• COVID-19

The keynote presentation at UCT's event for Open Data Day 2021. Addresses the difficulty of gaining access to sensitive data in South Africa during the Covid-19 pandemic.

As the COVID-19 pandemic progresses, the demands on the health care system will intensify and will result in critical shortages of resources (hospital beds, intensive care unit (ICU) beds, ventilators, medical workforce), particularly in the public sector. Given the expected downturn in an already weak economy coupled with the increased demand for government resources for economic relief and other measures, the ability of government to commit additional funding to an already under-funded public health sector is limited. Within this context, it is imperative that available public resources are used fairly, optimally and efficiently - we simply cannot afford to waste any resources. This rapid economic evaluation intends to inform Covid-19 health system decision-making. In this report, we respond to the question: What is the cost-effectiveness of ICU care versus treatment in general ward in supporting severe Covid-19 cases? Given the emergent nature of Covid-19 and the huge uncertainty in key parameters, a simple excel-based model is provided. This simplicity is designed to facilitate understanding. In addition, the framework allows for parameters to be varied and ultimately updated as sufficient local data become available.

This model assesses the potential economic burden of alcohol-related injuries in South Africa. In addition to provider costs for each injury, the model calculates number of outpatient visits, general ward inpatient days and ICU days needed per event.

MODEL STRATEGIESThis model assesses costs and outcomes for admitted severe and critical Covid-19 patients from time of admission to discharge or death.Two competing strategies are modelled:1. The "status quo" summarizes costs and outcomes for patients assuming usual care (no dexamethasone).2. The "dexamethasone" comparator summarizes costs and outcomes for patients assuming usual care with dexamethasone at 6mg/day over 10 days. MODEL OUTCOMESCost: mean cost (general ward and ICU) per admitted patient from the health care provider's perspective.Health outcomes: mean DALYs and deaths per admitted patient. Other: mean ICU days and inpatient days per admitted patient.

Abstract Background The new coronavirus outbreak originated in Wuhan, China, started in January 2020 is escalating as a pandemic across the globe in March 2020. It causes unprecedented morbidity and shocked health systems and the supply chains in new epicenters such as Italy, Spain, and the USA, claiming thousands of lives. Meanwhile, the pandemic is reaching swiftly and silently to low-income countries where international media cover less. How likely health outcomes among the countries with different economies may differ during the pandemic has not been reported yet. Methodologically, we conducted an analysis of COVID-19 deaths comparing case fatality rate (CFR) among countries with different income categories, applying COVID-19 global data from the European Centre for Disease Control including 199 countries’ data as of 31 March 2020, in the early phase of the pandemic. We categorized countries into high-income countries (HIC), upper-middle-income countries (UMIC), lower-middle-income countries (LMIC), and low-income countries (LIC) according to World Bank classification by income as of 2020. Finding Statistically, countries in different income groups are significantly different in terms of new cases identified in the last 2 weeks and the case fatality rate (MANOVA, P value < 0.001). New tests and detected case numbers shot up in HICs where CFR shot up in LMICs and LICs. The results of this analysis pointed out an important gap among countries with different economic status during the ongoing pandemic. Conclusion In the HIC, contact tracing, testing capacity, and outbreak response, as well as clinical services, are strong. In the LICs, there is a low capacity of outbreak response which is reflected by the significantly lower number of diagnostic tests. Consequently, the reported number of COVID-19 cases in LICs may not reflect the actual burden of the pandemic. Without effective prevention, the pandemic can readily break into the weak health system and over-burden the hospitals and clinical services in poor countries. This finding is showing health inequality between the rich and the poor being amplified by the COVID-19 pandemic. Addressing such a gap through the local governance and integrated global responses will not only prevent unprecedented deaths, but also preserve the momentum towards Sustainable Development Goals (SDGs).

The outbreak of COVID-19 started in mid-December 2019 in Wuhan, China. Up to 29 February 2020, SARS-CoV-2 (HCoV-19 / 2019-nCoV) had infected more than 85 000 people in the world. In this study, we used 93 complete genomes of SARS-CoV-2 from the GISAID EpiFlu TM database to investigate the evolution and human-to-human transmissions of SARS-CoV-2 in the first two months of the outbreak. We constructed haplotypes of the SARS-CoV-2 genomes, performed phylogenomic analyses and estimated the potential population size changes of the virus. The date of population expansion was calculated based on the expansion parameter tau ( τ) using the formula t= τ/2 u. A total of 120 substitution sites with 119 codons, including 79 non-synonymous and 40 synonymous substitutions, were found in eight coding-regions in the SARS-CoV-2 genomes. Forty non-synonymous substitutions are potentially associated with virus adaptation. No combinations were detected. The 58 haplotypes (31 found in samples from China and 31 from outside China) were identified in 93 viral genomes under study and could be classified into five groups. By applying the reported bat coronavirus genome (bat-RaTG13-CoV) as the outgroup, we found that haplotypes H13 and H38 might be considered as ancestral haplotypes, and later H1 was derived from the intermediate haplotype H3. The population size of the SARS-CoV-2 was estimated to have undergone a recent expansion on 06 January 2020, and an early expansion on 08 December 2019. Furthermore, phyloepidemiologic approaches have recovered specific directions of human-to-human transmissions and the potential sources for international infected cases.

An online search of government websites and published literature was performed for regional data reports on COVID-19 cases that included sex as a variable from 1st January 2020 up until 1st June 2020 (Search terms: COVID-19/case/sex/country/data/death/ICU/ITU). In order to ensure unbiased representation from as many regions as possible, a cross check was done using the list of countries reporting data on ‘Worldometer’, and an attempt was made to include as many regions reporting sex data as possible. Reports were translated using Google translate if they were not in English.Data selection, extraction and synthesisReports were included if they contained sex as a variable in data describing case number, intensive treatment unit (ITU) admission, or mortality. Data were entered directly by individual researchers into an online structured data extraction table. For some sources, counts of male confirmed cases or male deaths were not provided, but percentages of male cases or male deaths were provided instead. To include these sources and avoid biases that might be introduced by their exclusion, we calculated counts of male confirmed cases and male deaths from the reported percentages with rounding to the nearest integer. We acknowledge that this approach assumes that the reported percentages are reflective of the true percentages. For some sources, data included confirmed cases and deaths of unknown sex. For these sources, the reported totals were used where the proportion of unknown sex was small. This approach was preferred to excluding cases of unknown sex in order to avoid bias. The estimates represent the proportion of known male infections and odds ratios for mortality associated with known male sex, and will differ slightly from what the true values would be if the sex had been reported for all cases. Data were available at the level of country or regional summary data representing distinct individuals for each report, but not at the level of covariates for all individuals within a study. Consequently, covariates such as lifestyle, comorbidities, testing method and case type (hospital vs. community) could not be controlled for.

The coronavirus disease 2019 (COVID-19) pandemic continues to pose a global threat to the human population. Identifying animal species susceptible to infection with the SARS-CoV-2/ HCoV-19 pathogen is essential for controlling the outbreak and for testing valid prophylactics or therapeutics based on animal model studies. Here, different aged Chinese tree shrews (adult group, 1 year old; old group, 5–6 years old), which are close relatives to primates, were infected with SARS-CoV-2. X-ray, viral shedding, laboratory, and histological analyses were performed on different days post-inoculation (dpi). Results showed that Chinese tree shrews could be infected by SARS-CoV-2. Lung infiltrates were visible in X-ray radiographs in most infected animals. Viral RNA was consistently detected in lung tissues from infected animals at 3, 5, and 7 dpi, along with alterations in related parameters from routine blood tests and serum biochemistry, including increased levels of aspartate aminotransferase (AST) and blood urea nitrogen (BUN). Histological analysis of lung tissues from animals at 3 dpi (adult group) and 7 dpi (old group) showed thickened alveolar septa and interstitial hemorrhage. Several differences were found between the two different aged groups in regard to viral shedding peak. Our results indicate that Chinese tree shrews have the potential to be used as animal models for SARS-CoV-2 infection.

The global pandemic of COVID-19 caused by SARS-CoV-2 (also known as 2019-nCoV and HCoV-19) has posed serious threats to public health and economic stability worldwide, thus calling for development of vaccines against SARS-CoV-2 and other emerging and reemerging coronaviruses. Since SARS-CoV-2 and SARS-CoV have high similarity of their genomic sequences and share the same cellular receptor (ACE2), it is essential to learn the lessons and experiences from the development of SARS-CoV vaccines for the development of SARS-CoV-2 vaccines. In this review, we summarized the current knowledge on the advantages and disadvantages of the SARS-CoV vaccine candidates and prospected the strategies for the development of safe, effective and broad-spectrum coronavirus vaccines for prevention of infection by currently circulating SARS-CoV-2 and other emerging and reemerging coronaviruses that may cause future epidemics or pandemics.

Remaining a major healthcare concern with nearly 29 million confirmed cases worldwide at the time of writing, novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused more than 920 thousand deaths since its outbreak in China, December 2019. First case of a person testing positive for SARS-CoV-2 infection within the territory of the Republic of Latvia was registered on 2nd of March 2020, 9 days prior to the pandemic declaration by WHO. Since then, more than 277,000 tests were carried out confirming a total of 1,464 cases of coronavirus disease 2019 (COVID-19) in the country as of 12th of September 2020. Rapidly reacting to the spread of the infection, an ongoing sequencing campaign was started mid-March in collaboration with the local testing laboratories, with an ultimate goal in sequencing as much local viral isolates as possible, resulting in first full-length SARS-CoV-2 isolate genome sequences from the Baltics region being made publicly available in early April. With 133 viral isolates representing ~9.1% of the total COVID-19 cases during the “first coronavirus wave” in the country (early March, 2020—mid-September, 2020) being completely sequenced as of today, here, we provide a first report on the genetic diversity of Latvian SARS-CoV-2 isolates.