# An Overview of the World Current and Future Assessment of Novel COVID-19 Trajectory, Impact, and Potential Preventive Strategies at Healthcare Settings

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## Abstract

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^{2}> 0.97, which is an indication of the outbreak control. The SIR models for the countries in this group showed that they passed the expected 99% end of pandemic dates. Group B, however, exhibited a continuous increase of the total COVID-19 new cases, that best suited an exponential growth model with r

^{2}> 0.97, which meant that the outbreak is still uncontrolled. The SIR models for the countries in this group showed that they are still relatively far away from reaching the expected 97% end of pandemic dates. The maximum death percentage varied from 3.3% (India) to 7.2% with USA recording the highest death percentage, which is virtually equal to the maximum death percentage of the world (7.3%). The power of the exponential model determines the severity of the country’s trajectory that ranged from 11 to 19 with the USA and Brazil having the highest values. The maximum impact of this COVID-19 pandemic occurred during the uncontrolled stage (2), which mainly depended on the deceptive stage (1). Further, some novel potential containment strategies are discussed. Results from both models showed that the Group A countries contained the outbreak, whereas the Group B countries still have not reached this stage yet. Early measures and containment strategies are imperative in suppressing the spread of COVID-19.

## 1. Introduction

_{0}[22]. Iwato et al. conducted simulations using the SEIR model to assess the impact of secondary outbreaks outside China, assuming that one infected patient travelled to an outside community [23]. While applying the SEIR compartmental model, Kuniya T. predicted the epidemic peak for the coronavirus in Japan using real data from January to February 2020 [24]. Al Qaness et al. developed a novel forecasting model that forecasted and estimated the COVID-19 cases for the upcoming ten days using the Adaptive Neuro-Fuzzy Inference System (ANFIS), which uses the enhanced Flower Pollination Algorithm (FPA) and the Salp Swarm Algorithm (SSA) [25]. Roosa et al., in their study, generated forecasts based on two popular models used previously for forecasting infectious diseases outbreaks, i.e., Richards growth model, and a sub-epidemic wave model [26]. Jung et al. modeled the epidemic growth using two methods, Scenario-1, from a single case recorded on 8 December 2019, and Scenario-2, using the growth rate fitted along with the other parameters based on data from 20 exported cases reported by 24 January 2020 [27].

## 2. Materials and Methods

## 3. Results and Discussion

#### 3.1. Actual Data of New Cases

#### 3.2. Reported Deaths

#### 3.2.1. Total Deaths

#### 3.2.2. Death Percentage

#### 3.2.3. Novel COVID-19 Death Percentages Comparison

#### 3.3. COVID-19 Future Regression-Based Trajectory

^{2}> 0.9) for each case, it was used to project the future behavior of COVID-19 to provide potential statistics. This will help in developing proactive action plans and the necessary strategic measures to contain such pandemics in the future too.

^{2}> 0.97 (Figure 12) to fit the “S” shaped trend. The fitted model of total COVID-19 cases (TCOV) for both countries is expressed by Equation (8):

^{2}≥ 0.97 as expressed below:

^{2}= 0.99. The world’s total COVID-19 infected cases trajectory was suited more for the exponential growth model (Equation (10)) in the second interval (after 11 March). If the pandemic is not contained, the model (TCOV

_{W}) predicts that the world’s total new infected cases would reach 28,000,000 by 15 August with total deaths equal to 1,500,000.

#### 3.4. COVID-19 Future SIR-Based Trajectory

#### 3.5. Local COVID-19 Study

^{2}> 0.96 as expressed in Equation (5). One can clearly visualize that the slope of the curve has changed since the end of April indicating the start of the exponential growth second stage/interval (2) as those in Group B and the world. This suggests that the state of Kuwait has done a commendable job in reacting right away with this COVID-19 outbreak and took all the necessary measures to control it, which resulted in delaying stage 2 as much as possible. With the current trend, the model predicted that the total infected cases in the state of Kuwait by 15 August would be about 120,000.

## 4. Potential Prevention/Containment Strategies

#### 4.1. Development of Biodegradable Antiviral Masks

#### 4.2. Antiviral Nano-Coatings of Surfaces

## 5. Conclusions and Recommendations

## Author Contributions

## Funding

## Conflicts of Interest

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**Figure 4.**(

**a**) Switzerland and (

**b**) Ireland as examples of Group A, and (

**c**) USA and (

**d**) India as examples of Group B.

**Figure 11.**Death percentage rates of some of the selected countries along with the total death cases of the world.

**Figure 12.**Logistic function regression fit of the total COVID-19 infected cases of Group A countries (

**a**) Switzerland and (

**b**) Ireland.

**Figure 13.**Exponential growth regression fit of the total COVID-19 infected cases of Group B countries (

**a**) Brazil and (

**b**) India.

**Figure 14.**Exponential growth regression fit of the world’s current and future trajectory of COVID-19 total infected cases.

**Figure 21.**Exponential growth regression fit of Kuwait’s current and future trajectory of COVID-19 total infected cases.

What | When | Where | Deaths |
---|---|---|---|

Black Death | 1347–1351 | Europe | 50,000,000 |

HIV | 1980 | Global | 39,000,000 |

Spanish Flu | 1918–1920 | Global | 20,000,000 |

Asian Flu | 1957–1961 | Global | 2,000,000 |

Seventh cholera pandemic | 1961 | Global | 570,000 |

Swine Flu | 2009 | Global | 284,000 |

Ebola | 2014 | West Africa | 4877 |

Measles | 2011 | Congo | 4555 |

SARS | 2002–2003 | Global | 774 |

Group A: Controlled | Croup B: Uncontrolled | ||||
---|---|---|---|---|---|

Country | Total Infected | Total Deaths | Country | Total Infected | Total Deaths |

China | 85,071 | 4641 | USA | 3,247,684 | 134,814 |

Switzerland | 32,713 | 1685 | Brazil | 1,839,850 | 71,469 |

Ireland | 25,611 | 1746 | India | 849,553 | 22,674 |

Coefficient | M | k | n | r^{2} | |
---|---|---|---|---|---|

Country | |||||

Switzerland | 31,962.5 | 5.0 × 10^{−9} | 4.1 | 0.99 | |

Ireland | 26,000 | 3.7 × 10^{−9} | 4.1 | 0.97 |

Country | F | R | r^{2} |
---|---|---|---|

Brazil | 316 | 29 | 0.99 |

India | 247 | 26 | 0.997 |

USA | 1300 | 78 | 0.97 |

Group | Country | Expected Number Of Cases | Expected 97% End of Pandemic | Expected 99% End of Pandemic |
---|---|---|---|---|

A | China | 84,294 | 21-March-20 | 9-April-20 |

Switzerland | 31,376 | 11-May-20 | 25-May-20 | |

Ireland | 25,486 | 25-May-20 | 7-June-20 | |

B | USA | 4,007,934 | 6-August-20 | 18-August-20 |

Brazil | 2,339,875 | 3-August-20 | 14-August-20 | |

India | 1,305,506 | 10-August-20 | 22-August-20 |

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**MDPI and ACS Style**

Al-Anzi, B.S.; Alenizi, M.; Al Dallal, J.; Abookleesh, F.L.; Ullah, A.
An Overview of the World Current and Future Assessment of Novel COVID-19 Trajectory, Impact, and Potential Preventive Strategies at Healthcare Settings. *Int. J. Environ. Res. Public Health* **2020**, *17*, 7016.
https://doi.org/10.3390/ijerph17197016

**AMA Style**

Al-Anzi BS, Alenizi M, Al Dallal J, Abookleesh FL, Ullah A.
An Overview of the World Current and Future Assessment of Novel COVID-19 Trajectory, Impact, and Potential Preventive Strategies at Healthcare Settings. *International Journal of Environmental Research and Public Health*. 2020; 17(19):7016.
https://doi.org/10.3390/ijerph17197016

**Chicago/Turabian Style**

Al-Anzi, Bader S., Mohammad Alenizi, Jehad Al Dallal, Frage Lhadi Abookleesh, and Aman Ullah.
2020. "An Overview of the World Current and Future Assessment of Novel COVID-19 Trajectory, Impact, and Potential Preventive Strategies at Healthcare Settings" *International Journal of Environmental Research and Public Health* 17, no. 19: 7016.
https://doi.org/10.3390/ijerph17197016