Charting the challenges behind the testing of COVID-19 in developing countries: Nepal as a case study

A proof of concept study for the differentiation of SARS-CoV-2, hCoV-NL63, and IAV-H1N1 in vitro cultures using ion mobility spectrometry

Rapid, high-throughput diagnostic tests are essential to decelerate the spread of the novel coronavirus disease 2019 (COVID-19) pandemic. While RT-PCR tests performed in centralized laboratories remain the gold standard, rapid point-of-care antigen tests might provide faster results. However, they are associated with markedly reduced sensitivity. Bedside breath gas analysis of volatile organic compounds detected by ion mobility spectrometry (IMS) may enable a quick and sensitive point-of-care testing alternative. In this proof-of-concept study, we investigated whether gas analysis by IMS can discriminate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from other respiratory viruses in an experimental set-up. Repeated gas analyses of air samples collected from the headspace of virus-infected in vitro cultures were performed for 5 days. A three-step decision tree using the intensities of four spectrometry peaks correlating to unidentified volatile organic compounds allowed the correct classification of SARS-CoV-2, human coronavirus-NL63, and influenza A virus H1N1 without misassignment when the calculation was performed with data 3 days post infection. The forward selection assignment model allowed the identification of SARS-CoV-2 with high sensitivity and specificity, with only one of 231 measurements (0.43%) being misclassified. Thus, volatile organic compound analysis by IMS allows highly accurate differentiation of SARS-CoV-2 from other respiratory viruses in an experimental set-up, supporting further research and evaluation in clinical studies.

Smart testing and critical care bed sharing for COVID-19 control

During the early months of the current COVID-19 pandemic, social distancing measures effectively slowed disease transmission in many countries in Europe and Asia, but the same benefits have not been observed in some developing countries such as Brazil. In part, this is due to a failure to organise systematic testing campaigns at nationwide or even regional levels. To gain effective control of the pandemic, decision-makers in developing countries, particularly those with large populations, must overcome difficulties posed by an unequal distribution of wealth combined with low daily testing capacities. The economic infrastructure of these countries, often concentrated in a few cities, forces workers to travel from commuter cities and rural areas, which induces strong nonlinear effects on disease transmission. In the present study, we develop a smart testing strategy to identify geographic regions where COVID-19 testing could most effectively be deployed to limit further disease transmission. By smart testing we mean the testing protocol that is automatically designed by our optimization platform for a given time period, knowing the available number of tests, the current availability of ICU beds and the initial epidemiological situation. The strategy uses readily available anonymised mobility and demographic data integrated with intensive care unit (ICU) occupancy data and city-specific social distancing measures. Taking into account the heterogeneity of ICU bed occupancy in differing regions and the stages of disease evolution, we use a data-driven study of the Brazilian state of Sao Paulo as an example to show that smart testing strategies can rapidly limit transmission while reducing the need for social distancing measures, even when testing capacity is limited.

Molecular diagnostic assays for COVID-19: an overview

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the cardinal importance of rapid and accurate diagnostic assays. Since the early days of the outbreak, researchers with different scientific backgrounds across the globe have tried to fulfill the urgent need for such assays, with many assays having been approved and with others still undergoing clinical validation. Molecular diagnostic assays are a major group of tests used to diagnose COVID-19. Currently, the detection of SARS-CoV-2 RNA by reverse transcription polymerase chain reaction (RT-PCR) is the most widely used method. Other diagnostic molecular methods, including CRISPR-based assays, isothermal nucleic acid amplification methods, digital PCR, microarray assays, and next generation sequencing (NGS), are promising alternatives. In this review, we summarize the technical and clinical applications of the different COVID-19 molecular diagnostic assays and suggest directions for the implementation of such technologies in future infectious disease outbreaks.

Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets

The Coronavirus pandemic caused by the novel SARS-CoV-2 has significantly impacted human health and the economy, especially in countries struggling with financial resources for medical testing and treatment, such as Brazil's case, the third most affected country by the pandemic. In this scenario, machine learning techniques have been heavily employed to analyze different types of medical data, and aid decision making, offering a low-cost alternative. Due to the urgency to fight the pandemic, a massive amount of works are applying machine learning approaches to clinical data, including complete blood count (CBC) tests, which are among the most widely available medical tests. In this work, we review the most employed machine learning classifiers for CBC data, together with popular sampling methods to deal with the class imbalance. Additionally, we describe and critically analyze three publicly available Brazilian COVID-19 CBC datasets and evaluate the performance of eight classifiers and five sampling techniques on the selected datasets. Our work provides a panorama of which classifier and sampling methods provide the best results for different relevant metrics and discuss their impact on future analyses. The metrics and algorithms are introduced in a way to aid newcomers to the field. Finally, the panorama discussed here can significantly benefit the comparison of the results of new ML algorithms.

DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image

Recently, the destructive impact of Coronavirus 2019, commonly known as COVID-19, has affected public health and human lives. This catastrophic effect disrupted human experience by introducing an exponentially more damaging unpredictable health crisis since the Second World War (Kursumovic et al. in Anaesthesia 75: 989–992, 2020). Strong communicable characteristics of COVID-19 within human communities make the world's crisis a severe pandemic. Due to the unavailable vaccine of COVID-19 to control rather than cure, early and accurate detection of the virus can be a promising technique for tracking and preventing the infection from spreading (e.g., by isolating the patients). This situation indicates improving the auxiliary COVID-19 detection technique. Computed tomography (CT) imaging is a widely used technique for pneumonia because of its expected availability. The artificial intelligence-aided images analysis might be a promising alternative for identifying COVID-19. This paper presents a promising technique of predicting COVID-19 patients from the CT image using convolutional neural networks (CNN). The novel approach is based on the most recent modified CNN architecture (DenseNet-121) to predict COVID-19. The results outperformed 92% accuracy, with a 95% recall showing acceptable performance for the prediction of COVID-19.

Prediction of individual COVID-19 diagnosis using baseline demographics and lab data

The global surge in COVID-19 cases underscores the need for fast, scalable, and reliable testing. Current COVID-19 diagnostic tests are limited by turnaround time, limited availability, or occasional false findings. Here, we developed a machine learning-based framework for predicting individual COVID-19 positive diagnosis relying only on readily-available baseline data, including patient demographics, comorbidities, and common lab values. Leveraging a cohort of 31,739 adults within an academic health system, we trained and tested multiple types of machine learning models, achieving an area under the curve of 0.75. Feature importance analyses highlighted serum calcium levels, temperature, age, lymphocyte count, smoking, hemoglobin levels, aspartate aminotransferase levels, and oxygen saturation as key predictors. Additionally, we developed a single decision tree model that provided an operable method for stratifying sub-populations. Overall, this study provides a proof-of-concept that COVID-19 diagnosis prediction models can be developed using only baseline data. The resulting prediction can complement existing tests to enhance screening and pandemic containment workflows.

Truncating a densely connected convolutional neural network with partial layer freezing and feature fusion for diagnosing COVID-19 from chest X-rays

Deep learning and computer vision revolutionized a new method to automate medical image diagnosis. However, to achieve reliable and state-of-the-art performance, vision-based models require high computing costs and robust datasets. Moreover, even with the conventional training methods, large vision-based models still involve lengthy epochs and costly disk consumptions that can entail difficulty during deployment due to the absence of high-end infrastructures. Therefore, this method modified the training approach on a vision-based model through layer truncation, partial layer freezing, and feature fusion. The proposed method was employed on a Densely Connected Convolutional Neural Network (CNN), the DenseNet model, to diagnose whether a Chest X-Ray (CXR) is well, has Pneumonia, or has COVID-19. From the results, the performance to parameter size ratio highlighted this method's effectiveness to train a DenseNet model with fewer parameters compared to traditionally trained state-of-the-art Deep CNN (DCNN) models, yet yield promising results.•This novel method significantly reduced the model's parameter size without sacrificing much of its classification performance.•The proposed method had better performance against some state-of-the-art Deep Convolutional Neural Network (DCNN) models that diagnosed samples of CXRs with COVID-19.•The proposed method delivered a conveniently scalable, reproducible, and deployable DCNN model for most low-end devices.

A multispectral-sensor-based colorimetric reader for biological assays

Tests that depend on changes in color are commonly used in biosensing. Here, we report on a colorimetric reader for such applications. The device is simple to construct and operate, making it ideal for research laboratories with limited resources or skilled personnel. It consists of a commercial multispectral sensor interfaced with a Raspberry Pi and a touchscreen. Unlike camera-based readers, this instrument requires no calibration of wavelengths by the user or extensive image processing to obtain results. We demonstrate its potential for colorimetric biosensing by applying it to the birefringent enzyme-linked immunosorbent assay. It was able to prevent certain false positives that the assay is susceptible to and lowered its limit of detection for glucose by an order of magnitude.

Finding the real COVID-19 case-fatality rates for SAARC countries

The crude case fatality rate (CFR), because of the calculation method, is the most accurate when the pandemic is over since there is a possibility of the delay between disease onset and outcomes. Adjusted crude CFR measures can better explain the pandemic situation by improving the CFR estimation. However, no study has thoroughly investigated the COVID-19 adjusted CFR of the South Asian Association For Regional Cooperation (SAARC) countries. This study estimated both survival interval and underreporting adjusted CFR of COVID-19 for these countries. Moreover, we assessed the crude CFR between genders and across age groups and observed the CFR changes due to the imposition of fees on COVID-19 tests in Bangladesh. Using the daily records up to October 9, we implemented a statistical method to remove the delay between disease onset and outcome bias, and due to asymptomatic or mild symptomatic cases, reporting rates lower than 50% (95% CI: 10%-50%) bias in crude CFR. We found that Afghanistan had the highest CFR, followed by Pakistan, India, Bangladesh, Nepal, Maldives, and Sri Lanka. Our estimated crude CFR varied from 3.708% to 0.290%, survival interval adjusted CFR varied from 3.767% to 0.296% and further underreporting adjusted CFR varied from 1.096% to 0.083%. Furthermore, the crude CFRs for men were significantly higher than that of women in Afghanistan (4.034% vs. 2.992%) and Bangladesh (1.739% vs. 1.337%) whereas the opposite was observed in Maldives (0.284% vs. 0.390%), Nepal (0.006% vs. 0.007%), and Pakistan (2.057% vs. 2.080%). Besides, older age groups had higher risks of death. Moreover, crude CFR increased from 1.261% to 1.572% after imposing the COVID-19 test fees in Bangladesh. Therefore, the authorities of countries with higher CFR should be looking for strategic counsel from the countries with lower CFR to equip themselves with the necessary knowledge to combat the pandemic. Moreover, caution is needed to report the CFR.

SARS-CoV-2: sewage surveillance as an early warning system and challenges in developing countries

Transmission of novel coronavirus (SARS-CoV-2) in humans happens either through airway exposure to respiratory droplets from an infected patient or by touching the virus contaminated surface or objects (fomites). Presence of SARS-CoV-2 in human feces and its passage to sewage system is an emerging concern for public health. Pieces of evidence of the occurrence of viral RNA in feces and municipal wastewater (sewage) systems have not only warned reinforcing the treatment facilities but also suggest that these systems can be monitored to get epidemiological data for checking trend of COVID-19 infection in the community. This review summarizes the occurrence and persistence of novel coronavirus in sewage with an emphasis on the possible water environment contamination. Monitoring of novel coronavirus (SARS-CoV-2) via sewage-based epidemiology could deliver promising information regarding rate of infection providing a valid and complementary tool for tracking and diagnosing COVID-19 across communities. Tracking the sewage systems could act as an early warning tool for alerting the public health authorities for necessary actions. Given the impracticality of testing every citizen with limited diagnostic resources, it is imperative that sewage-based epidemiology can be tested as an early warning system. The need for the development of robust sampling strategies and subsequent detection methodologies and challenges for developing countries are also discussed.

Copan eNAT Transport System To Address Challenges in COVID-19 Diagnostics in Regions with Limited Testing Access

Community-based health care clinics and hospital outreach services have the potential to expand coronavirus disease 2019 (COVID-19) diagnostics to rural areas. However, reduced specimen stability during extended transport, the absence of a cold chain to centralized laboratories, and biosafety concerns surrounding specimen handling have limited this expansion.

Community-based health care clinics and hospital outreach services have the potential to expand coronavirus disease 2019 (COVID-19) diagnostics to rural areas. However, reduced specimen stability during extended transport, the absence of a cold chain to centralized laboratories, and biosafety concerns surrounding specimen handling have limited this expansion. In the following study, we evaluated eNAT (Copan Italia, Brescia, Italy) as an alternative transport system to address the biosafety and stability challenges associated with expanding COVID-19 diagnostics to rural and remote regions. In this study, we demonstrated that high-titer severe acute respiratory virus syndrome coronavirus 2 (SARS-CoV-2) lysate placed into eNAT medium cannot be propagated in cell culture, supporting viral inactivation. To account for off-site testing in these settings, we assessed the stability of contrived nasopharyngeal (NP) specimens stored for up to 14 days in various transport media (eNAT, eSwab, viral transport medium [VTM], saline, and phosphate-buffered saline [PBS]) at 4°C, 22 to 25°C, and 35°C. The molecular detection of SARS-CoV-2 was unaffected by sample storage temperature over the 2 weeks when stored in eNAT or PBS (change in cycle threshold, ≤1). In contrast, variable stability was observed across test conditions for other transport media. As eNAT can inactivate SARS-CoV-2, it may support COVID-19 diagnostics at the point of care. Evaluation of compatibility of eNAT with Cepheid Xpert Xpress SARS-CoV-2 assay demonstrated diagnostic accuracy and sensitivity equivalent to those of VTM. Taken together, these findings suggest that the implementation of eNAT as a collection device can expand COVID-19 testing to areas with limited health care access.

Diagnosing Covid-19 chest x-rays with a lightweight truncated DenseNet with partial layer freezing and feature fusion

Due to the unforeseen turn of events, our world has undergone another global pandemic from a highly contagious novel coronavirus named COVID-19. The novel virus inflames the lungs similarly to Pneumonia, making it challenging to diagnose. Currently, the common standard to diagnose the virus's presence from an individual is using a molecular real-time Reverse-Transcription Polymerase Chain Reaction (rRT-PCR) test from fluids acquired through nasal swabs. Such a test is difficult to acquire in most underdeveloped countries with a few experts that can perform the test. As a substitute, the widely available Chest X-Ray (CXR) became an alternative to rule out the virus. However, such a method does not come easy as the virus still possesses unknown characteristics that even experienced radiologists and other medical experts find difficult to diagnose through CXRs. Several studies have recently used computer-aided methods to automate and improve such diagnosis of CXRs through Artificial Intelligence (AI) based on computer vision and Deep Convolutional Neural Networks (DCNN), which some require heavy processing costs and other tedious methods to produce. Therefore, this work proposed the Fused-DenseNet-Tiny, a lightweight DCNN model based on a densely connected neural network (DenseNet) truncated and concatenated. The model trained to learn CXR features based on transfer learning, partial layer freezing, and feature fusion. Upon evaluation, the proposed model achieved a remarkable 97.99 % accuracy, with only 1.2 million parameters and a shorter end-to-end structure. It has also shown better performance than some existing studies and other massive state-of-the-art models that diagnosed COVID-19 from CXRs.

COVID-19 pandemic: the implications of the natural history, challenges of diagnosis and management for care in sub-Saharan Africa

BACKGROUND

Coronavirus disease (COVID-19) is a severe acute respiratory infection which has afflicted virtually almost all nations of the earth. It is highly transmissible and represents one of the most serious pandemics in recent times, with the capacity to overwhelm any healthcare system and cause morbidity and fatality.

MAIN CONTENT

The diagnosis of this disease is daunting and challenging as it is dependent on emerging clinical symptomatology that continues to increase and change very rapidly. The definitive test is the very expensive and scarce polymerase chain reaction (PCR) viral identification technique. The management has remained largely supportive and empirical, as there are no officially approved therapeutic agents, vaccines or antiviral medications for the management of the disease. Severe cases often require intensive care facilities and personnel. Yet there is paucity of facilities including the personnel required for diagnosis and treatment of COVID-19 in sub-Saharan Africa (SSA). It is against this backdrop that a review of key published reports on the pandemic in SSA and globally is made, as understanding the natural history of a disease and the documented responses to diagnosis and management is usually a key public health strategy for designing and improving as appropriate, relevant interventions. Lead findings were that responses by most nations of SSA were adhoc, paucity of public health awareness strategies and absence of legislations that would help enforce preventive measures, as well as limited facilities (including personal protective equipment) and institutional capacities to deliver needed interventions.

CONCLUSION

COVID-19 is real and has overwhelmed global health care system especially low-income countries of the sub-Sahara such as Nigeria. Suggestions for improvement of healthcare policies and programs to contain the current pandemic and to respond more optimally in case of future pandemics are made herein.

Design of a point-of-care facility for diagnosis of COVID-19 using an off-grid photovoltaic system

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is one of the biggest public health issues in the last years. The WHO has reported more than 50,000 confirmed cases and more than 1,000,000 confirmed deaths around the world. Early diagnosis is essential for an appropriate patient care and infection control, so laboratory where molecular tests are held plays a main role. However, laboratory facilities for testing are limited in rural areas. Therefore, it is important to have an effective and practical point-of-care diagnostic system in order to be implemented in developing countries with limited energy access. The objective of this research is to develop an energetically autonomous point-of-care diagnostic system for molecular detection of SARS-CoV-2. This design consists of a retractable system with an area of 15.79 m² and 3 well-distributed interior areas to guaranty appropriate sample processing. Our point-of-care diagnostic system can be installed at a fixed place (stationary), and it can also be transported to various strategic places (itinerant). The off-grid photovoltaic system feasibility was evaluated using the PVsyst software, presenting an installed capacity of 2.79 KWp, consisting of 4 monocrystalline photovoltaic modules, a 45 A charge regulator and 4 batteries (6 V, 453 Ah). The results showed a performance ratio of 0.522, with higher losses by the full battery (31.77%). This research determines that the proposed point-of-care diagnostic system meets all requirements to set and operate molecular techniques to diagnose infectious diseases, such as COVID-19, with good laboratory conditions, secure and eco-efficient energy, supporting the health scheme to prevent and control the spread of the virus.

An alternate prospect in detecting presymptomatic and asymptomatic COVID-19 carriers through odor differentiation by HeroRATs

The need for a cheap, ubiquitous, sensitive, rapid, noninvasive means of screening large numbers of presymptomatic and asymptomatic samples at departure or arrival into ports of countries, high-risk areas, and within communities forms the subject of this review. The widely used diagnostic test for the SARS-CoV 2 is the real-time reverse transcription–polymerase chain reaction assay while antibody-based techniques are being introduced as supplemental tools, but the lack of specialized nucleic acid extraction and amplification laboratories hampers/slows down timely large-scale testing. The use of animals with sensitive olfactory cue as an alternate testing model could serve as an alternative to detect COVID-19 in the saliva of carriers. The African giant rats are highly versatile and detect odorant molecules from carriers of pathogens with high percentage success after few months of training, hence can be taught to detect odor differences of COVID-19 in asymptomatic and presymptomatic individuals. If these are trained, they could help to curtail further spread of COVID infections.

Strategies at points of entry to reduce importation risk of COVID-19 cases and reopen travel

BACKGROUND

With more countries exiting lockdown, public health safety requires screening measures at international travel entry points that can prevent the reintroduction or importation of the severe acute respiratory syndrome-related coronavirus-2. Here, we estimate the number of cases captured, quarantining days averted and secondary cases expected to occur with screening interventions.

METHODS

To estimate active case exportation risk from 153 countries with recorded coronavirus disease-2019 cases and deaths, we created a simple data-driven framework to calculate the number of infectious and upcoming infectious individuals out of 100 000 000 potential travellers from each country, and assessed six importation risk reduction strategies; Strategy 1 (S1) has no screening on entry, S2 tests all travellers and isolates test-positives where those who test negative at 7 days are permitted entry, S3 the equivalent but for a 14 day period, S4 quarantines all travellers for 7 days where all are subsequently permitted entry, S5 the equivalent for 14 days and S6 the testing of all travellers and prevention of entry for those who test positive.

RESULTS

The average reduction in case importation across countries relative to S1 is 90.2% for S2, 91.7% for S3, 55.4% for S4, 91.2% for S5 and 77.2% for S6. An average of 79.6% of infected travellers are infectious upon arrival. For the top 100 exporting countries, an 88.2% average reduction in secondary cases is expected through S2 with the 7-day isolation of test-positives, increasing to 92.1% for S3 for 14-day isolation. A substantially smaller reduction of 30.0% is expected for 7-day all traveller quarantining, increasing to 84.3% for 14-day all traveller quarantining.

CONCLUSIONS

The testing and isolation of test-positives should be implemented provided good testing practices are in place. If testing is not feasible, quarantining for a minimum of 14 days is recommended with strict adherence measures in place.

COVID-19 pandemic crisis-a complete outline of SARS-CoV-2

BACKGROUND

Coronavirus (SARS-CoV-2), the cause of COVID-19, a fatal disease emerged from Wuhan, a large city in the Chinese province of Hubei in December 2019.

MAIN BODY OF ABSTRACT

The World Health Organization declared COVID-19 as a pandemic due to its spread to other countries inside and outside Asia. Initial confirmation of the pandemic shows patient exposure to the Huanan seafood market. Bats might be a significant host for the spread of coronaviruses via an unknown intermediate host. The human-to-human transfer has become a significant concern due to one of the significant reasons that is asymptomatic carriers or silent spreaders. No data is obtained regarding prophylactic treatment for COVID-19, although many clinical trials are underway.

CONCLUSION

The most effective weapon is prevention and precaution to avoid the spread of the pandemic. In this current review, we outline pathogenesis, diagnosis, treatment, ongoing clinical trials, prevention, and precautions. We have also highlighted the impact of pandemic worldwide and challenges that can help to overcome the fatal disease in the future.

Cavitary pulmonary tuberculosis with COVID-19 coinfection

The COVID-19 pandemic has strained the healthcare system worldwide, leading to an approach favoring judicious resource allocation. A focus on resource preservation can result in anchoring bias and missed concurrent diagnosis. Coinfection of Mycobacterium tuberculosis (TB) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has implications beyond morbidity at the individual level and can lead to unintended TB exposure to others. We present six cases of COVID-19 with newly diagnosed cavitating pulmonary tuberculosis to highlight the significance of this phenomenon and favorable outcomes if recognized early.

The current situation of COVID-19 in Sudan

The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global public health concern with rapid growth in the number of patients with significant mortality rates. The first case in Sudan was reported on 13 March 2020, and up to 3 July 2020 there are 9894 confirmed cases and 616 deaths. The case fatality rate was 6.23%. There is variation in case fatality rate (CFR), which in some cities (like Khartoum) was low (3.8%), but in others (like North Darfur) it was very high (31.7%). The government of Sudan has implemented preventive measures during the current coronavirus disease pandemic, such as partial lockdown, contact monitoring, risk communication, social distance, quarantine and isolation to prevent the spread of SARS-CoV-2. However, there are new community cases every day; this could be as a result of the weak application of these measures by the government, and the lack of commitment of people to these measures. The number of COVID-19 cases is currently decreasing in Sudan, but we are expected to see an increase in numbers of cases as a result of the massive demonstrations that occurred in Sudan recently, and as a result of the expected reopening and restoration of normal life. The government must increase testing facilities, and maintain social distancing and necessary precautions to limit the spread of infection after life returns to normal.