and Case Study: Caldas COVID-19 Evolution

#### 3.1. Extraction of Input Variables

#### 3.2. SIR Model

#### 3.3. Coefficient Extrapolation

#### 3.4. Expert System for Restraint Measure Modeling

#### 3.5. Platform Achitecture

- Data extraction: periodically (once a day) extracts statistics on the impact of the COVID-19 pandemic from Colombia’s open portal (https://www.datos.gov.co/, accessed on 22 February 2021).
- Deep intelligence: This is a platform used to manage the different workflows in projects in the field of Big Data, Artificial Intelligence and Smart Cities. Specifically, it is used in the following ways:
- -
- Main data warehouse, as it stores both the data resulting from extraction and the data resulting from predictions.
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- Visualization engine: it is able to generate interactive visualizations with the stored data to show the result of the whole process in the most summarized way possible to the user.

- Data analysis: this is a system that periodically takes Deep Intelligence data on the indicators of COVID-19 impact in Colombia, in order to make the relevant predictions with the hybrid model explained above. Finally, it stores the results in Deep Intelligence.

- Personal data
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- ID: it is the patient identifier; the identification document is encrypted to preserve the privacy of the information.
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- Municipality: name of the municipality where the patient resides.
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- Department: name of the department where the patient resides.
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- Age Criteria: identifies whether the patient is an adult (A) or a minor (M)
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- Age: it is the numerical value of the patient’s age at the time of registration.
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- Gender: gender identifier of the patient, may be female (F) or male (M).
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- Neighborhood/Village: indicates the name of the neighborhood or village of the patient’s residence.
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- Address: the exact location of the patient.
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- Patient state: identifies if the patient has recovered or deceased.
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- Management: Describes whether the person was hospitalized (H) or stayed at home (C)
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- Type of contagion: states if the contagion has been within the community, from a relative or imported.
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- State of severity of the patient: a difference between mild, moderate or serious.

- General section:
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- Alert: allows to register the test status of the patient: in study, discarded, confirmed.
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- Record date: corresponds to the date on which the patient information was recorded.
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- Onset of symptoms date: corresponds to the date the patient started having symptoms.
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- Diagnosis date: date the patient’s infection was confirmed.
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- Recovered date: corresponds to the date on which the patient was recovered.
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- Date of death: date of the patient’s death.

## 4. Results

## 5. Conclusions and Future Work

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**A historical dataset is used to calculate how COVID-19 evolves over a certain period of time and the curves S (the time-dependent susceptible population), I (the time-dependent infected population) and R (the time-dependent removed (recovered, death) population) are extracted over this period of time. These variables are used to fit an SIR model using sliding windows. The differential equations are solved using a Runge–Kutta method and the fit is performed in the sense of mean squares, thus extracting the unknown parameters of the model: $\beta $ and $\gamma $, as well as the basic reproductive number ${R}_{0}$, all of them being functions of time. To extrapolate these parameters to higher time values (${\beta}^{\prime},{\gamma}^{\prime},{R}_{0}^{\prime}$), an LSTM neural network is used, the results of which are further refined by using an expert system that takes into account possible future changes in the constraints imposed by the government (${\beta}^{\u2033},{\gamma}^{\u2033},{R}_{0}^{\u2033}$ in the diagram). Finally, by solving the SIR model again with these extrapolated coefficients, the predictions in the evolution of the ${S}^{\prime}$, ${I}^{\prime}$ and ${R}^{\prime}$ curves are obtained.

**Figure 2.**Representation of the variation of the $\beta $ parameter without applying the expert system (blue curve). The horizontal coordinate represents time, while the vertical coordinate represents this coefficient. The higher the value is, the more contacts have infected individuals. The solid vertical lines represent the dates on which the government imposed a new measure and the dotted vertical lines are the approximate dates on which the consequences of these measures are expected to become visible. This time lag, which has been considered to be 10 days, is due to the existence of an incubation period of the disease, where no symptoms are present even though contagion has occurred, and an adaptation period until the measure is fully established.

**Figure 3.**Representation of the sigmoidal function that has been used when applying a strong relaxation measure, assuming an incubation period t of 3 days and an adaptation period k to the new measure of 7 days. As a result, a smooth transition transition is obtained when a measure is considered in the expert model.

**Figure 4.**Architecture used to implement the proposed predictive model. The data is obtained from the CKAN server in Colombia’s open portal (

**upper left**), using a Python-implemented API served with Flask (

**lower left**), which also accesses additional information sources. The interaction with the Deep Intelligence platform (

**lower right**) is performed by means of its API. Users (

**upper right**) are able to access the platform using its graphical interface.

**Figure 5.**Some of the steps performed to build the system in Deep Intelligence [34]. (

**a**) A data source in the platform, showing structured information. (

**b**) Monitoring of additional indicators using the visualization tools provided by the platform. (

**c**) Global curve visualization from an external source. (

**d**) Visualization of the predictions obtained by the model.

**Figure 6.**Prediction of the evolution of the number of positive cases from 4 September. The series show the different predictions when various hypothesis on the restriction measures are used in the system. The prediction curves extend up to 20 days, which is the intended prediction range.

**Figure 7.**Prediction of the evolution of the number of positive cases from 4 September with a confidence interval of 80 %, assuming that no restrictive measures are applied. The prediction extends up to 20 days, which is the intended prediction range.

**Figure 8.**Analysis of the error distribution in the prediction of infected individuals. (

**a**) Distribution of the relative error for a 7-day prediction horizon. (

**b**) Error as a function of the prediction horizon. Solid lines depict the mean of the absolute value of the relative error, while the shaded regions depict its sample standard deviation.

Case ID | Symptoms Onset Date | Diagnostic Date | Recovery Date | Death Date |
---|---|---|---|---|

2592 | 2020-03-26 | 2020-04-11 | 2020-04-23 | - |

2593 | 2020-03-25 | 2020-04-11 | 2020-05-25 | - |

2594 | 2020-03-25 | 2020-04-11 | 2020-05-05 | - |

2595 | 2020-03-25 | 2020-04-11 | 2020-05-16 | - |

2596 | 2020-03-28 | 2020-04-11 | - | 2020-04-01 |

2597 | 2020-03-22 | 2020-04-11 | 2020-04-16 | - |

2699 | 2020-04-09 | 2020-04-11 | 2020-04-21 | - |

**Table 2.**Percentage of change according to the type of measure applied, based on the date from the measures taken in Caldas (Figure 2).

Government Measures | Percentage Change | Target Change |
---|---|---|

Strong restriction | −30% | 0.7 |

Slight restriction | −10% | 0.9 |

Slight relaxation | +10% | 1.1 |

Strong relaxation | +30% | 1.3 |

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**MDPI and ACS Style**

Castillo Ossa, L.F.; Chamoso, P.; Arango-López, J.; Pinto-Santos, F.; Isaza, G.A.; Santa-Cruz-González, C.; Ceballos-Marquez, A.; Hernández, G.; Corchado, J.M.
A Hybrid Model for COVID-19 Monitoring and Prediction. *Electronics* **2021**, *10*, 799.
https://doi.org/10.3390/electronics10070799

**AMA Style**

Castillo Ossa LF, Chamoso P, Arango-López J, Pinto-Santos F, Isaza GA, Santa-Cruz-González C, Ceballos-Marquez A, Hernández G, Corchado JM.
A Hybrid Model for COVID-19 Monitoring and Prediction. *Electronics*. 2021; 10(7):799.
https://doi.org/10.3390/electronics10070799

**Chicago/Turabian Style**

Castillo Ossa, Luis Fernando, Pablo Chamoso, Jeferson Arango-López, Francisco Pinto-Santos, Gustavo Adolfo Isaza, Cristina Santa-Cruz-González, Alejandro Ceballos-Marquez, Guillermo Hernández, and Juan M. Corchado.
2021. "A Hybrid Model for COVID-19 Monitoring and Prediction" *Electronics* 10, no. 7: 799.
https://doi.org/10.3390/electronics10070799