Limited containment options of COVID-19 outbreak revealed by regional agent-based simulations for South Africa

Introduction

The first cases of the coronavirus disease COVID-19 were confirmed on December 29, 2019 in China¹. Due to high contagiousness², the disease has spread rapidly^3,4 and by April 2020 the World Health Organization (WHO) had confirmed⁵ about 1.6 million cases worldwide. Consequently, the COVID-19 epidemic was declared a pandemic as it started to impact economies worldwide. However, epidemiological expertise lagged behind the exponential spread in this specific situation. In an attempt to contribute to the scientific assessment of the COVID-19 pandemic, we quickly derived first results and were able to make first predictions for the South African COVID-19 development. The significance of timely countermeasures could be seen in the development of infections in the United States, where cases surged from 1,678 to 307,318 between March 16, and April 6, 2020^6,7. Therefore, our research applied agent-based simulations to assess the effectiveness of countermeasures at an early stage of the COVID-19 pandemic and the following paper displays the state of literature and infection figures for the period of March and April 2020.

Most countries of the Global South were affected as well, often with reported case numbers just beginning to surge⁸. However, the reported low numbers in April, e.g. about 9000 cases in all of Africa, may be biased as only few individuals have been tested, see⁹ for the example of South Africa, and also¹⁰. Furthermore, such countries operate under vastly different socio-economic conditions, with larger social inequality, distinct transportation options and weaker health care systems compared to those of most countries in the global north^11,12,13, such that core characteristics of potential COVID-19 spreading dynamics and thus the effectiveness of specific countermeasures remain largely unknown for countries of the Global South.

The transmission of COVID-19 is currently thought to occur through direct inter-person droplet-based infections through coughing and sneezing, with possible additional infection paths through aerosols and via contaminated surfaces. Symptoms often occur only after an incubation period of several days, with many infected responding entirely asymptomatic. Contagion may also occur via such symptom-free carriers, posing a challenge for tracing and estimating spreading patterns¹⁴. Transmission predominantly occurs through interactions while being at home, being at work, using shared transportation modes or during group-based leisure activities. In many countries of the Global South, most of these social activities occur under conditions drastically different from those in, for example, Europe or the U.S.. For instance, these countries feature, on average, more people per household, higher unemployment rates, more manual and lower payed work^15,16 and much of publicly available transportation services used by the middle and low-income population are offered as paratransit shared-mobility services^17,18. Moreover, public health care services are different and often less well equipped than in countries of the Global North^13,19.

As a paradigmatic region offering large-scale data availability, we consider the Nelson Mandela Bay Municipality (NMBM) in South Africa to study scenarios of COVID-19 spreading dynamics and the impact of countermeasures. We combine socio-economic and travel survey data from more than 100,000 people²⁰, about 10% of the local population, based on employment status, household size, age group and income level together with a detailed 24-hour travel diary component integrated into an agent-based traffic simulation (see Methods). The resulting contact network forms the basis for extended Susceptible-Infected-Recovered (SIR) model dynamics with parameters adapted to COVID-19²¹. Such agent-based simulations capture the inhomogeneity among the agents and are capable of modeling intricate nonlinear dynamic relationships between them, including a spreading rate that depends on the individual agent’s detailed activities, their modes of transportation used, and their distance to each other²².

To evaluate the impact of various policy measures on the course of the disease, we systematically compare several scenarios by varying simulation parameters accordingly (see Methods). First, a baseline scenario without any countermeasures; second, a default scenario in line with the current measures implemented in South Africa as of early April 2020²³. These include the shutdown of childcare and educational institutions, the prohibition of leisure activities of any kind and cutting shopping activity options by about 70%. Moreover, work related and “other” activities (for example trips to health care facilities and visits to public institutions) and travelling as passenger in a car are reduced by 80% and formal and informal public transport is reduced by 30%. Third, as a harsher variation of the lockdown that may be achievable in principle, we study the effect of a realistic lockdown scenario, increasing the restriction of activities related to work, shopping, leisure, and “other” by 90% while childcare, educational activities as well as formal and informal public transport are completely shut down. Finally, we consider a theoretical complete lockdown where all travel and outside activities are prohibited. All countermeasures come into effect 7 days after an initial infection of 100 people. We furthermore investigate additional simulations that start with the (currently enacted) default measures and introduce the realistic lockdown scenario after a number of days. Our results may inform and complement the ongoing discussion around tightening, loosening, introducing or repealing certain countermeasures.

Methods

Results and discussion

Direct model simulations in the four scenarios (Figure 1) reveal that a large fraction of the population becomes infected, not only for the baseline scenario without countermeasures (as expected), but also with the default countermeasures active as of this writing (April 11 2020), see Figure panels 1A and 1B. Even if scenarios of harsh but realistically possible or complete lockdown were enacted only seven days after reaching 100 infected people, the outbreaks would still become macroscopic, with tens of thousands of infected people (Figure 1C and 1D) in the NMBM alone. Across all scenarios enacted, outbreak dynamics also causes overload of the approximately ICU beds estimated as baseline for NMBM (Figure 1E,F). As a precondition for successful containment, lockdown needs to be implemented sufficiently early on (in particular less than 7 days after the first 100 infected, not the first 100 confirmed infected).

Figure 1. Distinct outbreak scenarios of COVID-19.

Dynamics for the Nelson Mandela Bay Municipality, Eastern Cape, South Africa, with a total population of 1.15 million people. (A-D) Evolution of the number of infected (dotted lines), seriously sick (dashed) and critical (solid) patients in four different scenarios: (A) baseline without any countermeasures, (B) with default countermeasures in line with current conditions in South Africa, (C) with harsh realistic countermeasures shutting down 90% of all activities, and (D) with complete lockdown prohibiting any movement and group activities (infections within a household are still possible, see Supplementary Material for details). All countermeasures are initiated 7 days (grey shading) after the an initial infection of 100 people. (E) and (F) Number of critical cases requiring intensive care compared to the available ICU capacity (horizontal dashed line) for all four countermeasure scenarios. None of the scenarios reduces the number of critical cases sufficiently to guarantee intensive care (ICU) treatment for all critical patients.

In all four scenarios, the outbreak strongly overloads the available intensive care unit (ICU) beds, with ten- to 100-fold overload in default and baseline scenario, respectively (see Figure 2A). Out of the total 267 ICU beds available in (public and private) hospitals in the whole Eastern Cape Province (data from 2008,³²), only about 50 would be available for critical cases from the NMBM (scaled proportional to the relative population counts in 2011^33,34). We additionally quantify the sustained pressure on the health care system by computing the cumulative overload λ of the health system as the total number of person days critical patients go without intensive care (Figure 2B),

The potential severity of the outbreak in NMBM even with strong countermeasures, is especially evident when compared to that of the United Kingdom (UK) (Figure 2C). In the UK, ICU capacity would be exceeded by a factor of about 35 without any countermeasures, by a factor of about 2 with social distancing (similar to default in our simulations) and not at all with measures currently enacted³⁵. In our NMBM scenario simulations, ICU capacity would be exceeded by a factor of more than 200 without any countermeasures, by factor of about 20 with default measures, by a factor of 5 with realistic lockdown and still by a factor of 3 even with complete lockdown. These numbers underline the strong fragility of the health care services by example of the NMBM. We expect similar orders of magnitude of capacity exceedance throughout South Africa and in many countries of the Global South.

Figure 2. Overload of the health care system.

(A) Maximum number of concurrent critical patients during the peak of the outbreak. The available ICU capacity (dashed line) is widely exceeded in all scenarios. (B) The measure λ for the sustained overload, counting the total number of person days that critical patients are without intensive care. (C) Relative exceedance of the ICU capacity at the peak of the outbreak. The potential consequences of COVID-19 in countries of the Global South becomes clear from the comparison to UK estimates³⁵ with about 35-fold overload without interventions (UK baseline, upper dotted line) and no overload with UK lockdown (lower dotted line) enforcing school and university closure, case isolation and general social distancing. Note the logarithmic vertical axes in all panels to make vast case number differences visible simultaneously with the capacity.

Therefore, all realistic scenarios, including the default scenario currently enacted in South Africa, hardly seem capable of containing the COVID-19 pandemic without substantially overloading ICU availability. Our results suggest that such overload could even be expected if all the 267 ICU beds in both private and public hospitals within the entire Eastern Cape Province would be exclusively available for critical COVID-19 patients in the Nelson Mandela Bay Municipality only.

Realistic or complete lockdown offer options of greatly reducing the pressure on the health care system if enacted rapidly enough. However, economic boundary conditions and in particular the large inequality in South Africa – and similarly in many other countries of the Global South – pose additional problems. Thus, in our opinion, in particular complete lockdown seems hardly enforceable and would appear unsustainable especially for a large share of people with lower income, as already weaker containment will likely lead to severe economic consequences in addition to disease related deaths^36,37.

How can the number of critical cases be confined given that the default scenario is already enacted? On April 9th, 2020, the number of cases reported in NMBM was 30³⁸. As the number of reported cases seem to systematically underestimate their actual number by at least a factor of three, as predicted for Austria on April 10, 2020¹⁰, we estimate that on 9th of April, at least 100 cases existed in NMBM and take that as our initial condition. Starting simulations with the default scenario active, we find that critical cases are likely to vastly exceed the available ICU capacity (Figure 3A) within two months. In contrast, introducing the realistic lockdown scenario immediately, i.e. starting April 13th, might support a successful confinement of the number of critical COVID-19 patients in NMBM to manageable numbers (Figure 3B). In this scenario, the simulations suggest that there is about 80% likelihood that ICU capacity becomes overloaded at some time and that ,if overload occurs, it will be relatively mild (factor of 2-3 of the capacity, in contrast to factor of about 30 when keeping the default measures as they are).

Figure 3. Confining the impact by rapid lockdown.

Ten stochastic realizations of agent-based simulations (gray lines in (A) and (B)) show increasing typical number of critical cases, quantified by the ensemble mean (dark) and median (light). (A) Stochastic dynamics of the number of critical cases when keeping the currently active default measures in NMBM. The ICU capacity is exceeded in almost all realizations within two months. (B) Switching from default (currently roughly enacted in South Africa) to the realistic lockdown scenario, limiting all activity by 90%, four days after crossing 100 infected people (estimated from 30 actually reported cases) in NMBM (that is on April 13, 2020), yields an exceeded ICU capacity in 80% of realizations, at most five times above the available capacity. (C) Relative exceedance of available ICU capacity in both scenarios. Keeping a default would cause roughly 3000% overload whereas enacting a stricter lockdown on April 13th substantially reduces expected overload to less than 300% and may completely avoid an overload in best cases.

We remark that the success of such lockdown countermeasures crucially relies on several restrictions and our simulations likely under- rather than overestimate future case numbers. First in our simulations, activities are immediately reduced to the set low values (complete shutdown of public transport, child care and educational facilities, 90% reduction of all other activities) and all inhabitants fully comply with these restrictions. Second, our simulations are based on estimated parameters that thereby come with uncertainties, and stochastic dynamics may create large deviations from the predicted values, in particular also earlier growth and larger total number of critical patients, not last due to an exponential growth of the outbreak in its initial phase without severe lockdown (compare³⁹). Third, our estimates assume that all ICU beds would be available exclusively for COVID-19 patients during the entire time of the outbreak. Those and other constraints call for a more conservative, especially earlier introduction of realistic countermeasures.

We provide additional measures recorded from simulations on the location of infection events and exact values of the health care system overload in Tables 3 and 4, respectively.

Across all scenarios studied, the results thus indicate that it may be hard to enact realistic, socially and economically feasible countermeasures without exceeding ICU capacity and that more drastic measures beyond the current default are rapidly needed. Finally, it seems reasonable to assume that the consequences of countermeasures would be qualitatively the same across South Africa as well as many countries of the Global South.

Data availability

All data underlying the results are available as part of the article and no additional source data are required.