A complex, high-performance agent-based model used to explore tuberculosis and COVID-19 case-finding interventions

Tuberculosis (TB) claimed an estimated 1.5 million lives in 2021 and the COVID-19 pandemic resulted in 14.9 million excess deaths in 2020 and 2021 combined. With both these infectious diseases substantial pathogen transmission takes place before people report to health facilities. Diagnosing more people more quickly and placing them on treatment and/or isolating them is thus critical to achieving epidemiological control. Early diagnosis interventions include contact tracing and isolation, testing of asymptomatic people thought to be at high risk of infection, and in the case of TB, screening using chest X-rays. The impact of several such early diagnosis interventions on case detection have been studied in clinical trials, but the longer-term impact of these interventions on infections (incidence) and deaths (mortality) is not known. There are also unanswered questions as to the impact hypothetical future TB tests, for example allowing for more frequent testing, may have on TB incidence and mortality. We developed an agent-based model (ABM) called ABM Spaces and used it to ask: (i) What is the impact of four different TB case-finding interventions on TB detection rates, incidence and mortality? (ii) What is the impact of test frequency and test sensitivity on tuberculosis incidence? And (iii) What is the impact of contact tracing and isolation and variable test turnaround times on COVID-19 diagnosis and mortality? Such agent-based modelling, in which disease transmission and progression is modelled at the level of discrete individuals, has increasingly been used in recent decades to model infectious disease interventions. Relatively few ABMs in the literature contain substantial social structure (for example associating agents with specific households, workplaces, and school classes). We illustrate that such ABMs with substantial social structure can be developed in a way that is epidemiologically sound and show that this type of ABM is well-suited to the modelling of social interventions such as contact tracing. In the ABM Spaces model we found that testing of people at considerable risk of TB has a greater impact on TB incidence and mortality than mass X-ray screening, that the impact of the two interventions is additive, and that the impact of annual testing of high TB risk individuals is highly sensitive to HIV prevalence. We found that the relationship between test frequency and TB mortality and incidence is non-linear, with an inflection point at around the four-month mark. The COVID-19 version of ABM Spaces confirmed the potential of contact tracing and isolation to reduce incidence and mortality, but the effect was highly sensitive to test turnaround times.

Grantor

Department of Computer Science

Year dc:date.issued

2024

Author dc:creator

• Low, Marcus

Advisor dc:contributor.advisor

• Kuttel, Michelle

• Computer Science

Handle dc:identifier.uri

http://hdl.handle.net/11427/40759

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/40759