Synthetic populations of South African urban areas

Joubert, Johan W.

doi:10.1016/j.dib.2018.05.126

Cited by 13 publications

(5 citation statements)

References 6 publications

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“…2 and 3 are based on a detailed agent-based simulation of 10% of the population (114,346 agents, such that each agent represents 10 people) of NMBM conducted with MATSim (version 12.0-SNAPSHOT) 32 . The (synthetic) population of agents that serves as input for the MATSim simulation is that suggested by Joubert et al 33 , aggregating survey data on social and economic conditions as well as detailed travel diaries. In the simulation, each agent chooses a transport mode and route based on their activity schedule (type and place of planned activities such as work, school or shopping).…”

Section: Methodsmentioning

confidence: 99%

COVID-19 in South Africa: outbreak despite interventions

Schröder¹,

Bossert

Kersting

et al. 2021

Sci Rep

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The future dynamics of the Corona Virus Disease 2019 (COVID-19) outbreak in African countries is largely unclear. Simultaneously, required strengths of intervention measures are strongly debated because containing COVID-19 in favor of the weak health care system largely conflicts with socio-economic hardships. Here we analyze the impact of interventions on outbreak dynamics for South Africa, exhibiting the largest case numbers across sub-saharan Africa, before and after their national lockdown. Past data indicate strongly reduced but still supracritical growth after lockdown. Moreover, large-scale agent-based simulations given different future scenarios for the Nelson Mandela Bay Municipality with 1.14 million inhabitants, based on detailed activity and mobility survey data of about 10% of the population, similarly suggest that current containment may be insufficient to not overload local intensive care capacity. Yet, enduring, slightly stronger or more specific interventions, combined with sufficient compliance, may constitute a viable option for interventions for South Africa.

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Section: Methodsmentioning

confidence: 99%

COVID-19 in South Africa: outbreak despite interventions

Schröder¹,

Bossert

Kersting

et al. 2021

Sci Rep

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“…In order to obtain a realistic simulation of a region, geo- and census- time-use or similar data are usually processed. For the NMBM, both a network and a synthetic population representing 10 % of the inhabitants processed from demographic data are taken from previous publications 37 . Since the agents are generated from census data and instructed to behave rationally, their movements and especially their contacts with other agents represent a realistic approximation of human daily routines.…”

Section: Methodsmentioning

confidence: 99%

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

Schlüter

Sørensen

Bossert

et al. 2021

Sci Rep

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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.

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“…This paper benefits from publicly available synthetic populations generated using a Bayesian network approach [28]. Similar to earlier approaches like Iterative Proportional Fitting and Iterative Proportional Updating, a Bayesian network uses the detailed Public Use Micro Sample (PUMS) data and improves its geographic granularity by using the Community Profiles data, which is spatially more detailed but aggregated, as control totals.…”

Section: Synthetic Populationsmentioning

confidence: 99%