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Advances, Systems and Applications

CIA-CVD: cloud based image analysis for COVID-19 vaccination distribution


Due to the huge impact of COVID-19, the world is currently facing a medical emergency and shortage of vaccine. Many countries do not have enough medical equipment and infrastructure to tackle this challenge. Due to the lack of a central administration to guide the countries to take the necessary precautions, they do not proactively identify the cases in advance. This has caused Covid-19 cases to be on the increase, with the number of cases increasing at a geometric progression. Rapid testing, RT-PCR testing, and a CT-Scan/X-Ray of the chest are the primary procedures in identifying the covid-19 disease. Proper immunization is delivered on a priority basis based on the instances discovered in order to preserve human lives. In this research paper, we suggest a technique for identifying covid-19 positive cases and determine the most affected locations of covid-19 cases for vaccine distribution in order to limit the disease's impact. To handle the aforementioned issues, we propose a cloud based image analysis approach for using a COVID-19 vaccination distribution (CIA-CVD) model. The model uses a deep learning, machine learning, digital image processing and cloud solution to deal with the increasing cases of COVID-19 and its priority wise distribution of the vaccination.

Graphical Abstract

Highlights of the research

  1. 1.

    The study discusses the COVID-19 pandemic's significant effects on a global level, emphasizing the lack of vaccines and medical supplies.

  2. 2.

    The study shows how a lack of centralized management leds to a reactive response to the crisis, which results in an astonishing number of instances around the world.

  3. 3.

    The study acknowledges the significance of RT-PCR testing, chest imaging (CT-Scan/X-Ray), and rapid testing as essential diagnostic techniques for detecting COVID-19 cases. For accurate patient treatment and early discovery, these procedures are crucial.

  4. 4.

    The study suggests a data-driven strategy to rank the COVID-19 vaccine distribution.

  5. 5.

    The methodology shows how vaccines are provided effectively, prioritizing locations with the need to save lives by identifying the most impacted regions and positive cases.

  6. 6.

    A Cloud-Based Image Analysis approach was presented using a COVID-19 Vaccination Distribution (CIA-CVD) model.

  7. 7.

    The proposed model makes use of cloud computing, DL, ML, and digital image processing technologies. It provides a thorough procedure for handling the rising number of COVID-19 cases and optimizing the vaccination schedule using data-driven insights.


The present COVID-19 pandemic has resulted in loss of human lives and resources. Owing to the increasing number of cases, there is a high demand to deal with COVID-19 cases. Techniques adopted for COVID-19 detection must be autonomous and should be able to find the person who is infected with COVID-19 or not by checking the basic COVID-19 symptoms such as cough, fever, cold, and throat itching, and so on. If a person is found with these symptoms, they need to go for a more advanced check of the disease via RT-PCR test, Rapid Antigen Testing, or through CT-Scan/X-Ray images [1]. As we know, these basic processes take more time to find identify the disease. Therefore, some advanced mechanism are required to detect the disease, treat it, and distribute vaccines to those with COVID-19.

For that, the suggested CIA-CVD model is intended to give the best detection accuracy in identifying the disease as well as the diving areas in respective and concerned regions. The test samples are collected and sent for testing and the results are stored at the Central Administrative Authority (CAA). CAA is used as a cloud for storing the whole testing and vaccination data of regions and after the data is stored, processing is done to find the highest positive case regions by applying analytic algorithms [2]. Priority is given to that resultant regions for the vaccination distribution process. After getting the Highest Positive cases in the regions based on 7 days [3], the CAA gives instruction to Vaccine Distribution Authority (VDA) to Dispatch vaccine to the priority regions first and with the interval of 7 days, the same task takes place and distribution will be done by VDA and the result will be sent back to the CAA.

But when the COVID-19 cases increase, there will be shortage of vaccines for distribution. In that, the cases will increase and vaccination will be affected in those regions as shown in Fig. 1 [4]. Therefore, we gave a 7 days interval in analysing the data [5] at CAA and thereafter, passed them to VDA to supply vaccination as soon as possible. Figure 1 depicts how COVID-19 instances in India are emerging, implying that technological approaches are essential for the administration of healthcare systems [6, 7].

Fig. 1
figure 1

COVID-19 cases on the rise in INDIA in the month of March 2020: Source Ministry of health and family welfare, business standard calculations

Motivated by these factors, we suggested a Cloud Based Image Analysis for COVID19 Vaccination Distribution (CIA-CVD) system for managing healthcare services utilizing cloud computing to reduce the effect of the COVID-19 pandemic. This patient data is delivered and saved in a relational database so that the CAA can view it as an instance or a trend and respond accordingly. To make things easier, concepts like cloud-based image analysis and vaccine dissemination may be mapped using CIA-CVD systems. CAA and VDA can help physicians, nurses, patients, and their families in a variety of ways [8]. Better patient route organization, medication management, assistance in emergency circumstances or first aid, and finding a solution to the COVID-19 disease are all viable instances for CAA to be used to lessen the load on medical workers. The suggested framework has been used to illustrate this in Sect. 2.


The following are the motivations for doing this research.

  • A framework for organizing COVID-19 patient data and severity analysis by area has been presented.

  • Identification of COVID-19 positive patients by area for faster vaccination delivery which help to reduce the impact of delays in the vaccine distribution method.

  • -The management of patients will be conducted by harnessing the capabilities of Cloud Computing (CC) and Machine Learning (ML)/Deep Learning (DL) algorithms.

The suggested method is used to identify locations where immunization must be provided on a priority basis. This will allow healthcare systems to provide COVID-19 patients with faster and more secure services. The framework proposed here will be used for the identification of the COVID-19 cases and highest positive cases regions to be process of vaccination. This will be beneficial for the healthcare systems to provide faster and secured services to the patients and physicians via cloud.

The remainder of the paper is organized as follows; Sect. 2 discusses the methods utilized. Section 3 goes through the numerous strategies utilized to combat the invaders and thereafter the suggested framework is discussed and its outcomes, followed by the CC SWOT analysis are presented by Sect. 4 and the conclusion is presented in Sect. 5.

Related work

With the ever-changing risk panorama and the emergence of new emerging risks and vulnerabilities, an increase in COVID-19 instances causes more deaths. Table 1 displays a number of reviewed literature and their descriptions. CAA stores patient information. The CT filters serve as the foundation for the planned COVID-19 screening technique. To do this, we expanded the engineering of the EfficientNet family and constructed models utilizing CT scans of solid and SaR-CoV-2-infected individuals. The CT images are extracted from the preceding section's datasets and processed using the pre-handling approach, which is a straightforward cycle in PC vision applications [9]. Pre-handling approaches can help to reduce unneeded disturbance, emphasize areas of the image that can help with the cognitive job, and even aid in the deep learning phase. In this study, a basic pixel power normalisation is applied to some extent. Without this pre-planning, model intermingling at the arranging step is improbable [10, 11]. To preserve similarity, data images for convolutional network models are often adjusted. Because EfficientNets have a low computational cost in terms of lethargy, they can include more significant standard knowledge images.

Table 1 Literature review and their descriptions

As a result, in the idea of the model [11], we also look at the effect of changing the data goal. As a consequence, this pre-taking care of move becomes yet another association cap. Deep learning assumes that a major back to back or reformist model is better than shallow models at game plan or backslide tasks. Discontinuous neural connections have hidden states that span time, allowing them to retain a large amount of knowledge about the past. Because of their ability to handle variable length successive data, they are most commonly used in determining applications. Irregular neural connections have a significant disadvantage in that they cannot resolve the vanishing propensity or exploding incline problem, and they can only store transient memory because they contain hidden layer inception components of the past time venture.

In this paper, we have utilized information created by regions sent to CAA and CAA track down the most elevated number of cases and afterward send back to CAA. After that C train the VDA to apply the antibodies and inform at whatever point it’s finished and proceed with this chain until all areas not done vaccination.

Major contributions of the paper

  1. 1.

    This research introduces a novel approach called the Cloud-Based Image Analysis for COVID-19 Vaccination Distribution (CIA-CVD) model. This model leverages advanced technologies such as Deep Learning, Machine Learning, and Digital Image Processing, coupled with cloud solutions, to address the pressing issue of efficiently distributing COVID-19 vaccines.

  2. 2.

    The study emphasizes the importance of data-driven decision-making in the fight against COVID-19. By utilizing CT-Scan/X-Ray images of chests and other medical data, the CIA-CVD model empowers healthcare authorities with a decision support system. This system aids in identifying COVID-19 positive cases and determining priority areas for vaccine distribution, ensuring a more effective allocation of limited medical resources.

  3. 3.

    This research offers a complete strategy to lessen the pandemic's overall impact with a focus on areas lacking central administrative supervision. The CIA-CVD model presents a viable approach for targeting COVID-19 imapct on a global scale, ultimately protecting human lives, by integrating medical infrastructure, CC, and DL algorithms.

This research represents a significant step towards addressing the challenges posed by the COVID-19 pandemic, offering a data-driven, technologically advanced approach to optimize vaccination distribution and healthcare resource allocation.


Proposed model and algorithms

As we discuss the Methodology of the CIA-CVD is needed to do Image analysis, trends of regions and Vaccination process under the observation of CAA. The model architecture for CIA-CVD is shown in Fig. 2.

Fig. 2
figure 2

Architecture of the proposed model

In the architecture, we proposed that for a smooth process of COVID-19 identification and vaccination, the image areas be divided into respective regions, and in each cycle, images are collected from the respective defined regions, i.e., A, B, C, D, and the Leaky ReLU algorithm 2 is used to classify whether the given image is Covid infected or not, and region-wise results are sent to CAA. CAA then identifies the regions with the highest number of positive cases, and each result analysis process is completed after a predetermined period of time, and the same result analysis is sent to CAA, a cloud-based administration, to handle the process as well as data in relation to the applied algorithms. After getting the result analysis, CAA needs to share this information and instruct VDA to do vaccination to the resultant regions with higher number of cases and from that, a trigger will be fired with notification to the respective regions people about the vaccination task and gets completed as soon as possible. After the completion of task, VDA needs to send the status to CAA and it instructs new regions as per result analysis and updates the respected data. This Process continues until all the regions are not vaccinated and the new cases will not be stopped. The identification and vaccination task runs in a parallel way, so that it is not delayed to get the harvest effect of the COVID-19. For the same, different algorithm was prepared to give more idea about the process and mark a flow using the flowchart which direct reader about the process and give clarity about the model. Figure 3 shows the flowchart of the CIA-CVD process.

Fig. 3
figure 3

Flowchart of the CIA-CVD process

Algorithm 1 shows the strategy of how COVID-19 detection and what is the process to run Vaccination Program region wise is mention with association of different types of module [19], where collection of dataset and processing of dataset is included and how region wise data (R1(D)) was stored in the Central Administration in charge (Y) or Cloud (C). After identifying the positive/negative cases, the formula for finding highest positive cases was described and at last how the vaccination process takes place towards the priority regions was mentioned. Algorithm 2 defines how to detect the positive/negative cases was found by using deep learning models and classifiers [20].

Algorithm 1. Covid-19 Detection and Vaccination Region wise

figure b

Algorithm 2. Covid-19 Disease Detection Based on CT-Scan Images Using Deep Learning

figure c

The pre-processing of different sizes and types of images are done by reshaping the images to (128, 128, 3) and Zoom up to 0.4% and all the images are in horizontal form. Thereafter, the images are fed into the pre-trained model to find the accuracy of the normal and COVID images [21]. Then Convolutional Layer is applied on the images of the given model (Y) and flatten the dimension by reducing the X dimensions to X-1 and then apply dense layer which is fully connected with all the neurons by using the architecture ResNet and Xception Net and Inception Net. Then activation of Dense layer is done by sending the Parameters to the Function Leaky ReLU(z) and at last the classification is done by using Softmax classifier. Algorithm 3 describes how the data was collected from Y or C in the form of S and from different regions n to find out the highest region/state that reports the highest number of cases on weekly data [22, 23]. Algorithm 4 describes how the vaccination process takes place. Using the data from Y or C about R, the first thing to be done is to send information to the Vaccination Distributor (VD) [24] and the VD apply the vaccination to R. This process will continue until all the people of all the regions/states are not vaccinated [25].

Algorithm 3. Identifying Higher Positive Covid-19 Cases Regions using Administrative Data

figure d

Algorithm 4. Do Vaccination Task on Higher Cases Region

figure e

For finding the region with the highest positive cases one procedure was defined namely Find_Large_Region which processes each month's data and gives the result about which region has the largest amount of cases and sends it to Y or C with Region specification R [23].

In this research paper we are focusing on the concept of detecting COVID-19 Cases and Vaccination Process by making use of intelligent AI and deep learning based CT-Scan Images to identify and handled data and vaccination process on the cloud computing. The proposed framework’s outcome analysis (CIA-CVD) is defined in Sect. 4.

Result analysis of the proposed framework

Figure 3 shows the proposed architecture where the CAA are used to manage the information between the Regions (R) and the VDA for smooth management of the services. New images are generated from different regions who aim to find the positive cases and lives better are in demand. Images, CAA, computer programs or identifying highest regions algorithm and Region Vaccination Distributor are the one that conducts conversations using imaginary or textual methods are becoming increasingly common and popular. CAA is the Central authority, that maintains the region wise detection data and highest region cases data with which vaccination status of the regions by using this frameworks have expanded and take their place in healthcare system, too. The Medical Futurist claim that they can reduce physician fatigue and teach individuals how to properly care for their health [26]. Numerous activities can be carried out using CAA in Cloud-based medical systems, such as disease detection, providing patient information to CAA regions/states, providing Highest cases regions from the previous month, the ability to scale range of dates when patient numbers increase, offering vaccination priorities to the specified region by CAA to VDA, and sharing data with the VDA / stakeholders and the Algorithms. This study, therefore focused on detection and vaccination using the concepts of CNN based ResNext architecture for detection of COVID-19 and CAA for the central administration of vaccination using region based data. It is obvious that the CAA will be making the decisions based on the “dataset available for the analysis purpose” in the cloud computing environment [27]. Hence, if the genuine/original dataset is available, CAA can pass the correct information to the VDA and other management authorities without the involvement of human need. Hence, the dataset from where the CAA is making the decisions play an important role. Table 2 presents a combination of COVID non-COVID dataset using randomization.

Table 2 Combination of COVID and non-COVID dataset using randomization

But when the dataset itself is anomaly-based, the resultant information passing to the patients/doctors and others will be of no use; instead, this will create havoc in the system and improper management of the computing resources. Hence, the CAA have to identify the anomalies along with concerning their own jobs. Therefore in this research paper, we are focusing on the region wise data for disease detection using deep learning concepts. The dataset in Table 2 contains COVID-19 X-ray/CT-Scan image consisting of Virtual Machines, and the parameter that we are considering here is the COVID or non-COVID cases. Figure 4 shows the readings of dataset of the images without the anomaly and data is clear with the disease COVID-19. Figure 5 shows the readings of the dataset of the images without the anomaly and data is clear with the result as non-COVID-19.

Fig. 4
figure 4

COVID-19 Disease Images of CT-Scan

Fig. 5
figure 5

Non-COVID-19 Disease Images of CT-Scan

Figure 6 depicts the increasing cases of COVID-19 in each region/state with counts, and this information is sent to the algorithm, which uses CAA to find the largest COVID-19 cases in each state/region [28]. As we all known, this identification of highest no. of cases is done after the given period of time which is called phases and this phases data of the particular region is shown in Fig. 7 with respect to dates and counts.

Fig. 6
figure 6

Total No. of COVID-19 cases state wise

Fig. 7
figure 7

Phase wise impact of COVID-19 in regions/states

After getting regional/state wise data from CAA to VDA, how the vaccination progress and how much amount of vaccination is done in respective regions/states are shown in Fig. 8 [29].

Fig. 8
figure 8

State wise vaccination data

The machine learning-based isolation graph, which is implemented in Figs. 9, 10, and 11, identifies consumption in terms of High, Low, and Average between timestamps of 0 ms and 8100 ms and in the range of CPU usages from 0 to 100% [30].

Fig. 9
figure 9

CPU utilization for High CPU usage with upper bound of 100%, in graph x-axis: Timestamp (ms) and in Y-axis: CPU Usage in [%]

Fig. 10
figure 10

CPU utilization for AVG CPU usage with upper bound of 70%, in graph x-axis: Timestamp (ms) and in Y-axis: CPU Usage in [%]

Fig. 11
figure 11

CPU utilization for LOW CPU usage with upper bound of 100% and lower bound of 0%, in graph X-axis: Timestamp (ms) and in Y-axis: CPU Usage in [%]

When the anomaly is discovered, CAA will take the necessary steps to reduce the effect of the anomaly or bug. If there is no abnormality, CAA can securely transmit the data to the end users. The computed accuracy was 0.92. These findings and analyses can assist CAA and VDA determine if the information provided to patients, clinicians, and others is accurate. We made the assumption that the cloud resource management had already created the workload patterns. If the model differs from the current one, machine learning-based isolation graphs will identify this and inform the CAA. Until the anomaly is averted, CAA will stop exchanging messages with the VDA of the healthcare system.

SWOT analysis for implementing cloud in the healthcare system

SWOT analysis helps us to find out the efficiency of deployment of evolving technology in the Healthcare domain. The parameters for the SWOT analysis is presented in Table 3.

Table 3 SWOT analysis

The results show that none of these models proved to be as reliable to replace RT-PCR test and still researchers are trying to improve these techniques. From our survey, it is noticeable that the X-Ray image dataset is more widely available than the CT Image dataset as a CT scan is costly and more time-consuming. As a result, the majority of the researchers relied on chest X-ray images to diagnose COVID-19. After getting results of disease, region wise cloud computing plays an important roles in the healthcare systems [31], and CAA can get and provide details of the appointment to health care professionals like VDA and help them update medical records into the Cloud and its security is also an important factor [32]. The service combines integrated medical information with natural language abilities, extendable techniques, and enforcement components to provide forecasts to healthcare organizations by simply running the model in a loop [33,34,35,36,37]. In this study report, the framework known as CIA-CVD has been applied, which works with Deep Learning and machine learning-based images to detect disease using a pre-defined dataset. This will increase the trustworthiness of CIA-CVD for medical services.

Conclusion and recommendation for future work


In this paper, we presented deep learning models for predicting the number of COVID-19 positive cases in Indian regions. An exploratory data evaluation of the growth in the number of positive cases in India was conducted. States are classified into mild, moderate, and extreme zones based on the number of cases and the regular growth rate in order to implement effective lockdown measures of state by state rather than locking down the entire country, which might cause socioeconomic concerns. As COVID-19 is spreading worldwide at a rapid rate, accurate and faster detection has become essential. In this study, we tried to present a comprehensive survey of AI assisted methods that used medical images to combat the COVID-19 pandemic challenge by detecting it at a small cost and relatively faster time. We surveyed 80 COVID-19 diagnosis models among which 28 used CT images, 50 used X-Ray images and 2 used both CT and X-Ray images.

Recommendation for future work

The following are potential directions for future research:

  1. 1.

    Enhancing Accuracy and Speed: Improve the CIA-CVD model's DL and ML algorithms to increase the efficiency and accuracy of COVID-19 identification from medical photos.

  2. 2.

    Integration with Real-Time Data: By adding the most recent epidemiological data and medical imaging technology, develop methods for real-time data integration to guarantee that the model can adapt to changing pandemic conditions.

  3. 3.

    Deployment and Scalability: Look into ways to make the CIA-CVD model widely available to medical facilities all across the world. To guarantee its efficacy in areas with different healthcare infrastructures, scalability is essential.

  4. 4.

    Privacy and Ethical Considerations: Utilize effective data anonymization techniques and adhere to data protection laws to address privacy issues related to medical picture data.

  5. 5.

    AI Explainability: To win the trust of medical professionals and legislators, look into ways to make the AI-driven decision-making process more visible and understandable.

  6. 6.

    Continuous Model Training: To keep the model updated and efficient, implement ongoing model training to adjust to new COVID-19 variants and adjustments in diagnostic procedures.

  7. 7.

    Security and Resilience: To safeguard sensitive medical data, the cloud-based solution's security should be strengthened. Put resilience mechanisms in place to guarantee the system's availability even in difficult circumstances.

  8. 8.

    Cost Optimization: For healthcare organizations with limited resources, looking into ways to lower the operational expenses related to cloud-based technologies to make the CIA-CVD model economically feasible.

  9. 9.

    Human-AI Interaction: Look into ways to make interactions between medical experts and the AI system better, making sure that the model's outputs are understandable and useful.

  10. 10.

    Long-Term Pandemic Preparedness: Research should be expanded to address long-term pandemic preparedness by creating flexible AI systems that may be used to combat future outbreaks of infectious diseases.

Availability of data and materials

The datasets generated during and/or analysed during the current study are not publicly available but are available from the corresponding author on reasonable request.



Cloud-Based Image Analysis for COVID-19 Vaccination Distribution


Cloud Computing


Central Administrative Authority


Computed Tomography


COVID-19 Cases Detected


Deep Learning


Higher Number of Positive Cases


CT-Scan Image


Insufficient Vaccine

Leaky ReLU:

Leaky Rectified Linear Unit


Machine Learning




Analysis Region Wise Record


Region Wise Data


Positive Cases Data


Region Wise Vaccination


Vaccine Distribution


Vaccine Distribution Authority


Weekly Wise Data


Central Administrative In charge


  1. Song Y, Zheng S, Li L, Zhang X, Zhang X, Huang Z, Chen J, Zhao H, Jie Y, Wang R, Chong Y (2020) Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. medRxiv

  2. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, Cao K (2020) Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology 19:200905

    Google Scholar 

  3. Zheng C, Deng X, Fu Q, Zhou Q, Feng J, Ma H, Liu W, Wang X (2020) Deep learning-based detectionfor COVID-19 from chest CT using weak label. medRxiv

  4. Shambhu S, Koundal D, Das P, Sharma C (2021) Binary classification of COVID-19 CT images using CNN: COVID diagnosis using CT. Int J E-Health Med Commun 13(2):1–13.

    Article  Google Scholar 

  5. Shankar S, Koundal D, Das P, Hoang VT, Tran-Trung K, Turabieh H (2022) "Computational methods for automated analysis of malaria parasite using blood smear images: recent advances." Comput Intell Neurosci 2022

  6. Ministry of Health and Family Welfare (2018) Coverage Evaluation Survey- Intensified Mission Indradhanush. MOHFW

  7. Shambhu S, Koundal D, Das P (2023) Deep learning-based computer assisted detection techniques for malaria parasite using blood smear images. Int J Adv Technol Eng Explor 10(105):990–1015.

    Article  Google Scholar 

  8. Chowdhury ME, Rahman T, Khandakar A, Mazhar R, Kadir MA, Mahbub ZB, Islam KR, KhanMS, Iqbal A, Al-Emadi N, Reaz MB (2020) Can AI help in screening viral and COVID-19 pneumonia?.arXiv preprint arXiv:2003.13145

  9. Misra P, Panigrahi N, Gopal Krishna Patro S, Salau AO, Aravinth SS (2023) PETLFC: Parallel ensemble transfer learning based framework for COVID-19 differentiation and prediction using deep convolutional neural network models. Multimed Tools Appl

  10. Ayalew AM, Salau AO, Tamyalew Y, Abeje BT (2023) X-Ray image-based COVID-19 detection using deep learning. Multimed Tools Appl.

  11. Salau AO (2021) Detection of Corona Virus Disease Using a Novel Machine Learning Approach. 2021 International Conference on Decision Aid Sciences and Application (DASA), pp. 587–590.

  12. Keeling MJ, Hollingsworth TD (2020) Read JMEfficacy of contact tracing for the containment of the 2019 novel coronavirus (COVID-19). J Epidemiol Community Health 74:861–866

    Google Scholar 

  13. Hu, Zeng-Yun Cui, Qianqian Han, Junmei Wang, Xia Sha, Wei Teng, Zhidong (2020) Evaluation and prediction of the COVID-19 variations at different input population and quarantine strategies, a case study in Guangdong province, China. Int J Infect Dis 95.

  14. Gostic, Katelyn Gomez, Ana Mummah, Riley Kucharski, Adam Lloyd-Smith, James (2020) Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19. eLife 9.

  15. S. Shambhu, D. Koundal and P. Das, "Edge-Based Segmentation for Accurate Detection of Malaria Parasites in Microscopic Blood Smear Images: A Novel Approach using FCM and MPP Algorithms," 2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN), Villupuram, India, 2023, pp. 1–6,

  16. Shambhu, Shankar, and Deepika Koundal (2019) "Recent Trends in Image Processing Using Granular Computing. " In International Conference on Advanced Communication and Computational Technology, pp. 469–479. Singapore: Springer Nature Singapore

  17. Pallasch G, Salman R, Hartwig C (2005) Effectiveness of interventions to improve the uptakeof immunisation in primary care, with specific focus on Mumps, Measles and Rubella (MMR). University of Huddersfield, Huddersfield ISBN 9781 862180772

    Google Scholar 

  18. Heuvelings, Charlotte Vries, Sophia Greve, Patrick Visser, Benjamin Belard, Sabine Janssen,SaskiaCremers, A. Spijker, René Shaw, Beth Hill, RuaraidhZumla, AlimuddinSandgren, Andreas van der Werf, Marieke Grobusch, Martin (2017) Effectiveness of interventions for diagnosis and treatment of tuberculosis in hard-to-reach populations in countries of low and medium tuberculosis incidence: A systematic review. Lancet Infect Dis 17.

  19. Mobiny A, Cicalese PA, Zare S, Yuan P, Abavisani M, Wu CC, Ahuja J, de Groot PM, VanNguyen H (2020) Radiologist-Level COVID-19 Detection Using CT Scans with Detail-Oriented Capsule Networks. arXiv preprint arXiv:2004.07407

  20. Wang L, Li J, Guo S, Xie N, Yao L, Cao Y et al (2020) Real-time estimation and prediction of mortality caused by COVID-19 with patient information-based algorithm. Sci Total Environ 727

    Article  Google Scholar 

  21. Abadi M, Barham P, Chen Z, Chen A, Davis J, Dean J et al (2016) TensorFlow: a system for large-scale machine learning 12th USENIX Symposium on operating systems design and implementation (OSDI 16). pp 265–283

  22. McKinney W et al (2010) Data structures for statistical computing in python. Proceedings of the 9thPython in science conference, vol. 445, Austin, pp 51–56

    Google Scholar 

  23. Prasad VK, Bhavsar MD, Tanwar S (2019) Influence of montoring: fog and edge computing. Scalable Computing 20(2):365–376

    Google Scholar 

  24. National Cold Chain Assessment India", July 2008 by partner organization WHO, Immunization Basics and UNICEF. Available online:

  25. Gunadi W. Nurcahyo, Rose Alinda Alias, Sm Mamyam, Shasuddin and Mohd. NoorMD.SAP (2002) “Sweep Algorithm in Vehicle Routing Problem For Public Transport”, JurnalAntarabangsa 2:51-64

  26. Prasad VK, Bhavsar MD (2020) Monitoring IaaS cloud for healthcare systems: healthcare information management and cloud resources utilization. Int J E-Health Med Commun 11(3):54–70

    Article  Google Scholar 

  27. Prasad VK, Bhavsar M (2017) Efficient Resource Monitoring and Prediction Techniques in an IaaS Level of Cloud Computing: Survey. International Conference on Future Internet Technologies and Trends. Springer, Cham, pp 47–55

    Google Scholar 

  28., Worldometer, last accesses: 01 Feb 2023

  29., last accesses: 01 Feb 2023

  30. Vivek Kumar P, Bhavsar MD (2021) SLAMMP framework for cloud resource management and its impact on healthcare computational techniques. Int J E-Health Med Commun 12(2):1–31

    Article  Google Scholar 

  31. Prasad VK, Mehta H, Gajre P, Sutaria V, Bhavsar M (2017) Capacity Planning Through Monitoring of Context-Aware Tasks at IaaS Level of Cloud Computing. International Conference on Future Internet Technologies and Trends. Springer, Cham, pp 66–74

    Google Scholar 

  32. Zhao Y, Guang Cheng Yu, Duan ZG, Zhou Y, Tang Lu (2021) Secure IoT edge: threat situation awareness based on network traffic. Comput Netw 201:108525

    Article  Google Scholar 

  33. Daskalopoulos I, Ahmed M, Hailes S, Roussos G, Delamothe T, Kwon K, Brown L (2014) Policy-enabled internet of things deployable platforms for vaccine cold chains. Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. pp 295–302

    Google Scholar 

  34. Ayalew AM, Salau AO, Abeje BT, Enyew B (2022) Detection and classification of COVID-19 disease from X-ray images using convolutional neural networks and histogram of oriented gradients. Biomed Signal Process Control 74(103530):1–11.

    Article  Google Scholar 

  35. Wubineh BZ, Salau AO, Braide SL (2023) Knowledge based expert system for diagnosis of COVID-19. Journal of Pharmaceutical Negative Results 14(3):1242–1249.

    Article  Google Scholar 

  36. Indumathi N, Shanmuga Eswari M, Salau AO, Ramalakshmi R, Revathy R (2022) Prediction of COVID-19 Outbreak with Current Substantiation Using Machine Learning Algorithms. Intelligent Interactive Multimedia Systems for e-Healthcare Applications. Springer, Singapore.

  37. Frimpong SA, Salau AO, Quansah A, Hanson I, Abubakar R, Yeboah V (2022) Innovative IoT-Based wristlet for early COVID-19 detection and monitoring among students. Math Model Eng Probl 9 6:1557–1564.

    Article  Google Scholar 

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Vivek Kumar Prasad: Conceptualization, Methodology, Software, Writing- Original draft preparation Debabrata Dansana: Data curation, Visualization, Investigation.S Gopal Krishna Patro: Investigation, Visualization, Investigation. Ayodeji Olalekan Salau: Data curation, Methodology, Writing- Reviewing, Editing and Validation. Divyang Yadav: Data curation, Visualization, Investigation. Madhuri Bhavsar: Validation.

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Correspondence to Ayodeji Olalekan Salau.

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Prasad, V.K., Dansana, D., Patro, S.G.K. et al. CIA-CVD: cloud based image analysis for COVID-19 vaccination distribution. J Cloud Comp 12, 163 (2023).

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