Optimization and Operations Research in Mitigation of a Pandemic

The pandemic of COVID-19 initiated in 2019 and spread all over the world in 2020 has caused significant damages to the human society, making troubles to all aspects of our daily life. Facing the serious outbreak of the virus, we consider possible solutions from the perspectives of both governments and enterprises. Particularly, this paper discusses several applications of supply chain management, public resource allocation, and pandemic prevention using optimization and machine learning methods. Some useful insights in mitigating the pandemic and economy reopening are provided at the end of this paper. These insights might help governments to reduce the severity of the current pandemic and prevent the next round of outbreak. They may also improve companies’ reactions to the increasing uncertainties appearing in the business operations. Although the coronavirus imposes challenges to the entire society at the moment, we are confident to develop new techniques to prevent and eradicate the disease.

The clinical characteristics and mortal causes analysis of COVID-19 death patients (preprint)

Abstract Purpose: Currently, COVID-19 is causing a large number of deaths globally. However, few researches focused on the clinical features of death patients. This study conducted a retrospective analysis of clinical characteristics and mortal causes in Chinese COVID-19 death patients. Patients and methods: The clinical characteristics of death patients were collected from publicized by local health authorities in China. Expressions of virus targets in human organs were obtained from GTEx database. Results: 159 patients from 24 provinces in China were recruited in our study, including 26 young patients under 60 and 133 aged 60 or older. The median age was 71 years, which indicated that most death patients were elderly. More male patients died of COVID-19 than females (1.65 fold). Hypertension was the most common coexisting disorder and respiratory failure was the most common direct cause of death. Fever (71.19%) and cough (55.08%) were the predominant presenting symptoms. There was one asymptomatic patient. In addition, by comparing young and old patients, heart disease was identified as an important risk factor for death in the aged patients. ACE2 and TMPRSS2 were the targets of SARS-CoV-2, we analyzed their expression in different organs. TMPRSS2 and ACE2 had a high expression in the organs which had corresponding clinical features in death patients. Conclusion: Male, age and heart disease were the main risk factors of death. Beside, asymptomatic patients with serious coexisting disorders may also die of SARS-CoV-2. Thus, more attention should be paid to the old patients with heart disease and asymptomatic patients in the treatment . Keywords: COVID-19, SARS-Cov-2, death, coexisting disorder, cause of death

Effect of large-scale testing platform in prevention and control of the COVID-19 pandemic: an empirical study with a novel numerical model (preprint)

Background: China adopted an unprecedented province-scale quarantine since January 23rd 2020, after the novel coronavirus (COVID-19) broke out in Wuhan in December 2019. Responding to the challenge of limited testing capacity, large-scale standardized and fully-automated laboratory (Huo-Yan) was built as an ad-hoc measure. There was so far no empirical data or mathematical model to reveal the impact of the testing capacity improvement since the quarantine. Methods: We integrated public data released by the Health Commission of Hubei Province and Huo-Yan Laboratory testing data into a novel differential model with non-linear transfer coefficients and competitive compartments, to evaluate the trends of suspected cases under different nucleic acid testing capacities. Results: Without the establishment of Huo-Yan, the suspected cases would increased by 47% to 33,700, the corresponding cost of the quarantine would be doubled, and the turning point of the increment of suspected cases and the achievement of "daily settlement" (all daily new discovered suspected cases were diagnosed according to the nucleic acid testing results) would be delayed for a whole week and 11 days. If the Huo-Yan Laboratory could ran at its full capacity, the number of suspected cases could started to decrease at least a week earlier, the peak of suspected cases would be reduced by at least 44% and the quarantine cost could be reduced by more than 72%. Ideally, if a daily testing capacity of 10,500 could achieved immediately after the Hubei lockdown, "daily settlement" for all suspected cases would be achieved immediately. Conclusions: Large-scale and standardized clinical testing platform with nucleic acid testing, high-throughput sequencing and immunoprotein assessment capabilities need to be implemented simultaneously in order to maximize the effect of quarantine and minimize the duration and cost. Such infrastructure like Huo-Yan, is of great significance for the early prevention and control of infectious diseases for both common times and emergencies.