Maj Gen (ret) Johan Jooste (67) retired from the South African Army in 2006 after 35 years of active service. He is a war veteran and the last part of his military career he served in the Army General Staff in various capacities. In his second career he spent 7 years with a multinational company, BAE Systems, in the military vehicle industry. At the beginning of this 2013 he was appointed as Commanding Officer Special Projects at SAN Parks, responsible for all matters related to the anti – poaching strategy, planning and execution. He subsequently established a Wildlife Crime and Corruption Combatting Coordination Centre at the SANParks Head office. He is currently employed by PPF and seconded to the Department of Environmental Affairs where he manages a program to establish wild life zone in all provinces.
Keynote title: Technology makes things possible, only people can make it happen
Abstract – The protection of the wild life of Africa against the odds of international organized crime that feeds an insatiable demand, poses unique and severe challenges. As part of a grand strategy ranger corpse are transformed in to Anti – Poaching Units (APU). To ensure the intelligent deployment and sustainability of these units, superior connectivity and situational awareness is required. Both of these comes at a price in the African bush where there are no or little infra structure, harsh conditions, not so tech savvi staff and ever evolving poacher tactics. Unique and customized solutions enhance the anti – poaching effort with some smack of the 4IR and not “blue sky” solutions, but “brown earth” options.
Dr. Jian Chen is the founder and CEO of CreditWise Technologies, Co. Ltd. He also serves as the senior advisor of MSCI’s China business, and the senior advisor of Caixin Insight Group, a prestigious think tank in China. He previously held the positions of Managing Partner of RQuest Financial Services Group, Managing Director in IFE Group, Risk Modeling Director in Freddie Mac, Director of Credit Risk Management in Fannie Mae.
Dr. Chen holds the academic position of adjunct professor at Shanghai Institute of Advanced Finance (SAIF). He also serves as the visiting professor of PKU-Fordham joint PhD program. He previously served as an adjunct professor at Johns Hopkins Carey Business School, where he taught MBA-level finance courses. Dr. Chen’s academic research interests include discrete event modeling, real estate finance and economics, fixed income securities pricing & hedging, consumer behavior modeling, and quantitative risk management.
Keynote title: Fusion of Finance and Epidemiology Models
Abstract – Dr. Chen Jian has recently used a quantitative finance model, transition matrix model, widely used in credit risk analysis for the prediction of COVID-19 outbreaks. This approach is proven to be more robust, flexible, and accurate, than many traditional epidemiology models.
Since the onset of the COVID-19 outbreak in Wuhan, China, numerous forecasting models have been proposed to project the trajectory of coronavirus infection cases. Most of these forecasts are based on the traditional epidemiological model. However, many of these forecasts have performed poorly, mainly due to two reasons: 1) these type of forecast models are highly sensitive to model parameters, which have wide confidence intervals; 2) the models fail to incorporate the non-pharmaceutical-intervention (or NPI, mainly government policies, like “lock-down”, “shut-down”, “stay-at-home” directives) effects successfully. We propose a new discrete-time Markov chain model that directly incorporates stochastic behavior and for which parameter estimation is straightforward from available data. Transition matrix models (TMM) have been widely used in financial industry, mainly in credit analysis, especially in predicting credit rating migration of corporate bonds, or delinquency migration of consumer loans. The event chain of a consumer loan’s “early delinquency”, “serious delinquency”, “default”, is very like the event chain of COVID-19’s “mild case”, “severe case”, “critical case”, “death”.
Using such data from China’s Hubei province (for which Wuhan is the provincial capital city and which accounted for approximately 82% of the total reported COVID-19 cases in the entire country), the model is shown to be flexible, robust, and accurate. As a result, it has been adopted by the first Shanghai assistance medical team in Wuhan’s Jinyintan Hospital, which was the first designated hospital to take COVID-19 patients in the world. The forecast has been used for preparing medical staff, intensive care unit (ICU) beds, ventilators, and other critical care medical resources and for supporting real-time medical management decisions.
Empirical data from China’s first two months (January/February) of fighting against COVID-19 was collected and used to enhance the model by embedding NPI efficiency into the model. We applied the model to forecast Italy, South Korea, and Iran on March 9. Later we made forecasts for Spain, Germany, France, US on March 24. Again, the model has performed very well, proven to be flexible, robust, and accurate for most of these countries/regions outside China.
Compared to widely used SIR-type models, we find TMM more flexible, robust, and accurate for COVID-19 forecasts. More importantly, it has been adopted by frontline medical professionals to support real-time COVID-19 medical management decisions and proven to be a more pragmatic forecasting tool. Three out of the four authors were invited to provide insights on this novel model at the Brookings event “Fighting COVID-19: Experiences and lessons from the frontlines in Asia” , which was broadcasted international news agencies, including Caixin Global , China Daily , Reuters, etc. The model has also received wide attention in news media, including Caixin, with a full feature article on its forecasts .
We suggest that modeling teams around the world should take a closer look at this discrete-time Markov chain model and examine its usefulness in the battle against COVID-19, including the support for real-time decision making of preparing medical staff, equipment, and other medical resources, as well as government/business planning of when and how to impose/lift non-pharmaceutical invention (NPI) policies.
Kristian Soltesz is an associate professor with the department of Automatic Control at Lund University, Lund, Sweden. His current research interests are within biomedical applications of control systems, with a particular focus on organ preservation and evaluation in heart transplantation. During 2020, Kristian has focused on modelling aspects of the SARS-CoV-2 pandemic. he holds a master degree in engineering physics from Lund, and conducted his degree project on autonomous vehicle control at Caltech in 2007. During his licentiate work, Kristian focused on automatic controller tuning within the process industries, and the subject has remained one of his interests. In 2013 Kristian defended his PhD on automaton in anesthesia, based on a clinical project conducted at the University of British Columbia and the BC Childrens Hospital. He has since worked as a postdoc at the Norwegian University of Science and Technology in Trondheim, before returning to Lund in 2016.
Keynote title: Fusion in the fight against COVID-19: possibilities and limitations