COVID-19 Pandemic Development in Jordan—Short-Term and Long-Term Forecasting

Hussein, Tareq; Hammad, Mahmoud; Fung, Pak Lun; Al-Kloub, Marwan; Odeh, Issam; Zaidan, Martha Arbayani; Wraith, Darren

doi:10.3390/vaccines9070728

Cited by 12 publications

(34 citation statements)

References 21 publications

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“…Vaccines are one of the most efficient medical interventions for the control of infectious diseases [ 1 ]. The emergence of the COVID-19 pandemic, has caused over 5 million deaths globally and 11,197 deaths in Jordan as of 13 November 2021 [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Willingness of the Jordanian Population to Receive a COVID-19 Booster Dose: A Cross-Sectional Study

et al. 2022

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SARS-CoV-2 (COVID-19) vaccines are critical for containing serious infections. However, as COVID-19 evolves toward more transmissible varieties and serum antibody levels in vaccinated persons steadily decline over time, the likelihood of breakthrough infections increases. This is a cross-sectional study based on an online questionnaire for Jordanian adults (n = 915) to determine how individuals who have finished the current vaccination regimen feel about a prospective COVID-19 booster shot and what factors might influence their decision. Almost half of the participants (44.6%) intended to get the booster dose of the COVID-19 vaccine. The most frequently mentioned reasons for participants’ reluctance to get the COVID-19 vaccine booster dose were “The benefits of booster dose have not been scientifically proven” (39.8%), followed by “I took the last dose a short time ago, and there will be no need to take the booster dose for at least a year” (24.6%). In turn, “I was infected with COVID-19; thus, I do not require the booster dose” was the least reported reason (13.1%). These findings highlight the considerable hesitancy toward COVID-19 booster immunization among Jordanians, as well as the variables associated with vaccine hesitancy and the most frequently mentioned reasons for hesitancy, which will aid in creating excellent campaigns regarding booster doses.

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Section: Introductionmentioning

confidence: 99%

Willingness of the Jordanian Population to Receive a COVID-19 Booster Dose: A Cross-Sectional Study

et al. 2022

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“…8 Due to the high contiguity of COVID-19, preventative measures should be implemented to reduce its transmission such as lockdown, social distancing and vaccinations. 9 , 10 Despite the seriousness of the disease and its ability to spread rapidly, people still have low readiness for getting the COVID-19 vaccines. 11 , 12 …”

Section: Introductionmentioning

confidence: 99%

Community pharmacists’ willingness and barriers to provide vaccination during COVID-19 pandemic in Jordan

Jarab

Al‐Qerem

Mukattash

2022

Human Vaccines & Immunotherapeutics

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Providing vaccination in community pharmacies could increase the vaccination coverage rate as well as help reducing the workload of the healthcare system. The current study was conducted to evaluate community pharmacists’ willingness and barriers to provide vaccination in community pharmacy setting. A validated questionnaire which included eight items to evaluate willingness and eleven items to evaluate the barriers to provide vaccines was distributed online. Binary logistic regression was conducted to explore the factors that are significantly associated with willingness and barriers to provide the vaccine. Among the 201 participating pharmacists, 174 (86.6%) had a high willingness level. Lack of authorization (91.6%), lack of collaboration with other healthcare professionals (85.6%), and lack of space for storage (74.1%) were the most recognized barriers to vaccinate. Pharmacists with BSc degree demonstrated less willingness (OR = 0.18 (0.07–0.46), and increased barriers (OR = 4.86 (1.56–15.17) to provide the vaccine when compared with Pharm D and postgraduate pharmacists P < .01. Factors including male gender (OR: 6.10), working in chain pharmacy (OR: 8.98) and rural areas (OR: 4.31), moderate income (OR: 19.34) and less years of experience (OR:0.85) were significantly associated with increased barriers to provide the vaccine ( P < .05). Despite the high willingness of the community pharmacists to vaccinate, several barriers were present. Enhancing pharmacists’ authorization and collaboration with other healthcare professionals and providing space for storage along with providing training courses and workshops should be considered to enhance pharmacist’s engagement in vaccination service.

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“…In [47], the authors proposed a hybrid mathematical model based on the short-term forecast (STF) and long-term forecast (LTF) models. Their system was applied to Jordan only.…”

Section: Review Of Predictions Modelsmentioning

confidence: 99%

A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting

Shahin

Almotairi

2021

Fractal Fract

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The COVID-19 pandemic has widely spread with an increasing infection rate through more than 200 countries. The governments of the world need to record the confirmed infectious, recovered, and death cases for the present state and predict the cases. In favor of future case prediction, governments can impose opening and closing procedures to save human lives by slowing down the pandemic progression spread. There are several forecasting models for pandemic time series based on statistical processing and machine learning algorithms. Deep learning has been proven as an excellent tool for time series forecasting problems. This paper proposes a deep learning time-series prediction model to forecast the confirmed, recovered, and death cases. Our proposed network is based on an encoding–decoding deep learning network. Moreover, we optimize the selection of our proposed network hyper-parameters. Our proposed forecasting model was applied in Saudi Arabia. Then, we applied the proposed model to other countries. Our study covers two categories of countries that have witnessed different spread waves this year. During our experiments, we compared our proposed model and the other time-series forecasting models, which totaled fifteen prediction models: three statistical models, three deep learning models, seven machine learning models, and one prophet model. Our proposed forecasting model accuracy was assessed using several statistical evaluation criteria. It achieved the lowest error values and achieved the highest R-squared value of 0.99. Our proposed model may help policymakers to improve the pandemic spread control, and our method can be generalized for other time series forecasting tasks.

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COVID-19 Pandemic Development in Jordan—Short-Term and Long-Term Forecasting

Cited by 12 publications

References 21 publications

Willingness of the Jordanian Population to Receive a COVID-19 Booster Dose: A Cross-Sectional Study

Willingness of the Jordanian Population to Receive a COVID-19 Booster Dose: A Cross-Sectional Study

Community pharmacists’ willingness and barriers to provide vaccination during COVID-19 pandemic in Jordan

A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting

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