Anticipating the impact of COVID19 and comorbidities on the South African healthcare system by agent-based simulations

Abstract

Tuberculosis (TB) is the 10th leading cause of death worldwide, and since 2007 it has been the main cause of death from a single infectious agent, ranking above HIV/AIDS. The current COVID-19 is a pandemic which caused many deaths around the world. The danger is not only a coinfection as observed for TB and HIV for a long time, but that both TB and SARS-CoV-2 affect the respiratory organs and thus potentiate their effect or accelerate the critical course. A key public health priority during the emergence of a novel pathogen is the estimation of the clinical need to assure adequate medical treatment. This requires a correct adjustment to the critical case detection rate and the prediction of possible scenarios based on known patterns. The African continent faces constraining preconditions in regard to healthcare capacities and social welfare which may hinder required countermeasures. However, given the high TB prevalence rates, COVID-19 may show a particular severe course in respective African countries, e.g. South Africa. Using WHO's TB and public infrastructure data, we conservatively estimate that the symptomatic critical case rate, which affects the healthcare system, is between 8 and 12% due to the interaction of COVID-19 and TB, for a TB population of 0.52% in South Africa. This TB prevalence leads to a significant increase in the peak load of critical cases of COVID-19 patients and potentially exceeds current healthcare capacities.

Figure 1

Policy impacts on peaks of critical cases (summary). Properties of the maximum number of critical cases throughout the epidemics for 130 days. The three policy scenarios are conducted and model lifting, maintaining and tightening the lockdown. Each scenario is simulated with and without consideration of TB as a risk factor to assess the impact. All policy scenarios start with 100 infected inhabitants and a default lockdown is applied until day 25. The dashed red lines represent the number of available ICUs. Each setup is based on ten simulations.

Figure 2

Policy impacts on the critical cases (time). Simulated course of COVID-19 in three policy scenarios corresponding to Fig. 1. The dashed green lines indicate the number of critical patients in scenarios without consideration of TB, the dashed red lines accordingly represent the critical cases under consideration of a TB risk group. For each scenario 10 simulations are computed, the solid lines represent their averages.

Figure 3

Varying TB rates. Each panel depicts 10 simulated courses of COVID-19 measured in critical cases per 100,000 inhabitants with a TB rate varying from 0% (green line) to 1% (red line). The small panels depict the course over a longer time horizon (200 days).