Journal of Infectious Diseases and Diagnosis

The Effect of COVID-19 Vaccination Status on RT-PCR Ct Values: A Comprehensive Study

Abebe M Aga^1*, Demise Mulugeta¹, Jemal Mohammed¹, Anberber Alemu¹, Yewenshet Tesera¹, Dereje Nigussei¹, Bedasa Gidisa¹, Jaleta Bulti², Frehiwot Mulugeta², Atsbeha Gebreegziabxier² and Gemechu Tadesse^{2 1}Department of Infectious Diseases and Diagnosis, Armauer Hansen Research Institute, Pastor Square, Ethiopia
²Department of Infectious Diseases and Diagnosis, Ethiopian Public Health Institute, Pastor Square, Ethiopia
^*Correspondence: Abebe M Aga, Department of Infectious Diseases and Diagnosis, Armauer Hansen Research Institute, Pastor Square, Ethiopia, Email:

Received: 22-May-2024, Manuscript No. JIDD-24-25812; Editor assigned: 25-May-2024, Pre QC No. JIDD-24-25812 (PQ); Reviewed: 12-Jun-2024, QC No. JIDD-24-25812; Revised: 07-Aug-2025, Manuscript No. JIDD-24-25812 (R); Published: 14-Aug-2025, DOI: 10.35248/2576-389X.25.10.338

Keywords

Clinical symptoms; COVID-19; Diagnostics; Test result; RDT; RT-PCR

Introduction

The COVID-19 pandemic, which originated in Wuhan, China, in December 2019, rapidly escalated into a global crisis, prompting the World Health Organization (WHO) to declare it a pandemic on March 11, 2020. This viral disease, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2), primarily spreads through respiratory droplets and manifests with symptoms ranging from mild respiratory issues to severe pneumonia and fatalities.

The global response to the pandemic varied significantly, influenced by factors such as healthcare infrastructure, governmental measures, public compliance and the emergence of different virus variants. In Africa, the response was diverse across countries, with initial forecasts predicting severe outbreaks due to limited health infrastructure and socio economic challenges. However, many African nations implemented swift measures including travel bans, curfews and lockdowns, potentially contributing to lower infection rates in the early stages of the pandemic. Despite proactive measures, by September 2021, South Africa reported one of the highest case counts on the continent, partly attributed to robust testing infrastructure and the emergence of virus variants [1].

Ethiopia, during the pandemic, implemented measures such as school closures, partial lockdowns and public health campaigns upon confirming its first case in March 2020. However, the virus spread throughout the country, particularly affecting urban areas like the capital, Addis Ababa. Challenges such as testing constraints, stigma, misinformation and healthcare strain were prevalent. Additionally, the emergence of new variants raised concerns about test efficacy and accuracy.

Testing played a crucial role in identifying and isolating infected individuals, tracing contacts and adjusting strategies. It also aided in monitoring vaccine efficacy and detecting new virus variants. Nasopharyngeal and oropharyngeal swabs were recommended specimens for testing, with RT-PCR considered the gold standard for active infections. Rapid Diagnostic Tests (RDTs), including antigen detection, offered quicker results but with lower sensitivity compared to RT-PCR. Concerns about false negatives, especially in regions with low prevalence, were notable. Clinical symptoms, though non-specific, aided in suspecting COVID-19 but required confirmation through testing, preferably RT-PCR.

Vaccination campaigns have significantly altered the landscape of the pandemic, reducing the incidence of severe illness, hospitalizations and deaths among vaccinated individuals. As a result, the prevalence of COVID-19 in vaccinated populations may differ from that in unvaccinated individuals, impacting the interpretation of diagnostic test results. Vaccination can also influence the spectrum of clinical symptoms observed in infected individuals, potentially complicating the reliance on symptom-based screening. Accordingly, the positive rate among clinically suspected cases reported to be relatively high, ranging from 50% to 80%, especially in areas where prevalence is high. In others, particularly in areas with lower prevalence or where testing resources are limited, the positive rate among clinical cases may be lower, ranging from 10% to 50% or less. On the other hand, pockets of unvaccinated populations remain vulnerable to outbreaks, potentially leading to differential patterns of transmission and diagnostic test outcome.

Therefore, comprehensive comparisons between RDTs and RT PCR were essential for informing public health policies and resource allocation. Understanding the prevalence, sensitivity, specificity and overall agreement between these methods aided in the efficient management of COVID-19. Balancing the advantages and limitations of each diagnostic tool was crucial in the broader strategy to control the pandemic and mitigate its impact on global health and economies. Continued research and collaboration are imperative for improving testing methodologies and effectively managing COVID-19 outbreaks worldwide [2].

Materials and Methods

Study design

This study follows a cross-sectional investigation that enrolled a cohort of participants who were suspected of having COVID-19 based on clinical symptoms such as cough, joint pain, fever, headache and sore throat. The individuals were selected from a population of patients in healthcare facilities, ensuring diversity for representation and were chosen based on the fulfillment of clinical symptoms.

Study setting

The research was carried out at healthcare in Addis Ababa, including hospitals and health centers, to identify clinical cases of COVID-19 and perform RDT testing. Additionally, COVID-19 RT-PCR testing conducted at the Ethiopian Public Health Institute (EPHI).

Study participants

Individuals of both genders, aged 18 years and above, both vaccinated and unvaccinated with COVID-19 vaccine, suspected of having the disease based on their symptoms and fulfilling clinical criteria for the disease, were included in the study after providing consent and signing the necessary documentation.

Sample collection

Samples were collected with record of predefined sign and symptoms of COVID-19 including cough, joint pain, fever, headache and sore throat. In order to conduct RDT testing, nasal swabs were collected in accordance with the manufacturer's guidelines for sample collection and processing. For RT-PCR testing, nasopharyngeal swabs were collected in Viral Transport Media (VTM) and subsequently transported to EPHI under cold box storage. The samples were then stored at a temperature of -70°C until processing, ensuring stored under optimal conditions. Accordingly, a total of 453 samples that met the predefined criteria were collected following appropriate procedures. Total of 453 samples collected and from these, 76 RDT negatives samples fulfilling predefined clinical criteria were randomly chosen for inclusion in further confirmation with the RT-PCR [3].

Testing procedures

As a clinical diagnosis, patients seeking medical attention at healthcare facilities are screened based on symptoms associated with COVID-19, which meet the primary criteria including cough, joint pain, fever, headache and sore throat. RDT test was conducted using Panbio COVID-19 antigen rapid diagnostic kit produced by Abbott, following the manufacturer's instructions. The results were recorded within the specified reaction time.

For RT-PCR test, RNA extraction done by lysing the viral particles in the sample and then isolating the RNA using BioFlux RNA extraction kit with Bioer automated extraction machine, following the manufacturer's protocol. The extracted RNA was reverse transcribed into complementary DNA (cDNA) using reverse transcriptase and cDNA is then amplified using primers for SARS-CoV-2. This was done in a PCR machine which undergoes various cycles of heating and cooling to allow for DNA denaturation, primer annealing and DNA extension. The presence of the virus was detected through fluorescence in real-time giving a Cycle threshold (Ct) value. Internal control was used to ensure the RNA extraction was successful and that there are no PCR inhibitors in the sample.

Data analysis

SPSS version 25 was employed for this data analysis, utilizing descriptive statistics to summarize and depict the primary characteristics of the data, including frequencies and percentages. Cross-tabulation was employed to explore the connection between two categorical variables, aiding in the visualization of variable frequency distribution and the identification of patterns or associations. The chi-square test was utilized to determine whether the observed frequencies significantly deviate from the expected frequencies, offering insights into the strength and direction of the variable association. The findings from the descriptive statistics, cross tabulation and chi-square tests were analyzed to derive meaningful conclusions about the relationship between variables [4].

Results

The Table 1 provides a detailed breakdown of COVID-19 symptoms observed in a study population, highlighting both the frequency and percentage of positive and negative responses for each symptom (Table 1). Cough emerges as the most prevalent symptom, with 38.9% of individuals reporting it as a positive symptom. This aligns with previous research indicating cough as one of the hallmark symptoms of COVID-19. However, it's notable that 9.1% of individuals who underwent testing did not exhibit this symptom despite other positive test results. This discrepancy underscores the variability in symptom presentation among COVID-19 cases, suggesting that while cough is commonly associated with the disease, its absence does not rule out infection. Similarly, fever, another commonly recognized symptom of COVID-19, is reported by 32.2% of individuals. However, 15.7% of individuals who underwent testing did not experience fever, indicating that its absence does not necessarily indicate a negative test result.

Table 1: Clinical symptom frequency.

This finding emphasizes the importance of considering a range of symptoms in COVID-19 diagnosis, as not all infected individuals may present with fever. Shortness of breath, while less common, is still reported by 5.1% of individuals. However, it's notable that 42.8% of individuals who underwent testing did not report this symptom despite other positive test results. This highlights the importance of recognizing that shortness of breath may not be present in all COVID-19 cases and its absence does not preclude the possibility of infection. Sore throat emerges as another moderately common symptom, reported by 28.7% of individuals. However, 19.2% of individuals tested negative for COVID-19 despite experiencing a sore throat. This discrepancy underscores the need for clinicians to consider a range of symptoms and employ diagnostic tests judiciously to accurately identify COVID-19 cases (Table 2) [5].

Table 2: RDT and RT-PCR test result.

For RT-PCR, the results provide insights into the presence and prevalence of the tested condition within the sampled population (Table 2). Out of 453 total tests conducted, 291 cases tested positive (64.2%) and 162 cases tested negative (35.8%) as indicated in table below. Upon further examination of the discrepancy between the RDT positive results and the RT-PCR negative results, we can determine the absolute and percentage differences between these two groups.

RDT positive result: 298 cases

RT-PCR negative result: 162 cases

Absolute difference: |298−162|=136|298−162|=136

Percentage difference: |298−162|298×100%298|298−162|×100% =136298×100%=298136×100% ≈ 45.64% ≈ 45.64%

Therefore, the absolute difference between the RDT positive results and the RT-PCR negative results is 136 cases. Moreover, the percentage difference between them is approximately 45.64%. This analysis highlights a notable contrast between the positive outcomes of the RDT test and the negative outcomes of the RT-PCR test, suggesting potential discrepancies in the precision and sensitivity of these testing methodologies. In terms of frequencies for each possible scenario, there are 291 instances where individuals tested positive on both RDT and RT-PCR tests. Furthermore, there are 7 cases where individuals tested positive on RDT but negative on RT-PCR. There are four cases where individuals tested negative on RDT but positive on RT PCR. Lastly, there are 154 cases where individuals tested negative on both RDT and RT-PCR tests. These combinations encompass all feasible outcomes of RDT and RT-PCR test results [6].

The table also shows combined RDT and RT-PCR test results (Table 2). Among those who tested negative on the RDT, 148 were also negative on the RT-PCR test, while 6 tested positive on the RT-PCR test. Among those who tested positive on the RDT, 284 were also positive on the RT-PCR test and 14 tested negatives on the RT-PCR test. There was 1 case where the RDT result was categorized as "invalid" and tested positive on the RT PCR test. The chi-square tests indicate a statistically significant association between the RDT and RT-PCR test results, as the p value is less than 0.05 (p<0.05).

The RDT results with COVID-19 vaccination status shown in table below indicating those who tested negative on the RDT, all were not vaccinated against COVID-19 (Table 5). Among those who tested positive on the RDT, 92 were not vaccinated, 136 were vaccinated and 53 cases were not categorized, along with 17 categorized as vaccinated. There was 1 case where the RDT result was categorized as "invalid" and it was not vaccinated against COVID-19. The chi-square tests indicate a statistically significant association between the RDT results and COVID-19 vaccination status, as the p-value is less than 0.05 (p<0.05) (Table 3) [7].

Table 3: Frequency table for RDT and COVID-19 vaccination.

This result describes the distribution of COVID-19 vaccination status among a group of individuals. Out of the total 453 individuals surveyed, 246 individuals have valid responses regarding their COVID-19 vaccination status, constituting 54.3% of the total and 137 individuals or 30.2% of the total, have not received the COVID-19 vaccination, 53 individuals or 11.7% of the total, have received the COVID-19 vaccination, 17 individuals or 3.8% of the total, have received the COVID-19 vaccination. In general, the majority of individuals in the surveyed group have valid responses regarding their COVID-19 vaccination status, with a significant portion having received the vaccination. The data demonstrates that among individuals who tested negative on the RDT, the majority (154 out of 154 or 100%) were not vaccinated against COVID-19. This suggests a potential correlation between a negative RDT result and lack of vaccination, implying that unvaccinated individuals may be at a higher risk of testing negative on RDTs, possibly due to their susceptibility to COVID-19 infection. Conversely, among individuals who tested positive on the RDT, 136 out of 298 cases (45.6%) were vaccinated against COVID-19. While the majority of RDT-positive cases were vaccinated, this proportion is significantly lower compared to RDT-negative cases. This finding may indicate breakthrough infections among vaccinated individuals or the presence of false positive RDT results, highlighting the need for caution in interpreting positive RDT outcomes in vaccinated populations (Table 4) [8].

Table 4: Frequency table for RT-PCR and COVID-19 vaccination.

The table represents cross-tabulation of RT-PCR test results with COVID-19 vaccination status (Table 4). Among those with a negative RT-PCR test result, 150 were not vaccinated against COVID-19, 9 were vaccinated and 2 cases were not categorized, along with 1 categorized as "Yes" for vaccination. Among those with a positive RT-PCR test result, 96 were not vaccinated, 128 were vaccinated and 51 cases were not categorized, along with 16 categorized as "Yes" for vaccination (Table 5) [9].

Table 5: Association between test result and vaccination status.

Among individuals who tested negative on the RT-PCR test, the majority (150 out of 162 cases or 92.6%) were not vaccinated against COVID-19 (Table 5). This indicates a notable correlation between a negative RT-PCR result and lack of vaccination, suggesting that unvaccinated individuals may be more susceptible to COVID-19 infection or severe disease progression. Conversely, among individuals who tested positive on the RT PCR test, 145 out of 291 cases (49.8%) were vaccinated against COVID-19. While the majority of RT-PCR-positive cases were vaccinated, this proportion is notably lower compared to RT PCR-negative cases. This finding may suggest breakthrough infections among vaccinated individuals or the presence of false positive RT-PCR results, underscoring the importance of interpreting positive RT-PCR outcomes in vaccinated populations with caution [10].

Among the individuals who tested positive on the RDT, 4 have not been vaccinated against COVID-19, while 92 have received the vaccine. Conversely, out of those who tested negative on the RDT, 136 have not been vaccinated and 53 have received the COVID-19 vaccine. The breakdown of categories based on RDT test results and vaccination status offers valuable insights into the distribution of COVID-19 vaccination status among individuals with varying combinations of RDT and RT-PCR test results, aiding in the analysis of vaccination trends and their correlation with test outcomes. The chi-square tests reveal a statistically significant association between the RT-PCR test results and COVID-19 vaccination status, with a p-value less than 0.05 (p<0.05). The association between RT-PCR test results and COVID-19 vaccination status underscores the critical role of vaccination in reducing the risk of COVID-19 infection and its implications for diagnostic test performance. Continued surveillance and research are essential to elucidate the dynamics between vaccination status and RT-PCR test outcomes, inform public health strategies and optimize COVID-19 testing and management approaches in vaccinated populations (Table 6) [11].

Table 6: Viral load CT value in association to vaccination status.

This study examined the relationship between receiving either the first or second dose of the COVID-19 vaccine and RT-PCR cycle threshold (CT) values, which serve as indicators of viral load. The CT values were categorized into five groups: not available, high viral load (<20), intermediate viral load (20-30), low viral load (30-40) and negative (>40) [12].

In terms of first dose analysis, among 298 participants, the majority fell into the "High viral load (<20)" category, with 82 vaccinated and 120 unvaccinated individuals. Fewer participants were in the "Intermediate viral load (20-30)" category, with 19 vaccinated and 39 unvaccinated. The "Low viral load (30-40)" and "Negative (>40)" categories had even fewer participants, with a fairly balanced distribution between vaccinated and unvaccinated individuals. Statistical analysis using the chi-square test revealed a Pearson chi-square value of 3.901 with a p-value of 0.420 and a likelihood ratio of 3.873 with a p-value of 0.424. Both p-values are above the 0.05 threshold, indicating no significant association between receiving the first dose of the vaccine and CT value categories [13].

Among 120 participants, 67 had received the second dose, while 53 had not. Most participants were in the "High viral load (<20)" category, with 47 vaccinated and 35 unvaccinated individuals. The "Intermediate viral load (20-30)" category included 10 vaccinated and 9 unvaccinated. The "Low viral load (30-40)" and "Negative (>40)" categories had a similar distribution between vaccinated and unvaccinated individuals. The chi-square test for the second dose showed a Pearson chi-square value of 0.887 with a p-value of 0.926 and a likelihood ratio of 0.885 with a p-value of 0.927. Both p-values are significantly above 0.05, indicating no significant association between receiving the second dose and the CT value categories [14].

The findings of this study are significant as they suggest no clear association between receiving either the first or second dose of the COVID-19 vaccine and viral load categories, as indicated by CT values. This implies that neither the first nor the second dose alone significantly alters the viral load among infected individuals or that other factors might play a more crucial role in influencing viral load. The results indicate no significant association, but the presence of cells with low expected counts highlights the necessity for further research with larger sample sizes to ensure more robust and reliable conclusions. These findings can inform future studies and vaccination strategies, emphasizing the need for comprehensive data to better understand the impacts of vaccination on viral dynamics [15].

Discussion

The data on clinical symptoms provides insight into the prevalence and distribution of various symptoms among COVID-19 positive and negative individuals. The most frequently reported symptom among positive cases is a headache, with 39.5% (179 individuals) of the infected group experiencing this symptom, compared to only 8.4% (38 individuals) of those testing negative. This significant difference underscores the prominence of headaches as a symptom associated with COVID-19. Cough is the second most common symptom in the positive group, reported by 38.9% (176 individuals). This finding aligns with existing literature that identifies cough as a key symptom of COVID-19. However, 9.1% (41 individuals) of those who tested negative also reported cough, indicating that while cough is a common symptom, it is not exclusive to COVID-19 and can occur in other respiratory conditions. Fever, another hallmark symptom of COVID-19, was reported by 32.2% (146 individuals) of the positive group, while 15.7% (71 individuals) of the negative group also experienced fever. This overlap suggests that fever, although significant, should not be used as a sole diagnostic criterion. Shortness of breath, while often highlighted as a severe symptom of COVID-19, was reported by only 5.1% (23 individuals) of the positive group. In contrast, a substantial 42.8% (194 individuals) of the negative group reported this symptom, suggesting that shortness of breath is less indicative of COVID-19 and may be more commonly associated with other conditions. Sore throat was noted by 28.7% (130 individuals) of the positive group, compared to 19.2% (87 individuals) of the negative group. This moderate prevalence highlights the importance of considering sore throat in conjunction with other symptoms. Loss of taste and smell, often highlighted as specific indicators of COVID-19, were reported by 12.1% (55 individuals) and 9.5% (43 individuals) of the positive group, respectively. Conversely, a significant portion of the negative group also reported these symptoms (35.5% and 38.0%, respectively), suggesting these symptoms are not as exclusive to COVID-19 as initially thought. Easy fatigue and joint pain were reported by 22.3% (101 individuals) and 30.7% (139 individuals) of the positive group, respectively. These symptoms were also present in 25.6% (116 individuals) and 16.8% (76 individuals) of the negative group, indicating their commonality in other conditions as well [16].

The study also compared the results of RDT and RT-PCR tests to understand discrepancies and reliability. Out of 453 tests conducted, 65.8% (298 individuals) tested positive on RDT, while 34.0% (154 individuals) tested negative. The RT-PCR results showed 64.2% (291 individuals) positive and 35.8% (162 individuals) negative. A detailed comparison revealed that 7 individuals who tested positive on RDT were negative on RT PCR, while 4 individuals who tested negative on RDT were positive on RT-PCR. This discrepancy suggests potential limitations in the sensitivity and specificity of RDT compared to RT-PCR. The absolute difference between RDT and RT-PCR positive results was 136 cases, with a percentage difference of approximately 45.64%. This substantial difference indicates a significant variance in the results of these testing methods, highlighting the need for caution when relying solely on RDT results for COVID-19 diagnosis [17].

The association between test results (both RDT and RT-PCR) and COVID-19 vaccination status. Among the RDT-negative group, all 154 individuals were not vaccinated, suggesting a potential correlation between lack of vaccination and negative RDT results. In contrast, among the RDT-positive group, 45.6% (136 individuals) were vaccinated, indicating a presence of breakthrough infections or false positives in vaccinated individuals. For RT-PCR results, 92.6% (150 out of 162) of the negative group were not vaccinated, compared to 49.8% (145 out of 291) of the positive group who were vaccinated. This again highlights a potential correlation between vaccination status and test outcomes, with a notable proportion of positive cases being among vaccinated individuals [18].

The relationship between vaccination status and viral load, as indicated by CT values, was also explored. Among those who received the first dose, 82 out of 298 participants had a high viral load (<20). Statistical analysis using the chi-square test showed no significant association between the first dose and CT value categories (p-value=0.420). For the second dose, 47 out of 120 participants had a high viral load. The chi-square test again showed no significant association between the second dose and CT value categories (p-value=0.926). The study's findings suggest no clear association between receiving either the first or second dose of the COVID-19 vaccine and viral load categories. This implies that the vaccine doses alone do not significantly alter the viral load among infected individuals, indicating the possible influence of other factors on viral dynamics. Understanding these relationships is crucial for public health strategies, particularly in managing transmission and severity of infections. The presence of cells with low expected counts in the statistical tests highlights the need for further research with larger sample sizes to ensure more robust conclusions. These findings can guide future studies and vaccination policies, emphasizing comprehensive data collection to better understand the impacts of vaccination on viral dynamics [19].

Conclusion

This study explored the intricate relationship between COVID-19 vaccination status, viral load measured by CT values and diagnostic test outcomes. By carefully analyzing symptomatology, test results and vaccination status, several crucial findings have emerged. To begin with, although certain symptoms such as cough, headache and fever were commonly observed among individuals who tested positive for COVID-19, these symptoms were not exclusive to positive cases. This highlights the importance of comprehensive diagnostic approaches that go beyond relying solely on symptomatology. Furthermore, a comparison between Rapid Diagnostic Tests (RDT) and RT-PCR tests revealed significant differences, underscoring the need for confirmatory RT-PCR testing due to its higher sensitivity and specificity. Moreover, the association between vaccination status and test outcomes revealed a significant proportion of vaccinated individuals among both RDT and RT-PCR positive cases. This suggests the possibility of breakthrough infections or false positives, emphasizing the importance of interpreting test results cautiously in vaccinated populations. Lastly, the examination of viral load in relation to vaccination status showed no significant correlation between receiving either the first or second dose of the COVID-19 vaccine and viral load categories, as indicated by CT values. Overall, these findings highlight the complexity of COVID-19 diagnosis and vaccination dynamics.

Ethical Considerations

The study has received ethical approval from the Institutional Review Board of the Ethiopian Public Health Institute (EPHI_IRB). Furthermore, consent have acquired from the administrators of every healthcare facility to carry out our research. Before enrolling them, all participants in the study provided written informed consent. Data collectors have also informed each participant that their involvement in the study is voluntary and that they retain the right to withdraw at any point without any adverse effects on their access to health services.

Acknowledgments

We would like to express our gratitude to the healthcare professionals for their commitment and dedication during data and sample collection. We also appreciate the participants who willingly volunteered for data collection, as their involvement has significantly contributed to our knowledge of COVID-19 detection methods. Lastly, we want to thank the genomics laboratory division at EPHI for their assistance in performing the RT-PCR tests.

Conflict of Interest

We declare that there are no conflicts of interest relevant to this study. No financial or personal relationships with individuals or organizations that could inappropriately influence or bias the content of this comparative study on COVID-19 detection methods have been disclosed.

References

1. Alhassan RK, Nketiah-Amponsah E, Afaya A, Salia SM, Abuosi AA, Nutor JJ, et al. Global Health Security Index not a proven surrogate for health systems capacity to respond to pandemics: The case of COVID-19. J Infect Public Health. 2023;16(2):196-205.

   [Crossref][Google Scholar][PubMed]

2. Altawalah H, Alfouzan W, Al-Fadalah T, Ezzikouri S. Diagnostic performance of automated SARS-CoV-2 antigen assay in nasal swab during COVID-19 vaccination campaign. Diagnostics. 2011;11:2110.

   [Crossref][Google Scholar][PubMed]

3. Aruleba RT, Adekiya TA, Ayawei N, Obaido G, Aruleba K, Mienye ID, et al. COVID-19 diagnosis: A review of rapid antigen, RT-PCR and artificial intelligence methods. Bioengineering. 2021;9(4):153.

   [Crossref][Google Scholar][PubMed]

4. Chong KC, Tay CJ, Khan MS, Hassan MA. COVID-19 testing and vaccination in Ethiopia: Challenges and recommendations. Ethiop J Health Sci. 2007;33:45-53.

   [Google Scholar]

5. Desalegn Z, Sebre S, Yohannes M, Seman A, Shiferaw W, Ademe M, et al. Comparison of the diagnostic performance of a rapid antigen test with real-time polymerase chain reaction for detection of SARS-CoV-2 among patients diagnosed with COVID-19 at selected hospitals in Addis Ababa, Ethiopia. Infect Drug Res. 2010;11:4299-4305.

   [Crossref][Google Scholar][PubMed]

6. Filchakova O, Dossym D, Ilyas A, Kuanysheva T, Abdizhamil A, Bukasov R. Review of COVID-19 testing and diagnostic methods. Talanta. 2010;244:123409.

   [Crossref][Google Scholar][PubMed]

7. Gize A, Kassa M, Ali S, Tadesse Y, Fantahun B, Habtu Y, et al. Epidemiological, clinical and laboratory profile of patients presenting with severe acute respiratory syndrome (SARS-CoV-2) in Ethiopia. Plos One. 2023;18:0295177.

   [Crossref][Google Scholar][PubMed]

8. Humer E, Keil T, Stupp C, Schlee W, Wildner M, Heuschmann P, et al. Associations of country-specific and sociodemographic factors with self-reported COVID-19â??related symptoms: Multivariable analysis of data from the coronacheck mobile health platform. JMIR Pub Health Surv. 2012;9:e40958.

   [Crossref][Google Scholar][PubMed]

9. Jarrom D, Elston L, Washington J, Prettyjohns M, Cann K, Myles S, et al. Effectiveness of tests to detect the presence of SARS-CoV-2 virus and antibodies to SARS-CoV-2, to inform COVID-19 diagnosis: A rapid systematic review. BMJ. 2022;27:33-45.

   [Crossref][Google Scholar][PubMed]

10. Kitara DL, Ikoona EN. Proposed strategies for easing COVID-19 lockdown measures in Africa. Pan African Med J. 2002;36:12-17.

    [Crossref][Google Scholar][PubMed]

11. Kliegr T, Jarkovsky J, Jirincova H, Kuchar J, Karel T, Chudan D, et al. Can variants, reinfection, symptoms and test types affect COVID-19 diagnostic performance? A large-scale retrospective study of AG-RDTs during circulation of Delta and Omicron variants, Czechia. Eurosurveillance. 2010;28(38):2200938.

    [Crossref][Google Scholar][PubMed]

12. Liu C, Lee J, Ta C, Soroush A, Rogers JR, Kim JH, et al. A retrospective analysis of COVID-19 mRNA vaccine breakthrough infections risk factors and vaccine effectiveness. Medrxiv.  2021;8(9): e524-e526.

    [Crossref][Google Scholar][PubMed]

13. Mina MJ, Parker R, Larremore DB. Rethinking COVID-19 test sensitivity a strategy for containment. New Eng J Med. 2001;383(22):120.

    [Crossref][Google Scholar][PubMed]

14. Olalekan A, Iwalokun B, Akinloye OM, Popoola O, Samuel TA, Akinloye O, et al. COVID-19 rapid diagnostic test could contain transmission in low-and middle-income countries. Afri J Lab Med. 2010;9:1-8.

    [Crossref][Google Scholar][PubMed]

15. Qian Y, Zeng T, Wang H, Xu M, Chen J, Hu N, et al. Safety management of nasopharyngeal specimen collection from suspected cases of coronavirus disease 2019. Int J Nurs Sci. 2011;7(2):153-156.

    [Crossref][Google Scholar][PubMed]

16. Reiner R, Collins JK. Forecasting the trajectory of the COVID-19 pandemic into 2023 under plausible variant and intervention scenarios: A global modelling study. MedRxiv. 2023; 12:2023-03.

    [Google Scholar]

17. Soni A, Herbert C, Filippaios A, Broach J, Colubri A, Fahey N, et al. Comparison of rapid antigen tests' performance between Delta and Omicron variants of SARS-CoV-2: A secondary analysis from a serial home self-testing study. Anna inter med. 2011;175:1685-1692.

    [Crossref][Google Scholar][PubMed]

18. Wagenhauser I, Knies K, Pscheidl T, Eisenmann M, Flemming S, Petri N, et al. SARS-CoV-2 Antigen Rapid Detection Tests: test performance during the COVID-19 pandemic and the impact of COVID-19 vaccination. medRxiv. 2021;2024-04.

    [Google Scholar]

19. Watare SH, Alemu MA, Tayachew A, Yohannes N, Gizachew L, Kebede A, et al. An investigation of a hundred COVID-19 cases and close contacts in Ethiopia, May to June, 2020: A prospective case-ascertained study. PLoS One. 2010;17:e0275596.

    [Crossref][Google Scholar][PubMed]