Leveraging epidemiology as a decision support tool during the COVID-19 epidemic in South Africa.

By May 2021, South Africa (SA) had experienced two 'waves' of COVID-19 infections, with an initial peak of infections reached in July 2020, followed by a larger peak of infections in January 2021. Public health decisions rely on accurate and timely disease surveillance and epidemiological analyses, and accessibility of data at all levels of government is critical to inform stakeholders to respond effectively. In this paper, we describe the adaptation, development and operation of epidemiological surveillance and modelling systems in SA in response to the COVID-19 epidemic, including data systems for monitoring laboratory-confirmed COVID-19 cases, hospitalisations, mortality and recoveries at a national and provincial level, and how these systems were used to inform modelling projections and public health decisions. Detailed descriptions on the characteristics and completeness of individual datasets are not provided in this paper. Rapid development of robust data systems was necessary to support the response to the SA COVID-19 epidemic. These systems produced data streams that were used in decision-making at all levels of government. While much progress was made in producing epidemiological data, challenges remain to be overcome to address gaps to better prepare for future waves of COVID-19 and other health emergencies.

Mind the gap: Patterns of red blood cell product usage in South Africa, 2014 - 2019.

BACKGROUND: A key component of any successful healthcare system is the availability of sufficient, safe blood products delivered in an equitable manner. South Africa (SA) has a two-tiered healthcare system with public and privately funded sectors. Blood utilisation data for both sectors are lacking. Evaluation of blood utilisation patterns in each healthcare sector will enable implementation of systems to bring about more equality. OBJECTIVES: To conduct a critical evaluation of red blood cell (RBC) product utilisation patterns at the South African National Blood Service (SANBS). METHODS: Operationally collected data from RBC requests submitted to SANBS blood banks for the period 1 January 2014 - 31 March 2019 were used to determine temporal RBC product utilisation patterns by healthcare sector. Demographic patterns were determined, and per capita RBC utilisation trends calculated. RESULTS: Of the 2 356 441 transfusion events, 65.9% occurred in the public and 34.1% in the private sector. Public sector patients were younger (median (interquartile range (IQR)) 33 (22 - 49) years) than in the private sector (median (IQR) 54 (37 - 68) years), and mainly female in both sectors (66.2% in the public sector and 53.4% in the private sector). Between 2014 and 2018, per capita RBC utilisation decreased from 11.9 to 11.0/1 000 population in the public sector, but increased from 34.8 to 38.2/1 000 population in the private sector. CONCLUSIONS: We confirmed distinctly different RBC utilisation patterns between the healthcare sectors in SA. Possible drivers for these differences may be healthcare access, differing patient populations and prescriber habits. Better understanding of these drivers may help inform equitable public health policy.

The effect of seasonal birth pulses on pathogen persistence in wild mammal populations.

The notion of a critical community size (CCS), or population size that is likely to result in long-term persistence of a communicable disease, has been developed based on the empirical observations of acute immunizing infections in human populations, and extended for use in wildlife populations. Seasonal birth pulses are frequently observed in wildlife and are expected to impact infection dynamics, yet their effect on pathogen persistence and CCS have not been considered. To investigate this issue theoretically, we use stochastic epidemiological models to ask how host life-history traits and infection parameters interact to determine pathogen persistence within a closed population. We fit seasonal birth pulse models to data from diverse mammalian species in order to identify realistic parameter ranges. When varying the synchrony of the birth pulse with all other parameters being constant, our model predicted that the CCS can vary by more than two orders of magnitude. Tighter birth pulses tended to drive pathogen extinction by creating large amplitude oscillations in prevalence, especially with high demographic turnover and short infectious periods. Parameters affecting the relative timing of the epidemic and birth pulse peaks determined the intensity and direction of the effect of pre-existing immunity in the population on the pathogen's ability to persist beyond the initial epidemic following its introduction.