Table 1 Literature review and their descriptions

Keeling et al. [12]

The effectiveness of contact tracing for the containment of the 2019 novel coronavirus (COVID-19)

The aim of this study was to see how effective contact tracing was at containing Covid-19

A postal and online cross-sectional survey was used to characterize contact trends

All of the contact tracking can be done quickly, and the basic reproductive ratio can be reduced from 3.11 to 0.21, allowing the outbreak to be contained. Each new case necessitates the tracing of an average of 36 people, with 8.7% of cases having more than 100 near-traceable contacts

Hu et al. [13]

Evaluation and prediction of the COVID-19 variations at different input population and quarantine strategies

Simulate and forecast disease variations in Guangdong province, as well as investigate the effects of the input population and quarantine policies

The simulation was used to assess the influence of the input population

The confirmed cases’ simulated peak value is 1002 on

February 10, 2020. With a peak value of 1397 on May 11, 2020, the disease will become extinct. Increased input population numbers will primarily shorten disease extinction days and increase the percentages of exposed individuals

Gostic et al. [14]

Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19

Provided current knowledge of the main COVID-19 life history and epidemiological parameters, estimate the effect of various screening programs

We gathered information on confirmed and suspected COVID-19 acute respiratory disease cases recorded by cities and provinces throughout the United States

The median proportion of infected travelers found in a spreading epidemic is just 0.30, even under ideal conditions when only one infection in twenty is subclinical and all travelers undergo departure and arrival screening. Based on the middle-case assumption that 25% of cases are subclinical, it is predicted that arrival screening alone would catch around one-third of infected travelers in a stable epidemic, and that arrival and departure screening together would catch about half of them

Wang et al. [15]

A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)

Screening large numbers of reported cases for effective quarantine and care is a priority for managing the spread of Corona Virus Disease (COVID-19). Based on COVID-19 CT picture radiographic changes

They gathered 1,119 CT images of COVID-19 cases with pathogen confirmation as well as those who had previously been diagnosed with standard viral pneumonia. They changed the Inception transfer-learning model to construct the algorithm, which was then evaluated both internally and externally

With a precision of 0.88 and a sensitivity of 0.87, internal validation had an overall accuracy of 89.5%. The external research dataset's total accuracy was 79.3%, with precision of 0.83 and sensitivity of 0.67. Furthermore, the system accurately identified 46 of the 54 COVID-19 photos as COVID-19 positive, with an accuracy of 85.2%, despite the first two nucleic acid test results of the 54 COVID-19 images being negative

Shah et al. [16]

A Comprehensive Survey of COVID-19 Detection using Medical Images

To estimate the cost and time required for standard Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests to detect COVID-19 is uneconomical and unnecessary, researchers are attempting to use medical images such as X-Ray and Computed Tomography (CT) images to detect this disease using Artificial Intelligence (AI) based systems to assist in automating the scanning process

COVID-19 can now be detected using AI-based models from X-ray or CT lung images

They looked at datasets, preprocessing techniques, segmentation processes, feature extraction, classification, and experimental findings to see where future research could go in the field of COVID-19 disease automated diagnosis using AI-based frameworks. There is also a shortage of annotated medical images/datasets of COVID-19 affected individuals, which necessitates enhancing, segmentation in preprocessing, and domain adaptation in transfer learning for a model, resulting in the best possible model output

Pallasch et al. [17]

Cost-effectiveness of tuberculosis control strategies among immigrants and refugees

Immigrants and refugees from high-to-low incidence countries will be vaccinated

Both Tb-related diagnostics rely on screening and disease identification, as well as prioritising vaccination based on regional data

The effect of a previously used chest X-ray is negligible. Global investment in high-incidence countries will be an ideal control technique. Since cell-mediated strategies are costly, they were not tested for screening purposes

Charlotte et al. [18]

Effectiveness of interventions for diagnosis and treatment of tuberculosis in hard-to-reach populations in countries of low and medium tuberculosis incidence

To vaccinate the refugees and immigration people and give aeffictive approach

Treatment of active tuberculosis in OECD, EU, EEA, and EU candidate countries

The quality assessment’s findings. A meta-analysis was not sufficient due to the heterogeneity of the included studies in terms of the form of hard-to-reach population, treatments, published results, and study design. Active referral to Tb clinics has been shown to improve care adherence in migrants. A group DOT led by non-family members tends to be the most successful, despite some inconsistencies